2,654 results on '"Aditya V"'
Search Results
2. End-to-Side Venous Anastomosis with IJV: Improving Outcomes of Microvascular Anastomosis in Head and Neck Reconstruction
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Anupam Golash, Sudipta Bera, Aditya V. Kanoi, Swaraj Hanspal, and Abhijit Golash
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end to side ,IJV ,venous anastomosis ,head and neck reconstruction ,re-exploration ,Surgery ,RD1-811 - Abstract
Background End-to-side (ES) venous anastomosis is an established approach for head and neck reconstruction and has several benefits over conventional end-to-end (EE) anastomosis. However, this is not preferred by all, which may be due to technical preferences for an EE anastomosis by many surgeons. We present here our experience of routine ES venous anastomosis for head and neck reconstruction over the past 8 years.
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- 2024
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3. Novel Effective Therapeutic Regimen for Recurrent/Metastatic Head and Neck Squamous Cell Cancer: Concurrent Triple Oral Metronomic Chemotherapy and Immunotherapy
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Sewanti Limaye, Mohan Menon, Shambhavi Singh, Pritam Kataria, Aditya V. Shreenivas, Rajan Datar, Darshana Patil, Prashant Kumar, Niyati Shah, Hardik Sheth, Suku Sneha, Chetan Madre, Ruturaj Deshpande, Narayan K. Menon, Prasad Dandekar, and Vijay Haribhakti
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recurrent metastatic head and neck cancer ,metronomic chemotherapy ,immunotherapy ,solid tumors ,Neoplasms. Tumors. Oncology. Including cancer and carcinogens ,RC254-282 ,Immunologic diseases. Allergy ,RC581-607 - Published
- 2024
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4. Obesity-driven mitochondrial dysfunction in human adipose tissue-derived mesenchymal stem/stromal cells involves epigenetic changes
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Alfonso Eirin, Roman Thaler, Logan M. Glasstetter, Li Xing, Xiang-Yang Zhu, Andrew C. Osborne, Ronscardy Mondesir, Aditya V. Bhagwate, Amir Lerman, Andre J. van Wijnen, and Lilach O. Lerman
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Cytology ,QH573-671 - Abstract
Abstract Obesity exacerbates tissue degeneration and compromises the integrity and reparative potential of mesenchymal stem/stromal cells (MSCs), but the underlying mechanisms have not been sufficiently elucidated. Mitochondria modulate the viability, plasticity, proliferative capacity, and differentiation potential of MSCs. We hypothesized that alterations in the 5-hydroxymethylcytosine (5hmC) profile of mitochondria-related genes may mediate obesity-driven dysfunction of human adipose-derived MSCs. MSCs were harvested from abdominal subcutaneous fat of obese and age/sex-matched non-obese subjects (n = 5 each). The 5hmC profile and expression of nuclear-encoded mitochondrial genes were examined by hydroxymethylated DNA immunoprecipitation sequencing (h MeDIP-seq) and mRNA-seq, respectively. MSC mitochondrial structure (electron microscopy) and function, metabolomics, proliferation, and neurogenic differentiation were evaluated in vitro, before and after epigenetic modulation. hMeDIP-seq identified 99 peaks of hyper-hydroxymethylation and 150 peaks of hypo-hydroxymethylation in nuclear-encoded mitochondrial genes from Obese- versus Non-obese-MSCs. Integrated hMeDIP-seq/mRNA-seq analysis identified a select group of overlapping (altered levels of both 5hmC and mRNA) nuclear-encoded mitochondrial genes involved in ATP production, redox activity, cell proliferation, migration, fatty acid metabolism, and neuronal development. Furthermore, Obese-MSCs exhibited decreased mitochondrial matrix density, membrane potential, and levels of fatty acid metabolites, increased superoxide production, and impaired neuronal differentiation, which improved with epigenetic modulation. Obesity elicits epigenetic changes in mitochondria-related genes in human adipose-derived MSCs, accompanied by structural and functional changes in their mitochondria and impaired fatty acid metabolism and neurogenic differentiation capacity. These observations may assist in developing novel therapies to preserve the potential of MSCs for tissue repair and regeneration in obese individuals.
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- 2024
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5. Management outcomes of cervical radiculopathy with conservative treatment, anterior cervical discectomy fusion (ACDF), and anterior cervical disc replacement (ACDR)—Retrospective single center matched cohort study
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Saumyajit Basu, Piyush Joshi, Vikas Hanasoge, Aditya V Guduru, Piyush W Gadegone, and Mitul Jain
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acdf ,acdr ,cervical radiculopathy ,conservative ,functional outcomes ,radiological outcomes ,Orthopedic surgery ,RD701-811 - Abstract
Background: Degenerative cervical radiculopathy results from nerve root compression in the cervical neural foramina, often due to a herniated disc, osteophyte, or facetal/ligamentum flavum hypertrophy. Typically, 80% of patients show improvement within the initial 12 weeks through nonoperative measures, with surgical intervention considered for non-responders. This study aimed to compare clinical and radiological outcomes in cervical radiculopathy patients undergoing prolonged conservative care, anterior cervical discectomy and fusion (ACDF), or anterior cervical disc replacement (ACDR) after a 1‐year follow‐up. Materials and Methods: Our study was a retrospective single‐center study involving 780 cervical radiculopathy patients from January 2012 to December 2021. About 80.12% found relief with conservative management within 12 weeks. Remaining 155 patients were offered surgery, with 73 opting for continued conservative care, and 82 undergoing surgery (55 with ACDF and 27 with ACDR). Evaluation was done using visual analogue scale (VAS), Neck Disability Index (NDI), and radiographic parameters. Results: The mean follow‐up was 11.58 ± 6.7 months. ACDR group: Mean age 43.38 ± 8.56, VAS 7.81 ± 1.04 preoperatively, improved significantly to 2.07 ± 1.34 (P < 0.05) at 1‐year follow‐up. ACDF group: Mean age 44.85 ± 10.65, VAS 8.22 ± 1.21 preoperatively, improved significantly to 2.09 ± 1.01 (P < 0.05) at 1‐year follow‐up. Conservative group: Mean age 45.04 ± 11.19, VAS 7.77 ± 1.86 preoperatively, improved significantly to 2.08 ± 1.40 (P < 0.05) at 1‐year follow‐up. Radiographic parameters significantly improved in all groups at 1‐year follow‐up (P < 0.05). Range of motion (ROM) changes varied across groups. Miyazaki’s grading and Kim’s score showed comparable results. Conclusion: Comparable clinical and radiological outcomes were observed among conservative, ACDF, and ACDR approaches. ACDR approach demonstrated a better NDI score outcome. Neck ROM was better maintained or improved in the ACDR approach, decreased in ACDF, and remained almost similar in the conservative group.
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- 2024
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6. Comparison of eight modern preoperative scoring systems for survival prediction in patients with extremity metastasis
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Tse‐Ying Lee, Yu‐An Chen, Olivier Q. Groot, Hung‐Kuan Yen, Bas J. J. Bindels, Robert‐Jan Pierik, Hsiang‐Chieh Hsieh, Aditya V. Karhade, Ting‐En Tseng, Yi‐Hsiang Lai, Jing‐Jen Yang, Chia‐Che Lee, Ming‐Hsiao Hu, Jorrit‐Jan Verlaan, Joseph H. Schwab, Rong‐Sen Yang, and Wei‐Hsin Lin
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Asian cohort ,external validation ,extremity metastasis ,survival prediction models ,Neoplasms. Tumors. Oncology. Including cancer and carcinogens ,RC254-282 - Abstract
Abstract Background Survival is an important factor to consider when clinicians make treatment decisions for patients with skeletal metastasis. Several preoperative scoring systems (PSSs) have been developed to aid in survival prediction. Although we previously validated the Skeletal Oncology Research Group Machine‐learning Algorithm (SORG‐MLA) in Taiwanese patients of Han Chinese descent, the performance of other existing PSSs remains largely unknown outside their respective development cohorts. We aim to determine which PSS performs best in this unique population and provide a direct comparison between these models. Methods We retrospectively included 356 patients undergoing surgical treatment for extremity metastasis at a tertiary center in Taiwan to validate and compare eight PSSs. Discrimination (c‐index), decision curve (DCA), calibration (ratio of observed:expected survivors), and overall performance (Brier score) analyses were conducted to evaluate these models’ performance in our cohort. Results The discriminatory ability of all PSSs declined in our Taiwanese cohort compared with their Western validations. SORG‐MLA is the only PSS that still demonstrated excellent discrimination (c‐indexes>0.8) in our patients. SORG‐MLA also brought the most net benefit across a wide range of risk probabilities on DCA with its 3‐month and 12‐month survival predictions. Conclusions Clinicians should consider potential ethnogeographic variations of a PSS's performance when applying it onto their specific patient populations. Further international validation studies are needed to ensure that existing PSSs are generalizable and can be integrated into the shared treatment decision‐making process. As cancer treatment keeps advancing, researchers developing a new prediction model or refining an existing one could potentially improve their algorithm's performance by using data gathered from more recent patients that are reflective of the current state of cancer care.
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- 2023
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7. A Prospective Observational Study on Outcomes of Single Stage Posterior Decompression and Fixation for Dorsolumbar Spine Tuberculosis
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Hari J. Menon, Aditya V. Tripathi, Nrutik M. Patel, and Chandan Narang
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Koch's spine ,posterior approach ,Pott's spine ,spondylodiscitis ,Medicine ,Orthopedic surgery ,RD701-811 - Abstract
Abstract Objective To study the results of only posterior decompression and instrumentation in dorsal and dorsolumbar spine tuberculosis. Methods The patients (n = 30) who were included in this study had dorsal or dorsolumbar spine tuberculosis, with or without neurological deficit, and with or without deformity. All 30 patients were managed by only posterior approach decompression and instrumentation. We studied cases for correction and maintenance of deformity at dorsal and dorsolumbar spine, functional outcome by the Oswestry disability index (ODI) and visual analogue scale (VAS) scores, as well as neurological outcome by the Frankel grade. Results In the current series, 30 patients were operated with single stage posterior decompression and instrumentation, and showed significant improvement in neurological status and functional outcomes, which were accessed by the ODI score, VAS score, and Frankel grade. Conclusion The posterior (extracavitary) approach provides optimum access to the lateral and anterior aspects of the spinal cord for good decompression. It facilitates early mobilization and avoids problems of prolonged recumbency, provides better functional outcome, and significantly better sagittal plane kyphosis correction.
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- 2023
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8. Community engagement in health promotion campaigns: A qualitative photo content analysis from vitalizing communities against NCD risk factors (V-CaN) field trial in rural central India
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Aditya V. Samanthapudi, Devyani Wanjari, Radhika Sharma, M Rajashekhar, Ashwini Kalantri, Abhishek V. Raut, and Subodh S. Gupta
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chronic disease ,community participation ,preventive health services ,qualitative research ,Special aspects of education ,LC8-6691 ,Public aspects of medicine ,RA1-1270 - Abstract
BACKGROUND: India faces a critical challenge with 5.8 million annual deaths from non-communicable diseases (NCDs). Maharashtra, where NCDs constitute 66% of the disease burden. The youth, lacking awareness, are vulnerable. Vitalizing communities against NCD risk factors (V-CaN) melawa, inspired by the “Pandharpur Wari” pilgrimage, aims to bridge implementation gaps and empower communities. “Arogya chi wari” integrates health practices with cultural events, offering a unique approach. Photo documentation from V-CaN melawa becomes a powerful tool for assessing community engagement qualitatively. The aim of the study was to qualitatively analyze photos from V-CaN melawas, exploring community engagement in health promotion against NCD risk factors. MATERIALS AND METHODS: V-CaN melawas were organized in the field practice area of the department of community medicine. These melawas were part of the cluster randomized field trial named V-CaN, which is being implemented in a rural area of the Wardha district of Maharashtra. The V-CaN days, also known as melawas, were organized with the aim of facilitating behavioral change among participants. A qualitative study using photo content analysis was conducted, reviewing 2000 pictures from 59 V-CaN melawas. Thematic content analysis was employed, with researchers selecting 61 photos based on uniqueness. RESULTS: Six major themes emerged: health promotion, health system involvement, intersectoral coordination, inclusiveness, community resource mobilization, and innovation. Examples include nutrition exhibitions, health screenings, and innovative games. CONCLUSIONS: The analysis showcases diverse community participation in V-CaN melawas, emphasizing inclusivity, collaboration, and innovation. While qualitative, the study lays the foundation for future quantitative assessments of the intervention’s impact on health outcomes and community attitudes.
