Immunoinformatics and Reverse Vaccinology Approach for the Identification of Potential Vaccine Candidates against Vandammella animalimors.

Vandammella animalimorsus is a Gram-negative and non-motile bacterium typically transmitted to humans through direct contact with the saliva of infected animals, primarily through biting, scratches, or licks on fractured skin. The absence of a confirmed post-exposure treatment of V. animalimorsus bacterium highlights the imperative for developing an effective vaccine. We intended to determine potential vaccine candidates and paradigm a chimeric vaccine against V. animalimorsus by accessible public data analysis of the strain by utilizing reverse vaccinology. By subtractive genomics, five outer membranes were prioritized as potential vaccine candidates out of 2590 proteins. Based on the instability index and transmembrane helices, a multidrug transporter protein with locus ID A0A2A2AHJ4 was designated as a potential candidate for vaccine construct. Sixteen immunodominant epitopes were retrieved by utilizing the Immune Epitope Database. The epitope encodes the strong binding affinity, nonallergenic properties, non-toxicity, high antigenicity scores, and high solubility revealing the more appropriate vaccine construct. By utilizing appropriate linkers and adjuvants alongside a suitable adjuvant molecule, the epitopes were integrated into a chimeric vaccine to enhance immunogenicity, successfully eliciting both adaptive and innate immune responses. Moreover, the promising physicochemical features, the binding confirmation of the vaccine to the major innate immune receptor TLR-4, and molecular dynamics simulations of the designed vaccine have revealed the promising potential of the selected candidate. The integration of computational methods and omics data has demonstrated significant advantages in discovering novel vaccine targets and mitigating vaccine failure rates during clinical trials in recent years.

The utilization of renal dialysis: a comprehensive study in Saudi Arabia.

BACKGROUND: Understanding the trend of utilization of renal dialysis in Saudi Arabia (SA) is fundamental as it provides a general overview of renal care. The practice of renal dialysis assists in identifying challenges, opportunities, and potential areas for improvement in the provision of the services. OBJECTIVES: This research investigated the utilization of renal dialysis services in SA by exploring the number of renal dialysis centers, hemodialysis machines (HD), and peritoneodialysis patients. METHODS: The dataset for this study was derived from a collaboration between the General Authority of Statistics (GaStat) and the Ministry of Health (MoH), focusing on indicators for renal dialysis centers and patients across health sectors in 2021. Analysis was conducted using MS Excel 365 and IBM SPSS Version 29, incorporating multiple regression techniques. The health sector was treated as the dependent variable. At the same time, the number of hemodialysis (HD) machines and the counts of HD and peritoneal dialysis patients were considered independent variables. RESULTS: Around 275 renal dialysis centers, over 8000 HD machines, 20,440 HD patients, and 1,861 peritoneal patients were tallied from two resources. The findings revealed a negative relationship between the health sector and several renal dialysis centers and peritoneodialysis patients, as demonstrated by p < 0.05 in multiple regression analysis. CONCLUSION: The number of renal dialysis centers influences the availability of HD machines, affecting the number of HD and peritoneodialysis patients. Most national patients preferred MoH over other semi-governmental and private sectors, and vice versa for non-Saudis.

Exploring sustainable healthcare: Innovations in health economics, social policy, and management.

The healthcare sector faces several challenges, such as rising costs, rising demand, and the need for sustainability. A new area of healthcare has emerged due to these problems, focusing on long-term improvements in management, social policy, and health economics. This research explores the cutting edge of healthcare, concentrating on long-term advancements in management, social policy, and health economics. To better understand the problems affecting the healthcare sector and to pinpoint the areas where sustainable solutions are most required, a survey of 2000 healthcare professionals and policymakers was performed. The data were analyzed using structural equation modeling (SEM), and a thorough sustainable healthcare model was created. According to the survey's findings, the healthcare sector now faces three significant challenges: growing prices, increased demand, and the need for sustainability. According to the respondents, the three main areas where sustainable innovations are most required are management, social policy, and health economics. These conclusions were supported by the (SEM) analysis, which also showed that sustainable practices in these fields significantly impact the sustainability of the healthcare system. These findings lead this research to conclude that to guarantee the accessibility and affordability of healthcare for everyone, a move towards sustainable practices in health economics, social policy, and management is needed. Cooperation between healthcare providers, policymakers, and other stakeholders is required to create creative solutions that support sustainability in the healthcare sector. This study offers a thorough framework for sustainable healthcare that may act as a guide for further research and the formulation of new regulations.

Exploring the safety reporting culture among healthcare practitioners in Saudi hospitals: a comprehensive 2022 national study.

