Accelerating the Hydrogen Evolution Kinetics with a Pulsed Laser-Synthesized Platinum Nanocluster-Decorated Nitrogen-Doped Carbon Electrocatalyst for Alkaline Seawater Electrolysis.

Efficient and durable electrocatalysts for the hydrogen evolution reaction (HER) in alkaline seawater environments are essential for sustainable hydrogen production. Zeolitic imidazolate framework-8 (ZIF-8) is synthesized through pulsed laser ablation in liquid, followed by pyrolysis, producing N-doped porous carbon (NC). NC matrix serves as a self-template, enabling Pt nanocluster decoration (NC-Pt) via pulsed laser irradiation in liquid. NC-Pt exhibits a large surface area, porous structure, high conductivity, N-rich carbon, abundant active sites, low Pt content, and a strong NC-Pt interaction. These properties enhance efficient mass transport during the HER. Remarkably, the optimized NC-Pt-4 catalyst achieves low HER overpotentials of 52, 57, and 53 mV to attain 10 mA cm-2 in alkaline, alkaline seawater, and simulated seawater, surpassing commercial Pt/C catalysts. In a two-electrode system with NC-Pt-4(-)ǀǀIrO2(+) as cathode and anode, it demonstrates excellent direct seawater electrolysis performance, with a low cell voltage of 1.63 mV to attain 10 mA cm-2 and remarkable stability. This study presents a rapid and efficient method for fabricating cost-effective and highly effective electrocatalysts for hydrogen production in alkaline and alkaline seawater environments.

Probing and gauging of D-Penicillamine xenobiotics in hepatic Wilson disease patients.

D-penicillamine (PA) is the primary chelator of choice to treat Wilson disease (WD). There are limitations in obtaining comprehensive data on PA metabolites in biological specimens by conventional approaches. Hence, the aim of the present was to identify the major hepatic PA metabolites and draw clear conclusions of the drug's xenobiotic in WD. Urine samples were collected from children with hepatic WD (n = 63, aged 14.8 ± 4 years) 5 h after PA administration (16.3 ± 3.8 mg/kg/day) and age-matched healthy volunteers comprised as controls (n = 30). High-resolution 800 MHz nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry was applied to reveal unambiguous appraisals of different excretory by-products of PA metabolism. Four new products comprising penicillamine disulphide (PD), penicillamine cysteine disulphide (PCD), S-methyl penicillamine (SMP), and N-acetyl penicillamine (NAP) of PA xenobiotic metabolites were identified using high-resolution NMR spectroscopy. Quantitative levels of PCD and SMP were approximately three-fold higher than those of PD and NAP, respectively. High-resolution NMR identifies the major PA metabolites with certainty. Reduction, sulfation, and methylation are the predominant pathways of PA metabolism. There is a potential application for assessing therapeutic monitoring of chelation in hepatic WD.

Facile room-temperature synthesis of layered transition metal phosphonates via hitherto unknown alkali metal tert-butyl phosphonates.

A facile room-temperature synthetic method is presented to produce alkali metal salts of tert-butyl phosphonic acid. The reaction between equimolar amounts of alkali metal carbonates and tert-butyl phosphonic acid in methanol results in the formation of [(tBuPO3H)Li(H2O)3·(H2O)] (1), [(tBuPO3)Na2(H2O)4]n (2), and [(tBuPO3H)K(H2O)]n (3). Solid-state structures of these compounds have been confirmed by single-crystal X-ray diffraction studies and further validated using numerous spectroscopic and analytical techniques. Compounds 1-3 are polymeric solids that are predominantly made up of a 1-D polymeric metal phosphonate chain. This synthetic approach leads to the formation of network structures/polymers in the solid state that otherwise are absent in solution due to the ionic nature of the interaction between the alkali metal ions and phosphonate anions. Apart from the multidentate nature of the phosphonate ligands, additional hydrogen bonding interactions involving water molecules, free P-OH groups, and PO moieties allow these chains to be propagated into 2-D sheets. We have further utilized the completely metalated sodium phosphonate 2 to synthesize layered metal phosphonates [(tBuPO3)Ca(H2O)]n (4), [(tBuPO3)Mn(H2O)]n (5) and [(tBuPO3)Co(H2O)]n (6) via a simple metathesis reaction.