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- 2024
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9. Acceptance, safety and efficacy of postpartum intrauterine contraceptive device
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Neelam Nalini, Bijeta Singh, Saumaya Jha, and Aditya V. Singh
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complications ,contraception ,efficacy ,ppiucd ,Medicine - Abstract
Background: Postpartum intrauterine contraceptive device (PPIUCD) is safe method of contraception, but with low acceptability rate. Factors that govern acceptability needs to be addressed for increasing its rate. This study was done to assess the acceptance, efficiency, and complications of PPIUCD in tertiary centre of Jharkhand, India. Methods: This prospective study included antenatal women >34 weeks of gestational age who attended antenatal women in the department of Obstetrics and Gynaecology between 1st January 2020 to 1st September 2020. Details related to age, parity, education, awareness of PPIUCD, reasons for acceptance/refusal of PPIUCD were recorded. The types of insertion were postplacental, postcaesarean, and postabortal. Postinsertion counselling was done for PPIUCD, and women were followed-up at 6 weeks and 10 weeks for assessing complications. Results: The overall acceptance rate was 36.23% (n = 100). The main reasons for rejecting the use of PPIUCD included fear of pain, bleeding, and other complications (59.09%) and COVID-19 (10.23%). In majority (80%), type of insertion was postplacental with postcaesarean in 18% and postabortal in 2%. Complications were present in 14% women that included abdominal pain (8%), heavy menstrual bleeding (6%), infection (4%), thread not visible (1%), and IUCD not located by USG or X-ray (1%). At 6 months, expulsion occurred in 2 women. There was no significant association of age (P = 0.312), religion (P = 1), tribal/non-tribal (P = 1), education level (P = 0.628), and type of insertion (P = 0.356) with complications. At 1 year of follow up, none of the women conceived again showing the efficacy to be 100% as a contraceptive. Conclusion: In spite of limited awareness, PPIUCD proved to be an effective and safe method of long-acting reversible contraception. However, it had low rate of acceptability. PPIUCD was related to lesser complications as expulsion occurred in only 2 women at 6 months. Factors such as age, religion, tribal/non-tribal, education level, and type of insertion were not associated with acceptability rate. PPIUCD was 100% effective as a contraceptive.
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- 2023
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10. Treatment of Severe COVID-19 Infection With Remdesivir in Peritoneal Dialysis
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Ryan Geffin MD, Shazia Raheem PharmD, Lavannya M. Pandit MD, MS, Andrew S. Hunter PharmD, Michael W. Holliday MD, PhD, Aditya V. Rao, and Maulin K. Shah MD
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Medicine (General) ,R5-920 ,Pathology ,RB1-214 - Abstract
End-stage kidney disease (ESKD) has been shown to be correlated with an increased risk of COVID-19 infection and mortality. Remdesivir is an effective non-EUA U.S. Food and Drug Administration (FDA)-approved antiviral agent for the treatment of COVID-19 in hospitalized adult and pediatric patients, though a lack of data has prevented its use in patients with severe kidney disease including dialysis patients. Some observational studies report the use of remdesivir in hemodialysis patients, but there are no reports of patients treated with remdesivir on peritoneal dialysis. Dialysis modalities may affect drug pharmacokinetics, and safety and efficiency of remdesivir in peritoneal dialysis is unknown. We report the first case, to our knowledge, of using remdesivir in a patient treated with peritoneal dialysis with no significant adverse events. This case illustrates the potential for remdesivir to be considered in peritoneal dialysis patients with severe COVID infection. Proper risk analysis and careful monitoring should be done, given the unpredictable clearance of the drug.
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- 2023
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11. Dysfunctional ERG signaling drives pulmonary vascular aging and persistent fibrosis
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Nunzia Caporarello, Jisu Lee, Tho X. Pham, Dakota L. Jones, Jiazhen Guan, Patrick A. Link, Jeffrey A. Meridew, Grace Marden, Takashi Yamashita, Collin A. Osborne, Aditya V. Bhagwate, Steven K. Huang, Roberto F. Nicosia, Daniel J. Tschumperlin, Maria Trojanowska, and Giovanni Ligresti
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Science - Abstract
Vascular dysfunction is associated with ageing and chronic diseases, but its role in lung repair and fibrosis is unclear. Here, the authors show that the endothelial transcription factor ERG is a mediator of vascular repair whose function declines in aged lungs resulting in sustained fibrosis
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- 2022
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12. Tinospora Cordifolia (Giloy)–Induced Liver Injury During the COVID‐19 Pandemic—Multicenter Nationwide Study From India
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Anand V. Kulkarni, Pavan Hanchanale, Vikash Prakash, Chetan Kalal, Mithun Sharma, Karan Kumar, Saptarshi Bishnu, Aditya V. Kulkarni, Lovkesh Anand, Ajay Kumar Patwa, Sandeep Kumbar, Sumeet Kainth, Cyriac Abby Philips, and for the Liver Research Club India
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Diseases of the digestive system. Gastroenterology ,RC799-869 - Abstract
Tinospora cordifolia (Giloy) is an herbal supplement commonly used in the Indian alternative medicine system Ayurveda. This herb has been promoted to the public in India as an immune booster to prevent novel coronavirus disease 2019. However, small reports have recently shown an association between Giloy use and the development of herb‐induced liver injury (HILI) with autoimmune features in some patients. This large retrospective Indian multicenter study spanning 13 centers at nine locations was designed to identify features and outcomes of HILI temporally associated with Giloy use. Chemical and toxicological analyses of retrieved Giloy samples using state‐of‐the‐art methods were also performed. We report 43 patients, of whom more than half were female, with a median time from initial Giloy consumption to symptom onset of 46 days. Patients presented with acute hepatitis, acute worsening of chronic liver disease (CLD, the most common clinical presentation), or acute liver failure. Causality assessment revealed probable liver injury in 67.4%. The most common autoantibody detected was anti‐nuclear antibody. Liver biopsy in a subset revealed HILI associated with autoimmune features and hepatocyte and canalicular cholestasis and neutrophilic and eosinophilic infiltration. Conclusion: Giloy is associated with acute hepatitis with autoimmune features and can unmask autoimmune hepatitis (AIH) in people with silent AIH‐related CLD. Further studies on the safety (and efficacy) of untested but heavily promoted herbals in alternative systems of medicine are an unmet need in the interests of public health and are especially important during this global health emergency.
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- 2022
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13. Towards Virtual Clinical Trials of Radiology AI with Conditional Generative Modeling
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Killeen, Benjamin D., Wan, Bohua, Kulkarni, Aditya V., Drenkow, Nathan, Oberst, Michael, Yi, Paul H., and Unberath, Mathias
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Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Artificial Intelligence ,Computer Science - Machine Learning - Abstract
Artificial intelligence (AI) is poised to transform healthcare by enabling personalized and efficient care through data-driven insights. Although radiology is at the forefront of AI adoption, in practice, the potential of AI models is often overshadowed by severe failures to generalize: AI models can have performance degradation of up to 20% when transitioning from controlled test environments to clinical use by radiologists. This mismatch raises concerns that radiologists will be misled by incorrect AI predictions in practice and/or grow to distrust AI, rendering these promising technologies practically ineffectual. Exhaustive clinical trials of AI models on abundant and diverse data is thus critical to anticipate AI model degradation when encountering varied data samples. Achieving these goals, however, is challenging due to the high costs of collecting diverse data samples and corresponding annotations. To overcome these limitations, we introduce a novel conditional generative AI model designed for virtual clinical trials (VCTs) of radiology AI, capable of realistically synthesizing full-body CT images of patients with specified attributes. By learning the joint distribution of images and anatomical structures, our model enables precise replication of real-world patient populations with unprecedented detail at this scale. We demonstrate meaningful evaluation of radiology AI models through VCTs powered by our synthetic CT study populations, revealing model degradation and facilitating algorithmic auditing for bias-inducing data attributes. Our generative AI approach to VCTs is a promising avenue towards a scalable solution to assess model robustness, mitigate biases, and safeguard patient care by enabling simpler testing and evaluation of AI models in any desired range of diverse patient populations., Comment: 35 pages
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- 2025
14. Wide range of applications for machine-learning prediction models in orthopedic surgical outcome: a systematic review
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Paul T Ogink, Olivier Q Groot, Aditya V Karhade, Michiel E R Bongers, F Cumhur Oner, Jorrit-Jan Verlaan, and Joseph H Schwab
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Orthopedic surgery ,RD701-811 - Abstract
Background and purpose — Advancements in software and hardware have enabled the rise of clinical prediction models based on machine learning (ML) in orthopedic surgery. Given their growing popularity and their likely implementation in clinical practice we evaluated which outcomes these new models have focused on and what methodologies are being employed. Material and methods — We performed a systematic search in PubMed, Embase, and Cochrane Library for studies published up to June 18, 2020. Studies reporting on non-ML prediction models or non-orthopedic outcomes were excluded. After screening 7,138 studies, 59 studies reporting on 77 prediction models were included. We extracted data regarding outcome, study design, and reported performance metrics. Results — Of the 77 identified ML prediction models the most commonly reported outcome domain was medical management (17/77). Spinal surgery was the most commonly involved orthopedic subspecialty (28/77). The most frequently employed algorithm was neural networks (42/77). Median size of datasets was 5,507 (IQR 635–26,364). The median area under the curve (AUC) was 0.80 (IQR 0.73–0.86). Calibration was reported for 26 of the models and 14 provided decision-curve analysis. Interpretation — ML prediction models have been developed for a wide variety of topics in orthopedics. Topics regarding medical management were the most commonly studied. Heterogeneity between studies is based on study size, algorithm, and time-point of outcome. Calibration and decision-curve analysis were generally poorly reported.
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- 2021
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15. Availability and reporting quality of external validations of machine-learning prediction models with orthopedic surgical outcomes: a systematic review
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Olivier Q Groot, Bas J J Bindels, Paul T Ogink, Neal D Kapoor, Peter K Twining, Austin K Collins, Michiel E R Bongers, Amanda Lans, Jacobien H F Oosterhoff, Aditya V Karhade, Jorrit-Jan Verlaan, and Joseph H Schwab
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Orthopedic surgery ,RD701-811 - Abstract
Background and purpose — External validation of machine learning (ML) prediction models is an essential step before clinical application. We assessed the proportion, performance, and transparent reporting of externally validated ML prediction models in orthopedic surgery, using the Transparent Reporting for Individual Prognosis or Diagnosis (TRIPOD) guidelines. Material and methods — We performed a systematic search using synonyms for every orthopedic specialty, ML, and external validation. The proportion was determined by using 59 ML prediction models with only internal validation in orthopedic surgical outcome published up until June 18, 2020, previously identified by our group. Model performance was evaluated using discrimination, calibration, and decision-curve analysis. The TRIPOD guidelines assessed transparent reporting. Results — We included 18 studies externally validating 10 different ML prediction models of the 59 available ML models after screening 4,682 studies. All external validations identified in this review retained good discrimination. Other key performance measures were provided in only 3 studies, rendering overall performance evaluation difficult. The overall median TRIPOD completeness was 61% (IQR 43–89), with 6 items being reported in less than 4/18 of the studies. Interpretation — Most current predictive ML models are not externally validated. The 18 available external validation studies were characterized by incomplete reporting of performance measures, limiting a transparent examination of model performance. Further prospective studies are needed to validate or refute the myriad of predictive ML models in orthopedics while adhering to existing guidelines. This ensures clinicians can take full advantage of validated and clinically implementable ML decision tools.
