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1. RISE-iEEG: Robust to Inter-Subject Electrodes Implantation Variability iEEG Classifier

Author

Memar, Maryam Ostadsharif, Ziaei, Navid, Nazari, Behzad, and Yousefi, Ali

Subjects
Quantitative Biology - Neurons and Cognition, Computer Science - Human-Computer Interaction, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Signal Processing
Abstract

Utilization of intracranial electroencephalography (iEEG) is rapidly increasing for clinical and brain-computer interface applications. iEEG facilitates the recording of neural activity with high spatial and temporal resolution, making it a desirable neuroimaging modality for studying neural dynamics. Despite its benefits, iEEG faces challenges such as inter-subject variability in electrode implantation, which makes the development of unified neural decoder models across different patients difficult. In this research, we introduce a novel decoder model that is robust to inter-subject electrode implantation variability. We call this model RISE-iEEG, which stands for Robust Inter-Subject Electrode Implantation Variability iEEG Classifier. RISE-iEEG employs a deep neural network structure preceded by a patient-specific projection network. The projection network maps the neural data of individual patients onto a common low-dimensional space, compensating for the implantation variability. In other words, we developed an iEEG decoder model that can be applied across multiple patients' data without requiring the coordinates of electrode for each patient. The performance of RISE-iEEG across multiple datasets, including the Audio-Visual dataset, Music Reconstruction dataset, and Upper-Limb Movement dataset, surpasses that of state-of-the-art iEEG decoder models such as HTNet and EEGNet. Our analysis shows that the performance of RISE-iEEG is 10\% higher than that of HTNet and EEGNet in terms of F1 score, with an average F1 score of 83\%, which is the highest result among the evaluation methods defined. Furthermore, the analysis of projection network weights in the Music Reconstruction dataset across patients suggests that the Superior Temporal lobe serves as the primary encoding neural node. This finding aligns with the auditory processing physiology.

Published
2024

2. Genome-wide detection of somatic mosaicism at short tandem repeats.

Author

Sehgal, Aarushi, Ziaei Jam, Helyaneh, Shen, Andrew, and Gymrek, Melissa

Subjects
Humans, Mosaicism, Microsatellite Repeats, Genome, Human, High-Throughput Nucleotide Sequencing, Alleles, Software
Abstract

MOTIVATION: Somatic mosaicism has been implicated in several developmental disorders, cancers, and other diseases. Short tandem repeats (STRs) consist of repeated sequences of 1-6 bp and comprise >1 million loci in the human genome. Somatic mosaicism at STRs is known to play a key role in the pathogenicity of loci implicated in repeat expansion disorders and is highly prevalent in cancers exhibiting microsatellite instability. While a variety of tools have been developed to genotype germline variation at STRs, a method for systematically identifying mosaic STRs is lacking. RESULTS: We introduce prancSTR, a novel method for detecting mosaic STRs from individual high-throughput sequencing datasets. prancSTR is designed to detect loci characterized by a single high-frequency mosaic allele, but can also detect loci with multiple mosaic alleles. Unlike many existing mosaicism detection methods for other variant types, prancSTR does not require a matched control sample as input. We show that prancSTR accurately identifies mosaic STRs in simulated data, demonstrate its feasibility by identifying candidate mosaic STRs in Illumina whole genome sequencing data derived from lymphoblastoid cell lines for individuals sequenced by the 1000 Genomes Project, and evaluate the use of prancSTR on Element and PacBio data. In addition to prancSTR, we present simTR, a novel simulation framework which simulates raw sequencing reads with realistic error profiles at STRs. AVAILABILITY AND IMPLEMENTATION: prancSTR and simTR are freely available at https://github.com/gymrek-lab/trtools. Detailed documentation is available at https://trtools.readthedocs.io/.

Published
2024

3. LongTR: genome-wide profiling of genetic variation at tandem repeats from long reads.

Author

Ziaei Jam, Helyaneh, Zook, Justin, Javadzadeh, Sara, Park, Jonghun, Sehgal, Aarushi, and Gymrek, Melissa

Subjects
Long reads, Microsatellites, Tandem repeats, Humans, Tandem Repeat Sequences, Genome, Human, Genetic Variation, Software, Sequence Analysis, DNA, High-Throughput Nucleotide Sequencing, Nanopore Sequencing
Abstract

Tandem repeats are frequent across the human genome, and variation in repeat length has been linked to a variety of traits. Recent improvements in long read sequencing technologies have the potential to greatly improve tandem repeat analysis, especially for long or complex repeats. Here, we introduce LongTR, which accurately genotypes tandem repeats from high-fidelity long reads available from both PacBio and Oxford Nanopore Technologies. LongTR is freely available at https://github.com/gymrek-lab/longtr and https://zenodo.org/doi/10.5281/zenodo.11403979 .

