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BackgroundAutofluorescence-based imaging has the potential to non-destructively characterize the biochemical and physiological properties of plants regulated by genotypes using optical properties of the tissue. A comparative study of stress tolerant and stress susceptible genotypes of Brassica rapa with respect to newly introduced stress-based phenotypes using machine learning techniques will contribute to the significant advancement of autofluorescence-based plant phenotyping research.MethodsAutofluorescence spectral images have been used to design a stress detection classifier with two classes, stressed and non-stressed, using machine learning algorithms. The benchmark dataset consisted of time-series image sequences from three Brassica rapa genotypes (CC, R500, and VT), extreme in their morphological and physiological traits captured at the high-throughput plant phenotyping facility at the University of Nebraska-Lincoln, USA. We developed a set of machine learning-based classification models to detect the percentage of stressed tissue derived from plant images and identified the best classifier. From the analysis of the autofluorescence images, two novel stress-based image phenotypes were computed to determine the temporal variation in stressed tissue under progressive drought across different genotypes, i.e., the average percentage stress and the moving average percentage stress.ResultsThe study demonstrated that both the computed phenotypes consistently discriminated against stressed versus non-stressed tissue, with oilseed type (R500) being less prone to drought stress relative to the other two Brassica rapa genotypes (CC and VT).ConclusionAutofluorescence signals from the 365/400 nm excitation/emission combination were able to segregate genotypic variation during a progressive drought treatment under a controlled greenhouse environment, allowing for the exploration of other meaningful phenotypes using autofluorescence image sequences with significance in the context of plant science.

Urine is a highly advantageous biological specimen for biomarker research and is a non-invasive source. Most of the urinary biomarkers are non-specific, volatile and need extensive validation before clinical adoption. Extracellular vesicles are secreted by almost all cells and are involved in homoeostasis, intercellular communication, and cellular processes in healthy and pathophysiological states. Urinary extracellular vesicles (UEVs) are released from the urogenital system and mirror the molecular processes of physiological and pathological states of their source cells. Therefore, UEVs serve as a valuable source of biomarkers for the non-invasive diagnosis of various pathologies. They hold a promising source of multiplex biomarkers suitable for prognosis, diagnosis, and therapy monitoring. UEVs are easily accessible, non-invasive, and suited for longitudinal sampling. Although various techniques are available for isolating UEVs, there is yet to be a consensus on a standard and ideal protocol. We have optimized an efficient, reliable, and easily adoptable polyethylene glycol (PEG) based UEV isolation technique following MISEV guidelines. The method is suitable for various downstream applications of UEVs. This could be a cost-effective, consistent, and accessible procedure for many clinical labs and is most suited for longitudinal analysis. Adopting the protocol will pave the way for establishing UEVs as the ideal biomarker source. • Urine can be collected non-invasively and repeatedly, hence a very useful specimen for biomarker discovery. Urinary EVs (UEVs), derived from urine, offer a stable diagnostic tool, but standardised isolation and analysis approaches are warranted. • To have enough UEVs for any study, large volumes of urine sample are necessary, which limits different isolation methods by cost, yield, and time. • The protocol developed could help researchers by offering a cost-effective and dependable UEV isolation method and may lay the foundation for UEVs adoption in clinical space.

Magnetic resonance imaging and computed tomography from multiple batches (e.g. sites, scanners, datasets, etc.) are increasingly used alongside complex downstream analyses to obtain new insights into the human brain. However, significant confounding due to batch-related technical variation, called batch effects, is present in this data; direct application of downstream analyses to the data may lead to biased results. Image harmonization methods seek to remove these batch effects and enable increased generalizability and reproducibility of downstream results. In this review, we describe and categorize current approaches in statistical and deep learning harmonization methods. We also describe current evaluation metrics used to assess harmonization methods and provide a standardized framework to evaluate newly-proposed methods for effective harmonization and preservation of biological information. Finally, we provide recommendations to end-users to advocate for more effective use of current methods and to methodologists to direct future efforts and accelerate development of the field.

Drug expenditure in the United States has continued to increase unsustainably; the specialty of dermatology has been particularly affected. Resources are limited - someone has to make decisions about what treatments will be covered and how they will be reimbursed. Step therapy is a cost-control method used by insurers to encourage the use of the most cost-effective treatments before more expensive options are attempted. However, a rigid step therapy policy can be problematic when protocols are out of date, or delay necessary treatment leading to unnecessary suffering, increased morbidity, and overall cost. To address some of these concerns, the proposed Safe Step Act (S. 2546 and H.R. 2279) attempts to create a requirement that insurers provide a transparent, expeditious exceptions process for step therapy protocols. Increased flexibility in this process will allow for the unique circumstances of individual patients and improve access to expensive drugs for special cases. However, this bill may be exploited, further weakening insurers' ability to negotiate on cost. We should be cautious about measures that reduce the effectiveness of this tool, particularly if we, as a society, aim to expand access to basic care to all Americans.