Correlation of Internal Jugular Vein and Inferior Vena Cava Collapsibility Index with Direct Central Venous Pressure Measurement in Critically-ill Patients: An Observational Study.

Background and aims: Prompt assessments and quick replacement of intravascular fluid are critical steps to resuscitate hypovolemic patients. Intravascular volume assessment by direct central venous pressure (CVP) measurement is an invasive, time-consuming, and labor-intensive procedure. Nowadays, bedside ultrasound-guided volume assessment of the internal jugular vein (IJV) or inferior vena cava (IVC) is commonly employed as a proxy for direct CVP.Therefore, we examined the strength of association between CVP and collapsibility index (CI) of the IJV and IVC for evaluating the volume status of critically ill patients. Methods: Bedside USG-guided A-P diameter and cross-sectional area of the right IJV and IVC were measured, and their corresponding collapsibility indices were deduced. The results of the IJV and IVC indices were correlated with CVP. Results: About 60 out of 70 enrolled patients were analyzed. The baseline clinical parameters of patients are shown in Table 1. For CSA and AP diameter, the correlations between CVP and IJV-CI at 0° were r = -0.107 (p = 0.001) and r = -0.092 (p = 0.001). Correlations between CVP and IJV-CI at 30° for CSA and diameter, however, were (r = -0.109, p = 0.001) and (r = -0.117, p = 0.001), respectively. Table 2 depicts the correlation between CVP and IVC-CI r = -0.503, p = 0.001 for CSA and r = -0.452, p = 0.001 for diameter. Conclusion: The IVC and IJV collapsibility indices can be used in place of invasive CVP monitoring to assess fluid status in critically ill patients. How to cite this article: Kumar A, Bharti AK, Hussain M, Kumar S, Kumar A. Correlation of Internal Jugular Vein and Inferior Vena Cava Collapsibility Index with Direct Central Venous Pressure Measurement in Critically-ill Patients: An Observational Study. Indian J Crit Care Med 2024;28(6):595-600.

Recent advancements and prospects in noble and non-noble electrocatalysts for materials methanol oxidation reactions.

The direct methanol fuel cell (DMFC) represents a highly promising alternative power source for small electronics and automobiles due to its low operating temperatures, high efficiency, and energy density. The methanol oxidation process (MOR) constitutes a fundamental chemical reaction occurring at the positive electrode of a DMFC. Pt-based materials serve as widely utilized MOR electrocatalysts in DMFCs. Nevertheless, various challenges, such as sluggish reaction rates, high production costs primarily attributed to the expensive Pt-based catalyst, and the adverse effects of CO poisoning on the Pt catalysts, hinder the commercialization of DMFCs. Consequently, endeavors to identify an alternative catalyst to Pt-based catalysts that mitigate these drawbacks represent a critical focal point of DMFC research. In pursuit of this objective, researchers have developed diverse classes of MOR electrocatalysts, encompassing those derived from noble and non-noble metals. This review paper delves into the fundamental concept of MOR and its operational mechanisms, as well as the latest advancements in electrocatalysts derived from noble and non-noble metals, such as single-atom and molecule catalysts. Moreover, a comprehensive analysis of the constraints and prospects of MOR electrocatalysts, encompassing those based on noble metals and those based on non-noble metals, has been undertaken.

Efficient internalization of nano architectured 177Lu-hyaluronic acid@ zirconium-based metal-organic framework for the treatment of neuroblastoma: Unravelling toxicity, stability, radiolabelling and bio-distribution.

