MoS2, WS2, and MoWS2 Flakes as Reversible Host Materials for Sodium-Ion and Potassium-Ion Batteries.

Transition-metal dichalcogenides (TMDs) and their alloys are vital for the development of sustainable and economical energy storage alternatives due to their large interlayer spacing and hosting ability for alkali-metal ions. Although the Li-ion chemically correlates with the Na-ion and K-ion, research on batteries with TMD anodes for K+ is still in its infancy. This research explores TMDs such as molybdenum disulfide (MoS2) and tungsten disulfide (WS2) and TMD alloys such as molybdenum tungsten disulfide (MoWS2) for both sodium-ion batteries (NIBs) and potassium-ion batteries (KIBs). The cyclic stability test analysis indicates that in the initial cycle, the MoS2 NIB demonstrates exceptional performance, with a peak charge capacity of 1056 mAh g-1, while retaining high Coulombic efficiency. However, the WS2 KIB underperforms, with the least charge capacity of 130 mAh g-1 in the first cycle and exceptionally low retention at a current density of 100 mA g-1. The MoWS2 TMD alloy exhibits a moderate charge capacity and cyclic efficiency for both NIBs and KIBs. This comparison study shows that decreasing sizes of alkali-metal ions and constituent elements in TMDs or TMD alloys leads to decreased resistance and slower degradation processes as indicated by cyclic voltammetry and electrochemical impedance spectroscopy after 10 cycles. Furthermore, the study of probable electrochemical intercalation and removal processes of Na-ions and K-ions demonstrates that large geometrically shaped TMD flakes are more responsive to intercalation for Na-ions than K-ions. These performance comparisons of different TMD materials for NIBs and KIBs may promote the future development of these batteries.

The Impact of Inflammation on Thermal Hyperpnea: Relevance for Heat Stress and Febrile Seizures.

Extreme heat caused by climate change is increasing transmission of infectious diseases resulting in a sharp rise in heat-related illness and mortality. Understanding mechanistic link between heat, inflammation and disease is thus important for public health. Thermal hyperpnea, and consequent respiratory alkalosis is crucial in febrile seizures and convulsions induced by heat stress in humans. Here we address what causes thermal hyperpnea in neonates and how is it affected by inflammation. TRPV1, a heat-activated channel is sensitized by inflammation and modulates breathing, and thus may play a key role. To investigate whether inflammatory sensitization of TRPV1 modifies neonatal ventilatory responses to heat stress, leading to respiratory alkalosis and an increased susceptibility to hyperthermic seizures we treated neonatal rats with bacterial lipopolysaccharide, and breathing, arterial pH, in-vitro vagus nerve activity, and seizure susceptibility were assessed during heat stress in the presence or absence of a TRPV1 antagonist (AMG-9810) or shRNA-mediated TRPV1 suppression. Lipopolysaccharide-induced inflammatory preconditioning lowered the threshold temperature and latency of hyperthermic seizures. This was accompanied by increased tidal volume, minute ventilation, expired CO2, and arterial pH (alkalosis). Lipopolysaccharide exposure also elevated vagal spiking and intracellular calcium levels in response to hyperthermia. TRPV1 inhibition with AMG-9810 or shRNA reduced the lipopolysaccharide-induced susceptibility to hyperthermic seizures and altered the breathing pattern to fast shallow breaths (tachypnea), making each breath less efficient and restoring arterial pH. These results indicate that inflammation exacerbates thermal hyperpnea-induced respiratory alkalosis associated with increased susceptibility to hyperthermic seizures, primarily mediated by TRPV1 localized to vagus neurons. This article is open access and distributed under the terms of the Creative Commons Attribution Non-Commercial No Derivatives License 4.0 (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Addressing Irreversibility and Structural Distortion in WS2 Inorganic Fullerene-Like Nanoparticles: Effects of Voltage Cutoff Experiments in Beyond Li+-Ion Storage Applications.

