Molnupiravir for treating COVID-19

Objectives: This is a protocol for a Cochrane Review (intervention). The objectives are as follows:. To assess the effects of molnupiravir in people with confirmed SARS-CoV-2 infection and mild-to-moderate symptoms, with or without risk factors for severe disease.

CONTROLLED TESTING AND ISOLATION FOR SUPPRESSING COVID-19

The Corona virus disease 2019 (COVID-19) has significantly affected lives of people around the world. Today, isolation policy is mostly enforced by identifying infected individuals based on symptoms when these appear or by testing people and quarantining those who have been in close contact with infected people. In addition, many countries have imposed complete or partial lock-downs to control the spread of the disease. While lock-downs have succeeded to slow down the spread of the virus, they have devastating effects on the economy and social life. We argue that controlling the spread of the virus can be done by using active feedback to control testing for infection by actively testing individuals with a high probability of being infected. We develop an active testing strategy to achieve this goal, and demonstrate that it would have tremendous success in controlling the spread of the virus. Our results show up to a 50% reduction in quarantine rate and morbidity rate in typical settings as compared to existing methods.

Meta-analytic magic, ivermectin, and socially responsible reporting.

Some clinicians prescribe ivermectin for COVID-19 despite a lack of support from any credible South African professional body. They argue that when faced by clinical urgency, weak signals of efficacy should trigger action if harm is unlikely. Several recent reviews found an apparent mortality benefit by including studies at high risk of bias and with active rather than placebo controls. If these studies are discounted, the pooled mortality effect is no longer statistically significant, and evidence of benefit is very weak. Relying on this evidence could cause clinical harm if used to justify vaccine hesitancy. Clinicians remain responsible for ensuring that guidance they follow is both legitimate and reliable. In the ivermectin debate, evidence-based medicine (EBM) principles have largely been ignored under the guise thatin a pandemic the 'rules are different', probably to the detriment of vulnerable patients and certainly to the detriment of the profession's image. Medical schools and professional interest groups are responsible for transforming EBM from a taught but seldom-used tool into a process of lifelong learning, promoting a consistent call for evidence-based and unconflicted debate integral to clinical practice.

WORSE OUTCOMES OF VARIANT COVID-19 VIRUSES IN CHRONIC HEMODYALISYS PATIENTS

TOPIC: Chest Infections TYPE: Original Investigations PURPOSE: The COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to challenge the world. Even though effective vaccines have been developed, new variants of concern continue to emerge. While there is evidence of higher transmissibility rates of the B.1.1.7 variant and other variants, concerns regarding disease severity of these variants have not yet been confirmed. Patients undergoing maintenance hemodialysis are at increased risk for infection, with several reports of COVID-19 outbreaks in hemodialysis centers. The clinical outcomes of the SARS-CoV-2 variant viruses have not been reported or compared to non-variant SARS-CoV-2 among this unique population. The goal of the study was to compare the clinical outcomes and related mortality of infection with variant SARS-CoV-2 in chronic hemodialysis patients, and to compare it with infection by previous, non-variant strains of the virus. METHODS: This is a retrospective observational study comparing COVID-19 outbreaks of variant and non-variant SARS-CoV-2 strains in 2 hemodialysis centers in Israel. In one dialysis center ("center 1") an outbreak of COVID-19 caused by variant SARS-CoV-2 occurred starting from 28 December 2020. Subjects from a second hemodialysis center ("center 2") infected by non-variant SARS-CoV-2 in an earlier outbreak of COVID-19 which occurred from April 2020 to July 2020 served as control group. Complete SARS-CoV-2 genomes were sequenced via next generation sequencing (NGS).Primary outcome measures were 30-days mortality rates and in-hospital mortality rates. Secondary outcomes included mortality rates during follow-up, disease severity (according to NIH guidelines), need for respiratory support, type of respiratory support and need for hemodynamic support. RESULTS: Baseline subjects' characteristics were comparable. Chronic hemodialysis patients infected with SARS-CoV-2 variants had more severe infection and required more respiratory support, such as NIV (p=0.05), HFOT (p=0.021) and mechanical ventilation (p=0.05), as well as more hemodynamic support (p=0.05). Among patients from center 1, who were infected with virus variants, 71% were classified as critical vs. 8% of patients from center 2 (non-variant, p=0.005). 30-day mortality was higher among patients from center 1 as compared to center 2 (57.1% vs. 7.7.%, odds ratio for 30-day mortality in center 1 was 16, with 95% confidence interval 2-128, p=0.003).Multivariate analysis model for predictors of all-cause mortality showed that infection with a variant was the most important predictor of mortality. CONCLUSIONS: Infection with variant SARS-CoV-2 among chronic hemodialysis patients was strongly related with severe disease and mortality. CLINICAL IMPLICATIONS: SARS-CoV-2 genetic variation may affect clinical outcomes. Vaccination of hemodialysis patients should be prioritized. DISCLOSURES: No relevant relationships by sydney Benchetrit, source=Web Response No relevant relationships by keren cohen, source=Web Response No relevant relationships by Ayman Fadeela, source=Web Response No relevant relationships by Orna Mor, source=Web Response no disclosure on file for Naomi Nacasch;No relevant relationships by ori wand, source=Web Response no disclosure on file for Neta Zuckerman;

Traitement automatique de la langue pour une réponse rapide dans le cadre d’une maladie émergente : exemple de la COVID-19