Zirconium-based metal-organic frameworks (UiO-66) have gained considerable attention owing to their versatile application. In the present research, UiO-66 was synthesized via a defect engineering approach, and its toxicity profile was explored. The synthesized nanomaterial was extensively characterized via spectroscopic methods such as FTIR and Raman spectroscopy, which confirmed the formation of the framework. X-ray diffraction (XRD) and transmission electron microscopy (TEM) were used to determine the crystallinity, shape and size of the nanoformulations. Thermal gravimetric analysis, 1H NMR spectroscopy and Brunauer-Emmett-Teller (BET) surface area analysis were used to identify the differences between pristine and defective UiO-66. Furthermore, the synthesized MOF was exposed to various pH conditions, serum protein and DMEM. Drug loading and release studies were evaluated using 5-fluorouracil as a model anticancer drug. The synthesized MOFs were modified with hyaluronic acid via mussel-inspired polymerization to increase their uptake and stability. More importantly, the toxicity of the nanoformulation was investigated via various toxicity studies, such as hemolysis assays and cell viability assays, and was further supported by in vivo acute and subacute toxicity data obtained from Wistar rats. Radiolabelling and bio-distribution studies were also performed using 177Lu to explore the bio-distribution profile of UiO-66.

Ultrafast Conversion of Water and Oxygen Molecules With Dissociation of Hydrogen Bonding Effect to Achieve Extra-High Energy Efficiency of Secondary Metal-Air Batteries.

Metal-air secondary batteries with ultrahigh specific energies have received vast attention and are considered new promising energy storage. The slow redox reactions between oxygen-water molecules lead to low energy efficiency (55-71%) and limited applications. Herein, it is proposed that the MIL-68(In)-derived porous carbon nanotube supports the CoNiFeP heteroconjugated alloy catalyst with an overboiling point electrolyte to achieve the ultrahigh oxidation rate of water molecules. Structural characterization and density functional theory calculations reveal that the new catalyst greatly reduces the free energy of the process, and the overboiling point further accelerates the dissociation of O─H and hydrogen bonds, and the release of O2 molecules, achieving an extra-low overpotential of 110 mV@10 mA cm-2 far lower than commercial Ir/C catalysts of 192 mV at 125 °C and state-of-the-art. Furthermore, the energy efficiency of assembled rechargeable zinc-air batteries begins to break through at 85 °C, jumps at 100 °C, and reaches ultrahigh energy efficiency of 88.1% at 125 °C with an ultralow decay rate of 0.0068% after 150 cycles far superior to those of reported metal-air batteries. This work provides a new catalyst and electrolyte joint-design strategy and reexamines the battery operating temperature to construct higher energy efficiency for secondary fuel cells.

Overlap in oncogenic and pro-inflammatory pathways associated with areca nut and nicotine exposure.

Background: Betel nut/areca nut/Areca catechu is one of the most commonly used psychoactive substance, and is also a major preventable cause of cancer. Unlike other psychoactive substances, such as nicotine, the mechanisms underlying addiction to areca nuts and related oncogenesis remain elusive. Recent reports suggest a possible overlap in the mechanisms of action of nicotine and areca nuts in the human body. Thus, this study aimed to investigate the interactome of human proteins associated with areca nut exposure and the intricate similarities and differences in the effects of the two psychoactive substances on humans. Methods: A list of proteins associated with areca nut use was obtained from the available literature using terms from Medical Subject Headings (MeSH). Protein-protein interaction (PPI) networks and functional enrichment were analyzed. The results obtained for both psychoactive substances were compared. Results: Given the limited number of common proteins (36/226, 16%) in the two sets, a substantial overlap (612/1176 nodes, 52%) was observed in the PPI networks, as well as in Gene Ontology. Areca nuts mainly affect signaling pathways through three hub proteins (alpha serine/threonine-protein kinase, tumor protein 53, and interleukin-6), which are common to both psychoactive substances, as well as two unique hub proteins (epidermal growth factor receptor and master regulator of cell cycle entry and proliferative metabolism). Areca nut-related proteins are associated with unique pathways, such as extracellular matrix organization, lipid storage, and metabolism, which are not found in nicotine-associated proteins. Conclusions: Areca nuts affect regulatory mechanisms, leading to systemic toxicity and oncogenesis. Areca nuts also affect unique pathways that can be studied as potential markers of exposure, as well as targets for anticancer therapeutic agents.

Multiple Protein Profiler 1.0 (MPP): A Webserver for Predicting and Visualizing Physiochemical Properties of Proteins at the Proteome Level.

