Impact of the COVID-19 Pandemic on Blood Donation Patterns: A Systematic Review and Meta-Analysis.

Blood centers, which are arguably the backbone of every hospital, depend on blood donors for a constant and regular supply of blood. Like many other fields, the COVID-19 pandemic severely affected blood donations. In this article, we aim to systematically search the studies done on blood donation during the COVID-19 pandemic period, analyze the pandemic's effect on blood donation, and examine the methodology used to overcome the problem. We performed a systematic review and meta-analysis to investigate the effect of the COVID-19 pandemic on blood donation. Two independent reviewers searched different databases, such as PubMed, ProQuest, Scopus, and Google Scholar. We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses and the Joanna Briggs Institute critical appraisal checklist for overall study characteristics. We included a total of 15 studies. There was an overall decrease in blood donation of 25%, with some regions showing a decrease of as much as 71%. However, some regions were able to experience a 2-10% increase in blood donation after taking stringent and early measures to prevent such decreases. The COVID-19 pandemic and consequent lockdown greatly affected blood transfusion services, resulting in a progressive decline in blood donations that threatened the lives of many patients who were fully dependent on blood transfusion. However, by making appropriate and early decisions and taking action, policymakers and the rest of society can prevent such shortages, potentially saving millions of lives.

Estimation of anti-SARS-CoV-2 IgG titre among blood donors in Ranchi.

Introduction: Coronavirus disease 2019 is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), also known as novel coronavirus (2019-nCoV). The disease presentation ranges from asymptomatic to severe acute respiratory failure requiring intensive care support. Anti-SARS-CoV-2 IgG antibodies are developed either by natural infection from SARS-CoV-2 or by vaccination against COVID-19. The persistence of IgG antibodies allows identification of the people who have been infected in the past, recovered from illness, and possibly become immune. 7 IgG detection and other serological assays will play an important role in research and surveillance. Aims and Objective: The objective of the study is to assess anti-SARS-CoV-2 IgG titre among blood donors and to assess the decreasing incidence of COVID-19 in the department of blood bank, Rajendra Institute of Medical Sciences, Ranchi, Jharkhand. Materials and Methods: An observational, cross-sectional study was conducted at the department of blood bank, Rajendra Institute of Medical Sciences, Ranchi, Jharkhand over a period of 2 months and 14 days from 06 February 2021 to 20 April 2021 who donated at least one unit of blood. Results: This study recorded a greater number of male donors with B+ blood group. The anti-SARS-CoV-2 titre were mostly young adults between 18 and 31 years of age. Conclusion: Seroprevalence was high in males having blood group B+ between 18 and 32 years of age.

DART: deep learning enabled topological interaction model for energy prediction of metal clusters and its application in identifying unique low energy isomers.

Recently, machine learning (ML) has proven to yield fast and accurate predictions of chemical properties to accelerate the discovery of novel molecules and materials. The majority of the work is on organic molecules, and much more work needs to be done for inorganic molecules, especially clusters. In the present work, we introduce a simple topological atomic descriptor called TAD, which encodes chemical environment information of each atom in the cluster. TAD is a simple and interpretable descriptor where each value represents the atom count in three shells. We also introduce the DART deep learning enabled topological interaction model, which uses TAD as a feature vector to predict energies of metal clusters, in our case gallium clusters with sizes ranging from 31 to 70 atoms. The DART model is designed based on the principle that the energy is a function of atomic interactions and allows us to model these complex atomic interactions to predict the energy. We further introduce a new dataset called GNC_31-70, which comprises structures and DFT optimized energies of gallium clusters with sizes ranging from 31 to 70 atoms. We show how DART can be used to accelerate the process of identification of low energy structures without geometry optimization. Albeit using a topological descriptor, DART achieves a mean absolute error (MAE) of 3.59 kcal mol-1 (0.15 eV) on the test set. We also show that our model can distinguish core and surface atoms in the Ga-70 cluster, which the model has never encountered earlier. Finally, we demonstrate the transferability of the DART model by predicting energies for about 6k unseen configurations picked up from molecular dynamics (MD) data for three cluster sizes (46, 57, and 60) within seconds. The DART model was able to reduce the load on DFT optimizations while identifying unique low energy structures from MD data.

Synthesis and Characterization of a Novel γ-Aminobutyric Acid Type A (GABAA) Receptor Ligand That Combines Outstanding Metabolic Stability, Pharmacokinetics, and Anxiolytic Efficacy.

1,4-Benzodiazepines are used in the treatment of anxiety disorders but have limited long-term use due to adverse effects. HZ-166 (2) has been shown to have anxiolytic-like effects with reduced sedative/ataxic liabilities. A 1,3-oxazole KRM-II-81 (9) was discovered from a series of six bioisosteres with significantly improved pharmacokinetic and pharmacodynamic properties as compared to 2. Oxazole 9 was further characterized and exhibited improved anxiolytic-like effects in a mouse marble burying assay and a rat Vogel conflict test.

Exploiting 4-sulphate N-acetyl galactosamine decorated gelatin nanoparticles for effective targeting to professional phagocytes in vitro and in vivo.

The present study was focused on the development of surface modified gelatin nanoparticles (SGNPs) using novel ligand 4-sulfated N-acetyl galactosamine (4-SO(4)GalNAc) for specific targeting to macrophages. The gelatin has been modified with the potential targeting moiety 4-SO(4)GalNAc, which was further used for the preparation of modified nanoparticles. The nanoparticles have been prepared by two step desolvation method. The SGNPs and unmodified gelatin nanoparticles (GNPs) were loaded with doxorubicin (DxR) and its targeting potential was compared. Developed DxR-loaded SGNPs (DxR-SGNPs) were found to have negative zeta potential (-19.8 ± 0.22 mV) whereas DxR-loaded GNPs (DxR-GNPs) have the positive zeta potential of around +12.2 ± 0.36 mV. The mean particle size of DxR-SGNPs and DxR-GNPs was found to be 283 ± 7 and 134 ± 5 nm, respectively. Flow cytometric data confirmed the enhanced uptake of DxR-SGNPs in J774A.1 and PBMC when compared with DxR-GNPs. Intracellular localization studies indicate that the fluorescence intensity of DxR-SGNPs was significantly higher when compared to DxR-GNPs. DxR-SGNPs rendered significantly higher localization of DxR in liver and spleen as compared to DxR-GNPs after i.v. administration. The study stipulates that 4-SO(4)GalNAc assures for targeting resident macrophages.