Introduction Une maladie émergente pose des problèmes spécifiques pour les outils informatiques. L’informatique biomédicale repose en grande partie sur les données structurées qui requièrent l’existence de données ou de modèles de connaissances. Cependant, une nouvelle maladie ne peut avoir de modèle de connaissances préexistant. Au cours d’une épidémie de maladie émergente, le traitement automatique de la langue (TAL) peut permettre la conversion rapide de données textuelles non structurées en un nouveau modèle de connaissances. Bien que cette idée ait déjà été suggérée, il n’y avait pas eu jusqu’à présent d’opportunité pour la tester en temps réel. La pandémie actuelle de COVID-19 en est une. L’objectif de cette étude était de montrer la valeur ajoutée de l’extraction par TAL de l’information clinique présente dans les textes pour répondre aux questions posées dans le cadre d’une maladie émergente. Méthodes Nous avons exploré les effets à long-terme des traitements par inhibiteurs calciques sur le devenir des patients hypertendus, hospitalisés pour une infection COVID-19. Dans l’entrepôt de données de santé de l’AP-HP, nous avons comparé deux sources différentes d’information : les données structurées (codes diagnostics CIM10, résultats biologiques, prescriptions médicamenteuses) et les données extraites des textes cliniques par TAL. Résultats Dans cette étude multicentrique sur les 39 hôpitaux de l’AP-HP, le TAL a permis d’augmenter suffisamment la puissance statistique pour rendre significatif un résultat de risque relatif ajusté alors qu’il ne l’était pas avec les données structurées uniquement (Fig. 1). En comparant aux données structurées, le nombre de patients incluables dans l’étude a été multiplié par 2,95, la quantité d’information sur les médicaments par 7,2 et les informations phénotypiques par 11,9. Conclusion Dans notre étude, l’utilisation d’inhibiteurs calciques était associée à une diminution de la mortalité intra-hospitalière chez les patients avec une infection COVID-19. Ces résultats ont été obtenus en adaptant rapidement des pipelines TAL au domaine d’une nouvelle maladie. Ce pipeline d’extraction était suffisamment performant pour extraire des informations utiles. Quand ces informations ont été utilisées pour enrichir les données structurées déjà disponibles, l’échantillon de l’étude a pu être suffisamment augmenté pour voir apparaitre un effet de traitement qui n’était jusqu’alors pas détectable.

Understanding the Effects of Human Factors on the Spread of COVID-19 Using a Neural Network

During the spread of an infectious disease such as COVID-19, the identification of human factors that affect the spread is a really important area of research. These factors directly impact the spread of such a disease and are important in identifying the various regions that are at a higher risk than others. This allows for an optimal distribution of resources according to predicted demand. Traditional infectious modeling techniques are good at representing the spread and can incorporate multiple factors that resemble real-life scenarios. The primary issue here is the identification of relevant variables. In this study, a residual analysis is presented to downsize the dataset available and shortlist the variables classified as absolutely necessary for disease modeling. The performance of different datasets is evaluated using an Artificial Neural Network and regression analysis. The results show that the drop in performance using the reduced dataset is reasonable as it is very difficult to obtain a perfect dataset covering only necessary variables. This approach can be automated in the future as it offers a small dataset containing a few variables against a large dataset with possibly hundreds of variables. © 2020 IEEE.

Identifying Factors in COVID-19 AI Case Predictions

Many machine learning methods are being developed to predict the spread of COVID-19. This paper focuses on the expansion of inputs that may be considered in these models. A correlation matrix is used to identify those variables with the highest correlation to COVID-19 cases. These variables are then used and compared in three methods that predict future cases: a Support Vector Machine Regression (SVR), Multidimensional Regression with Interactions, and the Stepwise Regression method. All three methods predict a rise in cases similar to the actual rise in cases, and importantly, are all able to predict to a certain degree the unexpected dip in cases on the 10th and 11th day of prediction. © 2020 IEEE.

Essential medicine selection during the COVID-19 pandemic: Enabling access in uncharted territory

The COVID-19 pandemic requires urgent decisions regarding treatment policy in the face of rapidly evolving evidence. In response, the South African Essential Medicines List Committee established a subcommittee to systematically review and appraise emerging evidence, within very short timelines, in order to inform the National Department of Health COVID-19 treatment guidelines. To date, the subcommittee has reviewed 14 potential treatments, and made recommendations based on local context, feasibility, resource requirements and equity. Here we describe the rapid review and evidence-to-decision process, using remdesivir and dexamethasone as examples. Our experience is that conducting rapid reviews is a practical and efficient way to address medicine policy questions under pandemic conditions.

MEDA 2020: The curative power of medical data

The focus of the MEDA 2020 workshop is biomedical data in digital form, especially biomedical literature on the one hand, and genomic data on the other. Like in previous editions, this edition will include presentation of original work, a keynote talk, and a panel discussion for robust machine translation abilities that can build on previous work in the field [1, 8, 12, 15-17, 25, 26] and be applied to the large amount of COVID-19-related scientific literature [6, 19, 24] and clinical records [7, 13] in multiple languages. Possibly it has never been more important to be able to apply multilingual language processing techniques to the problem of information retrieval by health care consumers [14]. © 2020. ACM ISBN.

Essential medicine selection during the COVID-19 pandemic: Enabling access in uncharted territory

The COVID-19 pandemic requires urgent decisions regarding treatment policy in the face of rapidly evolving evidence. In response, the South African Essential Medicines List Committee established a subcommittee to systematically review and appraise emerging evidence, within very short timelines, in order to inform the National Department of Health COVID-19 treatment guidelines. To date, the subcommittee has reviewed 14 potential treatments, and made recommendations based on local context, feasibility, resource requirements and equity. Here we describe the rapid review and evidence-to-decision process, using remdesivir and dexamethasone as examples. Our experience is that conducting rapid reviews is a practical and efficient way to address medicine policy questions under pandemic conditions.