Covid-19 diagnosis from x-ray using neural networks (preprint)

Corona virus or COVID-19 is a pandemic illness, which has influenced more than million of causalities worldwide and infected a few large number of individuals .Innovative instrument empowering quick screening of the COVID-19 contamination with high precision can be critically useful to the medical care experts. The primary clinical device presently being used for the analysis of COVID-19 is the Reverse record polymerase chain response as known as RT-PCR, which is costly, less-delicate and requires specific clinical work force. X-Ray imaging is an effectively available apparatus that can be a great option in the COVID-19 conclusion. This exploration was taken to examine the utility of computerized reasoning in the quick and exact recognition of COVID-19 from chest X-Ray pictures. The point of this paper is to propose a procedure for programmed recognition of COVID-19 from advanced chest X-Ray images applying pre-prepared profound learning calculations while boosting the discovery exactness. The point is to give over-focused on clinical experts a second pair of eyes through a learning picture characterization models. We distinguish an appropriate Convolutional Neural Network-CNN model through beginning similar investigation of a few mainstream CNN models.

Mathematical modelling of supply chain under current COVID’19 business scenario with the review study on the fuzzy logic based supply chains

Organisation should focus on raw material flow, and process flow for the smooth running of supply chain. And the success of the supply chain depends on the demand aspects of the market too. Designing a supply chain does involve many challenges in all the flows mentioned above. This article is a review study of Fuzzy logic has been applied in modelling the supply chain. Besides the article also suggests briefly the initial requirements of evolving a mathematical model for an integrated supply chain.

Expanding Access to Remdesivir via an Improved Pyrrolotriazine Synthesis: Supply Centered Synthesis.

Pyrrolotriazine 1 is an important precursor to remdesivir. Initial results toward an efficient synthesis are disclosed consisting of sequential cyanation, amination, and triazine formation beginning from pyrrole. This route makes use of highly abundant, commoditized raw material inputs. The yield of triazine was doubled from 31% to 59%, and the synthetic step count was reduced from 4 to 2. These efforts help to secure the remdesivir supply chain.

A Concise Route to MK-4482 (EIDD-2801) (preprint)

A two-step route to MK-4482 (EIDD-2801, 1) was developed consisting of an esterification and hydroxamination of cytidine. The reactions can be conducted in either order with overall yields of 67% (first step—esterification) and 37% (first step—hydroxamination). Selective esterification of the nucleoside’s primary alcohol by enzymatic means eliminated the need for diol protection/deprotection, and direct transamination with hydroxylamine precluded the necessity of activating the nucleobase for amine coupling. This results in a significant advancement over the reported synthesis which is formed in at best 17% yield. The step count is reduced from five transformation to two, and the more expensive uridine is replaced with the more available cytidine.

Outcomes and Cardiovascular Comorbidities in a Predominantly African-American Population with COVID-19 (preprint)

Importance: Racial disparities in COVID-19 outcomes have been amplified during this pandemic and reports on outcomes in African-American (AA) populations, known to have higher rates of cardiovascular (CV) comorbidities, remain limited. Objective: To examine prevalence of comorbidities, rates of hospitalization and survival, and incidence of CV manifestations of COVID-19 in a predominantly AA population in south metropolitan Chicago. Design, Setting, Participants: This was an observational cohort study of COVID-19 patients encountered from March 16 to April 16, 2020 at the University of Chicago. Deidentified data were obtained from an institutional data warehouse. Group comparisons and logistic regression modeling based on baseline demographics, clinical characteristics, laboratory and diagnostic testing was performed. Exposures: COVID-19 was diagnosed by nasopharyngeal swab testing and clinical management was at the discretion of treating physicians. Main Outcomes and Measures: Primary outcomes were hospitalization and in-hospital mortality, and secondary outcomes included incident CV manifestations of COVID-19 in the context of overall cardiology service utilization. Results: During the 30 day study period, 1008 patients tested positive for COVID-19 and 689 had available encounter data. Of these, 596 (87%) were AA and 356 (52%) were hospitalized, of which 319 (90%) were AA. Age > 60 years, tobacco use, BMI >40 kg/m2, diabetes mellitus (DM), insulin use, hypertension, chronic kidney disease, coronary artery disease (CAD), and atrial fibrillation (AF) were more common in hospitalized patients. Age > 60 years, tobacco use, CAD, and AF were associated with greater risk of in-hospital mortality along with several elevated initial laboratory markers including troponin, NT-proBNP, blood urea nitrogen, and ferritin. Despite this, cardiac manifestations of COVID-19 were uncommon, coincident with a 69% decrease in cardiology service utilization. For hospitalized patients, median length of stay was 6.2 days (3.4-11.9 days) and mortality was 13%. AA patients were more commonly hospitalized, but without increased mortality. Conclusions and Relevance: In this AA-predominant experience from south metropolitan Chicago, CV comorbidities and chronic diseases were highly prevalent and associated with increased hospitalization and mortality. Insulin-requiring DM and CKD emerged as novel predictors for hospitalization. Despite the highest rate of comorbidities reported to date, CV manifestations of COVID-19 and mortality were relatively low. The unexpectedly low rate of mortality merits further study.