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- 2021
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16. Sociodemographic Factors Are Associated with Patient-Reported Outcome Measure Completion in Orthopaedic Surgery
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David N. Bernstein, MD, MBA, MEI, Aditya V. Karhade, MD, MBA, Christopher M. Bono, MD, Joseph H. Schwab, MD, MS, Mitchel B. Harris, MD, and Daniel G. Tobert, MD
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Orthopedic surgery ,RD701-811 - Abstract
Background:. Patient-reported outcome measures (PROMs) and, specifically, the Patient-Reported Outcomes Measurement Information System (PROMIS), are increasingly utilized for clinical research, clinical care, and health-care policy. However, completion of these outcome measures can be inconsistent and challenging. We hypothesized that sociodemographic variables are associated with the completion of PROM questionnaires. The purposes of the present study were to calculate the completion rate of assigned PROM forms and to identify sociodemographic and other variables associated with completion to help guide improved collection efforts. Methods:. All new orthopaedic patients at a single academic medical center were identified from 2016 to 2020. On the basis of subspecialty and presenting condition, patients were assigned certain PROMIS forms and legacy PROMs. Demographic and clinical information was abstracted from the electronic medical record. Bivariate analyses were performed to compare characteristics among those who completed assigned PROMs and those who did not. A multivariable logistic regression model was created to determine which variables were associated with successful completion of assigned PROMs. Results:. Of the 219,891 new patients, 88,052 (40%) completed all assigned PROMs. Patients who did not activate their internet-based patient portal had a 62% increased likelihood of not completing assigned PROMs (odds ratio [OR], 1.62; 95% confidence interval [CI], 1.58 to 1.66; p < 0.001). Non-English-speaking patients had a 90% (OR, 1.90; 95% CI, 1.82 to 2.00; p < 0.001) increased likelihood of not completing assigned PROMs at presentation. Older patients (≥65 years of age) and patients of Black race had a 23% (OR, 1.23; 95% CI, 1.19 to 1.27; p < 0.001) and 24% (OR, 1.24; 95% CI, 1.19 to 1.30; p < 0.001) increased likelihood of not completing assigned PROMs, respectively. Conclusions:. The rate of completion of PROMs varies according to sociodemographic variables. This variability could bias clinical outcomes research in orthopaedic surgery. The present study highlights the need to uniformly increase completion rates so that outcomes research incorporates truly representative cohorts of patients treated. Furthermore, the use of these PROMs to guide health-care policy decisions necessitates a representative patient distribution to avoid bias in the health-care system. Level of Evidence:. Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence.
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- 2022
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17. Thromboprophylaxis for children hospitalized with COVID‐19 and MIS‐C
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Anna H. Schmitz, Kelly E. Wood, Elliot L. Burghardt, Bryan P. Koestner, Linder H. Wendt, Aditya V. Badheka, and Anjali A. Sharathkumar
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anticoagulants ,child ,COVID‐19 ,heparin ,low molecular weight ,MIS‐C ,Diseases of the blood and blood-forming organs ,RC633-647.5 - Abstract
Abstract Background Limited data exist about effective regimens for pharmacological thromboprophylaxis in children with acute coronavirus disease 2019 (COVID‐19) and multisystem inflammatory syndrome in children (MIS‐C). Objectives Study the outcomes of institutional thromboprophylaxis protocol for primary venous thromboembolism (VTE) prevention in children hospitalized with acute COVID‐19/MIS‐C. Methods This single‐center retrospective cohort study included consecutive children (aged less than 21 years) with COVID‐19/MIS‐C who received tailored intensity thromboprophylaxis, primarily with low‐molecular‐weight heparin, from April 2020 through October 2021. Thromboprophylaxis was given to those with moderate to severe disease based on the World Health Organization scale and exposure to two or more VTE risk factors. Therapeutic intensity was considered for severe illness. Clinical recovery along with D‐dimer improvement determined thromboprophylaxis duration. Outcomes were incident VTEs, bleeding, and mortality. Results Among 211 hospitalizations, 45 (21.3%) received thromboprophylaxis (COVID‐19, 16; MIS‐C, 29). Median age was 14.8 years (interquartile range [IQR], 8.9–16.1). Among 35 (77.8%) with severe illness, 27 (60.0%) required respiratory support, and 19 (42.2%) required an intensive care unit stay. Median hospitalization was 6 days (IQR, 5.0–10.5). Median thromboprophylaxis duration was 19 days (IQR, 6.0–31.0) with therapeutic intensity in 24 (53.3%) and prophylactic in 21 (46.7%). Outcomes were as follows: VTE, 1 (2.2%); death, 1 (2.2%, unrelated to bleeding/thrombosis); major/clinically relevant nonmajor bleeding, 0; and minor bleeding, 7 (15.5%). D‐dimer was elevated in a majority at diagnosis (median, 2.3; IQR, 1.2–3.3 mg/ml fibrinogen‐equivalent units) and was noninformative in assessing disease severity. D‐dimer normalized at thromboprophylaxis discontinuation. Conclusions Our experience of using clinically directed thromboprophylaxis with tailored intensity approach for children hospitalized with COVID‐19 and MIS‐C favors its inclusion in current standard of care. The role of D‐dimer in directing thromboprophylaxis management deserves further evaluation.
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- 2022
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18. Comprehensive and Comparative Analysis between Transfer Learning and Custom Built VGG and CNN-SVM Models for Wildfire Detection
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Jonnalagadda, Aditya V., Hashim, Hashim A., and Harris, Andrew
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Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Artificial Intelligence - Abstract
Contemporary Artificial Intelligence (AI) and Machine Learning (ML) research places a significant emphasis on transfer learning, showcasing its transformative potential in enhancing model performance across diverse domains. This paper examines the efficiency and effectiveness of transfer learning in the context of wildfire detection. Three purpose-built models -- Visual Geometry Group (VGG)-7, VGG-10, and Convolutional Neural Network (CNN)-Support Vector Machine(SVM) CNN-SVM -- are rigorously compared with three pretrained models -- VGG-16, VGG-19, and Residual Neural Network (ResNet) ResNet101. We trained and evaluated these models using a dataset that captures the complexities of wildfires, incorporating variables such as varying lighting conditions, time of day, and diverse terrains. The objective is to discern how transfer learning performs against models trained from scratch in addressing the intricacies of the wildfire detection problem. By assessing the performance metrics, including accuracy, precision, recall, and F1 score, a comprehensive understanding of the advantages and disadvantages of transfer learning in this specific domain is obtained. This study contributes valuable insights to the ongoing discourse, guiding future directions in AI and ML research. Keywords: Wildfire prediction, deep learning, machine learning fire, detection, Comment: In Proc. of the 2024 IEEE International Conference On Intelligent Computing in Data Sciences
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- 2024
19. Reasoning Elicitation in Language Models via Counterfactual Feedback
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Hüyük, Alihan, Xu, Xinnuo, Maasch, Jacqueline, Nori, Aditya V., and González, Javier
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Computer Science - Computation and Language ,Computer Science - Artificial Intelligence ,Computer Science - Machine Learning - Abstract
Despite the increasing effectiveness of language models, their reasoning capabilities remain underdeveloped. In particular, causal reasoning through counterfactual question answering is lacking. This work aims to bridge this gap. We first derive novel metrics that balance accuracy in factual and counterfactual questions, capturing a more complete view of the reasoning abilities of language models than traditional factual-only based metrics. Second, we propose several fine-tuning approaches that aim to elicit better reasoning mechanisms, in the sense of the proposed metrics. Finally, we evaluate the performance of the fine-tuned language models in a variety of realistic scenarios. In particular, we investigate to what extent our fine-tuning approaches systemically achieve better generalization with respect to the base models in several problems that require, among others, inductive and deductive reasoning capabilities.
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- 2024
20. Does Resident Participation Influence Surgical Time and Clinical Outcomes? An Analysis on Primary Bilateral Single-Staged Sequential Total Knee Arthroplasty
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Aditya V. Maheshwari, MD, Christopher T. Garnett, BA, Tzu H. Cheng, MD, Joshua R. Buksbaum, BS, Vivek Singh, MD, MPH, and Neil V. Shah, MD, MS
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Residency training ,Resident education ,Total knee arthroplasty ,Single-staged bilateral ,Postoperative outcomes ,Orthopaedic surgery ,Orthopedic surgery ,RD701-811 - Abstract
Background: Although several studies have indirectly compared teaching and nonteaching hospitals, results are conflicting, and evaluation of the direct impact of trainee involvement is lacking. We investigated the direct impact of resident participation in primary total knee arthroplasties (TKAs). Material and methods: Fifty patients undergoing single-staged sequential bilateral primary TKAs were evaluated. The more symptomatic side was performed by the attending surgeon first, followed by the contralateral side performed by a chief resident under direct supervision and assistance of the same attending surgeon. Surgery was subdivided into 8 critical steps on both sides. The overall time and critical stepwise surgical time and short-term clinical outcomes were then compared between the 2 sides. Results: The attending surgeon completed the surgery (skin incision to dressing) significantly faster than the resident (70.2 vs 96.9 minutes) by a mean of 26.7 minutes (P < .05) and was also faster in all steps. The most significant differences in time were in “exposure” (9.5 vs 16.5 minutes) and “closure” steps (13.2 vs 24.9 minites), all P < .001. Adverse events occurred in 7 patients; 5 of these resolved uneventfully. There were no significant differences in surgical complications, objective outcome scores, or patient satisfaction scores between both sides. Conclusion: Resident participation in TKA increased operative time without jeopardizing short-term patient clinical outcomes, satisfaction, and complications. This may alleviate concerns from patients and policymakers about TKA in an academic setting. Surgical “exposure” and “closure” were the most prolonged steps for the residents, and they may benefit with more focus and/or simulation studies during training.
