Investigating the role of hypothetical protein (AAB33144.1) in HIV-1 virus pathogenicity: A comparative study with FDA-Approved inhibitor compounds through In silico analysis and molecular docking.

Aim and objective: Due to the a lot of unexplored proteins in HIV-1, this research aimed to explore the functional roles of a hypothetical protein (AAB33144.1) that might play a key role in HIV-1 pathogenicity. Methods: The homologous protein was identified along with building and validating the 3D structure by searching several bioinformatics tools. Results: Retroviral aspartyl protease and retropepsin like functional domains and motifs, folding pattern (cupredoxins), and subcellular localization in cytoplasmic membrane were determined as biological activity. Besides, the functional annotation revealed that the chosen hypothetical protein possessed protease-like activity. To validate our generated protein 3D structure, molecular docking was performed with five compounds where nelfinavir showed (-8.2 kcal/mol) best binding affinity against HXB2 viral protease (PDB ID: 7SJX) and main protease (PDB ID: 4EYR) protein. Conclusions: This study suggests that the annotated hypothetical protein related to protease action, which may be useful in viral genetics and drug discovery.

Amelioration of CCl4-induced oxidative stress and hepatotoxicity by Ganoderma lucidum in long evans rats.

Liver disease is a serious health problem affecting people worldwide at an alarming rate. The present study aimed to investigate the protective effects of Ganoderma lucidum against CCl4-induced liver toxicity in rats. The experimental Long Evans rats were divided into five groups, of which four groups were treated with carbon tetrachloride (CCl4). Among the CCl4 treated groups, one of the groups was treated with silymarin and two of them with ethanolic extract of G. lucidum at 100 and 200 mg/Kg body weight. The oxidative stress parameters and endogenous antioxidant enzyme concentrations were assessed by biochemical tests. Liver enzymes ALT, AST, and ALP were determined spectrophotometrically. Histopathological examinations were carried out to assess hepatic tissue damage and fibrosis. Reverse transcription PCR (RT-PCR) was performed to determine the expression of IL-1ß, IL-6, IL-10, TNF-α, and TGF-ß genes. Gas Chromatography-Mass Spectroscopy (GC-MS) analysis revealed that G. lucidum is rich in several phytochemicals including 6-Octadecanoic acid (55.81%), l-( +)-Ascorbic acid 2,6-dihexadecanoate (18.72%), Cis-11-Eicosenamide (5.76%), and Octadecanoic acid (5.26%). Treatment with the G. lucidum extract reduced the elevated ALT, AST, ALP levels, and cellular oxidative stress markers and increased the endogenous antioxidant levels. Histopathology observations revealed that the inflammation, infiltration of immune cells, and aberration of collagen fibers in the hepatocytes were altered by the G. lucidum treatment. The increased expression of inflammatory cytokines TNF-α, TGF-ß, IL-1 ß, and IL-6 were markedly suppressed by G. lucidum extract treatment. G. lucidum also prevented the suppression of protective IL-10 expression by CCl4. This study strongly suggests that G. lucidum extract possesses significant hepatoprotective activity as evidenced by reduced oxidative stress and inflammation mediated by suppression in inflammatory cytokine expression and increased protective IL-10 cytokine expression.

Evaluation of Total Phenolic Content, HPLC Analysis, and Antioxidant Potential of Three Local Varieties of Mushroom: A Comparative Study.