Published
2024

4. AgentClinic: a multimodal agent benchmark to evaluate AI in simulated clinical environments

Author

Schmidgall, Samuel, Ziaei, Rojin, Harris, Carl, Reis, Eduardo, Jopling, Jeffrey, and Moor, Michael

Subjects
Computer Science - Human-Computer Interaction, Computer Science - Computation and Language
Abstract

Evaluating large language models (LLM) in clinical scenarios is crucial to assessing their potential clinical utility. Existing benchmarks rely heavily on static question-answering, which does not accurately depict the complex, sequential nature of clinical decision-making. Here, we introduce AgentClinic, a multimodal agent benchmark for evaluating LLMs in simulated clinical environments that include patient interactions, multimodal data collection under incomplete information, and the usage of various tools, resulting in an in-depth evaluation across nine medical specialties and seven languages. We find that solving MedQA problems in the sequential decision-making format of AgentClinic is considerably more challenging, resulting in diagnostic accuracies that can drop to below a tenth of the original accuracy. Overall, we observe that agents sourced from Claude-3.5 outperform other LLM backbones in most settings. Nevertheless, we see stark differences in the LLMs' ability to make use of tools, such as experiential learning, adaptive retrieval, and reflection cycles. Strikingly, Llama-3 shows up to 92% relative improvements with the notebook tool that allows for writing and editing notes that persist across cases. To further scrutinize our clinical simulations, we leverage real-world electronic health records, perform a clinical reader study, perturb agents with biases, and explore novel patient-centric metrics that this interactive environment firstly enables.

Published
2024

5. Latent Variable Double Gaussian Process Model for Decoding Complex Neural Data

Author

Ziaei, Navid, Stim, Joshua J., Goodman-Keiser, Melanie D., Sponheim, Scott, Widge, Alik S., Krikorian, Sasoun, and Yousefi, Ali

Subjects
Computer Science - Machine Learning, Quantitative Biology - Neurons and Cognition
Abstract

Non-parametric models, such as Gaussian Processes (GP), show promising results in the analysis of complex data. Their applications in neuroscience data have recently gained traction. In this research, we introduce a novel neural decoder model built upon GP models. The core idea is that two GPs generate neural data and their associated labels using a set of low-dimensional latent variables. Under this modeling assumption, the latent variables represent the underlying manifold or essential features present in the neural data. When GPs are trained, the latent variable can be inferred from neural data to decode the labels with a high accuracy. We demonstrate an application of this decoder model in a verbal memory experiment dataset and show that the decoder accuracy in predicting stimulus significantly surpasses the state-of-the-art decoder models. The preceding performance of this model highlights the importance of utilizing non-parametric models in the analysis of neuroscience data.

Published
2024

7. Addressing cognitive bias in medical language models

Author

Schmidgall, Samuel, Harris, Carl, Essien, Ime, Olshvang, Daniel, Rahman, Tawsifur, Kim, Ji Woong, Ziaei, Rojin, Eshraghian, Jason, Abadir, Peter, and Chellappa, Rama

Subjects
Computer Science - Computation and Language, Computer Science - Human-Computer Interaction
Abstract

There is increasing interest in the application large language models (LLMs) to the medical field, in part because of their impressive performance on medical exam questions. While promising, exam questions do not reflect the complexity of real patient-doctor interactions. In reality, physicians' decisions are shaped by many complex factors, such as patient compliance, personal experience, ethical beliefs, and cognitive bias. Taking a step toward understanding this, our hypothesis posits that when LLMs are confronted with clinical questions containing cognitive biases, they will yield significantly less accurate responses compared to the same questions presented without such biases. In this study, we developed BiasMedQA, a benchmark for evaluating cognitive biases in LLMs applied to medical tasks. Using BiasMedQA we evaluated six LLMs, namely GPT-4, Mixtral-8x70B, GPT-3.5, PaLM-2, Llama 2 70B-chat, and the medically specialized PMC Llama 13B. We tested these models on 1,273 questions from the US Medical Licensing Exam (USMLE) Steps 1, 2, and 3, modified to replicate common clinically-relevant cognitive biases. Our analysis revealed varying effects for biases on these LLMs, with GPT-4 standing out for its resilience to bias, in contrast to Llama 2 70B-chat and PMC Llama 13B, which were disproportionately affected by cognitive bias. Our findings highlight the critical need for bias mitigation in the development of medical LLMs, pointing towards safer and more reliable applications in healthcare.