Large interlayer spacing beneficially allows Na+- and K+-ion storage in transition-metal dichalcogenide (TMD)-based electrodes, but side reactions and volume change, which pulverize the TMD crystalline structure, are persistent challenges for the utilization of these materials in next-generation devices. This study first determines whether irreversibility due to structural distortion, which results in poor cycling stability, is also apparent in the case of inorganic fullerene-like (IF) tungsten disulfide (WS2) nanocages (WS2IF). To address these problems, this study proposes upper and lower voltage cutoff experiments to limit specific reactions in Na+/WS2IF and K+/WS2IF half-cells. Three-dimensional (3D) differential capacity curves and derived surface plots highlight the continuation of reversible reactions when a high upper cutoff technique is applied, thereby indirectly suggesting restricted structural dissolution. This resulted in improved capacity retention with stable performance and a higher Coulombic efficiency, laying the ground for the use of TMD-based materials beyond Li+-ion storage devices.

Green synthesis of copper nanoparticles by using pineapple peel waste: in vitro characterizations and antibacterial potential.

A considerable amount of fruit waste is being produced every day worldwide. The green synthesis of metal nanoparticles from fruit peel waste can be an innovative, cost-effective, and eco-friendly alternative to traditional methods. Copper nanoparticles (CuNPs) were synthesized by a green method using the pineapple peels extract (PLX) and copper sulfate pentahydrate. The formation of CuNPs was visually identified and detected by UV-Visible spectroscopy. The CuNPs were characterized by Fourier-transform infrared (FTIR) spectroscopy, particle size analyzer, scanning electron microscopy (SEM), and X-ray diffraction (XRD). The antioxidant and reducing power of CuNPs were conducted by %DPPH scavenging and electron transfer-based ferric reducing antioxidant power (FRAP) assay, respectively. The antibacterial properties of CuNPs were determined in gram-positive, and gram-negative bacteria. The results showed that the CuNPs were spherical in shape with mean particle size 290.5 nm. The zeta potential of the nanoparticles was found to be - 12.3 mV indicating the instability in the colloidal state. The FTIR study confirmed the peaks of phytochemicals present in the PLX and the nanoparticles supporting the use of pineapple peels as stabilizing, reducing and capping agents. Both the DPPH and reducing power assay depicted that the synthesized CuNPs had significant antioxidant activity. However, the synthesized CuNPs had strong inhibitory capacity against both gram-positive and gram-negative test organisms. Thus, the CuNPS could be used for its viable antibacterial potential to preserve fruits, flowers, and vegetables from bacterial contamination.

Target-specific novel molecules with their recipe: Incorporating synthesizability in the design process.

Application of Artificial intelligence (AI) in drug discovery has led to several success stories in recent times. While traditional methods mostly relied upon screening large chemical libraries for early-stage drug-design, de novo design can help identify novel target-specific molecules by sampling from a much larger chemical space. Although this has increased the possibility of finding diverse and novel molecules from previously unexplored chemical space, this has also posed a great challenge for medicinal chemists to synthesize at least some of the de novo designed novel molecules for experimental validation. To address this challenge, in this work, we propose a novel forward synthesis-based generative AI method, which is used to explore the synthesizable chemical space. The method uses a structure-based drug design framework, where the target protein structure and a target-specific seed fragment from co-crystal structures can be the initial inputs. A random fragment from a purchasable fragment library can also be the input if a target-specific fragment is unavailable. Then a template-based forward synthesis route prediction and molecule generation is performed in parallel using the Monte Carlo Tree Search (MCTS) method where, the subsequent fragments for molecule growth can again be obtained from a purchasable fragment library. The rewards for each iteration of MCTS are computed using a drug-target affinity (DTA) model based on the docking pose of the generated reaction intermediates at the binding site of the target protein of interest. With the help of the proposed method, it is now possible to overcome one of the major obstacles posed to the AI-based drug design approaches through the ability of the method to design novel target-specific synthesizable molecules.

Reliable method for predicting the binding affinity of RNA-small molecule interactions using machine learning.

Ribonucleic acids (RNAs) play important roles in cellular regulation. Consequently, dysregulation of both coding and non-coding RNAs has been implicated in several disease conditions in the human body. In this regard, a growing interest has been observed to probe into the potential of RNAs to act as drug targets in disease conditions. To accelerate this search for disease-associated novel RNA targets and their small molecular inhibitors, machine learning models for binding affinity prediction were developed specific to six RNA subtypes namely, aptamers, miRNAs, repeats, ribosomal RNAs, riboswitches and viral RNAs. We found that differences in RNA sequence composition, flexibility and polar nature of RNA-binding ligands are important for predicting the binding affinity. Our method showed an average Pearson correlation (r) of 0.83 and a mean absolute error of 0.66 upon evaluation using the jack-knife test, indicating their reliability despite the low amount of data available for several RNA subtypes. Further, the models were validated with external blind test datasets, which outperform other existing quantitative structure-activity relationship (QSAR) models. We have developed a web server to host the models, RNA-Small molecule binding Affinity Predictor, which is freely available at: https://web.iitm.ac.in/bioinfo2/RSAPred/.

pBRICS: A Novel Fragmentation Method for Explainable Property Prediction of Drug-Like Small Molecules.