Determining the physicochemical properties of a protein can reveal important insights in their structure, biological functions, stability, and interactions with other molecules. Although tools for computing properties of proteins already existed, we could not find a comprehensive tool that enables the calculations of multiple properties for multiple input proteins on the proteome level at once. Facing this limitation, we developed Multiple Protein Profiler (MPP) 1.0 as an integrated tool that allows the profiling of 12 individual properties of multiple proteins in a significant manner. MPP provides a tabular and graphic visualization of properties of multiple proteins. The tool is freely accessible at https://mproteinprofiler.microbiologyandimmunology.dal.ca/ .

Immuno-informatics study identifies conserved T cell epitopes in non-structural proteins of Bluetongue virus serotypes: formulation of a computationally optimized next-generation broad-spectrum multi-epitope vaccine.

Introduction: Bluetongue (BT) poses a significant threat to the livestock industry, affecting various animal species and resulting in substantial economic losses. The existence of numerous BT virus (BTV) serotypes has hindered control efforts, highlighting the need for broad-spectrum vaccines. Methodology: In this study, we evaluated the conserved amino acid sequences within key non-structural (NS) proteins of BTV and identified numerous highly conserved murine- and bovine-specific MHC class I-restricted (MHC-I) CD8+ and MHC-II-restricted CD4+ epitopes. We then screened these conserved epitopes for antigenicity, allergenicity, toxicity, and solubility. Using these epitopes, we developed in silico-based broad-spectrum multiepitope vaccines with Toll-like receptor (TLR-4) agonists. The predicted proinflammatory cytokine response was assessed in silico using the C-IMMSIM server. Structural modeling and refinement were achieved using Robetta and GalaxyWEB servers. Finally, we assessed the stability of the docking complexes through extensive 100-nanosecond molecular dynamics simulations before considering the vaccines for codon optimization and in silico cloning. Results: We found many epitopes that meet these criteria within NS1 and NS2 proteins and developed in silico broad-spectrum vaccines. The immune simulation studies revealed that these vaccines induce high levels of IFN-γ and IL-2 in the vaccinated groups. Protein-protein docking analysis demonstrated promising epitopes with strong binding affinities to TLR-4. The docked complexes were stable, with minimal Root Mean Square Deviation and Root Mean Square Fluctuation values. Finally, the in silico-cloned plasmids have high % of GC content with > 0.8 codon adaptation index, suggesting they are suitable for expressing the protein vaccines in prokaryotic system. Discussion: These next-generation vaccine designs are promising and warrant further investigation in wet lab experiments to assess their immunogenicity, safety, and efficacy for practical application in livestock. Our findings offer a robust framework for developing a comprehensive, broad-spectrum vaccine, potentially revolutionizing BT control and prevention strategies in the livestock industry.

Bimodal alteration of cognitive accuracy for spintronic artificial neural networks.

Spintronics-based artificial neural networks (ANNs) exhibiting nonvolatile, fast, and energy-efficient computing capabilities are promising neuromorphic hardware for performing complex cognitive tasks of artificial intelligence and machine learning. Early experimental efforts focused on multistate device concepts to enhance synaptic weight precisions, albeit compromising on cognitive accuracy due to their low magnetoresistance. Here, we propose a hybrid approach based on the tuning of tunnel magnetoresistance (TMR) and the number of states in the compound magnetic tunnel junctions (MTJs) to improve the cognitive performance of an all-spin ANN. A TMR variation of 33-78% is controlled by the free layer (FL) thickness wedge (1.6-2.6 nm) across the wafer. Meanwhile, the number of resistance states in the compound MTJ is manipulated by varying the number of constituent MTJ cells (n = 1-3), generating n + 1 states with a TMR difference between consecutive states of at least 21%. Using MNIST handwritten digit and fashion object databases, the test accuracy of the compound MTJ ANN is observed to increase with the number of intermediate states for a fixed FL thickness or TMR. Meanwhile, the test accuracy for a 1-cell MTJ increases linearly by 8.3% and 7.4% for handwritten digits and fashion objects, respectively, with increasing TMR. Interestingly, a multifarious TMR dependence of test accuracy is observed with the increasing synaptic complexity in the 2- and 3-cell MTJs. By leveraging on the bimodal tuning of multilevel and TMR, we establish viable paths for enhancing the cognitive performance of spintronic ANN for in-memory and neuromorphic computing.

