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Abstract Direct exposure to the fungal species Alternaria alternata is a major risk factor for the development of asthma, allergic rhinitis, and inflammation. As of November 23rd 2020, the NCBI protein database showed 11,227 proteins from A. alternata genome as hypothetical proteins (HPs). Allergens are the main causative of several life-threatening diseases, especially in fungal infections. Therefore, the main aim of the study is to identify the potentially allergenic inducible proteins from the HPs in A. alternata and their associated functional assignment for the complete understanding of the complex biological systems at the molecular level. AlgPred and Structural Database of Allergenic Proteins (SDAP) were used for the prediction of potential allergens from the HPs of A. alternata. While analyzing the proteome data, 29 potential allergens were predicted by AlgPred and further screening in SDAP confirmed the allergic response of 10 proteins. Extensive bioinformatics tools including protein family classification, sequence-function relationship, protein motif discovery, pathway interactions, and intrinsic features from the amino acid sequence were used to successfully predict the probable functions of the 10 HPs. The functions of the HPs are characterized as chitin-binding, ribosomal protein P1, thaumatin, glycosyl hydrolase, and NOB1 proteins. The subcellular localization and signal peptide prediction of these 10 proteins has further provided additional information on localization and function. The allergens prediction and functional annotation of the 10 proteins may facilitate a better understanding of the allergenic mechanism of A. alternata in asthma and other diseases. The functional domain level insights and predicted structural features of the allergenic proteins help to understand the pathogenesis and host immune tolerance. The outcomes of the study would aid in the development of specific drugs to combat A. alternata infections.

Nanoscale copper oxide (CuO NPs) with diameters in the 80–150 nm range has been biosynthesized using lipopeptide biosurfactant derived from Bacillus vallismortis and characterized by XRD and FE-SEM. The CuO NPs could be introduced as nanocarrier systems for combination therapy and a potential candidate for antibacterial, antioxidant, anticancer, and anti-diabetic activity. The antibacterial activity of CuO NPs was studied by incorporating the nanoparticles with fluorescent antibiotic Ciprofloxacin HCL (CIP) to form CIP@CuO NPs and tracked inside HEK-293 cell lines. The MIC values of CIP@CuO NPs against 1 × 106 CFU ml−1 Pseudomonas aeruginosa was determined to be 76 µg ml−1and 69 µg ml−1 against 1 × 106 CFU ml−1 Staphylococcus aureus. The CuO NPs were conjugated with the anticancer drug Doxorubicin (DOX) to form DOX@CuONPs, improving delivery toward cancer (HeLa) cells. The intracellular uptake of the drug-loaded CuO NPs was confirmed from confocal micrographs. Finally, the in vitro anti-diabetic activity of lipopeptide-coated CuO NPs was confirmed by the inhibitory activity of α-amylase. In contrast, the in vivo anti-diabetic efficacy of CuO NPs was validated by a significant reduction in blood glucose and glutathione levels. The CuO NPs positively affected the histopathological changes of the pancreas in induced diabetic mice. Cytotoxicity testing with Zebrafish demonstrated abnormal organ development with varying viability and hatching rates at 72 and 96 hpf, with an LC50 of 45 µg/l. Aside from the various potential medicinal characteristics, the study provided valuable information on cytotoxic impact, which can be used in future investigations of their eco-toxicological impacts.

Abstract The alarming effect of antibiotic resistance prompted the search for alternative medicine to resolve the microbial resistance conflict. Over the last two decades, scientists have become increasingly interested in metallic nanoparticles to discover their new dimensions. Green nano synthesis is a rapidly expanding field of interest in nanotechnology due to its feasibility, low toxicity, eco‐friendly nature, and long‐term viability. Some plants have long been used in medicine because they contain a variety of bioactive compounds. Silver has long been known for its antibacterial properties. Silver nanoparticles have taken a special place among other metal nanoparticles. Silver nanotechnology has a big impact on medical applications like bio‐coating, novel antimicrobial agents, and drug delivery systems. This review aims to provide a comprehensive understanding of the pharmaceutical qualities of medicinal plants, as well as a convenient guideline for plant‐based silver nanoparticles and their antimicrobial activity.