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- 2022
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21. Coronal Heating as Determined by the Solar Flare Frequency Distribution Obtained by Aggregating Case Studies
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James Paul Mason, Alexandra Werth, Colin G. West, Allison Youngblood, Donald L. Woodraska, Courtney L. Peck, Arvind J. Aradhya, Yijian Cai, David Chaparro, James W. Erikson, Koushik Ganesan, T. R. Geerdts, Thi D Hoang, Thomas M. Horning, Yan Jin, Haixin Liu, Noah Lordi, Zheng Luo, Thanmay S. Menon, Josephine C. Meyer, Emma E Nelson, Kristin A. Oliver, Jorge L Ramirez Ortiz, Andrew Osborne, Alyx Patterson, Nick Pellatz, John Pitten, Nanako Shitara, Daniel Steckhahn, Aseem Visal, Hongda Wang, Chaoran Wang, Evan Wickenden, John Wilson, Mengyu Wu, Nikolay Yegovtsev, Ingrid H Zimmermann, James Holland Aaron, Jumana T. Abdullah, Jonathan M. Abrams, Riley Abrashoff, Andres B. Acevedo, Iker Acha, Daniela M. Meza Acosta, Megan M. Adam, Dante Q. Adams, Kalvyn N Adams, Elena R Adams, Zainab A. Akbar, Ushmi H. Akruwala, Adel Al-Ghazwi, Batool H. Alabbas, Areej A. Alawadhi, Yazeed A. Alharbi, Mohammed S. Alahmed, Mohammed A. Albakr, Yusef J. Albalushi, Jonathan Albaum, Ahmed Aldhamen, Nolan Ales, Mohammad Alesmail, Abdulelah Alhabeeb, Dania Alhamli, Isehaq Alhuseini, Suhail Alkaabi, Tameem Alkhezzi, Mohamed Alkubaisi, Nasser Allanqawi, Martin Allsbrook, Yousef A. Almohsen, Justin Thomas Almquist, Teeb Alnaji, Yousef A Alnasrallah, Nicholas Alonzi, Meshal Alosaimi, Emeen Alqabani, Mohammad Alrubaie, Reema A. Alsinan, Ava L. Altenbern, Abdullah Altokhais, Saleh A. Alyami, Federico Ameijenda, Hamzi Amer, Meggan Amos, Hunter J. Anderson, Carter Andrew, Jesse C Andringa, Abigail Angwin, Gabreece Van Anne, Andrew Aramians, Camila Villamil Arango, Jack. W. Archibald, Brian A. Arias-Robles, Maryam Aryan, Kevin Ash, Justin Astalos, N. S. Atchley-Rivers, Dakota N. Augenstein, Bryce W. Austin, Abhinav Avula, Matthew C. Aycock, Abdulrahman A. Baflah, Sahana Balaji, Brian Balajonda, Leo M Balcer, James O. Baldwin, David J Banda, Titus Bard, Abby Barmore, Grant M. Barnes, Logan D. W. Barnhart, Kevin M. Barone, Jessica L. Bartman, Claire Bassel, Catalina S Bastias, Batchimeg Bat-Ulzii, Jasleen Batra, Lexi Battist, Joshua Bay, Simone Beach, Sara Beard, Quinn I Beato, Ryan Beattie, Thomas Beatty, Tristan De La Beaujardiere, Jacob N. Beauprez, M. G. Beck, Lily Beck, Simone E. Becker, Braden Behr, Timothy A. Behrer, Joshua Beijer, Brennan J. Belei, Annelene L. Belknap, Aislyn Bell, Caden Bence, Evan Benke, Naomi Berhanu, Zachary D. Berriman-Rozen, Chrisanna Bertuccio, Owen A. Berv, Blaine B. Biediger, Samuel J Biehle, Brennen Billig, Jacob Billingsley, Jayce A. Billman, Connor J. Biron, Gabrielle E. Bisacca, Cassidy A. Blake, Guillermo Blandon, Olivia Blevins, Ethan Blouin, Michal Bodzianowski, Taylor A. Boeyink, Matthew Bondar, Lauren Bone, Alberto Espinosa De Los Monteros Bonilla, William T Borelli, Luke R. Borgerding, Troy Bowen, Christine Boyer, Aidan Boyer, Aidan P. Boyle, Tom Boyne, Donovan Branch, Ariana E. Brecl, David J. Brennan, Alexander J Brimhall, Jennifer L. Brockman, Sarah Brookins, Gabriel T. Brown, Cameron L. Brown, Ryan Brown, Jordi Brownlow, Grant Brumage-Heller, Preston J. Brumley, Samuel Bryan, A. Brzostowicz, Maryam Buhamad, Gigi Bullard-Connor, J. R. Ramirez Bunsow, Annemarie C. Burns, John J. Burritt, Nicholas David Burton, Taylor Burton, Celeste Busch, Dylan R. Butler, B. W. Buxton, Malena C. Toups, Carter C. Cabbage, Breonna Cage, Jackson R. Cahn, Andrew J Campbell, Braden P. Canales, Alejandro R. Cancio, Luke Carey, Emma L. Carillion, Michael Andrew Carpender, Emily Carpenter, Shivank Chadda, Paige Chambers, Jasey Chanders, Olivia M. Chandler, Ethan C. Chang, Mitchell G. Chapman, Logan T. Chapman, S. Chavali, Luis Chavez, Kevin Chen, Lily Chen, Sam Chen, Judy Chen, Jenisha Chhetri, Bradyn Chiles, Kayla M. Chizmar, Katherine E Christiansen, Nicholas A. Cisne, Alexis Cisneros, David B. Clark, Evelyn Clarke, Peter C Clarkson, Alexis R. Clausi, Brooke Cochran, Ryan W. Coe, Aislinn Coleman-Plante, Jake R. Colleran, Zachary Colleran, Curran Collier, Nathaniel A. Collins, Sarah Collins, Jack C. Collins, Michael Colozzi, Aurora Colter, Rebecca A. Cone, Thomas C. Conroy, Reese Conti, Charles J. Contizano, Destiny J. Cool, Nicholas M. Cooper, Jessica S Corbitt, Jonas Courtney, Olivia Courtney, Corben L. Cox, Wilmsen B. Craig, Joshua B. Creany, Anastasia Crews, K. A. Crocker, A. J. Croteau, Christian J. Crow, Zoe Cruse, Avril Cruz, Tyler L. Curnow, Hayden Current, Riley T. Curry, Libby Cutler, Aidan St. Cyr, Frederick M. Dabberdt, Johnston Daboub, Olivia Damgaard, Swagatam Das, Emma A. B. Davis, Elyse Debarros, Sean Deel, Megan E. Delasantos, Tianyue Deng, Zachary Derwin, Om Desai, Kai Dewey, John S. Dias, Kenzie A. Dice, R. Dick, Cyrus A. Dicken, Henry Dietrick, Alexis M. Dinser, Alyssa M. Dixon, Thomas J. Dixon, Helen C. Do, Chris H Doan, Connor Doane, Joshua Dodrill, Timothy Doermer, Lizbeth Montoya Dominguez, J. Dominguez, Emerson N. Domke, Caroline R. Doran, Jackson A. Dorr, Philip Dorricott, Danielle C. Dresdner, Michael Driscoll, Kailer H. Driscoll, Sheridan J. Duncan, Christian Dunlap, Gabrielle M. Dunn, Tien Q. Duong, Tomi Oshima Dupeyron, Peter Dvorak, Andrew East, Andrew N. East, Bree Edwards, Lauren Ehrlich, Sara I. Elbashir, Rasce Engelhardt, Jacob Engelstad, Colin England, Andrew Enrich, Abbey Erickson, Benjamin Erickson, Nathan Evans, Calvin A Ewing, Elizabeth A. Eyeson, Ian Faber, Avery M. Fails, John T Fauntleroy, Kevin Fell, Zitian Feng, Logan D. Fenwick, Nikita Feoktistov, Ryann Fife, John Alfred D. Figueirinhas, Jean-Paul Fisch, Emmalee Fischer, Jules Fischer-White, Aidan F. Fitton, Alexander Fix, Lydia Flackett, Fernando Flores, Aidan Floyd, Leonardo Del Foco, Adeduni Folarin, Aidan E. Forbes, Elise Fortino, Benjamin L. Fougere, Alexandra A. Fowler, Margaret Fox, James M. French, Katherine V. French, Florian G. Frick, Calvin R. Fuchs, Bethany E. S. Fuhrman, Sebastian Furney, Moutamen Gabir, Gabriela Galarraga, Skylar Gale, Keala C. Gapin, A. J. Garscadden, Rachel Gasser, Lily Gayou, Emily E. Gearhart, Jane Geisman, Julianne R. Geneser, Sl Genne, Julia G Gentile, Eleanor Gentry, Jacob D. George, Nathaniel James Georgiades, Phillip Gerhardstein, Clint Gersabeck, Bandar Abu Ghaith, Dorsa Ghiassi, B. C. Giebner, Dalton Gilmartin, Connor B. Gilpatrick, Michael Gjini, Olivia Golden, Nathan T. Golding, C. A. Goldsberry, Angel R. Gomez, Angel A. Gomez, Sean Gopalakrishnan, Mariam Gopalani, Nicholas Gotlib, Alaina S. Graham, Michael J Gray, Alannah H. Gregory, Joshua A. Gregory, Kristyn Grell, Justus Griego, Nicholas F. Griffin, Kyle J. Griffin, Matt Guerrero, Nicole Gunderson, Mutian Guo, E. R. Gustavsson, Grace K. Hach, L. N. Haile, Jessica Haines, Jack J. Mc Hale, Ryder Buchanan Hales, Mark S. Haley, Jacqueline L. Hall, Sean R. Hamilton, Soonhee Han, Tyler Hand, Luke C. Hanley, Connor M Hansen, Joshua A. Hansen, Jonathan Hansson, Tony Yunfei Hao, Nicholas Haratsaris, Isabelle Hardie, Dillon F. Hardwick, Cameron T. Hares, Logan Swous Harris, Coyle M. Harris, Omer Hart, Kyle Hashiro, Elsie Hattendorf, Calder Haubrich, Elijah Hawat, Griffin A. Hayrynen, Danielle A. Heintz, Tim Hellweg, Angel Hernandez, Emanuel Herrera, Robert N. Herrington, Tim Herwig, Troy M. Hesse, Quinn Hiatt, Lea Pearl Hibbard, Imari R. Hicks, Andrew J. Hicks, Nigel Highhouse, Annalise K. Hildebrand, Paula Hill, Hallie Hill, Evan Hintsa, Anna E. Hirschmann, Travis Hitt, Ella Ho, Isabelle J. Hoff, Alex Hoffman, Blake A. Hogen, Linda Horne, Timothy J Houck, Noah H. Howell, E. M. Hrudka, J. Hu, Jianyang Huang, Chenqi Huang, Shancheng Huang, Zachary A. Hudson, Nathan C. Hudson, Tyler J. Huebsch, Owen Hull, Samuel C Hunter, Troy Husted, Abigail P. Hutabarat, Leslie Huynh, Antonio E. Samour Ii, Yolande Idoine, Julia A. Ingram, Taro Iovan, Samuel A. Isert, Antonio Salcido-Alcontar Jr, Thomas Jacobsen, Alan A Jaimes, Connor Jameson, J. R. Jarriel, Sam Jarvis, Josh Jenkins, Alexander V. Jensen, Jacob Jeong, Luke A. Jeseritz, Trevor Jesse, Soo Yeun Ji, Yufan Jiang, Owen Johnson, Matthew Johnson, Sawyer Johnson, Julia Johnston, Braedon Y. Johnston, Olivia M. Jones, M. R. Jones, Tara Jourabchi, Tony A. House Jr., Parker Juels, Sabrina J. H. T. Kainz, Emily Kaiser, Nicolas Ian Kallemeyn, Madison H. Kalmus, Etash Kalra, Margaret Kamenetskiy, Jeerakit Kanokthippayakun, Shaun D. Kapla, Brennan J. Karsh, Caden J. Keating, Morgan A. Kelley, Michael P. Kelley, Nicholas Kelly, James Kelly, Teagan Kelly, Christopher M Kelly, Kellen Kennedy, Cayla J. Kennedy, Forrest Kennedy, Abigail Kennedy, Liana Kerr-Layton, Marilyn Ketterer, Ibraheem A. Khan, Usman Khan, Sapriya Khanal, Jack L. Kiechlin, Dominic Killian, Kevin Kim, Brian T. Kim, Matthew M. Kim, Jake Kim, Aspen Kimlicko, Isabel M Kipp, Hunter B. Kirkpatrick, Natalie Kissner, Emily R. Kite, Olivia R. Kleinhaus, Philip Whiting Knott, Will Koch, Greta Koenig, Emily Koke, Thomas Kokes, Yash S. Kothamdi, Zack Krajnak, Zoe M. Kresek, Dylan Kriegman, Jake E. Kritzberg, Davis J. Krueger, Bartlomiej Kubiak, Kirsten Kuehl, Chrisanne Kuester, Nicolas A. Kuiper, Aman Priyadarshi Kumar, Connor Kuybus, Daniel Kwiatkowski, Quintin Y. Lafemina, Kevin Lacjak, Kyle Lahmers, Antonia Lam, Kalin Landrey, Maxwell B. Lantz, Zachary Larter, Benjamin P. Lau, Megan Lauzon, Rian Lawlor, Tyler Learned, E. C. Lee, Junwon Lee, Adrianna J. Lee, Justin Lee, Alexis Ying-Shan Lee, Christian J Lee, Nathaniel F. Lee, Linzhi Leiker, Dylan Lengerich, Cecilia Leoni, Adrienne R. Lezak, David Y. Li, Isaac Li, Ryan Z. Liao, Bridget Linders, Morgan I Linger, Katherine B. Linnane, Sam Lippincott, Barrett Lister, Shelby D Litton, Nianzi Liu, Steven Y. Liu, Timothy W. Logan, Nathan Londres, Mia C. Lonergan, Emily Lookhoff, N. E. Loomis, Christian Lopez, Justin Loring, Jeffrey Lucca, Dax Lukianow, Nathan M. Cheang, William Macdonald, Claire A. Madonna, Kasey O. Madsen, Tiffany E. Maksimuk, Macguire Mallory, Ryan A. Malone, Blake Maly, Xander R. Manzanares, Aimee S. Maravi, Serafima M. Marcus, Nasreen Marikar, Josie A. Marquez, Mathew J. Marquez, Lauren Marsh, Toni Marsh, Logan S. Martin, Alexa