Generative artificial intelligence algorithms have shown to be successful in exploring large chemical spaces and designing novel and diverse molecules. There has been considerable interest in developing predictive models using artificial intelligence for drug-like properties, which can potentially reduce the late-stage attrition of drug candidates or predict the properties of novel AI-designed molecules. Concurrently, it is important to understand the contribution of functional groups toward these properties and modify them to obtain property-optimized lead compounds. As a result, there is an increasing interest in the development of explainable property prediction models. However, current explainable approaches are mostly atom-based, where, often, only a fraction of a fragment is shown to be significant. To address the above challenges, we have developed a novel domain-aware molecular fragmentation approach termed post-processing of BRICS (pBRICS), which can fragment small molecules into their functional groups. Multitask models were developed to predict various properties, including the absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties. The fragment importance was explained using the gradient-weighted class activation mapping (Grad-CAM) approach. The method was validated on data sets of experimentally available matched molecular pairs (MMPs). The explanations from the model can be useful for medicinal chemists to identify the fragments responsible for poor drug-like properties and optimize the molecule. The explainability approach was also used to identify the reason behind false positive and false negative MMP predictions. Based on evidence from the existing literature and our analysis, some of these mispredictions were justified. We propose that the quantity, quality, and diversity of the training data will improve the accuracy of property prediction algorithms for novel molecules.

Plasmodium Topoisomerase VIB and Spo11 Constitute Functional Type IIB Topoisomerase in Malaria Parasite: Its Possible Role in Mitochondrial DNA Segregation.

The human malaria parasite undergoes a noncanonical cell division, namely, endoreduplication, where several rounds of nuclear, mitochondrial, and apicoplast replication occur without cytoplasmic division. Despite its importance in Plasmodium biology, the topoisomerases essential for decatenation of replicated chromosome during endoreduplication remain elusive. We hypothesize that the topoisomerase VI complex, containing Plasmodium falciparum topiosomerase VIB (PfTopoVIB) and catalytic P. falciparum Spo11 (PfSpo11), might be involved in the segregation of the Plasmodium mitochondrial genome. Here, we demonstrate that the putative PfSpo11 is the functional ortholog of yeast Spo11 that can complement the sporulation defects of the yeast Δspo11 strain, and the catalytic mutant Pfspo11Y65F cannot complement such defects. PfTopoVIB and PfSpo11 display a distinct expression pattern compared to the other type II topoisomerases of Plasmodium and are induced specifically at the late schizont stage of the parasite, when the mitochondrial genome segregation occurs. Furthermore, PfTopoVIB and PfSpo11 are physically associated with each other at the late schizont stage, and both subunits are localized in the mitochondria. Using PfTopoVIB- and PfSpo11-specific antibodies, we immunoprecipitated the chromatin of tightly synchronous early, mid-, and late schizont stage-specific parasites and found that both the subunits are associated with the mitochondrial genome during the late schizont stage of the parasite. Furthermore, PfTopoVIB inhibitor radicicol and atovaquone show synergistic interaction. Accordingly, atovaquone-mediated disruption of mitochondrial membrane potential reduces the import and recruitment of both subunits of PfTopoVI to mitochondrial DNA (mtDNA) in a dose-dependent manner. The structural differences between PfTopoVIB and human TopoVIB-like protein could be exploited for development of a novel antimalarial agent. IMPORTANCE This study demonstrates a likely role of topoisomerase VI in the mitochondrial genome segregation of Plasmodium falciparum during endoreduplication. We show that PfTopoVIB and PfSpo11 remain associated and form the functional holoenzyme within the parasite. The spatiotemporal expression of both subunits of PfTopoVI correlates well with their recruitment to the mitochondrial DNA at the late schizont stage of the parasite. Additionally, the synergistic interaction between PfTopoVI inhibitor and the disruptor of mitochondrial membrane potential, atovaquone, supports that topoisomerase VI is the mitochondrial topoisomerase of the malaria parasite. We propose that topoisomerase VI may act as a novel target against malaria.