Formulation of next-generation polyvalent vaccine candidates against three important poxviruses by targeting DNA-dependent RNA polymerase using an integrated immunoinformatics and molecular modeling approach.

BACKGROUND: Poxviruses comprise a group of large double-stranded DNA viruses and are known to cause diseases in humans, livestock animals, and other animal species. The Mpox virus (MPXV; formerly Monkeypox), variola virus (VARV), and volepox virus (VPXV) are among the prevalent poxviruses of the Orthopoxviridae genera. The ongoing Mpox infectious disease pandemic caused by the Mpox virus has had a major impact on public health across the globe. To date, only limited repurposed antivirals and vaccines are available for the effective treatment of Mpox and other poxviruses that cause contagious diseases. METHODS: The present study was conducted with the primary goal of formulating multi-epitope vaccines against three evolutionary closed poxviruses i.e., MPXV, VARV, and VPXV using an integrated immunoinformatics and molecular modeling approach. DNA-dependent RNA polymerase (DdRp), a potential vaccine target of poxviruses, has been used to determine immunodominant B and T-cell epitopes followed by interactions analysis with Toll-like receptor 2 at the atomic level. RESULTS: Three multi-epitope vaccine constructs, namely DdRp_MPXV (V1), DdRp_VARV (V2), and DdRp_VPXV (V3) were designed. These vaccine constructs were found to be antigenic, non-allergenic, non-toxic, and soluble with desired physicochemical properties. Protein-protein docking and interaction profiling analysis depicts a strong binding pattern between the targeted immune receptor TLR2 and the structural models of the designed vaccine constructs, and manifested a number of biochemical bonds (hydrogen bonds, salt bridges, and non-bonded contacts). State-of-the-art all-atoms molecular dynamics simulations revealed highly stable interactions of vaccine constructs with TLR2 at the atomic level throughout the simulations on 300 nanoseconds. Additionally, the outcome of the immune simulation analysis suggested that designed vaccines have the potential to induce protective immunity against targeted poxviruses. CONCLUSIONS: Taken together, formulated next-generation polyvalent vaccines were found to have good efficacy against closely related poxviruses (MPXV, VARV, and VPXV) as demonstrated by our extensive immunoinformatics and molecular modeling evaluations; however, further experimental investigations are still needed.

Analysis of Hybrid Feature Optimization Techniques Based on the Classification Accuracy of Brain Tumor Regions Using Machine Learning and Further Evaluation Based on the Institute Test Data.

Aim: The goal of this study was to get optimal brain tumor features from magnetic resonance imaging (MRI) images and classify them based on the three groups of the tumor region: Peritumoral edema, enhancing-core, and necrotic tumor core, using machine learning classification models. Materials and Methods: This study's dataset was obtained from the multimodal brain tumor segmentation challenge. A total of 599 brain MRI studies were employed, all in neuroimaging informatics technology initiative format. The dataset was divided into training, validation, and testing subsets online test dataset (OTD). The dataset includes four types of MRI series, which were combined together and processed for intensity normalization using contrast limited adaptive histogram equalization methodology. To extract radiomics features, a python-based library called pyRadiomics was employed. Particle-swarm optimization (PSO) with varying inertia weights was used for feature optimization. Inertia weight with a linearly decreasing strategy (W1), inertia weight with a nonlinear coefficient decreasing strategy (W2), and inertia weight with a logarithmic strategy (W3) were different strategies used to vary the inertia weight for feature optimization in PSO. These selected features were further optimized using the principal component analysis (PCA) method to further reducing the dimensionality and removing the noise and improve the performance and efficiency of subsequent algorithms. Support vector machine (SVM), light gradient boosting (LGB), and extreme gradient boosting (XGB) machine learning classification algorithms were utilized for the classification of images into different tumor regions using optimized features. The proposed method was also tested on institute test data (ITD) for a total of 30 patient images. Results: For OTD test dataset, the classification accuracy of SVM was 0.989, for the LGB model (LGBM) was 0.992, and for the XGB model (XGBM) was 0.994, using the varying inertia weight-PSO optimization method and the classification accuracy of SVM was 0.996 for the LGBM was 0.998, and for the XGBM was 0.994, using PSO and PCA-a hybrid optimization technique. For ITD test dataset, the classification accuracy of SVM was 0.994 for the LGBM was 0.993, and for the XGBM was 0.997, using the hybrid optimization technique. Conclusion: The results suggest that the proposed method can be used to classify a brain tumor as used in this study to classify the tumor region into three groups: Peritumoral edema, enhancing-core, and necrotic tumor core. This was done by extracting the different features of the tumor, such as its shape, grey level, gray-level co-occurrence matrix, etc., and then choosing the best features using hybrid optimal feature selection techniques. This was done without much human expertise and in much less time than it would take a person.