M. Martinez, Jose R. Martinez, Hazelia K. Martinez, Cara Martyr, Mirna Masri, Giorgio Matessi, Adam Izz Khan Mohd Reduan Mathavan, Randi M. Mathieson, Kabir P. Mathur, Graham Mauer, Victoria A. Mayer, Liam Mazzotta, Glen S. Mccammon, Rowan Mcconvey, Tyler Mccormick, Andrew Mccoy, Kelleen Mcentee, Meaghan V. Mcgarvey, Riley M. Mcgill, James K. Mcintyre, Finbar K. Mckemey, Zane Mcmorris, Jesse J. Mcmullan, Ella Mcquaid, Caden Mcvey, Kyle Mccurry, Mateo M. Medellin, Melissa Medialdea, Amar Mehidic, Stella Meillon, Jonah B. Meiselman-Ashen, Sarah Mellett, Dominic Menassa, Citlali Mendez, Patricia Mendoza-Anselmi, Riley Menke, Sarah Mesgina, William J. Mewhirter, Ethan Meyer, Aya M. Miften, Ethan J. Miles, Andrew Miller, Joshua B. Miller, Emily B. Millican, Sarah J. Millican, Dylan P. Mills, Josh Minimo, Jay H. Misener, Alexander J. Mitchell, Alexander Z. Mizzi, Luis Molina-Saenz, Tyler S Moll, Hayden Moll, Maximus Montano, Michael Montoya, Eli Monyek, Jacqueline Rodriguez Mora, Gavin Morales, Genaro Morales, Annalise M. Morelock, Cora Morency, Angel J. Moreno, Remy Morgan, Alexander P. Moss, Brandon A. Muckenthaler, Alexander Mueller, Owen T. Mulcahy, Aria T. Mundy, Alexis A. Muniz, Maxwell J. Murphy, Madalyn C. Murphy, Ryan C. Murphy, Tyler Murrel, Andrew J. Musgrave, Michael S. Myer, Kshmya Nandu, Elena R. Napoletano, Abdulaziz Naqi, Anoothi Narayan, Liebe Nasser, Brenna K Neeland, Molly Nehring, Maya Li Nelson, Lena P. Nguyen, Lena Nguyen, Leonardo Nguyen, Valerie A. Nguyen, Khoa D Nguyen, Kelso Norden, Cooper Norris, Dario Nuñez, Rosemary O. Nussbaum, Cian O’Sullivan, Ian O’Neill, S. H. Oakes, Anand Odbayar, Caleb Ogle, Sean Oishi-Holder, Nicholas Olguin, Nathaniel P. Olson, Jason Ong, Elena N. Opp, Dan Orbidan, Ryan Oros, Althea E. Ort, Matthew Osborn, Austin Osogwin, Grant Otto, Jessica Oudakker, Igor Overchuk, Hannah M. Padgette, Jacqueline Padilla, Mallory Palizzi, Madeleine L. Palmgren, Adler Palos, Luke J. Pan, Nathan L. Parker, Sasha R. Parker, Evan J. Parkinson, Anish Parulekar, Paige J. Pastor, Kajal Patel, Akhil Patel, Neil S. Patel, Samuel Patti, Catherine Patton, Genevieve K. Payne, Matthew P. Payne, Harrison M. Pearl, Charles B. Beck Von Peccoz, Alexander J. Pedersen, Lily M. Pelster, Munisettha E. Peou, J. S. Perez, Freddy Perez, Anneliese Pesce, Audrey J. Petersen, B. Peterson, Romeo S. L. Petric, Joshua Pettine, Ethan J. Phalen, Alexander V. Pham, Denise M. Phan, Callie C Pherigo, Lance Phillips, Justin Phillips, Krista Phommatha, Alex Pietras, Tawanchai P. Pine, Sedique Pitsuean-Meier, Andrew M. Pixley, Will Plantz, William C. Plummer, Kaitlyn E. Plutt, Audrey E. Plzak, Kyle Pohle, Hyden Polikoff, Matthew Pollard, Madelyn Polly, Trevor J. Porter, David Price, Nicholas K. Price, Gale H. Prinster, Henry Austin Propper, Josh Quarderer, Megan S. Quinn, Oliver Quinonez, Devon Quispe, Cameron Ragsdale, Anna L. Rahn, M. Rakhmonova, Anoush K Ralapanawe, Nidhi Ramachandra, Nathaniel Ramirez, Ariana C. Ramirez, Sacha Ramirez, Parker Randolph, Anurag Ranjan, Frederick C Rankin, Sarah Grace Rapaport, Nicholas O Ratajczyk, Mia G. V. Ray, Brian D. Reagan, John C. Recchia, Brooklyn J. Reddy, Joseph Reed, Charlie Reed, Justin Reeves, Eileen N. Reh, Ferin J. Von Reich, Andrea B. Reyna, Alexander Reynolds, Hope Reynolds, Matthew Rippel, Guillermo A. Rivas, Anna Linnea Rives, Amanda M. Robert, Samuel M. Robertson, Maeve Rodgers, Stewart Rojec, Andres C. Romero, Ryan Rosasco, Beth Rossman, Michael Rotter, Tyndall Rounsefell, Charlotte Rouse, Allie C. Routledge, Marc G. Roy, Zoe A. Roy, Ryan Ruger, Kendall Ruggles-Delgado, Ian C. Rule, Madigan Rumley, Brenton M. Runyon, Collin Ruprecht, Bowman Russell, Sloan Russell, Diana Ryder, David Saeb, J. Salazar, Violeta Salazar, Maxwell Saldi, Jose A. Salgado, Adam D. Salindeho, Ethan S. Sanchez, Gustavo Sanchez-Sanchez, Darian Sarfaraz, Sucheta Sarkar, Ginn A. Sato, Carl Savage, Marcus T. Schaller, Benjamin T. Scheck, Jared A. W. Schlenker, Matthew J Schofer, Stephanie H. Schubert, Courtney Schultze, Grace K Schumacher, Kasper Seglem, Lauren Serio, Octave Seux, Hannan Shahba, Callie D. Shannahan, Shajesh Sharma, Nathan Shaver, Timothy Shaw, Arlee K. Shelby, Emma Shelby, Grace Shelchuk, Tucker Sheldrake, Daniel P. Sherry, Kyle Z. Shi, Amanda M. Shields, Kyungeun Shin, Michael C. Shockley, Dominick Shoha, Jadon Shortman, Mitchell Shuttleworth, Lisa Sibrell, Molly G. Sickler, Nathan Siles, H. K. Silvester, Conor Simmons, Dylan M. Simone, Anna Simone, Savi Singh, Maya A. Singh, Madeline Sinkovic, Leo Sipowicz, Chris Sjoroos, Ryan Slocum, Colin Slyne, Korben Smart, Alexandra N. Smith, Kelly Smith, Corey Smith, Elena K. Smith, Samantha M. Smith, Percy Smith, Trevor J Smith, G. L. Snyder, Daniel A. Soby, Arman S. Sohail, William J. Solorio, Lincoln Solt, Caitlin Soon, Ava A Spangler, Benjamin C. Spicer, Ashish Srivastava, Emily Stamos, Peter Starbuck, Ethan K. Stark, Travis Starling, Caitlyn Staudenmier, Sheen L. Steinbarth, Christopher H. Steinsberger, Tyler Stepaniak, Ellie N. Steward, Trey Stewart, T. C. Stewart, Cooper N. Stratmeyer, Grant L. Stratton, Jordin L. Stribling, S. A Sulaiman, Brandon J Sullivan, M. E. Sundell, Sohan N. Sur, Rohan Suri, Jason R. Swartz, Joshua D. Sweeney, Konner Syed, Emi Szabo, Philip Szeremeta, Michael-Tan D. Ta, Nolan C. Tanguma, Kyle Taulman, Nicole Taylor, Eleanor Taylor, Liam C. Taylor, K. E. Tayman, Yesica Tellez, Richard Terrile, Corey D Tesdahl, Quinn N. Thielmann, Gerig Thoman, Daniel Thomas, Jeffrey J. Thomas, William N. Thompson, Noah R. Thornally, Darien P. Tobin, Kelly Ton, Nathaniel J. Toon, Kevin Tran, Bryn Tran, Maedee Trank-Greene, Emily D. Trautwein, Robert B. Traxler, Judah Tressler, Tyson R. Trofino, Thomas Troisi, Benjamin L. Trunko, Joshua K. Truong, Julia Tucker, Thomas D Umbricht, C. H. Uphoff, Zachary T. Upthegrove, Shreenija Vadayar, Whitney Valencia, Mia M. Vallery, Eleanor Vanetten, John D. Vann, Ilian Varela, Alexandr Vassilyev, Nicholas J. Vaver, Anjali A. Velamala, Evan Vendetti, Nancy Ortiz Venegas, Aditya V. Vepa, Marcus T. Vess, Jenna S. Veta, Andrew Victory, Jessica Vinson, Connor Maklain Vogel, Michaela Wagoner, Steven P. Wallace, Logan Wallace, Caroline Waller, Jiawei Wang, Keenan Warble, N. R. D. Ward-Chene, James Adam Watson, Robert J. Weber, Aidan B. Wegner, Anthony A Weigand, Amanda M. Weiner, Ayana West, Ethan Benjamin Wexler, Nicola H. Wheeler, Jamison R. White, Zachary White, Oliver S. White, Lloyd C. Whittall, Isaac Wilcove, Blake C. Wilkinson, John S. Willard, Abigail K. Williams, Sajan Williams, Orion K. Wilson, Evan M. Wilson, Timothy R. Wilson, Connor B. Wilson, Briahn Witkoff, Aubrey M. Wolfe, Jackson R. Wolle, Travis M. Wood, Aiden L. Woodard, Katelynn Wootten, Catherine Xiao, Jianing Yang, Zhanchao Yang, Trenton J. Young, Isabel Young, Thomas Zenner, Jiaqi Zhang, Tianwei Zhao, Tiannie Zhao, Noah Y. Zhao, Chongrui Zhou, Josh J Ziebold, Lucas J. Ziegler, James C. Zygmunt, Jinhua Zhang, and H. J. Lewandowski
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Solar physics ,Solar flares ,Astrostatistics distributions ,Astrophysics ,QB460-466 - Abstract
Flare frequency distributions represent a key approach to addressing one of the largest problems in solar and stellar physics: determining the mechanism that counterintuitively heats coronae to temperatures that are orders of magnitude hotter than the corresponding photospheres. It is widely accepted that the magnetic field is responsible for the heating, but there are two competing mechanisms that could explain it: nanoflares or Alfvén waves. To date, neither can be directly observed. Nanoflares are, by definition, extremely small, but their aggregate energy release could represent a substantial heating mechanism, presuming they are sufficiently abundant. One way to test this presumption is via the flare frequency distribution, which describes how often flares of various energies occur. If the slope of the power law fitting the flare frequency distribution is above a critical threshold, α = 2 as established in prior literature, then there should be a sufficient abundance of nanoflares to explain coronal heating. We performed >600 case studies of solar flares, made possible by an unprecedented number of data analysts via three semesters of an undergraduate physics laboratory course. This allowed us to include two crucial, but nontrivial, analysis methods: preflare baseline subtraction and computation of the flare energy, which requires determining flare start and stop times. We aggregated the results of these analyses into a statistical study to determine that α = 1.63 ± 0.03. This is below the critical threshold, suggesting that Alfvén waves are an important driver of coronal heating.
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- 2023
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22. Multisystem Inflammatory Syndrome in a Child with Post-acute COVID-19 Infection Presenting as Kawasaki-like Illness
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Venkat Reddy, Aditya V Kabra, and Sudharshan Raj Chitgupiker
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covid-19 ,iv immunoglobulin ,kawasaki mimic ,multisystem inflammatory syndrome in childhood ,Pediatrics ,RJ1-570 - Abstract
Amidst increasing concerns of children presenting with multisystem inflammatory syndrome (MIS) simulating Kawasaki disease, we report a child with MIS-C presented with abdominal pain and fever. A 9-year-old child presented with acute febrile illness, abdominal pain, and later had skin rash with mild bulbar conjunctivitis, oral mucosal erythema, and posterior pharyngeal congestion. Investigations showed high inflammatory markers, leukopenia with neutrophil predominance with high CRP (205 mg/L), ESR (89), D-dimer, and ferritin. Reverse transcriptase polymerase chain reaction (RT-PCR) for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was negative, but antibodies for SARS-CoV-2 was strongly reactive. During hospital stay, he developed arthralgia and tachypnea requiring oxygen. He was treated with oxygen, IV immunoglobulins, aspirin, steroids, and low-molecular-weight (LMW) heparin. Child has responded well, with fever and rash subsiding in 24 hours and no further complications.
