Covid-19 and diabetes in primary care - How do hematological parameters present in this cohort?

Objectives: Changes in hematological parameters are becoming evident as important early markers of COVID-19. Type 2 Diabetes Mellitus (T2DM) has been shown to be associated with increased severity of COVID-19. In this study, we aim to explore the various hematological variables in COVID-19 positive patients with T2DM, so as to act early and improve patient outcomes.Methods: Medical e-records of seventy adult patients with T2DM who were COVID-19 positive have been analyzed in this retrospective cohort study. Demographic, clinical and laboratory parameters for these patients were examined.Results: Of the seventy patients with T2DM, 48.88% had poorly controlled diabetes. 70.69% were pyrexial, 56.25% were tachycardic and 38.58% were asymptomatic on presentation. Amongst the hematological parameters, anemia was seen in 10% of males and 15.38% of females. 20% had a high red-blood-cell-distribution-width (RDW). 7.27% had thrombocytosis and 3.64% had thrombocytopenia. 73.3% had a high platelet-distribution-width (PDW) and 44.44% had an increased mean-platelet-volume (MPV). 16.36% were neutropenic and 16.67% had lymphocytopenia.Conclusion: Diabetic COVID-19 positive patients have been shown to have prominent manifestations of the hemopoietic-system with varied hematological profiles. Recognizing the implications of these variables early in primary-care, can help clinicians aid management decisions and dictate early referral to secondary-care services, to help improve prognosis.

SARS-CoV-2 TRAUMA HANDLING AND HAZARD RESCUE THROUGH MACHINE LEARNING

COVID-2019 has led to the greatest world menace of livelihood communities. This is a catastrophic epidemic that is basically known for SARS-CoV-2 which is expanded as Severe Acute Respiratory Syndrome. Meanwhile, the Coronavirus disease that emerged from China had lots of devastating effects on society and exhausted the ultra modern health-care systems worldwide. Now, it's remarkable to assist ML to identify, detect and predict pandemic immense nature. The entire society is posing their phenomenal attempts to oppose the challenge of fatal disease in conditions with economic frameworks, data origins, shielding gears, life-hazard treatments, medicines and numerous resources. This article mainly focuses on the Machine Learning model techniques that are comprehensively used for Screening, Predicting, Forecasting, Tracing and Drug managing based on the allied outbreak. The highly demanded ML methods point towards wireless infrastructure, real-time collections and end-to-end user devices. This evaluation suggests machine learning is a powerful aid for pandemics cause. [ABSTRACT FROM AUTHOR] Copyright of International Journal of Pharmaceutical Research (09752366) is the property of Association of Indian Pharmacist and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)