Clinical efficacy of Azithromycin for COVID-19 management: A systematic meta-analysis of meta-analyses.

BACKGROUND: Azithromycin has been adopted as a component of the COVID-19 management protocol throughout the global healthcare settings but with a questionable if not downright unsubstantiated evidence base. OBJECTIVES: In order to amalgamate and critically appraise the conflicting evidence around the clinical efficacy of Azithromycin (AZO) vis a vis COVID-19 management outcomes, a meta-analysis of meta-analyses was carried out to establish an evidence-based holistic status of AZO vis a vis its efficacy as a component-in-use of the COVID-19 management protocol. METHODS: A comprehensive systematic search was carried out through PubMed/Medline, Cochrane and Epistemonikos with a subsequent appraisal of abstracts and full-texts, as required. The Quality of Reporting of Meta-analyses (QUOROM) checklist and the Assessment of Multiple Systematic Reviews (AMSTAR) methodology were adopted to assess the methodological quality of the included meta-analyses. Random-effects models were developed to calculate summarized pool Odds Ratios (with 95% confidence interval) for the afore determined primary and secondary outcomes. RESULTS: AZO, when compared with best available therapy (BAT) including or excluding Hydroxychloroquine, exhibited statistically insignificant reduction in mortality [(n= 27,204 patients) OR= 0.77 (95% CI: 0.51-1.16) (I2= 97%)], requirement of mechanical ventilation [(n= 14,908 patients) OR= 1.4 (95% CI: 0.58-3.35) (I2= 98%)], induction of arrhythmia [(n= 9,723 patients) OR= 1.21 (95% CI: 0.63-2.32) (I2= 92%)] and QTc prolongation (a surrogate for torsadogenic effect) [(n= 6,534 patients) OR= 0.62 (95% CI: 0.23-1.73) (I2= 96%)]. CONCLUSION: The meta-analysis of meta-analyses portrays AZO as a pharmacological agent that does not appear to have a comparatively superior clinical efficacy than BAT when it comes to COVID-19 management. Secondary to a very real threat of anti-bacterial resistance, it is suggested that AZO be discontinued and removed from COVID-19 management protocols.

Physiopathology and effectiveness of therapeutic vaccines against human papillomavirus.

Human papillomavirus (HPV) is a well-known sexually transmitted disorder globally. Human papillomavirus (HPV) is the 3rd most common cancer that causes cervical carcinoma, and globally it accounts for 275,000 deaths every year. The load of HPV-associated abrasions can be lessened through vaccination. At present, three forms of prophylactic vaccines, Cervarix, Gadrasil, and Gardasil 9, are commercially accessible but all these prophylactic vaccines have not the ability to manage and control developed abrasions or infections. Therefore, a considerable amount of the population is not secured from HPV infectivity. Consequently, the development of therapeutic HPV vaccines is a crucial requirement of this era, for the treatment of persisting infections, and to stop the progression of HPV-associated cancers. Therapeutic vaccines are a developing trial approach. Because of the constitutive expression of E6 and E7 early genes in cancerous and pre-cancerous tissues, and their involvement in disturbance of the cell cycle, these are best targets for this therapeutic vaccine treatment. For the synthesis and development of therapeutic vaccines, various approaches have been examined comprising cell-based vaccines, peptide/protein-based vaccines, nucleic acid-based vaccines, and live-vector vaccines all proceeding towards clinical trials. This review emphasizes the development, progress, current status, and future perspective of several vaccines for the cure of HPV-related abrasions and cancers. This review also provides an insight to assess the effectiveness, safety, efficacy, and immunogenicity of therapeutic vaccines in the cure of patients infected with HPV-associated cervical cancer.

Brain tumor detection and multi-classification using advanced deep learning techniques.