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- 2022
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23. Prediction of Postoperative Delirium in Geriatric Hip Fracture Patients: A Clinical Prediction Model Using Machine Learning Algorithms
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Jacobien H. F. Oosterhoff MD, Aditya V. Karhade MD, MBA, Tarandeep Oberai PT, Esteban Franco-Garcia MD, Job N. Doornberg MD, PhD, and Joseph H. Schwab MD, MS
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Orthopedic surgery ,RD701-811 ,Geriatrics ,RC952-954.6 - Abstract
Introduction Postoperative delirium in geriatric hip fracture patients adversely affects clinical and functional outcomes and increases costs. A preoperative prediction tool to identify high-risk patients may facilitate optimal use of preventive interventions. The purpose of this study was to develop a clinical prediction model using machine learning algorithms for preoperative prediction of postoperative delirium in geriatric hip fracture patients. Materials & Methods Geriatric patients undergoing operative hip fracture fixation were queried in the American College of Surgeons National Surgical Quality Improvement Program database (ACS NSQIP) from 2016 through 2019. A total of 28 207 patients were included, of which 8030 (28.5%) developed a postoperative delirium. First, the dataset was randomly split 80:20 into a training and testing subset. Then, a random forest (RF) algorithm was used to identify the variables predictive for a postoperative delirium. The machine learning-model was developed on the training set and the performance was assessed in the testing set. Performance was assessed by discrimination (c-statistic), calibration (slope and intercept), overall performance (Brier-score), and decision curve analysis. Results The included variables identified using RF algorithms were (1) age, (2) ASA class, (3) functional status, (4) preoperative dementia, (5) preoperative delirium, and (6) preoperative need for mobility-aid. The clinical prediction model reached good discrimination (c-statistic = .79), almost perfect calibration (intercept = −.01, slope = 1.02), and excellent overall model performance (Brier score = .15). The clinical prediction model was deployed as an open-access web-application: https://sorg-apps.shinyapps.io/hipfxdelirium/ . Discussion & Conclusions We developed a clinical prediction model that shows promise in estimating the risk of postoperative delirium in geriatric hip fracture patients. The clinical prediction model can play a beneficial role in decision-making for preventative measures for patients at risk of developing a delirium. If found to be externally valid, clinicians might use the available web-based application to help incorporate the model into clinical practice to aid decision-making and optimize preoperative prevention efforts.
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- 2021
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24. BailOut angioplasty in a case of thrombus migration
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Rohit Rai, Shrishail P Jalkote, Shakil S Shaikh, Aditya V Gupta, and Narender Omprakash Bansal
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angioplasty ,cardiogenic shock ,thrombus migration ,Diseases of the circulatory (Cardiovascular) system ,RC666-701 - Abstract
Acute myocardial infarction with high thrombus burden poses a challenge to the operating surgeon. Thrombus migration can worsen the clinical condition by further occluding flowing vessels. In our case of acute anterior wall myocardial infarction, thrombus migration from left anterior descending (LAD) caused obstruction of the left circumflex artery (LCX), and ramus. Bailout percutaneous transluminal coronary angioplasty (PTCA) was done to LCX, ramus, and left main which helped the patient hemodynamically but soon succumbed to sudden cardiorespiratory arrest. Hence, bailout PTCA remains a challenging procedure.
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- 2021
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25. Transient tunnel vision as initial presentation of anti-MOG antibody positive optic neuritis
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Appaswamy T Prabhakar, Murali Rayani, Vamsi Krishna, Atif Sheikh, Ajith Sivadasan, Aditya V Nair, and Vivek Mathew
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Neurology. Diseases of the nervous system ,RC346-429 - Published
- 2021
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26. The Revolving Door Flap: Revisiting an Elegant but Forgotten Flap for Ear Defect Reconstruction
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Anupam Golash, Sudipta Bera, Aditya V. Kanoi, and Abhijit Golash
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ear reconstruction ,conchal defect ,revolving door flap ,subcutaneous pedicle ,Surgery ,RD1-811 - Abstract
Background The revolving door flap, although well described in the literature, is not widely used in general plastic surgery practice. The flap has been used for anterior auricular and conchal defects and is considered elegant for its unique flap design and peculiarity of flap harvest. However, due to its use for a very specific purpose and unique flap harvest technique that may be difficult to grasp, the flap is not very popular in reconstructive practice. Objectives This study aims to evaluate the understanding and learning curve of the revolving door flap, assess surgical outcome, and reemphasize its utility and elegance in reconstruction of ear defects. Methodology This is a case series of nine surgeries performed between January 2014 and 2018. Three cases were performed by the senior author and six cases by two junior authors. Patients were observed for complications and aesthetic outcomes. Results The mean dimension of the flaps was 27.22 mm × 22.78 mm. The mean operative time was 56.56 minutes (standard deviation 22.50, standard error of the mean 7.5). Flap congestion was noted in three cases postoperatively which resolved completely by the second week. Major “pinning” of the ear was noted in four cases. Conclusion Though infrequently performed, the revolving door flap has an easy learning curve once the proper harvest technique and flap movement has been grasped. The flap harvest is convenient, safe, and yields predictable results. Not only is total or partial flap loss extremely rare, the flap is sensate, color match is good, auricular contour is maintained, and the donor site can be closed primarily and remains well hidden.
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- 2020
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27. Use of Human Lung Tissue Models for Screening of Drugs against SARS-CoV-2 Infection
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Alexander J. McAuley, Petrus Jansen van Vuren, Muzaffar-Ur-Rehman Mohammed, Faheem, Sarah Goldie, Shane Riddell, Nathan J. Gödde, Ian K. Styles, Matthew P. Bruce, Simran Chahal, Stephanie Keating, Kim R. Blasdell, Mary Tachedjian, Carmel M. O’Brien, Nagendrakumar Balasubramanian Singanallur, John Noel Viana, Aditya V. Vashi, Carl M. Kirkpatrick, Christopher A. MacRaild, Rohan M. Shah, Elizabeth Vincan, Eugene Athan, Darren J. Creek, Natalie L. Trevaskis, Sankaranarayanan Murugesan, Anupama Kumar, and Seshadri S. Vasan
- Subjects
COVID-19 ,CoviRx.org ,therapeutics ,drug repurposing ,3D tissue models ,Microbiology ,QR1-502 - Abstract
The repurposing of licenced drugs for use against COVID-19 is one of the most rapid ways to develop new and alternative therapeutic options to manage the ongoing pandemic. Given circa 7817 licenced compounds available from Compounds Australia that can be screened, this paper demonstrates the utility of commercially available ex vivo/3D airway and alveolar tissue models. These models are a closer representation of in vivo studies than in vitro models, but retain the benefits of rapid in vitro screening for drug efficacy. We demonstrate that several existing drugs appear to show anti-SARS-CoV-2 activity against both SARS-CoV-2 Delta and Omicron Variants of Concern in the airway model. In particular, fluvoxamine, as well as aprepitant, everolimus, and sirolimus, has virus reduction efficacy comparable to the current standard of care (remdesivir, molnupiravir, nirmatrelvir). Whilst these results are encouraging, further testing and efficacy studies are required before clinical use can be considered.
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- 2022
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28. Does Reasoning Emerge? Examining the Probabilities of Causation in Large Language Models
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González, Javier and Nori, Aditya V.
- Subjects
Computer Science - Machine Learning - Abstract
Recent advances in AI have been significantly driven by the capabilities of large language models (LLMs) to solve complex problems in ways that resemble human thinking. However, there is an ongoing debate about the extent to which LLMs are capable of actual reasoning. Central to this debate are two key probabilistic concepts that are essential for connecting causes to their effects: the probability of necessity (PN) and the probability of sufficiency (PS). This paper introduces a framework that is both theoretical and practical, aimed at assessing how effectively LLMs are able to replicate real-world reasoning mechanisms using these probabilistic measures. By viewing LLMs as abstract machines that process information through a natural language interface, we examine the conditions under which it is possible to compute suitable approximations of PN and PS. Our research marks an important step towards gaining a deeper understanding of when LLMs are capable of reasoning, as illustrated by a series of math examples.
- Published
- 2024
29. Towards Verifying Exact Conditions for Implementations of Density Functional Approximations
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Helal, Sameerah, Tao, Zhe, Rubio-González, Cindy, Gygi, Francois, and Thakur, Aditya V.
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Condensed Matter - Materials Science ,Computer Science - Logic in Computer Science - Abstract
Density Functional Theory (DFT) is used extensively in the computation of electronic properties of matter, with various applications. Approximating the exchange-correlation (XC) functional is the key to the Kohn-Sham DFT approach, the basis of most DFT calculations. The choice of this density functional approximation (DFA) depends crucially on the particular system under study, which has resulted in the development of hundreds of DFAs. Though the exact density functional is not known, researchers have discovered analytical properties of this exact functional. Furthermore, these exact conditions are used when designing DFAs. We present XCVerifier, the first approach for verifying whether a DFA implementation satisfies the DFT exact conditions. XCVerifier was evaluated on five DFAs from the popular Libxc library and seven exact conditions from recent work. XCVerifier was able to verify or find violations for a majority of the DFA/condition pairs, demonstrating the feasibility of using formal methods to verify DFA implementations., Comment: Accepted paper at Correctness 2024
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- 2024
30. Recent Academic Research on Clinically Relevant Digital Measures: Systematic Review
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Md Mobashir Hasan Shandhi, Jennifer C Goldsack, Kyle Ryan, Alexandra Bennion, Aditya V Kotla, Alina Feng, Yihang Jiang, Will Ke Wang, Tina Hurst, John Patena, Simona Carini, Jeanne Chung, and Jessilyn Dunn
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Computer applications to medicine. Medical informatics ,R858-859.7 ,Public aspects of medicine ,RA1-1270 - Abstract
BackgroundDigital clinical measures collected via various digital sensing technologies such as smartphones, smartwatches, wearables, ingestibles, and implantables are increasingly used by individuals and clinicians to capture health outcomes or behavioral and physiological characteristics of individuals. Although academia is taking an active role in evaluating digital sensing products, academic contributions to advancing the safe, effective, ethical, and equitable use of digital clinical measures are poorly characterized. ObjectiveWe performed a systematic review to characterize the nature of academic research on digital clinical measures and to compare and contrast the types of sensors used and the sources of funding support for specific subareas of this research. MethodsWe conducted a PubMed search using a range of search terms to retrieve peer-reviewed articles reporting US-led academic research on digital clinical measures between January 2019 and February 2021. We screened each publication against specific inclusion and exclusion criteria. We then identified and categorized research studies based on the types of academic research, sensors used, and funding sources. Finally, we compared and contrasted the funding support for these specific subareas of research and sensor types. ResultsThe search retrieved 4240 articles of interest. Following the screening, 295 articles remained for data extraction and categorization. The top five research subareas included operations research (research analysis; n=225, 76%), analytical validation (n=173, 59%), usability and utility (data visualization; n=123, 42%), verification (n=93, 32%), and clinical validation (n=83, 28%). The three most underrepresented areas of research into digital clinical measures were ethics (n=0, 0%), security (n=1, 0.5%), and data rights and governance (n=1, 0.5%). Movement and activity trackers were the most commonly studied sensor type, and physiological (mechanical) sensors were the least frequently studied. We found that government agencies are providing the most funding for research on digital clinical measures (n=192, 65%), followed by independent foundations (n=109, 37%) and industries (n=56, 19%), with the remaining 12% (n=36) of these studies completely unfunded. ConclusionsSpecific subareas of academic research related to digital clinical measures are not keeping pace with the rapid expansion and adoption of digital sensing products. An integrated and coordinated effort is required across academia, academic partners, and academic funders to establish the field of digital clinical measures as an evidence-based field worthy of our trust.
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- 2021
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31. Interleaving static analysis and LLM prompting with applications to error specification inference
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Chapman, Patrick J., Rubio-González, Cindy, and Thakur, Aditya V.
