Nuances of Interpreting X-ray Analysis by Deep Learning and Lessons for Reporting Experimental Findings

With the increase in the availability of annotated X-ray image data, there has been an accompanying and consequent increase in research on machine-learning-based, and ion particular deep-learning-based, X-ray image analysis. A major problem with this body of work lies in how newly proposed algorithms are evaluated. Usually, comparative analysis is reduced to the presentation of a single metric, often the area under the receiver operating characteristic curve (AUROC), which does not provide much clinical value or insight and thus fails to communicate the applicability of proposed models. In the present paper, we address this limitation of previous work by presenting a thorough analysis of a state-of-the-art learning approach and hence illuminate various weaknesses of similar algorithms in the literature, which have not yet been fully acknowledged and appreciated. Our analysis was performed on the ChestX-ray14 dataset, which has 14 lung disease labels and metainfo such as patient age, gender, and the relative X-ray direction. We examined the diagnostic significance of different metrics used in the literature including those proposed by the International Medical Device Regulators Forum, and present the qualitative assessment of the spatial information learned by the model. We show that models that have very similar AUROCs can exhibit widely differing clinical applicability. As a result, our work demonstrates the importance of detailed reporting and analysis of the performance of machine-learning approaches in this field, which is crucial both for progress in the field and the adoption of such models in practice.

Intercomparison of estimators of extreme value family of distributions for rainfall frequency analysis

Estimation of rainfall for a given return period is of utmost importance for planning and design of minor and major hydraulic structures. This can be achieved through Extreme Value Analysis (EVA) of rainfall by fitting Extreme Value family of Distributions (EVD) such as Generalized Extreme Value, Extreme Value Type-1, Extreme Value Type-2 and Generalized Pareto to the series of observed Annual 1-Day Maximum Rainfall (AMR) data. Based on the intended applications and the variate under consideration, Method of Moments (MoM), Maximum Likelihood Method (MLM) and L-Moments (LMO) are used for determination of parameters of probability distributions. The adequacy of fitting EVD to the AMR series was evaluated by quantitative assessment using Goodness-of-Fit (viz., Chi-square and Kolmogorov-Smirnov) and diagnostic test (viz., D-index) tests and qualitative assessment by the fitted curves of the estimated rainfall. The paper presents a study on intercomparison of EVD (using MoM, MLM and LMO) adopted in EVA of rainfall with illustrative example and the results obtained thereof.

Analysis of the Shear-Thinning Viscosity Behavior of the Johnson–Segalman Viscoelastic Fluids

This paper presents a numerical comparison of viscoelastic shear-thinning fluid flow using a generalized Oldroyd-B model and Johnson–Segalman model under various settings. Results for the standard shear-thinning generalization of Oldroyd-B model are used as a reference for comparison with those obtained for the same flow cases using Johnson–Segalman model that has specific adjustment of convected derivative to assure shear-thinning behavior. The modeling strategy is first briefly described, pointing out the main differences between the generalized Oldroyd-B model (using the Cross model for shear-thinning viscosity) and the Johnson–Segalman model operating in shear-thinning regime. Then, both models are used for blood flow simulation in an idealized stenosed axisymmetric vessel under different flow rates for various model parameters. The simulations are performed using an in-house numerical code based on finite-volume discretization. The obtained results are mutually compared and discussed in detail, focusing on the qualitative assessment of the most distinct flow field differences. It is shown that despite all models sharing the same asymptotic viscosities, the behavior of the Johnson–Segalman model can be (depending on flow regime) quite different from the predictions of the generalized Oldroyd-B model.

SARS-CoV-2 Slows Brain Rhythms with more Severe Effects in Younger Individuals

Coronavirus disease secondary to infection by SARS-CoV-2 (COVID19 or C19) causes respiratory illness, as well as severe neurological symptoms that have not been fully characterized. In a previous study, we developed a computational pipeline for the automated, rapid, high-throughput and objective analysis of brain encephalography (EEG) rhythms. In this retrospective study, we used this pipeline to define the quantitative EEG changes in patients with a PCR-positive diagnosis of C19 (n=31) in the intensive care unit (ICU) of Cleveland Clinic, compared to a group of age-matched PCR-negative (n=38) control patients in the same ICU setting. Qualitative assessment of EEG by two independent teams of electroencephalographers confirmed prior reports with regards to the high prevalence of diffuse encephalopathy in C19 patients, although the diagnosis of encephalopathy was inconsistent between teams. Quantitative analysis of EEG showed distinct slowing of brain rhythms in C19 patients compared to control (enhanced delta power and attenuated alpha-beta power). Surprisingly, these C19-related changes in EEG power were more prominent in patients below age 70. Moreover, machine learning algorithms showed consistently higher accuracy in the binary classification of patients as C19 versus control using EEG power for subjects below age 70 compared to older ones, providing further evidence for the more severe impact of SARS-CoV-2 on brain rhythms in younger individuals irrespective of PCR diagnosis or symptomatology, and raising concerns over potential long-term effects of C19 on brain physiology in the adult population and the utility of EEG monitoring in C19 patients.

The issue of the readiness of the Russian economy to implement digital transformation

In the article, the author carried out an integral qualitative assessment of the readiness of the domestic economy for the transition of its functioning to the format of industry 4.0. The degree of infrastructural and institutional compliance of the country’s economy with the criteria necessary for the implementation of the digital transition is investigated. The relevance of the article is due to the compression of the time available for the domestic economy to implement digital transformation, which are caused by the economic and infrastructural consequences of the Covid–19 pandemic and the tightening of competition between countries for the right to dominate the information space. The result of the scientific work done by the author is a comprehensive assessment of the readiness of the Russian economy for digital transformation, indicating its strengths and weaknesses. Taking into account the most significant factors determining Russia’s readiness for digital transformation, the framework directions for supporting the digital transformation of the country’s economy are proposed.