Gex2SGen: Designing Drug-like Molecules from Desired Gene Expression Signatures.

Drug-induced gene expression profiling provides a lot of useful information covering various aspects of drug discovery and development. Most importantly, this knowledge can be used to discover drugs' mechanisms of action. Recently, deep learning-based drug design methods are in the spotlight due to their ability to explore huge chemical space and design property-optimized target-specific drug molecules. Recent advances in accessibility of open-source drug-induced transcriptomic data along with the ability of deep learning algorithms to understand hidden patterns have opened opportunities for designing drug molecules based on desired gene expression signatures. In this study, we propose a deep learning model, Gex2SGen (Gene Expression 2 SMILES Generation), to generate novel drug-like molecules based on desired gene expression profiles. The model accepts desired gene expression profiles in a cell-specific manner as input and designs drug-like molecules which can elicit the required transcriptomic profile. The model was first tested against individual gene-knocked-out transcriptomic profiles, where the newly designed molecules showed high similarity with known inhibitors of the knocked-out target genes. The model was next applied on a triple negative breast cancer signature profile, where it could generate novel molecules, highly similar to known anti-breast cancer drugs. Overall, this work provides a generalized method, where the method first learned the molecular signature of a given cell due to a specific condition, and designs new small molecules with drug-like properties.

R-SIM: A Database of Binding Affinities for RNA-small Molecule Interactions.

Ribonucleic acids (RNAs) are involved in a multitude of crucial cellular functions by acting as a central conduit for information transfer. Due to their essential and versatile functional roles in the cell, RNAs have also been implicated in multiple disease conditions of therapeutic relevance including cancers, bacterial and viral infections and neurodegenerative disorders. Recently, several approaches have emerged to tap into the potentially unexplored regions of the druggable genome, which refers to the genes and gene products that are focused during drug development. For example, considering RNAs as viable alternative therapeutic targets for drug development can potentially expand the range of therapeutic targets. Consequently, the availability of adequate binding affinity measurements for RNA-small molecule interactions is essential to understand target selectivity and design more potent RNA-targeting drug-like molecules. To facilitate this growing need, we have curated a database of experimentally validated RNA-small molecule interactions, called RNA-Small molecule Interaction Miner (R-SIM). Each entry in R-SIM provides comprehensive information on sequence, structure and classification of the RNA target, various physicochemical properties of the small molecule, binding affinity value and corresponding experimental conditions, three-dimensional structure (experimental or modelled) of the RNA-small molecule complex, and the literature source for the data. It also provides a user-friendly web interface with several options for search, display, sorting, visualization, download and upload of the data. R-SIM is freely available at: https://web.iitm.ac.in/bioinfo2/R_SIM/index.html. We envisage that R-SIM has several potential applications in understanding and accelerating the development of novel RNA-targeted small molecule therapeutics.

De novo design of anti-tuberculosis agents using a structure-based deep learning method.

Mycobacterium tuberculosis (Mtb) is a pathogen of major concern due to its ability to withstand both first- and second-line antibiotics, leading to drug resistance. Thus, there is a critical need for identification of novel anti-tuberculosis agents targeting Mtb-specific proteins. The ceaseless search for novel antimicrobial agents to combat drug-resistant bacteria can be accelerated by the development of advanced deep learning methods, to explore both existing and uncharted regions of the chemical space. The adaptation of deep learning methods to under-explored pathogens such as Mtb is a challenging aspect, as most of the existing methods rely on the availability of sufficient target-specific ligand data to design novel small molecules with optimized bioactivity. In this work, we report the design of novel anti-tuberculosis agents targeting the Mtb chorismate mutase protein using a structure-based drug design algorithm. The structure-based deep learning method relies on the knowledge of the target protein's binding site structure alone for conditional generation of novel small molecules. The method eliminates the need for curation of a high-quality target-specific small molecule dataset, which remains a challenge even for many druggable targets, including Mtb chorismate mutase. Novel molecules are proposed, that show high complementarity to the target binding site. The graph attention model could identify the probable key binding site residues, which influenced the conditional molecule generator to design new molecules with pharmacophoric features similar to the known inhibitors.