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- 2025
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32. Author Correction: Dysfunctional ERG signaling drives pulmonary vascular aging and persistent fibrosis
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Nunzia Caporarello, Jisu Lee, Tho X. Pham, Dakota L. Jones, Jiazhen Guan, Patrick A. Link, Jeffrey A. Meridew, Grace Marden, Takashi Yamashita, Collin A. Osborne, Aditya V. Bhagwate, Steven K. Huang, Roberto F. Nicosia, Daniel J. Tschumperlin, Maria Trojanowska, and Giovanni Ligresti
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Science - Published
- 2022
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33. Clinical profile and management of patients with acute pulmonary thromboembolism – a single centre, large observational study from India
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Thoddi Ramamurthy Muralidharan, Sankaran Ramesh, Balakrishnan Vinod Kumar, Aditya V. Ruia, Mohan Kumar, Akshaya Gopalakrishnan, Gurpreet S. Johal, Amit Hooda, Rohit Malhotra, Reza Masoomi, Mahalakshmi Ramadoss, Vinodhini Subramanian, Maria J. Kalsingh, Panchanatham Manokar, Jebaraj Rathinasamy, Shanmugasundram Sadhanandham, Jayanthy V. Balasubramaniyan, Preetam Krishnamurthy, Jayanthy S. Murthy, Sadagopan Thanikachalam, and Nagendra Boopathy Senguttuvan
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Diseases of the circulatory (Cardiovascular) system ,RC666-701 ,Diseases of the respiratory system ,RC705-779 - Abstract
Acute pulmonary thromboembolism is associated with high mortality, similar to that of myocardial infarction and stroke. We studied the clinical presentation and management of pulmonary thromboembolism in the Indian population. An analysis of 140 patients who presented with acute pulmonary thromboembolism at a large volume center in India from June 2015 through December 2018 was performed. The mean age of our study population was 50 years with 59% being male. Comorbidities including deep vein thrombosis, diabetes mellitus, hypertension, and chronic obstructive pulmonary disease were present in 52.9%, 40%, 35.7% and 7.14% of patients, respectively. Out of 140 patients, 40 (28.6%) patients had massive pulmonary thromboembolism, 36 (25.7%) sub-massive pulmonary thromboembolism, and 64 (45.7%) had low-risk pulmonary thromboembolism. Overall, in-hospital mortality was 25.7%. Multivariate regression analysis found chronic kidney disease and pulmonary thromboembolism severity to be the only independent risk factors. Thrombolysis was performed in 62.5% of patients with a massive pulmonary thromboembolism and 63.9% of patients with a sub-massive pulmonary thromboembolism. In the massive pulmonary thromboembolism group, patients receiving thrombolytic therapy had lower mortality compared with patients who did not receive therapy ( p =0.022), whereas this difference was not observed in patients in the sub-massive pulmonary thromboembolism group. We conclude that patients with acute pulmonary thromboembolism in India presented more than a decade earlier than our western counterparts, and it was associated with poor clinical outcomes. Thrombolysis was associated with significantly reduced in-hospital mortality in patients with massive pulmonary thromboembolism.
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- 2021
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34. Agrowaste-Derived ‘Natural’ Carbon Nanomaterials (NCNM) with Versatile Applications: Bacterial Cellulose
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Rajwade, Jyutika M., Kulkarni, Snehal S., Wadekar, Aditya V., Khandagale, Aniket S., Lockwood, David J., Series Editor, Talreja, Neetu, editor, Chauhan, Divya, editor, and Ashfaq, Mohammad, editor
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- 2025
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35. Defining giant mandibular ameloblastomas – Is a separate clinical sub-entity warranted?
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Aditya V. Kanoi, Tibar Banerjee, Narayanamurthy Sundaramurthy, Arindam Sarkar, Pooja Kanoi, and Sushovan Saha
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ameloblastoma ,fibula free flap ,jaw neoplasms ,mandibular reconstruction ,non-vascularised bone graft ,segmental mandibulectomy ,Surgery ,RD1-811 - Abstract
Context: The term giant mandibular ameloblastoma (GMAs) while being in popular usage in the medical literature remains largely equivocal. Although a few authors have in the past attempted to ascribe definite criteria to this entity, these are by and large arbitrary and without any benefit in decision-making or contributing to its management. Aims: The aim of this study is to propose a set of objective criteria for GMAs that can be clinically correlated and thereby aid in the management of this entity. Patients and Methods: Of a total of 16 patients with ameloblastoma of the mandible presenting at our institute from August 2012 to September 2016, 11 patients were identified as having GMAs as per the criteria proposed. Results: The defects in the mandible following segmental resection ranged from 7 to 11.5 cm in length (mean: 9.3 cm). No clinical or radiological evidence of tumour recurrence was found during a mean follow-up period of 10.7 months (range: 2–28 months). Conclusions: Defining GMA based on objective inclusion and exclusion criteria allows segregation of these lesions, thereby helping to remove ambiguity, simplify decision-making and facilitate communication among treating reconstructive surgeons. Inclusion criteria include: (i) The segmental bone defect following resection with a minimum 1 cm margin of healthy bone should exceed 6 cm (ii) The segmental bone defect should involve the central mandibular segment.
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- 2018
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36. A Case of Multiple Myeloma Presenting with Diabetes Insipidus
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Rudrajit Paul, Aditya V. Ruia, Asim Saha, Jayati Mondal, T. J. Sau, Indranil Thakur, and Kunal Haldar
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multiple myeloma ,diabetes insipidus ,pituitary gland ,hypernatremia ,case report, india. ,Medicine - Abstract
Multiple myeloma (MM) can present with involvement of the central nervous system in the form of nerve palsy, plasma cell masses or, rarely, with endocrinological effects due to involvement of the pituitary gland. Usually, in such cases, the disease has a rapid progression and poor prognosis. We report a 52-year-old man who was admitted to the Kolkata Medical College, Kolkata, India, in 2016 with a prolonged low-grade fever and hypernatremia. Shortly afterwards, the patient began to complain of increased urinary frequency and drowsiness. The hypernatremia was treated with intranasal desmopressin and free water replacement. Serum protein electrophoresis and an immunofixation study revealed an immunoglobulin G-κ monoclonal band. Magnetic resonance imaging of the pituitary gland revealed the absence of a posterior bright spot and spotty infiltration of the pituitary fossa. A bone marrow biopsy confirmed a diagnosis of cranial diabetes insipidus due to posterior pituitary MM infiltration.
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- 2017
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37. Striatal Projection Neurons Require Huntingtin for Synaptic Connectivity and Survival
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Caley J. Burrus, Spencer U. McKinstry, Namsoo Kim, M. Ilcim Ozlu, Aditya V. Santoki, Francia Y. Fang, Annie Ma, Yonca B. Karadeniz, Atesh K. Worthington, Ioannis Dragatsis, Scott Zeitlin, Henry H. Yin, and Cagla Eroglu
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Biology (General) ,QH301-705.5 - Abstract
Summary: Huntington’s disease (HD) is caused by an autosomal dominant polyglutamine expansion mutation of Huntingtin (HTT). HD patients suffer from progressive motor, cognitive, and psychiatric impairments, along with significant degeneration of the striatal projection neurons (SPNs) of the striatum. HD is widely accepted to be caused by a toxic gain-of-function of mutant HTT. However, whether loss of HTT function, because of dominant-negative effects of the mutant protein, plays a role in HD and whether HTT is required for SPN health and function are not known. Here, we delete Htt from specific subpopulations of SPNs using the Cre-Lox system and find that SPNs require HTT for motor regulation, synaptic development, cell health, and survival during aging. Our results suggest that loss of HTT function in SPNs could play a critical role in HD pathogenesis. : Burrus et al. show that striatal projection neurons require Huntingtin, the gene mutated in Huntington’s disease, for normal synaptic connectivity, regulated gene expression, and neuronal survival with aging. Loss of Huntingtin from striatal neurons recapitulates several features of Huntington’s disease pathology, an important consideration for therapies non-specifically targeting Huntingtin expression. Keywords: Huntington's Disease, basal ganglia, striatum, synaptic connectivity, neuronal survival
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- 2020
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38. Temporal validation of the SORG 90-Day and 1-Year machine learning algorithms for survival of patients with spinal metastatic disease
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Zijlstra, Hester, Kuijten, R. H., Bhimavarapu, Anirudh V., Lans, Amanda, Cross, Rachel E., Alnasser, Ahmad, Karhade, Aditya V., Verlaan, Jorrit-Jan, Groot, Olivier Q., and Schwab, Joseph H.
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- 2024
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39. Posterior reversible encephalopathy syndrome and parkinsonism as the first manifestation of primary hyperparathyroidism - a report of two cases
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Rallapalli, Sindhu Sree, Rayani, Murali, Ninan, George Abraham, Hussain, Mohammed Anwar, Nair, Aditya V., Bal, Deepti, Cherian, Kripa Elizabeth, Prabhakar, A. T., Paul, Thomas V., and Thomas, Nihal
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- 2024
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40. SegNet: A Segmented Deep Learning based Convolutional Neural Network Approach for Drones Wildfire Detection
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Jonnalagadda, Aditya V. and Hashim, Hashim A.
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Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Artificial Intelligence ,Electrical Engineering and Systems Science - Image and Video Processing - Abstract
This research addresses the pressing challenge of enhancing processing times and detection capabilities in Unmanned Aerial Vehicle (UAV)/drone imagery for global wildfire detection, despite limited datasets. Proposing a Segmented Neural Network (SegNet) selection approach, we focus on reducing feature maps to boost both time resolution and accuracy significantly advancing processing speeds and accuracy in real-time wildfire detection. This paper contributes to increased processing speeds enabling real-time detection capabilities for wildfire, increased detection accuracy of wildfire, and improved detection capabilities of early wildfire, through proposing a new direction for image classification of amorphous objects like fire, water, smoke, etc. Employing Convolutional Neural Networks (CNNs) for image classification, emphasizing on the reduction of irrelevant features vital for deep learning processes, especially in live feed data for fire detection. Amidst the complexity of live feed data in fire detection, our study emphasizes on image feed, highlighting the urgency to enhance real-time processing. Our proposed algorithm combats feature overload through segmentation, addressing challenges arising from diverse features like objects, colors, and textures. Notably, a delicate balance of feature map size and dataset adequacy is pivotal. Several research papers use smaller image sizes, compromising feature richness which necessitating a new approach. We illuminate the critical role of pixel density in retaining essential details, especially for early wildfire detection. By carefully selecting number of filters during training, we underscore the significance of higher pixel density for proper feature selection. The proposed SegNet approach is rigorously evaluated using real-world dataset obtained by a drone flight and compared to state-of-the-art literature.
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- 2024
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41. Computed tomography angiographic study of internal mammary perforators and their use as recipient vessels for free tissue transfer in breast reconstruction
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Aditya V. Kanoi, Karnav B. Panchal, Saugata Sen, and Gautam Biswas
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computed tomography angiography ,free flap breast reconstruction ,hand-held doppler ,internal mammary perforators ,recipient vessels ,Surgery ,RD1-811 - Abstract
Context: The internal mammary artery perforator vessels (IMPV) as a recipient in free flap breast reconstruction offer advantages over the more commonly used thoracodorsal vessels and the internal mammary vessels (IMV). Aims: This study was designed to assess the anatomical consistency of the IMPV and the suitability of these vessels for use as recipients in free flap breast reconstruction. Patients and Methods: Data from ten randomly selected female patients who did not have any chest wall or breast pathology but had undergone a computed tomography angiography (CTA) for unrelated diagnostic reasons from April 2013 to October 2013 were analysed. Retrospective data of seven patients who had undergone mastectomy for breast cancer and had been primarily reconstructed with a deep inferior epigastric artery perforator free flap transfer using the IMPV as recipient vessels were studied. Results: The CTA findings showed that the internal mammary perforator was consistently present in all cases bilaterally. In all cases, the dominant perforator arose from the upper four intercostal spaces (ICS) with the majority (55%) arising from the 2nd ICS. The mean distance of the perforators from the sternal border at the level of pectoralis muscle surface on the right side was 1.86 cm (range: 0.9–2.5 cm) with a mode value of 1.9 cm. On the left side, a mean of 1.77 cm (range: 1.5–2.1 cm) and a mode value of 1.7 cm were observed. Mean perforator artery diameters on the right and left sides were 2.2 mm and 2.4 mm, respectively. Conclusions: Though the internal mammary perforators are anatomically consistent, their use as recipients in free tissue transfer for breast reconstruction eventually rests on multiple variables.