Sesame Genomic Web Resource (SesameGWR): a well-annotated data resource for transcriptomic signatures of abiotic and biotic stress responses in sesame (Sesamum indicum L.).

Sesame (Sesamum indicum L.) is a globally cultivated oilseed crop renowned for its historical significance and widespread growth in tropical and subtropical regions. With notable nutritional and medicinal attributes, sesame has shown promising effects in combating malnutrition cancer, diabetes, and other diseases like cardiovascular problems. However, sesame production faces significant challenges from environmental threats such as charcoal rot, drought, salinity, and waterlogging stress, resulting in economic losses for farmers. The scarcity of information on stress-resistance genes and pathways exacerbates these challenges. Despite its immense importance, there is currently no platform available to provide comprehensive information on sesame, which significantly hinders the mining of various stress-associated genes and the molecular breeding of sesame. To address this gap, here a free, web-accessible, and user-friendly genomic web resource (SesameGWR, http://backlin.cabgrid.res.in/sesameGWR/) has been developed This platform provides key insights into differentially expressed genes, transcription factors, miRNAs, and molecular markers like simple sequence repeats, single nucleotide polymorphisms, and insertions and deletions associated with both biotic and abiotic stresses.. The functional genomics information and annotations embedded in this web resource were predicted through RNA-seq data analysis. Considering the impact of climate change and the nutritional and medicinal importance of sesame, this study is of utmost importance in understanding stress responses. SesameGWR will serve as a valuable tool for developing climate-resilient sesame varieties, thereby enhancing the productivity of this ancient oilseed crop.

Metal-free adsorption and photodegradation methods for methylene blue dye removal using different reduction grades of graphene oxide.

The release of organic pollutants and dyes into the environment by industries has had profound and harmful effects on both humans and ecosystems. Graphene oxide (GO) and its reduced form have been investigated for their effectiveness in removing pollutant dyes. GO nano-powder was synthesized using an improved version of Hummer's method and subsequently thermally reduced at various temperatures, including 125, 150, 175, and 200 °C, under vacuum conditions. In the X-ray diffraction spectra, an intense (001) diffraction peak was initially observed at 9.136° (2θ) for pristine GO. This peak gradually shifted towards higher angles as the reduction process took place and eventually disappeared when the GO was reduced at 200 °C. The intensity ratio of the D and G bands (ID/IG ratio) for GO nano-powder in the Raman spectra decreased from 0.94 to 0.76 due to the reduction process. The FTIR spectra of GO and reduced graphene oxide (rGO) also illustrated the reduction process. The bandgap of pristine GO significantly decreased from 2.31 to 0.73 eV, as determined by ultraviolet-visible (UV-Vis) diffuse reflectance spectrophotometry during the reduction process. The surface area and pore volume of both pristine GO and rGO-150 were determined using the BET (Brunauer-Emmett-Teller) and BJH (Barrett-Joyner-Halenda) methods. The results indicated an increase in the BET surface area from 6.61 to 7.86 m2/g and a corresponding enhancement in pore volume from 0.118 to 0.128 cc/g after reduction. The adsorption and photocatalytic degradation behavior of pristine GO and reduced graphene oxides (rGOs) were examined using methylene blue dye. The pristine GO demonstrated impressive adsorption capability, effectively removing the dye by 85.78 % within just 15 min and achieving nearly 97 % removal after 4 h. In contrast, the highest photocatalytic degradation of methylene blue, about 47.58 %, was attained for the rGO sample reduced at 150 °C under the illumination of visible light.