Amentoflavone and methyl hesperidin, novel lead molecules targeting epitranscriptomic modulator in acute myeloid leukemia: in silico drug screening and molecular dynamics simulation approach.

INTRODUCTION: M6A modification in transcriptome is critical in regulating different cellular processes, including cancer. In human beings, METTL3 is the major m6A writer that works in association with METTL14, an accessory protein. Extensive study revealed that cancer progression for acute myeloid leukemia, gastric cancer, colorectal cancer, hepatocellular carcinoma, and lung cancer is directly contributed by irregular expression of METTL3. OBJECTIVE: Targeting METTL3 has opened a new window in the development of novel inhibitors/drugs. METHODS: In this study, commercially available natural compounds were randomly screened to avoid the bias of screening small molecules on the basis of structural similarity. From 810 compounds that were screened, 80 commercially available compounds were showing better score when compared with the existing substrate/substrate-analogue and the inhibitor bound crystal structures in terms of docking score and binding energy calculation. RESULTS AND CONCLUSION: Among this pool of compounds, the best seven small molecules have been selected and further validated by different computational tools like binding energy calculation, molecular dynamics simulation, ADME analysis, and toxicity prediction. The novel hits found in this study can function as lead compounds which can be developed into inhibitors as well as drugs, specific against METTL3.

Sonochemical synthesis and biological evaluation of isoquinolin-1(2H)-one/isoindolin-1-one derivatives: Discovery of a positive ago-allosteric modulator (PAAM) of 5HT2CR.

Efforts have been devoted for the discovery and development of positive allosteric modulators (PAMs) of 5-HT2CR because of their potential advantages over the orthosteric agonist like Lorcaserin that was withdrawn from the market. On the other hand, pursuing a positive ago-allosteric modulator (PAAM) is considered as beneficial particularly when an agonist is not capable of affecting the potency of the endogenous agonist sufficiently. In search of a suitable PAAM of 5-HT2CR we adopted an in silico based approach that indicated the potential of the 3-(1-hydroxycycloalkyl) substituted isoquinolin-1-one derivatives against the 5-HT2CR as majority of these molecules interacted with the site other than that of Lorcaserin with superior docking scores. These compounds along with the regioisomeric 3-methyleneisoindolin-1-one derivatives were prepared via the Cu(OAc)2 catalyzed coupling of 2-iodobenzamide with 1-ethynylcycloalkanol under ultrasound irradiation. According to the in vitro studies, most of these compounds were not only found to be potent and selective agonists but also emerged as PAAM of 5-HT2CR whereas Lorcaserin did not show PAAM activities. According to the SAR study the isoquinolin-1(2H)-ones appeared as better PAAM than isoindolin-1-ones whereas the presence of hydroxyl group appeared to be crucial for the activity. With the potent PAAM activity for 5-HT2CR (EC50 = 1 nM) and 107 and 86-fold selectivity towards 5-HT2C over 5-HT2A and 5-HT2B the compound 4i was identified as a hit molecule. The compound showed good stability in male BALB/c mice brain homogenate (∼85 % remaining after 2 h), moderate stability in the presence of rat liver microsomes (42 % remaining after 1 h) and acceptable PK properties with fast reaching in the brain maintaining âˆ¼ 1:1 brain/plasma concentration ratio. The compound at a dose of 50 mg/kg exhibited decreased trend in the food intake starting from day 3 in S.D. rats, which reached significant by 5th day, and the effect was comparable to Lorcaserin (10 mg/kg) on day 5. Thus, being the first example of PAAM of 5-HT2CR the compound 4i is of further medicinal interest.

Tissue-specific mitochondrial HIGD1C promotes oxygen sensitivity in carotid body chemoreceptors.