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- 2017
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42. Cautionary Tales on Synthetic Controls in Survival Analyses
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Curth, Alicia, Poon, Hoifung, Nori, Aditya V., and González, Javier
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Statistics - Methodology - Abstract
Synthetic control (SC) methods have gained rapid popularity in economics recently, where they have been applied in the context of inferring the effects of treatments on standard continuous outcomes assuming linear input-output relations. In medical applications, conversely, survival outcomes are often of primary interest, a setup in which both commonly assumed data-generating processes (DGPs) and target parameters are different. In this paper, we therefore investigate whether and when SCs could serve as an alternative to matching methods in survival analyses. We find that, because SCs rely on a linearity assumption, they will generally be biased for the true expected survival time in commonly assumed survival DGPs -- even when taking into account the possibility of linearity on another scale as in accelerated failure time models. Additionally, we find that, because SC units follow distributions with lower variance than real control units, summaries of their distributions, such as survival curves, will be biased for the parameters of interest in many survival analyses. Nonetheless, we also highlight that using SCs can still improve upon matching whenever the biases described above are outweighed by extrapolation biases exhibited by imperfect matches, and investigate the use of regularization to trade off the shortcomings of both approaches., Comment: To appear in the 3rd Conference on Causal Learning and Reasoning (CLeaR 2024)
- Published
- 2023
43. Beyond Words: A Mathematical Framework for Interpreting Large Language Models
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González, Javier and Nori, Aditya V.
- Subjects
Computer Science - Machine Learning ,Computer Science - Artificial Intelligence - Abstract
Large language models (LLMs) are powerful AI tools that can generate and comprehend natural language text and other complex information. However, the field lacks a mathematical framework to systematically describe, compare and improve LLMs. We propose Hex a framework that clarifies key terms and concepts in LLM research, such as hallucinations, alignment, self-verification and chain-of-thought reasoning. The Hex framework offers a precise and consistent way to characterize LLMs, identify their strengths and weaknesses, and integrate new findings. Using Hex, we differentiate chain-of-thought reasoning from chain-of-thought prompting and establish the conditions under which they are equivalent. This distinction clarifies the basic assumptions behind chain-of-thought prompting and its implications for methods that use it, such as self-verification and prompt programming. Our goal is to provide a formal framework for LLMs that can help both researchers and practitioners explore new possibilities for generative AI. We do not claim to have a definitive solution, but rather a tool for opening up new research avenues. We argue that our formal definitions and results are crucial for advancing the discussion on how to build generative AI systems that are safe, reliable, fair and robust, especially in domains like healthcare and software engineering., Comment: 4 figures, 18 pages
- Published
- 2023
44. Exploring the Boundaries of GPT-4 in Radiology
- Author
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Liu, Qianchu, Hyland, Stephanie, Bannur, Shruthi, Bouzid, Kenza, Castro, Daniel C., Wetscherek, Maria Teodora, Tinn, Robert, Sharma, Harshita, Pérez-García, Fernando, Schwaighofer, Anton, Rajpurkar, Pranav, Khanna, Sameer Tajdin, Poon, Hoifung, Usuyama, Naoto, Thieme, Anja, Nori, Aditya V., Lungren, Matthew P., Oktay, Ozan, and Alvarez-Valle, Javier
- Subjects
Computer Science - Computation and Language - Abstract
The recent success of general-domain large language models (LLMs) has significantly changed the natural language processing paradigm towards a unified foundation model across domains and applications. In this paper, we focus on assessing the performance of GPT-4, the most capable LLM so far, on the text-based applications for radiology reports, comparing against state-of-the-art (SOTA) radiology-specific models. Exploring various prompting strategies, we evaluated GPT-4 on a diverse range of common radiology tasks and we found GPT-4 either outperforms or is on par with current SOTA radiology models. With zero-shot prompting, GPT-4 already obtains substantial gains ($\approx$ 10% absolute improvement) over radiology models in temporal sentence similarity classification (accuracy) and natural language inference ($F_1$). For tasks that require learning dataset-specific style or schema (e.g. findings summarisation), GPT-4 improves with example-based prompting and matches supervised SOTA. Our extensive error analysis with a board-certified radiologist shows GPT-4 has a sufficient level of radiology knowledge with only occasional errors in complex context that require nuanced domain knowledge. For findings summarisation, GPT-4 outputs are found to be overall comparable with existing manually-written impressions., Comment: EMNLP 2023 main
- Published
- 2023
45. A Comparative study of Triamcinolone acetonide with Methylprednisolone sodium succinate in the management of chronic low back pain
- Author
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Nawab P. Jamadar, Aditya V. Vazalwar, and Basavraj S. Nagoba
- Subjects
Triamcinolone acetonide ,Methylprednisolone ,Low back Pain ,Medicine ,Medicine (General) ,R5-920 - Abstract
Background: Low back pain is the most common complaint of young adults in case of intervertebral disc herniation. Its incidence is high in India due to difficult working as well as living environment. Objectives: The present study was carried out to compare the efficacy of injecting epidural triamcinolone with methylprednisolone sodium succinate in the management of chronic low back pain. Patients and Methods: This study was carried on patients presenting with low back pain who had MRI proven lumbar disc prolapsed at different levels and were not responding to conservational management. The study was carried out on 50 subjects divided into 2 groups, Group A and Group B of 25 each. Group A was given injection triamcinolone 80 mg with 2 ml of 0.5%bupivacaine diluted in 8 ml of normal saline into the lumbar epidural space. Group B was given injection methylprednisolone sodium succinate 80 mg with 2 ml of 0.5% bupivacaine diluted in 8 ml of normal saline into the lumbar epidural space. Observations: The success rate in group A was found to be 68% and the success rate in group B was found to be 80%. The visual analog scale score in group A was decreased by 20% after one week and by 50 – 60% at the end of 6 months. However, in group B, the visual analog scale score decreased by 30% after one week and by 70-80% at the end of 6 months. Conclusion: Methylprednisolone sodium succinate was found to be more efficacious in the management of chronic low back pain than triamcinolone acetonide.
- Published
- 2015
46. Architecture-Preserving Provable Repair of Deep Neural Networks
- Author
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Tao, Zhe, Nawas, Stephanie, Mitchell, Jacqueline, and Thakur, Aditya V.
- Subjects
Computer Science - Machine Learning - Abstract
Deep neural networks (DNNs) are becoming increasingly important components of software, and are considered the state-of-the-art solution for a number of problems, such as image recognition. However, DNNs are far from infallible, and incorrect behavior of DNNs can have disastrous real-world consequences. This paper addresses the problem of architecture-preserving V-polytope provable repair of DNNs. A V-polytope defines a convex bounded polytope using its vertex representation. V-polytope provable repair guarantees that the repaired DNN satisfies the given specification on the infinite set of points in the given V-polytope. An architecture-preserving repair only modifies the parameters of the DNN, without modifying its architecture. The repair has the flexibility to modify multiple layers of the DNN, and runs in polynomial time. It supports DNNs with activation functions that have some linear pieces, as well as fully-connected, convolutional, pooling and residual layers. To the best our knowledge, this is the first provable repair approach that has all of these features. We implement our approach in a tool called APRNN. Using MNIST, ImageNet, and ACAS Xu DNNs, we show that it has better efficiency, scalability, and generalization compared to PRDNN and REASSURE, prior provable repair methods that are not architecture preserving., Comment: Accepted paper at PLDI 2023. Tool is available at https://github.com/95616ARG/APRNN/
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- 2023
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47. Electrophysiology of Heart Failure Using a Rabbit Model: From the Failing Myocyte to Ventricular Fibrillation.
- Author
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Aditya V S Ponnaluri, Luigi E Perotti, Michael Liu, Zhilin Qu, James N Weiss, Daniel B Ennis, William S Klug, and Alan Garfinkel
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Biology (General) ,QH301-705.5 - Abstract
Heart failure is a leading cause of death, yet its underlying electrophysiological (EP) mechanisms are not well understood. In this study, we use a multiscale approach to analyze a model of heart failure and connect its results to features of the electrocardiogram (ECG). The heart failure model is derived by modifying a previously validated electrophysiology model for a healthy rabbit heart. Specifically, in accordance with the heart failure literature, we modified the cell EP by changing both membrane currents and calcium handling. At the tissue level, we modeled the increased gap junction lateralization and lower conduction velocity due to downregulation of Connexin 43. At the biventricular level, we reduced the apex-to-base and transmural gradients of action potential duration (APD). The failing cell model was first validated by reproducing the longer action potential, slower and lower calcium transient, and earlier alternans characteristic of heart failure EP. Subsequently, we compared the electrical wave propagation in one dimensional cables of healthy and failing cells. The validated cell model was then used to simulate the EP of heart failure in an anatomically accurate biventricular rabbit model. As pacing cycle length decreases, both the normal and failing heart develop T-wave alternans, but only the failing heart shows QRS alternans (although moderate) at rapid pacing. Moreover, T-wave alternans is significantly more pronounced in the failing heart. At rapid pacing, APD maps show areas of conduction block in the failing heart. Finally, accelerated pacing initiated wave reentry and breakup in the failing heart. Further, the onset of VF was not observed with an upregulation of SERCA, a potential drug therapy, using the same protocol. The changes introduced at the cell and tissue level have increased the failing heart's susceptibility to dynamic instabilities and arrhythmias under rapid pacing. However, the observed increase in arrhythmogenic potential is not due to a steepening of the restitution curve (not present in our model), but rather to a novel blocking mechanism.
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- 2016
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48. In-Depth Comparison of Lysine-Based Antibody-Drug Conjugates Prepared on Solid Support Versus in Solution
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Keith J. Arlotta, Aditya V. Gandhi, Hsiao-Nung Chen, Christine S. Nervig, John F. Carpenter, and Shawn C. Owen
- Subjects
antibody drug conjugates ,protein A ,LC/MS ,Raman ,DSC ,DLS ,ITC ,trastuzumab ,DM1 ,one-step ,Immunologic diseases. Allergy ,RC581-607 - Abstract
Antibody drug conjugates are a rapidly growing form of targeted chemotherapeutics. As companies and researchers move to develop new antibody–drug conjugate (ADC) candidates, high-throughput methods will become increasingly common. Here we use advanced characterization techniques to assess two trastuzumab-DM1 (T-DM1) ADCs; one produced using Protein A immobilization and the other produced in solution. Following determination of payload site and distribution with liquid chromatography-mass spectrometry (LC/MS), thermal stability, heat-induced aggregation, tertiary structure, and binding affinity were characterized using differential scanning calorimetry (DSC), dynamic light scattering (DLS), Raman spectroscopy, and isothermal titration calorimetry (ITC), respectively. Small differences in the thermal stability of the CH2 domain of the antibody as well as aggregation onset temperatures were observed from DSC and DLS, respectively. However, no significant differences in secondary and tertiary structure were observed with Raman spectroscopy, or binding affinity as measured by ITC. Lysine-based ADC conjugation produces an innately heterogeneous population that can generate significant variability in the results of sensitive characterization techniques. Characterization of these ADCs indicated nominal differences in thermal stability but not in tertiary structure or binding affinity. Our results lead us to conclude that lysine-based ADCs synthesized following Protein A immobilization, common in small-scale conjugations, are highly similar to equivalent ADCs produced in larger scale, solution-based methods.
- Published
- 2018
- Full Text
- View/download PDF
49. Multi-Contrast Imaging and Digital Refocusing on a Mobile Microscope with a Domed LED Array.
- Author
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Zachary F Phillips, Michael V D'Ambrosio, Lei Tian, Jared J Rulison, Hurshal S Patel, Nitin Sadras, Aditya V Gande, Neil A Switz, Daniel A Fletcher, and Laura Waller
- Subjects
Medicine ,Science - Abstract
We demonstrate the design and application of an add-on device for improving the diagnostic and research capabilities of CellScope--a low-cost, smartphone-based point-of-care microscope. We replace the single LED illumination of the original CellScope with a programmable domed LED array. By leveraging recent advances in computational illumination, this new device enables simultaneous multi-contrast imaging with brightfield, darkfield, and phase imaging modes. Further, we scan through illumination angles to capture lightfield datasets, which can be used to recover 3D intensity and phase images without any hardware changes. This digital refocusing procedure can be used for either 3D imaging or software-only focus correction, reducing the need for precise mechanical focusing during field experiments. All acquisition and processing is performed on the mobile phone and controlled through a smartphone application, making the computational microscope compact and portable. Using multiple samples and different objective magnifications, we demonstrate that the performance of our device is comparable to that of a commercial microscope. This unique device platform extends the field imaging capabilities of CellScope, opening up new clinical and research possibilities.
- Published
- 2015
- Full Text
- View/download PDF
50. Towards Verifying Exact Conditions for Implementations of Density Functional Approximations.
- Author
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Sameerah Helal, Zhe Tao, Cindy Rubio-González, François Gygi, and Aditya V. Thakur
- Published
- 2024
- Full Text
- View/download PDF
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