Functional foods such as mushrooms are rich in polyphenolic compounds and secondary metabolites with health-promoting properties such as antioxidant, antimicrobial, antidiabetic and immunostimulatory effects. The present study is aimed to investigate the ethanolic extracts of three varieties of mushrooms, namely, G. lucidum, G. tropicum, and C. indica grown in Bangladesh for phenolic and flavonoid content and their antioxidant properties. Moreover, the phenolic composition of the extracts was analyzed by using the HPLC-DAD system. G. lucidum extract exhibited the highest antioxidant potential as evidenced by its lowest IC50 value in all the tested assay models (40.44 ± 2.09 µg/mL, 151.32 ± 0.35 µg/mL, 137.89 ± 1.85 µg/mL in DPPH, H2O2, and NO scavenging assay, respectively) along with the highest phenolic content (81.34 ± 0.68 GAE g-1 extract). G. tropicum and C. indica extracts also showed significant antioxidant properties and a good amount of phenolic content, 52.16 ± 0.25 GAE g-1 extract, and 47.1 ± 0.26 GAE g-1 extract, respectively. The scavenging activity increased with the increasing concentration of extracts in all cases. The total phenolic content of the ethanolic extracts of mushroom species was highly correlated with antioxidant effects with Pearson's correlation coefficient (r) values ranging from 0.8883-0.9851. The α-amylase inhibitory and antibacterial activity of G. lucidum was evaluated by using 3,5-dinitrosalicylic acid and disc diffusion method, respectively. The maximum inhibitory activity recorded against α-amylase was 70.98 ± 0.042% at a concentration of 500 µg/mL. G. lucidum extract exhibited the highest antibacterial activity against Pseudomonas aeruginosa with 23.00 ± 1.00 mm clear zone of inhibition and an MIC value of 3.5 mg/mL. The results indicate that the mushroom species tested in this study could serve as a potential source of natural antioxidants in the development of nutraceuticals and herbal drugs for the management of oxidative stress-associated diseases as well as infectious diseases.

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the US

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multi-model ensemble forecast that combined predictions from dozens of different research groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naive baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-week horizon 3-5 times larger than when predicting at a 1-week horizon. This project underscores the role that collaboration and active coordination between governmental public health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks. Significance StatementThis paper compares the probabilistic accuracy of short-term forecasts of reported deaths due to COVID-19 during the first year and a half of the pandemic in the US. Results show high variation in accuracy between and within stand-alone models, and more consistent accuracy from an ensemble model that combined forecasts from all eligible models. This demonstrates that an ensemble model provided a reliable and comparatively accurate means of forecasting deaths during the COVID-19 pandemic that exceeded the performance of all of the models that contributed to it. This work strengthens the evidence base for synthesizing multiple models to support public health action.

A Mechanistic Review of ß-Carotene, Lutein, and Zeaxanthin in Eye Health and Disease.

Carotenoids are natural lipid-soluble antioxidants abundantly found as colorful pigments in fruits and vegetables. At least 600 carotenoids occur naturally, although about 20 of them, including ß-carotene, α-carotene, lycopene, lutein, zeaxanthin, meso-zeaxanthin, and cryptoxanthin, are detectable in the human blood. They have distinct physiological and pathophysiological functions ranging from fetal development to adult homeostasis. ß-carotene is a precursor of vitamin A that essentially functions in many biological processes including vision. The human macula lutea and eye lens are rich in lutein, zeaxanthin, and meso-zeaxanthin, collectively known as macular xanthophylls, which help maintain eye health and prevent ophthalmic diseases. Ocular carotenoids absorb light from the visible region (400-500 nm wavelength), enabling them to protect the retina and lens from potential photochemical damage induced by light exposure. These natural antioxidants also aid in quenching free radicals produced by complex physiological reactions and, consequently, protect the eye from oxidative stress, apoptosis, mitochondrial dysfunction, and inflammation. This review discusses the protective mechanisms of macular xanthophylls in preventing eye diseases such as cataract, age-related macular degeneration, and diabetic retinopathy. Moreover, some preclinical animal studies and some clinical trials are discussed briefly to understand carotenoid safety and efficacy.

DeepCOVID: An Operational Deep Learning-driven Framework for Explainable Real-time COVID-19 Forecasting

How do we forecast an emerging pandemic in real time in a purely data-driven manner? How to leverage rich heterogeneous data based on various signals such as mobility, testing, and/or disease exposure for forecasting? How to handle noisy data and generate uncertainties in the forecast? In this paper, we present DO_SCPLOWEEPC_SCPLOWCO_SCPLOWOVIDC_SCPLOW, an operational deep learning frame-work designed for real-time COVID-19 forecasting. DO_SCPLOWEEPC_SCPLOW-CO_SCPLOWOVIDC_SCPLOW works well with sparse data and can handle noisy heterogeneous data signals by propagating the uncertainty from the data in a principled manner resulting in meaningful uncertainties in the forecast. The deployed framework also consists of modules for both real-time and retrospective exploratory analysis to enable interpretation of the forecasts. Results from real-time predictions (featured on the CDC website and FiveThirtyEight.com) since April 2020 indicates that our approach is competitive among the methods in the COVID-19 Forecast Hub, especially for short-term predictions.