Generative adversarial networks (GANs) are one powerful type of deep learning models that have been successfully utilized in numerous fields. They belong to the broader family of generative methods, which learn to generate realistic data with a probabilistic model by learning distributions from real samples. In the clinical context, GANs have shown enhanced capabilities in capturing spatially complex, nonlinear, and potentially subtle disease effects compared to traditional generative methods. This review critically appraises the existing literature on the applications of GANs in imaging studies of various neurological conditions, including Alzheimer's disease, brain tumors, brain aging, and multiple sclerosis. We provide an intuitive explanation of various GAN methods for each application and further discuss the main challenges, open questions, and promising future directions of leveraging GANs in neuroimaging. We aim to bridge the gap between advanced deep learning methods and neurology research by highlighting how GANs can be leveraged to support clinical decision making and contribute to a better understanding of the structural and functional patterns of brain diseases.

Abstract Multi-omic data, i.e., clinical measures, radiomic, and genetic data, capture multi-faceted tumor characteristics, contributing to a comprehensive patient risk assessment. Here, we investigate the additive value and independent reproducibility of integrated diagnostics in prediction of overall survival (OS) in isocitrate dehydrogenase (IDH)-wildtype GBM patients, by combining conventional and deep learning methods. Conventional radiomics and deep learning features were extracted from pre-operative multi-parametric MRI of 516 GBM patients. Support vector machine (SVM) classifiers were trained on the radiomic features in the discovery cohort (n = 404) to categorize patient groups of high-risk (OS

Background The novel coronavirus (COVID-19) has an affinity for almost every organ system, including the skin. This review article will compile and compare dermatology guidelines related to the dermatologic care of patients during the COVID-19 pandemic. Objective To review and compare the available guidelines and recommendations on use of biologics and other systemic therapies in dermatology patients. Methods We conducted a search for guidelines and recommendations for dermatology providers, with specific attention to the management of biologics and non-biologic systemic agents during COVID-19. International and United States-based guidelines specific to the field of dermatology and guidance for healthcare providers were used as inclusion criteria. Available registries were included as well. Conclusion The guidelines stress a shared decision-making approach with risk-benefit analysis and treatment strategy modifications tailored to each unique patient case. The guidelines were all concordant with one another in highlighting the importance that patients should not discontinue their medications without first speaking with their providers.

Background Systemic methotrexate (MTX) is a useful treatment for many dermatologic conditions, however, the risk of adverse events prevents its use in patients with minimal or localized disease. Topical application of MTX may be an option to avoid the systemic adverse effects of oral MTX. Objective To assess what is known about the efficacy and safety of topical methotrexate. Methods A search on Pubmed was conducted. There were no limits on publication date. Results A total of 963 articles were discovered. Using our exclusion criteria, 916 articles were excluded; 47 articles were used for full text assessment. Topical MTX has been used primarily in psoriasis but also in mycosis fungoides, lymphomatoid papulosis, and oral precancerous lesions. Optimal delivery system and formulation for adequate penetration is still under investigation. Conclusion The quality of evidence for the utility of topical methotrexate in psoriasis is good, however, for other dermatologic diseases, the quality is poor. Topical MTX with improved delivery methods may be a viable tool against certain localized dermatologic conditions for patients who do not tolerate oral MTX. Further double-blinded randomized controled studies are needed to substantiate the utility of topical methotrexate.

Long-term treatment outcomes for patients with high grade ovarian cancers have not changed despite innovations in therapies. There is no recommended assay for predicting patient response to second-line therapy, thus clinicians must make treatment decisions based on each individual patient. Patient-derived xenograft (PDX) tumors have been shown to predict drug sensitivity in ovarian cancer patients, but the time frame for intraperitoneal (IP) tumor generation, expansion, and drug screening is beyond that for tumor recurrence and platinum resistance to occur, thus results do not have clinical utility. We describe a drug sensitivity screening assay using a drug delivery microdevice implanted for 24 h in subcutaneous (SQ) ovarian PDX tumors to predict treatment outcomes in matched IP PDX tumors in a clinically relevant time frame. The SQ tumor response to local microdose drug exposure was found to be predictive of the growth of matched IP tumors after multi-week systemic therapy using significantly fewer animals (10 SQ vs 206 IP). Multiplexed immunofluorescence image analysis of phenotypic tumor response combined with a machine learning classifier could predict IP treatment outcomes against three second-line cytotoxic therapies with an average AUC of 0.91.

In this paper, design and analysis of a flexible antenna backed with Frequency Selective Surface (FSS) is presented for wireless body area communications. Textile based compact monopole antenna (50.5 × 38 mm2) is backed with a modified square loop FSS textile based superstrate (120 × 120 mm2), to improve the wearable antenna’s performance in terms of SAR reduction and gain enhancement. Modified square shaped monopole radiator with a rectangular slit defected ground structure provides the radiation of the 2.45 GHz ISM band with a bandwidth (S11 ≤ −10 dB) of 2.075 to 2.625 GHz. The bending analysis of antenna is also investigated in operating frequency band. Achiral FSS provides gradual increment in inductance and stable performance at both TE and TM polarization for oblique incidence of signals up to 60°. The proposed antenna system with the FSS, yields the fractional bandwidth of 22.44% and peak gain of 7.76 dBi with the dimensions of 120 × 120 × 30 mm3. The inclusion of FSS, reduces the back radiation there by decrease in SAR level is achieved from 3.197 W/kg to 0.164 W/kg. The reduced SAR value and its stable frequency performance over flexed conditions, ensures that the proposed system is a suitable entrant for wearable applications.