Identification of Marker Genes in Infectious Diseases from ScRNA-seq Data Using Interpretable Machine Learning.

A common result of infection is an abnormal immune response, which may be detrimental to the host. To control the infection, the immune system might undergo regulation, therefore producing an excess of either pro-inflammatory or anti-inflammatory pathways that can lead to widespread inflammation, tissue damage, and organ failure. A dysregulated immune response can manifest as changes in differentiated immune cell populations and concentrations of circulating biomarkers. To propose an early diagnostic system that enables differentiation and identifies the severity of immune-dysregulated syndromes, we built an artificial intelligence tool that uses input data from single-cell RNA sequencing. In our results, single-cell transcriptomics successfully distinguished between mild and severe sepsis and COVID-19 infections. Moreover, by interpreting the decision patterns of our classification system, we identified that different immune cells upregulating or downregulating the expression of the genes CD3, CD14, CD16, FOSB, S100A12, and TCRɣδ can accurately differentiate between different degrees of infection. Our research has identified genes of significance that effectively distinguish between infections, offering promising prospects as diagnostic markers and providing potential targets for therapeutic intervention.

Development, characterization, and evaluation of Zn-SA-chitosan bionanoconjugates on wheat seed, experiencing chilling stress during germination.

This study aimed to develop and characterize the chitosan bionanoconjugates (BNCs) loaded with zinc (Zn) and salicylic acid (SA) and test their efficacy on wheat seed exposed to chilling stress. BNCs developed were spherical (480 ± 6.0 nm), porous, and positively charged (+25.2 ± 2.4 mV) with regulated nutrient release properties. They possessed complexation efficiency of 78.4 and 58.9 % for Zn, and SA respectively. BET analysis further confirmed a surface area of 12.04 m2/g. Release kinetics substantiated the release rates of Zn and SA, as 0.579 and 0.559 % per hour, along with a half-life of 119.7 and 124.0 h, respectively. BNCs positively affected the germination potential of wheat seeds under chilling stress as observed by significantly (p < 0.05) reduced mean emergence time (18 %), and increased germination rate (22 %), compared to the control. Higher activities of reserve mobilizing enzymes (α-amylase- 6.5 folds, protease -10.2 folds) as well as faster reserve mobilization of starch (64.4 %) and protein (63.5 %) molecules were also observed. The application further led to increased levels of the antioxidant enzymes (SOD and CAT) and reduced oxidative damage (MDA and H2O2). Thus, it is inferred that the developed BNCs could help substantially improve the germination and reserve mobilization potential, thereby increasing the crop yield.

Comprehensive Review on Alzheimer's Disease: Natural Therapeutics, Gaps and Challenges.

More than 20 million people worldwide have Alzheimer's disease (AD), making it the most prevalent disease. Patients with AD may live for at least a decade after diagnosis, making it the most common cause of disability in the elderly. Each year, 1% to 4% of the population is affected by AD, with prevalence peaking between ages 65 and 70 and declining to 6% among those over 85. Researchers have accumulated evidence on medicinal herbs that may reverse the pathogenesis of Alzheimer's disease. Alzheimer's disease (AD) is associated with severe memory loss, which can negatively impact social and professional life. The first neurotransmitter linked to Alzheimer's was acetylcholine (ACh). There is no known cure, and the available treatments are ineffective. Multiple studies indicate that Ayurvedic restorative herbs and their constituents may be effective in treating Alzheimer's disease. This technique emphasizes the fact that delaying or preventing Alzheimer's disease with the help of natural bio-actives could reduce the number of cases over the next half-century. To provide detailed information, the pathology and pathophysiology of Alzheimer's Disease are discussed in the text of this review, along with an overview of the neurotransmitters involved in the progression of the disease. The importance of different natural bioactives for the treatment of Alzheimer's disease is also outlined in the paper. The information contained in this paper can serve as a template for future research expressing the more beneficial role of other bioactive in acting as an adjuvant in the prevention and treatment of this disease, facing certain challenges and gaps with conventional drugs used to treat Alzheimer's disease.