Mammalian carotid body arterial chemoreceptors function as an early warning system for hypoxia, triggering acute life-saving arousal and cardiorespiratory reflexes. To serve this role, carotid body glomus cells are highly sensitive to decreases in oxygen availability. While the mitochondria and plasma membrane signaling proteins have been implicated in oxygen sensing by glomus cells, the mechanism underlying their mitochondrial sensitivity to hypoxia compared to other cells is unknown. Here, we identify HIGD1C, a novel hypoxia-inducible gene domain factor isoform, as an electron transport chain complex IV-interacting protein that is almost exclusively expressed in the carotid body and is therefore not generally necessary for mitochondrial function. Importantly, HIGD1C is required for carotid body oxygen sensing and enhances complex IV sensitivity to hypoxia. Thus, we propose that HIGD1C promotes exquisite oxygen sensing by the carotid body, illustrating how specialized mitochondria can be used as sentinels of metabolic stress to elicit essential adaptive behaviors.

An interpretable machine learning model for selectivity of small-molecules against homologous protein family.

Aim: In the early stages of drug discovery, various experimental and computational methods are used to measure the specificity of small molecules against a target protein. The selectivity of small molecules remains a challenge leading to off-target side effects. Methods: We have developed a multitask deep learning model for predicting the selectivity on closely related homologs of the target protein. The model has been tested on the Janus-activated kinase and dopamine receptor families of proteins. Results & conclusion: The feature-based representation (extended connectivity fingerprint 4) with Extreme Gradient Boosting performed better when compared with deep neural network models in most of the evaluation metrics. Both the Extreme Gradient Boosting and deep neural network models outperformed the graph-based models. Furthermore, to decipher the model decision on selectivity, the important fragments associated with each homologous protein were identified.

An In Silico Explainable Multiparameter Optimization Approach for De Novo Drug Design against Proteins from the Central Nervous System.

The aim of drug design and development is to produce a drug that can inhibit the target protein and possess a balanced physicochemical and toxicity profile. Traditionally, this is a multistep process where different parameters such as activity and physicochemical and pharmacokinetic properties are optimized sequentially, which often leads to high attrition rate during later stages of drug design and development. We have developed a deep learning-based de novo drug design method that can design novel small molecules by optimizing target specificity as well as multiple parameters (including late-stage parameters) in a single step. All possible combinations of parameters were optimized to understand the effect of each parameter over the other parameters. An explainable predictive model was used to identify the molecular fragments responsible for the property being optimized. The proposed method was applied against the human 5-hydroxy tryptamine receptor 1B (5-HT1B), a protein from the central nervous system (CNS). Various physicochemical properties specific to CNS drugs were considered along with the target specificity and blood-brain barrier permeability (BBBP), which act as an additional challenge for CNS drug delivery. The contribution of each parameter toward molecule design was identified by analyzing the properties of generated small molecules from optimization of all possible parameter combinations. The final optimized generative model was able to design similar inhibitors compared to known inhibitors of 5-HT1B. In addition, the functional groups of the generated small molecules that guide the BBBP predictive model were identified through feature attribution techniques.

Art of breaking bad news: A qualitative study in Indian healthcare perspective.

Background: Health-care communication is essential for amiable provider-recipient relationship. This study explored various health-care experiences and expectations of service recipients and providers in private clinical establishments of West Bengal, India, while breaking difficult news, bad news, and death. Aim: The current study was framed with the following research question: What are the varying perceptions, experiences, and expectations of healthcare recipients and their providers while seeking/delivering support in situations of breaking bad news and communications on death? Materials and Methods: The data were collected through individual in-depth interviews-31 respondents that included 16 patients and their families (recipient) and 15 medical practitioners (provider). Inductive thematic analysis was used. Results: Three main themes and nine sub-themes were identified highlighting livid experiences and perceptions of respondents. The findings suggest that interpersonal communications involve language barriers, health literacy and COVID-19 pandemic, situations of sudden unexplained death, perceptual negativity surrounding healthcare, empathy as well as emotions and multiple affiliations leading to ethical moral conflicts to influence individual perception. Regarding treatment attributes, factors of inaccessibility misconceived as incompetence and waiting and contact time are involved. The behavior and personality dimensions include attitude and robustness of the patient party and capability to handle emotions that affect provider-recipient relationship during communications of bad news and death. Conclusion: This study provided a local perspective about the experiences and expectations of healthcare recipients and their providers. Understanding this critical realm shall help in bridging the gap between recipient expectations and provider practices. It will also attempt towards possible alignment to improve patient satisfaction.

Knowledge, attitude and practice survey towards COVID-19 vaccination: A mediation analysis.