Polyphenols in Matily Herbal & Spices Boosts Antibodies in an Elderly Following COVID 19 Infections – A Case Study

An elderly patient had COVID-19 infection in August 2020 and started the home remedies treatment for the first 18 hours, followed by Azithromycin 250 mg for 6 days. The patient revered well, and the infection was confirmed by antibodies in the patient's serum. Later on, the patient was on Matily Herbal Drink and Matily Herbal & Spices Mix to avoid complications connected with COVID-19 re-infection. After completing 12 months of post COVID-19 infection, the antibodies were assessed to find the status. It was found to be increased in qualitative assessment. The quantitative assessment of antibodies showed a much higher value compared with individuals who had both the dose of vaccines and tested the blood after two weeks since the second dose of vaccine. We suggest that the increased antibodies could be because of the bioavailability of polyphenols present in the Matily Herbal Drink and Matily Herbal & Spices Mix. Polyphenols inactivate COVID-19 virus and this may have helped the body to increase its antibody production. The bioavailability of polyphenols depends on various factors, including acidulants in the diet. Based on the present studies, we suggest India should have her own strategies to increase antibodies in the population instead of just following the norms laid by International Agencies for the booster dose of vaccine Keywords: COVID-19, Matily Herbal Drink, Matily Herbal, Spices Mix

A qualitative assessment of QCA: method stretching in large-N studies and temporality

Qualitative Comparative Analysis (QCA) is a descriptive research method that can provide causal explanations for an outcome of interest. Despite extensive quantitative assessments of the method, my objective is to contribute to the scholarly discussion with insights constructed through a qualitative lens. Researchers using the QCA approach have less ability to incorporate and nuance information on set membership as the number of cases grows. While recognizing the suggested ways to overcome such challenges, I argue that since setting criteria for membership, calibrating, and categorizing are crucial QCA aspects that require in-depth knowledge, QCA is unfit for larger-N studies. Additionally, I also discuss that while the method is able to identify various parts of a causal configuration—the ‘what’—it falls short to shed light on the ‘how’ and ‘why,’ especially when temporality matters. Researchers can complement it with other methods, such as process tracing and case studies, to fill in these missing explanatory pieces or clarify contradictions—which begs the question of why they would also choose to use QCA.

Impact of COVID-19 on Hematology-Oncology Trainees: A Quantitative and Qualitative Assessment

PURPOSE: Graduate medical and research training has drastically changed during the COVID-19 pandemic, with widespread implementation of virtual learning, redeployment from core rotations to the care of patients with COVID-19, and significant emotional and physical stressors. The specific experience of hematology-oncology (HO) fellows during the COVID-19 pandemic is not known. METHODS: We conducted a mixed-methods study using a survey of Likert-style and open-ended questions to assess the training experience and well-being of HO fellows, including both clinical and postdoctoral trainee members of the American Society of Hematology and ASCO. RESULTS: A total of 2,306 surveys were distributed by e-mail; 548 (23.8%) fellows completed the survey. Nearly 40% of fellows felt that they had not received adequate mental health support during the pandemic, and 22% reported new symptoms of burnout. Pre-existing burnout before the pandemic, COVID-19–related clinical work, and working in a primary research or nonclinical setting were associated with increased burnout on multivariable logistic regression. Qualitative thematic analysis of open-ended responses revealed significant concerns about employment after training completion, perceived variable quality of virtual education and board preparation, loss of clinical opportunities to prepare for independent clinical practice, inadequate grant funding opportunities in part because of shifting research priorities, variable productivity, and mental health or stress during the pandemic. CONCLUSION: HO fellows have been profoundly affected by the pandemic, and our data illustrate multiple avenues for fellowship programs and national organizations to support both clinical and postdoctoral trainees.

Systematic Review of the Socioeconomic Consequences in Patients With Multiple Sclerosis With Different Levels of Disability and Cognitive Function

Multiple sclerosis (MS) is a challenging and disabling condition, predominantly affecting individuals in early adulthood. MS affects the physical, cognitive, and mental health of persons suffering from the disease as well as having a great impact on their financial status and quality of life. However, there is a lack of systematic approach toward assessing the socioeconomic consequences of MS. Our objective was to systematically review analytical observational studies investigating the socioeconomic consequences in persons with MS with different levels of physical disability and cognitive function. We conducted a systematic review on socioeconomic consequences of MS with a focus on employment-, income-, work ability-, and relationship-related outcomes in persons with MS with special focus on disability and cognition. Additionally, the educational characteristics were examined. From 4,957 studies identified, 214 were assessed for eligibility and a total of 19 studies were included in this qualitative assessment; 21 different outcomes were identified. All identified studies reported higher unemployment, higher early retirement, and higher risk of unemployment in relation to higher physical disability. Also, cognitive function was found to be a predictor of employment (unemployment). The studies pointed out significant correlations between greater disability and lower earnings and higher income from benefits. A study found the same correlation in relation to cognitive function. The studies reported higher work disability in relation to higher physical disability and lower cognitive function. In conclusion, this systematic review summarizes the pronounced differences in various socioeconomic outcomes between patients with MS with regards to their physical disability and cognitive function. In addition, we identified a lack of studies with longitudinal design in this field that can provide more robust estimates with covariate adjustments, such as disease modifying treatments.