Background: Autofluorescence-based imaging has the potential to nondestructively characterize the biochemical and physiological properties of plants regulated by genotypes using optical properties of the tissue. A comparative study of stress tolerant and stress susceptible genotypes of Brassica rapa with respect to newly introduced stress-based phenotypes using machine learning techniques will contribute to the significant advancement of autofluorescence-based plant phenotyping research. Methods: Autofluorescence spectral images have been used to design a stress detection classifier with two classes, stressed and non-stressed, using machine learning algorithms. The benchmark dataset consisted of time-series image sequences from three Brassica rapa genotypes (CC, R500, and VT), extreme in their morphological and physiological traits captured at the high-throughput plant phenotyping facility at the University of Nebraska-Lincoln, USA. We developed a set of machine learning-based classification models to detect the percentage of stressed tissue derived from plant images and identified the best classifier. From the analysis of the autofluorescence images, two novel stressbased image phenotypes were computed to determine the temporal variation in stressed tissue under progressive drought across different genotypes, i.e., the average percentage stress and the moving average percentage stress. Results: The study demonstrated that both the computed phenotypes consistently discriminated against stressed versus non-stressed tissue, with oilseed type (R500) being less prone to drought stress relative to the other two Brassica rapa genotypes (CC and VT). Conclusion: Autofluorescence signals from the 365/400 nm excitation/emission combination were able to segregate genotypic variation during a progressive drought treatment under a controlled greenhouse environment, allowing for the exploration of other meaningful phenotypes using autofluorescence image sequences with significance in the context of plant science. [ABSTRACT FROM AUTHOR]

Introduction: Animal-Assisted Therapy (AAT) is being developed as an alternative approach to reduce the anxiety and comfort the apprehensive patients. Hence, to evaluate the child’s and parental acceptance of pets in dental operatory, their favourite pet, child’s choice of live pet over soft toy, their preference of own pet vs therapy, more hairy pets vs less hairy pets, play area vs child’s lap, the present study is undertaken. Aim: To evaluate the influence of pets in reducing anxiety of children in the dental operatory and acceptance of this technique by the children and the parents. Materials and Methods: This questionnaire-based cross-sectional study was conducted on 65 children of age group 3-12 years and their parents who attended the Department of Pedodontics and Preventive Dentistry, GITAM Dental college and Hospital, Visakhapatnam, Andhra Pradesh, India for a period of two weeks in the month of July 2018. They were assessed for anxiety and the influence of pets on anxiety levels using questionnaire and picture cards. The data was statistically analysed using Chi-square test. Results: An 83.3% of parents and 75.4% of children felt that pets in the dental operatory are useful in reducing the anxiety. A 58.5% of children preferred live pets whereas only 33.3% parents preferred them. Dog was found to be the most favourite pet for very young children (31.6%) whereas older children preferred rabbit (43.5%). Conclusion: Animal-assisted therapy was found to be useful in reducing the anxiety of children during dental visit.

The present study focused to improve material characteristics and quality in terms of the NaOH concentration for treating the coconut and bamboo fiber to enhance the mechanical properties of natural fiber polymer-based hybrid composites. The NaOH-treated fibers were washed thoroughly using distilled water and allowed to dry for 24 hours. Composition of each specimen, bamboo (B) and coconut (C) fiber with epoxy composite, was prepared by hand layup process as per the American Society for Testing and Materials (ASTM) standard. The proportionality of the material was carefully fulfilled according to the previous literature reports. The weight fraction of the composite material content was set to be 30% and 70% of epoxy (E) resin and isolated fibers. Three distinct criteria were used to calculate mechanical parameters such as tensile strength, flexural strength, and material hardness. It was found that the combination of 70% E with 30% BC of hybridized composite had a maximum tensile strength of 62.42 MPa, whereas the flexural strength and hardness of the other combinations, such as 70% E with 30% C and 70% E with 30% B, were observed to be 58 MPa and 185 HRC (Hardness Rockwell C), respectively.

This paper mainly concentrates on finding maximum displacement and Eigen frequency for microelectromechanical system (MEMS) inertial sensors with various beam structures. In this work, six different shapes of beams are attached with one set of electrodes to find which structure exhibits the maximum displacement with less cross-axis displacement and to observe the operating frequency. The momentum of the beams is directly proportional to the thickness and length, which provides the maximum flexibility to produce momentum on proof mass. The beam thickness is 5 um for all proposed sensors and the momentum has been observed by changing different shapes of beam structures. The momentum occurred on proof mass due to applied physical energy. The same amount of physical energy is applied to all structure and the momentum, quality factor, and displacement are measured which had been compared and the table is given.