Published
2024

8. A Bayesian Gaussian Process-Based Latent Discriminative Generative Decoder (LDGD) Model for High-Dimensional Data

Author

Ziaei, Navid, Nazari, Behzad, Eden, Uri T., Widge, Alik, and Yousefi, Ali

Subjects
Computer Science - Machine Learning, I.5.1, G.3
Abstract

Extracting meaningful information from high-dimensional data poses a formidable modeling challenge, particularly when the data is obscured by noise or represented through different modalities. This research proposes a novel non-parametric modeling approach, leveraging the Gaussian process (GP), to characterize high-dimensional data by mapping it to a latent low-dimensional manifold. This model, named the latent discriminative generative decoder (LDGD), employs both the data and associated labels in the manifold discovery process. We derive a Bayesian solution to infer the latent variables, allowing LDGD to effectively capture inherent stochasticity in the data. We demonstrate applications of LDGD on both synthetic and benchmark datasets. Not only does LDGD infer the manifold accurately, but its accuracy in predicting data points' labels surpasses state-of-the-art approaches. In the development of LDGD, we have incorporated inducing points to reduce the computational complexity of Gaussian processes for large datasets, enabling batch training for enhanced efficient processing and scalability. Additionally, we show that LDGD can robustly infer manifold and precisely predict labels for scenarios in that data size is limited, demonstrating its capability to efficiently characterize high-dimensional data with limited samples. These collective attributes highlight the importance of developing non-parametric modeling approaches to analyze high-dimensional data., Comment: 40 pages, 6 figures

Published
2024
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9. Gene-environment interactions: Epstein-Barr virus infection and risk of pediatric-onset multiple sclerosis.

Author

Solomon, Olivia, Casper, T, Waltz, Michael, Weinstock-Guttman, Bianca, Aaen, Greg, Wheeler, Yolanda, Graves, Jennifer, Benson, Leslie, Gorman, Mark, Rensel, Mary, Mar, Soe, Lotze, Tim, Chitnis, Tanuja, Waldman, Amy, Krupp, Lauren, James, Judith, Hart, Janace, Barcellos, Lisa, Waubant, Emmanuelle, Greenberg, Barry, and Ziaei, Amin

Subjects
CD68, DRB1*15, Epstein–Barr virus, Pediatric onset, gene–environment interaction, multiple sclerosis, Adult, Child, Humans, Epstein-Barr Virus Infections, Herpesvirus 4, Human, Multiple Sclerosis, Risk Factors, HLA-DRB1 Chains, Antibodies
Abstract

BACKGROUND AND OBJECTIVE: Prior Epstein-Barr virus (EBV) infection is associated with an increased risk of pediatric-onset multiple sclerosis (POMS) and adult-onset multiple sclerosis (MS). It has been challenging to elucidate the biological mechanisms underlying this association. We examined the interactions between candidate human leukocyte antigen (HLA) and non-HLA variants and childhood EBV infection as it may provide mechanistic insights into EBV-associated MS. METHODS: Cases and controls were enrolled in the Environmental and Genetic Risk Factors for Pediatric MS study of the US Network of Pediatric MS Centers. Participants were categorized as seropositive and seronegative for EBV-viral capsid antigen (VCA). The association between prior EBV infection and having POMS was estimated with logistic regression. Interactions between EBV serostatus, major HLA MS risk factors, and non-HLA POMS risk variants associated with response to EBV infection were also evaluated with logistic regression. Models were adjusted for sex, age, genetic ancestry, and the mothers education. Additive interactions were calculated using relative risk due to interaction (RERI) and attributable proportions (APs). RESULTS: A total of 473 POMS cases and 702 controls contributed to the analyses. Anti-VCA seropositivity was significantly higher in POMS cases compared to controls (94.6% vs 60.7%, p

Published
2024

16. Language models are susceptible to incorrect patient self-diagnosis in medical applications

Author

Ziaei, Rojin and Schmidgall, Samuel

Subjects
Computer Science - Computation and Language
Abstract

Large language models (LLMs) are becoming increasingly relevant as a potential tool for healthcare, aiding communication between clinicians, researchers, and patients. However, traditional evaluations of LLMs on medical exam questions do not reflect the complexity of real patient-doctor interactions. An example of this complexity is the introduction of patient self-diagnosis, where a patient attempts to diagnose their own medical conditions from various sources. While the patient sometimes arrives at an accurate conclusion, they more often are led toward misdiagnosis due to the patient's over-emphasis on bias validating information. In this work we present a variety of LLMs with multiple-choice questions from United States medical board exams which are modified to include self-diagnostic reports from patients. Our findings highlight that when a patient proposes incorrect bias-validating information, the diagnostic accuracy of LLMs drop dramatically, revealing a high susceptibility to errors in self-diagnosis., Comment: 4 pages, Deep Generative Models for Health NeurIPS 2023