Summary: Background: The COVID-19 pandemic highlighted gaps in health surveillance systems, disease prevention, and treatment globally. Among the many factors that might have led to these gaps is the issue of the financing of national health systems, especially in low-income and middle-income countries (LMICs), as well as a robust global system for pandemic preparedness. We aimed to provide a comparative assessment of global health spending at the onset of the pandemic; characterise the amount of development assistance for pandemic preparedness and response disbursed in the first 2 years of the COVID-19 pandemic; and examine expectations for future health spending and put into context the expected need for investment in pandemic preparedness. Methods: In this analysis of global health spending between 1990 and 2021, and prediction from 2021 to 2026, we estimated four sources of health spending: development assistance for health (DAH), government spending, out-of-pocket spending, and prepaid private spending across 204 countries and territories. We used the Organisation for Economic Co-operation and Development (OECD)'s Creditor Reporting System (CRS) and the WHO Global Health Expenditure Database (GHED) to estimate spending. We estimated development assistance for general health, COVID-19 response, and pandemic preparedness and response using a keyword search. Health spending estimates were combined with estimates of resources needed for pandemic prevention and preparedness to analyse future health spending patterns, relative to need. Findings: In 2019, at the onset of the COVID-19 pandemic, US$9·2 trillion (95% uncertainty interval [UI] 9·1–9·3) was spent on health worldwide. We found great disparities in the amount of resources devoted to health, with high-income countries spending $7·3 trillion (95% UI 7·2–7·4) in 2019; 293·7 times the $24·8 billion (95% UI 24·3–25·3) spent by low-income countries in 2019. That same year, $43·1 billion in development assistance was provided to maintain or improve health. The pandemic led to an unprecedented increase in development assistance targeted towards health; in 2020 and 2021, $1·8 billion in DAH contributions was provided towards pandemic preparedness in LMICs, and $37·8 billion was provided for the health-related COVID-19 response. Although the support for pandemic preparedness is 12·2% of the recommended target by the High-Level Independent Panel (HLIP), the support provided for the health-related COVID-19 response is 252·2% of the recommended target. Additionally, projected spending estimates suggest that between 2022 and 2026, governments in 17 (95% UI 11–21) of the 137 LMICs will observe an increase in national government health spending equivalent to an addition of 1% of GDP, as recommended by the HLIP. Interpretation: There was an unprecedented scale-up in DAH in 2020 and 2021. We have a unique opportunity at this time to sustain funding for crucial global health functions, including pandemic preparedness. However, historical patterns of underfunding of pandemic preparedness suggest that deliberate effort must be made to ensure funding is maintained. Funding: Bill & Melinda Gates Foundation.

Background: Carbapenem-resistant Acinetobacter baumannii is an opportunistic pathogen responsible for nosocomial infections and is one of the biggest global threats according to the World Health Organization (WHO), particularly causing substantial morbidity and mortality.Objectives: This study aimed at using computational approaches to screen meropenem and its analogs against OXA-23-positive Acinetobacter baumannii, analyzing the correlations between kinetic and phenotypic characteristics.Methods: A total of 5,450 compounds were screened using virtual screening workflow (HTVS, Glide-SP, and Glide-XP) to identify the best compounds based on their binding energy and interactions against OXA-23 and OXA-27 as they had phenotypic data available. Molecular dynamics simulation and density functional theory (DFT) studies were performed from the outcome of molecular docking analysis.Results: During simulations, meropenem and its analogs exhibited high-level stable interactions with Ser79, Ser126, Thr217, Trp219, and Arg259 of OXA-23. Meropenem displayed a CovDock energy of about −3.5 and −1.9 kcal mol-1 against OXA-23 and OXA-27, respectively. Among the 5,450 compounds, Pubchem_10645796, Pubchem_25224737, and ChEMBL_14 recorded CovDock energy between −6.0 and −9.0 kcal mol-1. Moreover, the infra-red (IR) spectrophotometric analysis revealed C=O and C-N atoms showing bands at 1,800 and 1,125 cm-1, respectively. These observed data are in congruence with the experimental observations.Conclusion: The identified compounds showed good agreement with the spectrophotometric analysis using DFT methods. In the earlier studies, meropenem’s MIC value was 32 μg mL−1 in OXA-23-positive isolate A2265 compared to the MIC of 1 μg mL−1 in ΔblaOXA-23 A2265. Comparing the CovDock energy and hydrogen-bonding interactions, the predicted results are in good agreement with the experimental data reported earlier. Our results highlight the importance of OXA-23 molecular docking studies and their compliance with the phenotypic results. It will help further in developing newer antibiotics for treating severe infections associated with carbapenem-resistant A. baumannii.