Meta-QTL analysis and identification of candidate genes for multiple-traits associated with spot blotch resistance in bread wheat.

In bread wheat, a literature search gave 228 QTLs for six traits, including resistance against spot blotch and the following five other related traits: (i) stay green; (ii) flag leaf senescence; (iii) green leaf area duration; (iv) green leaf area of the main stem; and (v) black point resistance. These QTLs were used for metaQTL (MQTL) analysis. For this purpose, a consensus map with 72,788 markers was prepared; 69 of the above 228 QTLs, which were suitable for MQTL analysis, were projected on the consensus map. This exercise resulted in the identification of 16 meta-QTLs (MQTLs) located on 11 chromosomes, with the PVE ranging from 5.4% (MQTL7) to 21.8% (MQTL5), and the confidence intervals ranging from 1.5 to 20.7 cM (except five MQTLs with a range of 36.1-57.8 cM). The number of QTLs associated with individual MQTLs ranged from a maximum of 17 in MQTL3 to 8 each in MQTL5 and MQTL8 and 5 each in MQTL7 and MQTL14. The 16 MQTLs, included 12 multi-trait MQTLs; one of the MQTL also overlapped a genomic region carrying the major spot blotch resistance gene Sb1. Of the total 16 MQTLs, 12 MQTLs were also validated through marker-trait associations that were available from earlier genome-wide association studies. The genomic regions associated with MQTLs were also used for the identification of candidate genes (CGs) and led to the identification of 516 CGs encoding 508 proteins; 411 of these proteins are known to be associated with resistance against several biotic stresses. In silico expression analysis of CGs using transcriptome data allowed the identification of 71 differentially expressed CGs, which were examined for further possible studies. The findings of the present study should facilitate fine-mapping and cloning of genes, enabling Marker Assisted Selection.

Complete Genome Sequencing, Annotation, and Mutational Profiling of the Novel Clade I Human Mpox Virus, Kamituga Strain.

INTRODUCTION: Human Mpox (formerly monkeypox) infection is an emerging zoonotic disease caused by the Mpox virus (MPXV). We describe the complete genome annotation, phylogeny, and mutational profile of a novel, sustained Clade I Mpox outbreak in the city of Kamituga in Eastern Democratic Republic of the Congo (DRC). METHODOLOGY: A cross-sectional, observational, cohort study was performed among patients of all ages admitted to the Kamituga Hospital with Mpox infection symptoms between late September 2023 and late January 2024. DNA was isolated from Mpox swabbed lesions and sequenced followed by phylogenetic analysis, genome annotation, and mutational profiling. RESULTS: We describe an ongoing Clade I Mpox outbreak in the city of Kamituga, South Kivu Province, Democratic Republic of Congo. Whole-genome sequencing of the viral RNA samples revealed, on average, 201.5 snps, 28 insertions, 81 deletions, 2 indels, 312.5 total variants, 158.3 amino acid changes, 81.66 intergenic variants, 72.16 synonymous mutations, 106 missense variants, 41.16 frameshift variants, and 3.33 inframe deletions across six samples. By assigning mutations at the proteome level for Kamituga MPXV sequences, we observed that seven proteins, namely, C9L (OPG047), I4L (OPG080), L6R (OPG105), A17L (OPG143), A25R (OPG151), A28L (OPG153), and B21R (OPG210) have emerged as hot spot mutations based on the consensuses inframe deletions, frameshift variants, synonymous variants, and amino acids substitutions. Based on the outcome of the annotation, we found a deletion of the D14L (OPG032) gene in all six samples. Following phylogenetic analysis and whole genome assembly, we determined that this cluster of Mpox infections is genetically distinct from previously reported Clade I outbreaks, and thus propose that the Kamituga Mpox outbreak represents a novel subgroup (subgroup VI) of Clade I MPXV. CONCLUSIONS: Here we report the complete viral genome for the ongoing Clade I Mpox Kamituga outbreak for the first time. This outbreak presents a distinct mutational profile from previously sequenced Clade I MPXV oubtreaks, suggesting that this cluster of infections is a novel subgroup (we term this subgroup VI). These findings underscore the need for ongoing vigilance and continued sequencing of novel Mpox threats in endemic regions.