Published
2023

17. Agreement of a Pyramidal Wavefront-Based Autorefraction with Dry, Cycloplegic, and Subjective Refraction in Myopic Refractive Surgery Candidates

Author

Siamak Zarei-Ghanavati, Marzieh Najjaran, Samira Hassanzadeh, Mehdi Khabazkhoob, Akilesh Gokul, and Mohammed Ziaei

Subjects
aberrometry, autorefraction, cycloplegic refraction, subjective refraction, wavefront refraction, Ophthalmology, RE1-994
Abstract

Purpose To evaluate the repeatability of a pyramidal wavefront-based refraction (WFR) measurement and its agreement with dry autorefraction (DR), cycloplegic autorefraction (CR), and subjective refraction (SR) in myopic refractive surgery candidates. Methods One hundred eighty-nine eyes from 189 participants were evaluated. PERAMIS aberrometry (PERAMIS; SCHWIND eye-tech-solutions, Kleinostheim, Germany), DR, and CR, as well as SR, were completed for all candidates. The repeatability of PERAMIS measurements was assessed, and the Bland–Altman plots were used to test the agreement between different methods. Results Repeatability of the PERAMIS aberrometer was very high in the measurement of all refractive elements (Sphere, cylinder, spherical equivalent [M], J0, and J45) (interclass correlation coefficient >0.980 for all). A significant myopic shift was found with WFR compared to CR (0.45 diopter [D]) and SR (0.28 D) (P < 0.05). For the M component, there was a significant difference between WFR and CR (P < 0.05). J0 component measured with WFR was significantly different from SR and CR (P < 0.05). For the J45 variable, all three refraction methods were comparable (all, P > 0.05). In M > 5.00 D, a difference of 0.79 D (limit of agreement: −3.50–1.90) was found between WFR and CR. Conclusions In lower degrees of myopia, WFR was in good agreement with the manifest autorefraction. WFR, CR, and SR techniques were comparable in measuring astigmatism, especially in oblique astigmatism.

Published
2024
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18. Trainees’ perspectives and recommendations for catalyzing the next generation of NeuroAI researchers

Author

Andrea I. Luppi, Jascha Achterberg, Samuel Schmidgall, Isil Poyraz Bilgin, Peer Herholz, Maximilian Sprang, Benjamin Fockter, Andrew Siyoon Ham, Sushrut Thorat, Rojin Ziaei, Filip Milisav, Alexandra M. Proca, Hanna M. Tolle, Laura E. Suárez, Paul Scotti, and Helena M. Gellersen

Subjects
Science
Abstract

At this critical juncture in the development of NeuroAI, we outline challenges and training needs of junior researchers working across AI and neuroscience. We also provide advice and resources to help trainees plan their NeuroAI careers.

Published
2024
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19. Evaluation and mitigation of cognitive biases in medical language models

Author

Samuel Schmidgall, Carl Harris, Ime Essien, Daniel Olshvang, Tawsifur Rahman, Ji Woong Kim, Rojin Ziaei, Jason Eshraghian, Peter Abadir, and Rama Chellappa

Subjects
Computer applications to medicine. Medical informatics, R858-859.7
Abstract

Abstract Increasing interest in applying large language models (LLMs) to medicine is due in part to their impressive performance on medical exam questions. However, these exams do not capture the complexity of real patient–doctor interactions because of factors like patient compliance, experience, and cognitive bias. We hypothesized that LLMs would produce less accurate responses when faced with clinically biased questions as compared to unbiased ones. To test this, we developed the BiasMedQA dataset, which consists of 1273 USMLE questions modified to replicate common clinically relevant cognitive biases. We assessed six LLMs on BiasMedQA and found that GPT-4 stood out for its resilience to bias, in contrast to Llama 2 70B-chat and PMC Llama 13B, which showed large drops in performance. Additionally, we introduced three bias mitigation strategies, which improved but did not fully restore accuracy. Our findings highlight the need to improve LLMs’ robustness to cognitive biases, in order to achieve more reliable applications of LLMs in healthcare.