BACKGROUND AND AIM: The COVID-19 pandemic has significantly impacted human lives across the world. In a country like India, with the second highest population in the world, impact of COVID-19 has been diverse and multidimensional. Under such circumstances, vaccination against COVID-19 infection is claimed to be one of the major solutions to contain the pandemic. Understanding of Knowledge, Attitude and Practice (KAP) measures are essential prerequisites to design suitable intervention programs. This paper examines the different KAP factors in Indians towards their decision of vaccine uptake. METHOD: An online questionnaire was administered to Indian respondents. (Pilot study: n = 100, Main study: n = 221) to assess their existing knowledge on COVID-19 infections and vaccination, attitude and intentions towards COVID-19 vaccines and their decision towards COVID-19 vaccine uptake. RESULT: The findings highlighted that existing knowledge on COVID-19 infections and vaccination directly impacted their attitude and intention towards vaccination. The attitude and intention towards COVID-19 vaccines directly impacted their practice of undergoing COVID-19 vaccination. Further, there was a statistically significant and considerably large indirect effect of existing knowledge on COVID-19 infections and vaccination on the practice of undergoing COVID-19 vaccination through attitude and intention towards the vaccine. There was no direct effect of Knowledge (existing knowledge on COVID-19 infections and vaccination) on Practice (decision to undergo COVID-19 vaccination). Therefore, Attitude and intention towards COVID-19 vaccine is the primary mediator between Knowledge (existing knowledge on COVID-19 infections and vaccination) and Practice (decision to undergo COVID-19 vaccination). CONCLUSION: Participants decision towards COVID-19 vaccination decisions are strongly related to their attitude and intentions that confirms the strong role of attitude towards success of COVID-19 vaccination programme. Therefore, 'person-centric' attitude based positive intervention strategies that links their prior knowledge on COVID-19 infections and vaccination must be designed for greater vaccine acceptance amongst Indians.

Novel oxygen sensing mechanism in the spinal cord involved in cardiorespiratory responses to hypoxia.

As blood oxygenation decreases (hypoxemia), mammals mount cardiorespiratory responses, increasing oxygen to vital organs. The carotid bodies are the primary oxygen chemoreceptors for breathing, but sympathetic-mediated cardiovascular responses to hypoxia persist in their absence, suggesting additional high-fidelity oxygen sensors. We show that spinal thoracic sympathetic preganglionic neurons are excited by hypoxia and silenced by hyperoxia, independent of surrounding astrocytes. These spinal oxygen sensors (SOS) enhance sympatho-respiratory activity induced by CNS asphyxia-like stimuli, suggesting they bestow a life-or-death advantage. Our data suggest the SOS use a mechanism involving neuronal nitric oxide synthase 1 (NOS1) and nicotinamide adenine dinucleotide phosphate (NADPH) oxidase (NOX). We propose NOS1 serves as an oxygen-dependent sink for NADPH in hyperoxia. In hypoxia, NADPH catabolism by NOS1 decreases, increasing availability of NADPH to NOX and launching reactive oxygen species-dependent processes, including transient receptor potential channel activation. Equipped with this mechanism, SOS are likely broadly important for physiological regulation in chronic disease, spinal cord injury, and cardiorespiratory crisis.

De Novo Structure-Based Drug Design Using Deep Learning.

In recent years, deep learning-based methods have emerged as promising tools for de novo drug design. Most of these methods are ligand-based, where an initial target-specific ligand data set is necessary to design potent molecules with optimized properties. Although there have been attempts to develop alternative ways to design target-specific ligand data sets, availability of such data sets remains a challenge while designing molecules against novel target proteins. In this work, we propose a deep learning-based method, where the knowledge of the active site structure of the target protein is sufficient to design new molecules. First, a graph attention model was used to learn the structure and features of the amino acids in the active site of proteins that are experimentally known to form protein-ligand complexes. Next, the learned active site features were used along with a pretrained generative model for conditional generation of new molecules. A bioactivity prediction model was then used in a reinforcement learning framework to optimize the conditional generative model. We validated our method against two well-studied proteins, Janus kinase 2 (JAK2) and dopamine receptor D2 (DRD2), where we produce molecules similar to the known inhibitors. The graph attention model could identify the probable key active site residues, which influenced the conditional molecule generator to design new molecules with pharmacophoric features similar to the known inhibitors.