Aim: The Insilco study uses deep learning algorithms to predict the protein-coding pg m RNA sequences. Material and methods: The NCBI GEO DATA SET GSE218606's GEO R tool discovered P.G's outer membrane vesicles' most differentially expressed mRNA. Genemania analyzed differentially expressed gene networks. Transcriptomics data were collected and labeled on P. gingivalis protein-coding mRNA sequence and pseudogene, lincRNA, and bidirectional promoter lincRNA. Orange, a machine learning tool, analyzed and predicted data after preprocessing. Naïve Bayes, neural networks, and gradient descent partition data into training and testing sets, yielding accurate results. Cross-validation, model accuracy, and ROC curve were evaluated after model validation. Results: Three models, Neural Networks, Naive Bayes, and Gradient Boosting, were evaluated using metrics like Area Under the Curve (AUC), Classification Accuracy (CA), F 1 Score, Precision, Recall, and Specificity. Gradient Boosting achieved a balanced performance (AUC: 0.72, CA: 0.41, F 1: 0.32) compared to Neural Networks (AUC: 0.721, CA: 0.391, F1: 0.314) and Naive Bayes (AUC: 0.701, CA: 0.172, F1: 0.114). While statistical tests revealed no significant differences between the models, Gradient Boosting exhibited a more balanced precision-recall relationship. Conclusion: In silico analysis using machine learning techniques successfully predicted protein-coding mRNA sequences within Porphyromonas gingivalis OMVs. Gradient Boosting outperformed other models (Neural Networks, Naive Bayes) by achieving a balanced performance across metrics like AUC, classification accuracy, and precision-recall, suggests its potential as a reliable tool for protein-coding mRNA prediction in P. gingivalis OMVs. [ABSTRACT FROM AUTHOR]

With their distinctive core–shell design, core–shell nanocrystals have drawn interest in catalysis, medicinal research, and nanotechnology. These nanocrystals have a variety of characteristics and possible uses. The application of core–shell nanocrystals offers significant potential in increasing diagnostic and therapeutic approaches for cancer research in apoptosis and in vitro cancer cell imaging. In the present study, we investigated the fluorescence behavior of hydrophilic CdSe (core-only) and CdSe@CdS (core–shell) nanocrystals (NCs) and their potential in cancer cell imaging. The addition of a CdS coating to CdSe NCs increased the fluorescence intensity tenfold. The successful fabrication of core–shell CdSe@CdS nanocrystals was proven by a larger particle size (evaluated via DLS and TEM) and their XRD pattern and surface morphology compared to CdSe (core-only) NCs. When these NCs were used for bioimaging in MCF-7 and HEK-293 cell lines, they demonstrated excellent cellular uptake due to higher fluorescence intensity within cancerous cells than normal cells. Comparative cytotoxicity studies revealed that CdSe NCs were more toxic to all three cell lines (HEK-293, MCF-7, and HeLa) than CdSe@CdS core–shell structures. Furthermore, a decrease in mitochondrial membrane potential and intracellular ROS production supported NCs inducing oxidative stress, which led to apoptosis via the mitochondria-mediated pathway. Increased cytochrome c levels, regulation of pro-apoptotic gene expression (e.g., p53, Bax), and down-regulation of Bcl-2 all suggested cellular apoptosis occurred via the intrinsic pathway. Significantly, at an equivalent dose of core–shell NCs, core-only NCs induced more oxidative stress, resulting in increased apoptosis. These findings shed light on the role of a CdS surface coating in reducing free radical release, decreasing cytotoxicity, and improving fluorescence, advancing the field of cell imaging.