Published
2024
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20. Investigating the Usability and Comfort of Safety Harness in Construction Projects: The Reasons for Non-Use among Iranian Workers

Author

Parvin Sepehr, Mousa Jabbari, Hassan Sadeghi Naeini, Ali Salehi Sahl Abadi, Mansour Ziaei, Vahid Ahmadi Moshiran, Maryam Ahmadian, and Younes Mehrifar

Subjects
safety harness, comfort and satisfaction, construction workers, usability, maxqda, falling from a height, Environmental pollution, TD172-193.5
Abstract

Introduction: The safety harness is a critical device for preventing falls from height, particularly in the construction industry. This study aimed to identify the factors contributing to the non-use of safety harnesses during work at height and to evaluate the comfort, satisfaction, and usability of these harnesses among construction workers in Tehran using a custom-designed tool. Material and Methods: A semi-structured interview was conducted with construction workers to identify the factors influencing the non-use of safety harnesses. The collected data were analyzed using MAXQDA 10 software. Based on the results, which revealed dissatisfaction with the current safety harnesses, the levels of comfort, satisfaction, and usability were assessed using the Safety Harness Usability and Comfort Assessment Tool (SHUCAT) questionnaire. Results: The reasons for not using safety harnesses were categorized into four main groups: management factors, worker attitudes, comfort, and harness design. These were further subdivided into 27 subgroups. The average satisfaction and comfort scores for safety harnesses were 26.8 ± 6.25, indicating that workers generally felt uncomfortable and dissatisfied with their harnesses. The average usability score was 38.70 ± 5.60, reflecting poor usability of the harnesses. Conclusion: Improving the safety harness design could enhance workers’ comfort and satisfaction, increasing their willingness to use the equipment. Incorporating feedback from users and experts into the design process can help address the identified shortcomings and result in better product development.

Published
2024

21. Clinical and laboratory characteristics of ocular syphilis, co-infection, and therapy response

Author

Sahin O and Ziaei A

Subjects
Ocular syphilis, Scleritis, Anterior uveitis, Intermediate uveitis, Retinochoroiditis, Panuveitis, Optic neuritis, Penicillin, Methotrexate, Human immunodeficiency virus, Toxoplasma gondii, Ophthalmology, RE1-994
Abstract

Ozlem Sahin,1 Alireza Ziaei1–3 1Department of Ophthalmology/Uveitis, Dunya Goz Hospital, Ankara, Turkey; 2Department of Ophthalmology, Boston University School of Medicine, Boston, MA, USA; 3Department of Ophthalmology, Dunya Eye Hospital Ltd, Ankara, Turkey Purpose: To describe the clinical presentation of patients diagnosed with presumed latent ocular syphilis and congenital ocular syphilis at tertiary referral center in Turkey, and to compare the clinical findings with patients described in other studies, specifically focusing on demographics and co-infections. Methods: This is a retrospective study reviewing the medical records of patients diagnosed with ocular inflammation between January 2012 and June 2014 at a tertiary referral center in Turkey. Ocular syphilis was diagnosed on the basis of non-treponemal and treponemal antibody tests, and cerebrospinal fluid analysis. All the patients diagnosed with ocular syphilis were tested for human immunodeficiency virus (HIV), Toxoplasma gondii, rubella, cytomegalovirus, and herpes. Results: A total of 1,115 patients were evaluated between January 2012 and June 2014, and 12 patients (1.07%) were diagnosed with ocular syphilis based on the inclusion criteria. None of the patients were seropositive for HIV. Two patients were seropositive for T. gondii-specific IgG. Clinical presentations include non-necrotizing anterior scleritis, non-necrotizing sclerokeratitis, anterior uveitis, intermediate uveitis, posterior uveitis, panuveitis, and optic neuritis. All of the patients showed clinical improvement in the level of ocular inflammation with intravenous penicillin 24 million U/day for 10 days. Three patients received additional oral methotrexate as an adjunctive therapy. Two cases received low-dose trimethoprim–sulfamethoxazole. Conclusion: Ocular syphilis is an uncommon cause of ocular inflammation in HIV-negative patients. Central retinochoroiditis is the most common ocular manifestation, and it is the most common cause of visual impairment. Ocular syphilis might present associated with co-infections such as T. gondii in developing countries. Oral methotrexate might be beneficial as an adjunctive therapy for ocular syphilis in resolving the residual intraocular inflammation and cystoid macular edema after specific therapy with intravenous penicillin. Keywords: ocular syphilis, scleritis, uveitis, methotrexate, human immunodeficiency virus, T. gondii

Published
2015

22. Bayesian Time-Series Classifier for Decoding Simple Visual Stimuli from Intracranial Neural Activity

Author

Ziaei, Navid, Saadatifard, Reza, Yousefi, Ali, Nazari, Behzad, Cash, Sydney S., and Paulk, Angelique C.