A brain tumor is an uncontrolled development of brain cells in brain cancer if not detected at an early stage. Early brain tumor diagnosis plays a crucial role in treatment planning and patients' survival rate. There are distinct forms, properties, and therapies of brain tumors. Therefore, manual brain tumor detection is complicated, time-consuming, and vulnerable to error. Hence, automated computer-assisted diagnosis at high precision is currently in demand. This article presents segmentation through Unet architecture with ResNet50 as a backbone on the Figshare data set and achieved a level of 0.9504 of the intersection over union (IoU). The preprocessing and data augmentation concept were introduced to enhance the classification rate. The multi-classification of brain tumors is performed using evolutionary algorithms and reinforcement learning through transfer learning. Other deep learning methods such as ResNet50, DenseNet201, MobileNet V2, and InceptionV3 are also applied. Results thus obtained exhibited that the proposed research framework performed better than reported in state of the art. Different CNN, models applied for tumor classification such as MobileNet V2, Inception V3, ResNet50, DenseNet201, NASNet and attained accuracy 91.8, 92.8, 92.9, 93.1, 99.6%, respectively. However, NASNet exhibited the highest accuracy.

Deep Learning-Based COVID-19 Detection Using CT and X-Ray Images: Current Analytics and Comparisons.

Currently, the world faces a novel coronavirus disease 2019 (COVID-19) challenge and infected cases are increasing exponentially. COVID-19 is a disease that has been reported by the WHO in March 2020, caused by a virus called the SARS-CoV-2. As of 10 March 2021, more than 150 million people were infected and 3v million died. Researchers strive to find out about the virus and recommend effective actions. An unprecedented increase in pathogens is happening and a major attempt is being made to tackle the epidemic. This article presents deep learning-based COVID-19 detection using CT and X-ray images and data analytics on its spread worldwide. This article's research structure builds on a recent analysis of the COVID-19 data and prospective research to systematize current resources, help the researchers, practitioners by using in-depth learning methodologies to build solutions for the COVID-19 pandemic.

Microscopic brain tumor detection and classification using 3D CNN and feature selection architecture.

Brain tumor is one of the most dreadful natures of cancer and caused a huge number of deaths among kids and adults from the past few years. According to WHO standard, the 700,000 humans are being with a brain tumor and around 86,000 are diagnosed since 2019. While the total number of deaths due to brain tumors is 16,830 since 2019 and the average survival rate is 35%. Therefore, automated techniques are needed to grade brain tumors precisely from MRI scans. In this work, a new deep learning-based method is proposed for microscopic brain tumor detection and tumor type classification. A 3D convolutional neural network (CNN) architecture is designed at the first step to extract brain tumor and extracted tumors are passed to a pretrained CNN model for feature extraction. The extracted features are transferred to the correlation-based selection method and as the output, the best features are selected. These selected features are validated through feed-forward neural network for final classification. Three BraTS datasets 2015, 2017, and 2018 are utilized for experiments, validation, and accomplished an accuracy of 98.32, 96.97, and 92.67%, respectively. A comparison with existing techniques shows the proposed design yields comparable accuracy.

Aloe barbadensis Miller peptide/polypeptide fraction alleviates inflammation through inhibition of proinflammatory cytokines and mediators in vitro and in rats with Freund's adjuvant-induced hind paw edema

Objective: To evaluate the anti-inflammatory potential of peptide/polypeptide fraction of Aloe vera through in vitro and in vivo studies. Methods: The peptide/polypeptide fraction from Aloe vera was obtained through trichloroacetic acid precipitation. The anti-inflammatory property of the peptide/polypeptide fraction was tested by protein denaturation, membrane stabilization assays. The effect of the fraction on RAW 264.7 cell viability was examined by MTT assays. The nitric oxide level was determined through Griess reagent. TNF-α and IL-6 levels were estimated using ELISA kits. In vivo studies were carried out in male Wistar rats through injection of Freund's adjuvant in the hind paw. Paw edema was measured through the Vernier scale and levels of alanine aminotransferase, aspartate transaminase, TNF-α, IL-6, and secretory phospholipase A2 were estimated through their respective kits after fourteen days of treatment. GraphPad Prism6 was used for analyzing the results. Results: The peptide/polypeptide extract inhibited protein denaturation with an IC