Phytochemicals: recent trends and future prospective in COVID-19

Coronavirus (COVID-19) is now growing aggressively over the globe and is exceedingly tricky to control due to the lack of available treatments or vaccines. Multiple investigations are now underway with the aim of identifying suitable herbal remedies and phytochemicals to reduce the incidence of COVID-19. In conclusion, certain herbal medications and phytopharmaceuticals could be a potential treatment strategy for mitigating SARS-CoV-2 hazards. Extensive research has been performed in pursuit of fresh options, including the use of phytochemical substances, which, in agreement with previous research, are not only promising against SARS-CoV-2, but also as coadjuvants in other diseases like diabetes. In addition, plants have been used for eras to cure a variety of infections, and exploration with plant-based natural products has been emphasized by the low toxicity of their metabolites and minimal side effects. In this chapter, we draw attention to various plant species and phytochemicals, a few of them belonging to the structural classes like phenolic, alkaloids, and terpenes with significant antiviral efficacy against SARS-CoV-2 that could be investigated as prospective medicines for the treatment of COVID-19. © 2023 Elsevier Inc. All rights reserved.

Multiclass Classification of Disease Using CNN and SVM of Medical Imaging

This paper proposed a model that deals with automatic prediction of the disease given the medical imaging. While most of the existing models deals with predicting disease in one part of the body either brain, heart or lungs, this paper focuses on three different organs brain, chest, and knee for better understanding the real word challenge where problems do not include crisp classification but the multiclass classification. For simplicity this paper focuses on just determining whether that organ is affected with the disease or not and future work can be done by further expanding the model for multiple disease detection of that organ. We have used CNN for multiclass image classification to determine the input medical image is brain, chest or knee and then SVM is used for binary classification to determine whether that input image is detected with the disease or not. Three different datasets from Kaggle are used: Brain Tumor MRI Dataset, COVID-19 Chest X-ray Image Dataset and Knee Osteoarthritis Dataset with KL Grading. Images from these datasets are used to make fourth datasets for training and testing the CNN for the prediction of the three different organs and after that output will be the input of respective SVM classifier based on the output result and predict the weather it is diagnostic with the disease or not. The proposed model can be employed as an effective and efficient method to detect different human diseases associated with different parts of the body without explicitly giving the input that it belongs to that part. For the transparency this model displays the accuracy of prediction made for the input image. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Administration of systemic steroids during hospitalization does not improve the clinical outcome in patients with COVID-19. (COVID-19 dedicated issue.)

The systemic steroids are recommended for cases with refractory septic shock or severe acute respiratory distress syndrome. Although systemic glucocorticoids help to resolve inflammation and treat cytokine storm, the time course for steroid use and which patients benefit from using systemic corticosteroids is unclear. In this study, we aimed to evaluate the therapeutic effect of corticosteroids in COVID-19 patients. Electronic medical records of hospitalized patients (n=7,980) from 178 hospitals across United States for confirmed COVID-19 between January 1st 2020 and May 8th 2020 were reviewed. Of the 7,980 patients, 3,951 (49.5%) were female and 4,029 (50.5%) were male. The mean age was 57.4 .. 19 years. Fifteen percent (n=1,219) died in hospital or were discharged to hospice care. Seventy-two percent (n=5,774) required non-ICU level of care, while 28% (n=2,206) of patients required ICU, and of those 1,157 (14.5%) needed ventilator support. The mean length of stay in the hospital was 6 days (range 0 - 84 days). Fourteen percent (n=1111) of patients received at least one dose of systemic steroids during hospitalization. Sixty precent of those had ICU level of care with 435 (39%) requiring ventilator support. Overall, the use of corticosteroids was associated with increased mortality (OR=1.273;p=0.0160) and 3.53 days longer hospital stay (p<0.0001). The corticosteroid exposed group was also noted to progress to a higher level of care and have longer time on a ventilator when compared with the patients who did not receive steroids. The length of hospital stay and mortality was higher especially in severe/critical patients. Based on these results, we recommend cautious use of corticosteroids in COVID-19. The etiology behind this association is still unclear and presents an area for future research.