Subjects
Computer Science - Machine Learning, Electrical Engineering and Systems Science - Signal Processing, Quantitative Biology - Neurons and Cognition
Abstract

Understanding how external stimuli are encoded in distributed neural activity is of significant interest in clinical and basic neuroscience. To address this need, it is essential to develop analytical tools capable of handling limited data and the intrinsic stochasticity present in neural data. In this study, we propose a straightforward Bayesian time series classifier (BTsC) model that tackles these challenges whilst maintaining a high level of interpretability. We demonstrate the classification capabilities of this approach by utilizing neural data to decode colors in a visual task. The model exhibits consistent and reliable average performance of 75.55% on 4 patients' dataset, improving upon state-of-the-art machine learning techniques by about 3.0 percent. In addition to its high classification accuracy, the proposed BTsC model provides interpretable results, making the technique a valuable tool to study neural activity in various tasks and categories. The proposed solution can be applied to neural data recorded in various tasks, where there is a need for interpretable results and accurate classification accuracy.

Published
2023
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44. Brain-inspired learning in artificial neural networks: a review

Author

Schmidgall, Samuel, Achterberg, Jascha, Miconi, Thomas, Kirsch, Louis, Ziaei, Rojin, Hajiseyedrazi, S. Pardis, and Eshraghian, Jason

Subjects
Computer Science - Neural and Evolutionary Computing, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Quantitative Biology - Neurons and Cognition
Abstract

Artificial neural networks (ANNs) have emerged as an essential tool in machine learning, achieving remarkable success across diverse domains, including image and speech generation, game playing, and robotics. However, there exist fundamental differences between ANNs' operating mechanisms and those of the biological brain, particularly concerning learning processes. This paper presents a comprehensive review of current brain-inspired learning representations in artificial neural networks. We investigate the integration of more biologically plausible mechanisms, such as synaptic plasticity, to enhance these networks' capabilities. Moreover, we delve into the potential advantages and challenges accompanying this approach. Ultimately, we pinpoint promising avenues for future research in this rapidly advancing field, which could bring us closer to understanding the essence of intelligence.

Published
2023

45. CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting

Author

Graham, Simon, Vu, Quoc Dang, Jahanifar, Mostafa, Weigert, Martin, Schmidt, Uwe, Zhang, Wenhua, Zhang, Jun, Yang, Sen, Xiang, Jinxi, Wang, Xiyue, Rumberger, Josef Lorenz, Baumann, Elias, Hirsch, Peter, Liu, Lihao, Hong, Chenyang, Aviles-Rivero, Angelica I., Jain, Ayushi, Ahn, Heeyoung, Hong, Yiyu, Azzuni, Hussam, Xu, Min, Yaqub, Mohammad, Blache, Marie-Claire, Piégu, Benoît, Vernay, Bertrand, Scherr, Tim, Böhland, Moritz, Löffler, Katharina, Li, Jiachen, Ying, Weiqin, Wang, Chixin, Kainmueller, Dagmar, Schönlieb, Carola-Bibiane, Liu, Shuolin, Talsania, Dhairya, Meda, Yughender, Mishra, Prakash, Ridzuan, Muhammad, Neumann, Oliver, Schilling, Marcel P., Reischl, Markus, Mikut, Ralf, Huang, Banban, Chien, Hsiang-Chin, Wang, Ching-Ping, Lee, Chia-Yen, Lin, Hong-Kun, Liu, Zaiyi, Pan, Xipeng, Han, Chu, Cheng, Jijun, Dawood, Muhammad, Deshpande, Srijay, Bashir, Raja Muhammad Saad, Shephard, Adam, Costa, Pedro, Nunes, João D., Campilho, Aurélio, Cardoso, Jaime S., S, Hrishikesh P, Puthussery, Densen, G, Devika R, C V, Jiji, Zhang, Ye, Fang, Zijie, Lin, Zhifan, Zhang, Yongbing, Lin, Chunhui, Zhang, Liukun, Mao, Lijian, Wu, Min, Vo, Vi Thi-Tuong, Kim, Soo-Hyung, Lee, Taebum, Kondo, Satoshi, Kasai, Satoshi, Dumbhare, Pranay, Phuse, Vedant, Dubey, Yash, Jamthikar, Ankush, Vuong, Trinh Thi Le, Kwak, Jin Tae, Ziaei, Dorsa, Jung, Hyun, Miao, Tianyi, Snead, David, Raza, Shan E Ahmed, Minhas, Fayyaz, and Rajpoot, Nasir M.

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome. To drive innovation in this area, we setup a community-wide challenge using the largest available dataset of its kind to assess nuclear segmentation and cellular composition. Our challenge, named CoNIC, stimulated the development of reproducible algorithms for cellular recognition with real-time result inspection on public leaderboards. We conducted an extensive post-challenge analysis based on the top-performing models using 1,658 whole-slide images of colon tissue. With around 700 million detected nuclei per model, associated features were used for dysplasia grading and survival analysis, where we demonstrated that the challenge's improvement over the previous state-of-the-art led to significant boosts in downstream performance. Our findings also suggest that eosinophils and neutrophils play an important role in the tumour microevironment. We release challenge models and WSI-level results to foster the development of further methods for biomarker discovery.

Published
2023

46. Peripapillary Retinal Nerve Fiber Layer Thickness Changes in Preterm Children with or without Retinopathy of Prematurity History

Author

Siamak Zarei-Ghanavati, Hadi Ostadimoghaddam, Marzieh Najjaran, Nasser Shoeibi, and Mohammed Ziaei

Subjects
intravitreal bevacizumab, peripapillary retinal nerve fiber layer, retinopathy of prematurity, Ophthalmology, RE1-994
Abstract

Purpose: To investigate peripapillary retinal nerve fiber layer (pRNFL) thickness changes in preterm children with or without retinopathy of prematurity (ROP) history compared to full-term children. Methods: A retrospective comparative cohort study assessing pRNFL thickness was completed in children aged 4–8 years. Four groups of children were included (n = 30 each group): children with a history of ROP who were treated with intravitreal bevacizumab, children with ROP who received no treatment, and preterm children without ROP compared to age- and gender-matched full-term children. Results: A total of 120 eyes from 120 children were enrolled in this study. Both treated and regressed ROP children showed a significantly thinner pRNFL in the nasal quadrant compared to full-term children (P = 0.017 and P = 0.008, respectively). The pRNFL in the superior quadrant of treated ROP children was thinner than the preterm and control groups (P = 0.015 and P = 0.023, respectively), whereas the inferior quadrant of treated ROP children was thinner than the preterm group alone (P = 0.008). The pRNFL thickness in the temporal quadrant was comparable between groups (P = 0.129). The average spatial distribution profile of pRNFL thickness in treated ROP children was significantly thinner than in the preterm group (P = 0.041). Conclusion: pRNFL thickness is significantly altered in children with a prior history of treated ROP with thinning of the nasal and superior quadrants compared to full-term children.

Published
2024
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47. An in vitro study for reducing the cytotoxicity and dose dumping risk of remdesivir via entrapment in nanostructured lipid carriers

Author

Fatemeh Amiri, Sepideh Ziaei Chamgordani, and Hedayatollah Ghourchian

Subjects
Medicine, Science
Abstract

Abstract The aim of this study was to synthesize and evaluate nanostructured lipid carriers (NLCs) loaded with Remdesivir (RDV) to control its side effects in COVID-19 patients. Due to the low solubility and short half-life of RDV in the blood, an injectable formulation was prepared using sulphobutylether-beta-cyclodextrin. However, it can accumulate in the kidney and cause renal impairment. NLCs improve the parenteral delivery of hydrophobic drugs such as RDV by increasing drug solubility and bioavailability. For the synthesis of RDV-NLCs, the aqueous phase containing Tween 80 was injected into the lipid phase under rapid stirring and was sonicated. The experimental conditions were optimized using Box-Behnken design and Design Expert software. The optimum formulation contained a total lipid of 2.13%, a total surfactant of 1%, and a hot bath time of 71 min. The optimum formulation showed particle size, polydispersity index, zeta potential, and entrapment efficiency values of 151.0 ± 1.7 nm (from 149.1 to 152.1), 0.4 ± 0.1 (from 0.3 to 0.5), −43.8 ± 1.2 mV (from −42.4 to −44.7), and 81.34 ± 1.57% (from 79.52 to 82.33%), respectively. RDV-NLCs showed acceptable stability for 30 days at 25 ℃ and were compatible with commonly used intravenous infusion fluids for 48 h. FE-SEM images of RDV-NLC showed spherical particles with a mean diameter of 207 nm. The NLC-RDV formulation showed a sustained release of RDV with a low risk of dose-dumping, minimizing potential side effects. In addition, RDV in the form of RDV-NLC causes less cytotoxicity to healthy normal kidney cells, which is expected to reduce renal impairment in COVID-19 patients.

Published
2024
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48. Intra-canopy LED lighting outperformed top LED lighting in improving tomato yield and expression of the genes responsible for lycopene, phytoene and vitamin C synthesis

Author

Negar Ziaei, Majid Talebi, Badraldin Ebrahim Sayed Tabatabaei, Mohammad R. Sabzalian, and Masoud Soleimani

Subjects
Intra-canopy lighting, LED, Lycopene, Metabolic genes, Tomato, Top-lighting, Medicine, Science
Abstract

Abstract Greenhouses located at high latitudes and in cloudy areas often experience a low quality and quantity of light, especially during autumn and winter. This low daily light integral (DLI) reduces production rate, quality, and nutritional value of many crops. This study was conducted on Sakhiya RZ F1 tomato plants to evaluate the impact of LED lights on the growth and nutritional value of tomatoes in a greenhouse with low daily light due to cloudy weather. The treatments included LED growth lights in three modes: top lighting, intra-canopy lighting, and combined top and intra-canopy lighting. The results showed that although the combined top and intra-canopy lighting reached the maximum increase in tomato yield, exposure to intra-canopy LED lighting alone outperformed in tomato fruit yield increase (28.46%) than exposure to top LED lighting alone (12.12%) when compared to no supplemental lighting during the entire production year. Intra-canopy exposure demonstrated the highest increase in tomato lycopene (31.3%), while top and intra-canopy lighting exhibited the highest increase in vitamin C content (123.4%) compared to the control. The LED light treatment also had a very positive effect on the expression of genes responsible for metabolic cycles, including Psy1, LCY-β, and VTC2 genes, which had collinearity with the increase in tomato fruit production.

Published
2024
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49. Healthy eating index and risk of diminished ovarian reserve: a case–control study

Author

Hatav Ghasemi-Tehrani, Gholamreza Askari, Fatemeh Zahra Allameh, Mahdi Vajdi, Reza Amiri Khosroshahi, Sepide Talebi, Rahele Ziaei, Abed Ghavami, and Farzaneh Askari

Subjects
Diminished ovarian reserve, Infertility, Ovarian function, Healthy eating index, Medicine, Science
Abstract

Abstract Diminished ovarian reserve (DOR) is associated with reduced fertility and poor reproductive outcomes. The association between dietary patterns and DOR was not well studied. The purpose of this study was to evaluate the relationship between adhering to the healthy eating index (HEI-2015) and the risk of DOR. In this case–control study, 370 Iranian women (120 with DOR and 250 age- and BMI-matched controls) were examined. A reliable semi-quantitative food frequency questionnaire was used to collect diet-related data. We analyzed the HEI-2015 and their dietary intake data to determine major dietary patterns. The multivariable logistic regression was used in order to analyze the association between HEI-2015 and risk of DOR. We found no significant association between HEI-2015 score and risk of DOR in the unadjusted model (OR 0.78; 95%CI 0.59, 1.03). After controlling for physical activity and energy intake, we observed that women in the highest quartile of the HEI-2015 score had 31% decreased odds of DOR (OR 0.69; 95%CI 0.46, 0.93). This association remained significant even after adjusting for all potential confounders. Overall, increased adherence to HEI may lead to a significant reduction in the odds ratio of DOR. Clinical trials and prospective studies are needed to confirm this association.

Published
2024
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50. Dislocated 4-haptic intraocular lens rescue with Gore-tex suture scleral re-fixation

Author

Luke Michaels, George Moussa, Hadi Ziaei, and Andrew Davies

Subjects
Dislocated, Subluxed, Intraocular lens, Akreos adapt, Gore-tex, Ophthalmology, RE1-994
Abstract

Abstract Background Dislocated IOL exchange conventionally involves manipulation within the anterior chamber which risks secondary injury to anterior chamber structures. We describe and evaluate a 4-haptic IOL rescue technique that avoids entering the anterior chamber and thus minimizes post operative inflammation, astigmatism and recovery time relative to conventional IOL explantation and replacement techniques. Methods Retrospective, non-randomized, interventional study of all patients undergoing 4-haptic IOL rescue performed by two independent vitreoretinal surgeons at a single UK centre over two years. Surgical technique: A limited peritomy is performed with four 25-gauge scleral ports placed to enable use of two forceps, an infusion and a chandelier. A further four 27-gauge sclerotomies are symmetrically placed on the nasal and temporal sclera at 3 mm from the limbus with a 5 mm vertical separation on either side. A pars plana vitrectomy is performed followed by chandelier illuminated, bimanual cleaning of the dislocated IOL using 27-gauge serrated forceps. Gore-tex sutures are threaded through the IOL islets within the vitreous cavity and externalized through the sclerotomies for scleral re-fixation followed by conjunctival closure. Results Seven patients underwent IOL recycling with Gore-Tex suture scleral re-fixation. All procedures were successful in repositioning the IOLs, with all patients satisfied with post-operative outcome. Mean (standard deviation) time to IOL dislocation was 13 (3) years. Median visual acuity significantly improved post-operatively from 0.85 logMAR (Interquartile Range [IQR]: 0.2–2.1) to 0.07 (0.02–0.60) logMAR (p = 0.02). No significant post-operative complications were noted apart from persistent cystoid macular oedema in one patient non-compliant with post-operative treatment. Conclusions Transscleral refixation using Gore-Tex suture is an effective, safe and practical approach in the management of dislocated 4-piece IOLs.

Published
2024
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