BACKGROUND: With the rise in medical errors, establishing a strong safety culture and an effective incident reporting system is crucial. As part of the Saudi National Health Transformation Vision of 2030, multiple projects have been initiated to periodically assess healthcare quality measures and ensure a commitment to continuous improvement. Among these is the Hospital Survey on Patient Safety Culture National Project (HSPSC), conducted regularly by the Saudi Patient Safety Center (SPSC). However, comprehensive tools for assessing reporting culture are lacking. Addressing this gap can enhance reporting, efficiency, and health safety. OBJECTIVE: This paper aims to investigate the reporting practices among healthcare professionals (HCPs) in Saudi Arabian hospitals and examine the relationship between reporting culture domains and other variables such as hospital bed capabilities and HCPs' work positions. METHODS: The study focuses on measuring the reporting culture-related items measures and employs secondary data analysis using information from the Hospital Survey on Patient Safety Culture conducted by the Saudi Center for Patient Safety in 2022, encompassing hospitals throughout Saudi Arabia. Data incorporated seven items in total: four items related to the Response to Error Domain, two related to the Reporting Patient Safety Events Domain, and one associated with the number of events reported in the past 12 months. RESULTS: The sample for the analyzed data included 145,657 HCPs from 392 hospitals. The results showed that the average positive response rates for reporting culture-related items were between 50% and 70%. In addition, the research indicated that favorable response rates were relatively higher among managerial and quality/patient safety/risk management staff. In contrast, almost half had not reported any events in the preceding year, and a quarter reported only 1 or 2 events. Pearson correlation analysis demonstrates a strong negative correlation between bed capacity and reporting safety events, response to error, and number of events reported (r = -0.935, -0.920, and - 0.911, respectively; p < 0.05), while a strong positive correlation is observed between reporting safety events and response to error (r = 0.980; p < 0.01). CONCLUSIONS: Almost 75% of the HCPs reported fewer safety events over the last 12 months, indicating an unexpectedly minimal recorded occurrence variance ranging from 0 to 2 incidents.

An integrated in silico approach for the identification of novel potential drug target and chimeric vaccine against Neisseria meningitides strain 331401 serogroup X by subtractive genomics and reverse vaccinology.

Neisseria meningitidis, commonly known as the meningococcus, leads to substantial illness and death among children and young adults globally, revealing as either epidemic or sporadic meningitis and/or septicemia. In this study, we have designed a novel peptide-based chimeric vaccine candidate against the N. meningitidis strain 331,401 serogroup X. Through rigorous analysis of subtractive genomics, two essential cytoplasmic proteins, namely UPI000012E8E0(UDP-3-O-acyl-GlcNAc deacetylase) and UPI0000ECF4A9(UDP-N-acetylglucosamine acyltransferase) emerged as potential drug targets. Additionally, using reverse vaccinology, the outer membrane protein UPI0001F4D537 (Membrane fusion protein MtrC) identified by subcellular localization and recognized for its known indispensable role in bacterial survival was identified as a novel chimeric vaccine target. Following a careful comparison of MHC-I, MHC-II, T-cell, and B-cell epitopes, three epitopes derived from UPI0001F4D537 were linked with three types of linkers-GGGS, EAAAK, and the essential PADRE-for vaccine construction. This resulted in eight distinct vaccine models (V1-V8). Among them V1 model was selected as the final vaccine construct. It exhibits exceptional immunogenicity, safety, and enhanced antigenicity, with 97.7 % of its residues in the Ramachandran plot's most favored region. Subsequently, the vaccine structure was docked with the TLR4/MD2 complex and six different HLA allele receptors using the HADDOCK server. The docking resulted in the lowest HADDOCK score of 39.3 ± 9.0 for TLR/MD2. Immune stimulation showed a strong immune response, including antibodies creation and the activation of B-cells, T Cytotoxic cells, T Helper cells, Natural Killer cells, and interleukins. Furthermore, the vaccine construct was successfully expressed in the Escherichia coli system by reverse transcription, optimization, and ligation in the pET-28a (+) vector for the expression study. The current study proposes V1 construct has the potential to elicit both cellular and humoral responses, crucial for the developing an epitope-based vaccine against N. meningitidis strain 331,401 serogroup X.

Multidrug resistance among uropathogenic clonal group A E. Coli isolates from Pakistani women with uncomplicated urinary tract infections.

OBJECTIVE: Multi-drug resistance (MDR) has notably increased in community acquired uropathogens causing urinary tract infections (UTIs), predominantly Escherichia coli. Uropathogenic E. coli causes 80% of uncomplicated community acquired UTIs, particularly in pre-menopausal women. Considering this high prevalence and the potential to spread antimicrobial resistant genes, the current study was conducted to investigate the presence of clinically important strains of E. coli in Pakistani women having uncomplicated cystitis and pyelonephritis. Women belonging to low-income groups were exclusively included in the study. Seventy-four isolates from urine samples were processed, phylotyped, and screened for the presence of two Single Nucleotide Polymorphisms (SNPs) particularly associated with a clinically important clonal group A of E. coli (CgA) followed by antibiotic susceptibility testing and genome sequence analysis. RESULTS: Phylogroup B2 was most prevalent in patients and 44% of isolates were positive for the presence of CgA specific SNPs in Fumarate hydratase and DNA gyrase subunit B genes. Antibiotic susceptibility testing showed widespread resistance to trimethoprim-sulfamethoxazole and extended-spectrum beta-lactamase production. The infection analysis revealed the phylogroup B2 to be more pathogenic as compared to the other groups. The genome sequence of E. coli strain U17 revealed genes encoding virulence, multidrug resistance, and host colonization mechanisms. CONCLUSIONS: Our research findings not only validate the significant occurrence of multidrug-resistant clonal group A E. coli (CgA) in premenopausal Pakistani women suffering from cystitis and pyelonephritis but also reveal the presence of genes associated withvirulence, and drug efflux pumps. The detection of highly pathogenic, antimicrobial-resistant phylogroup B2 and CgA E. coli strains is likely to help in understanding the epidemiology of the pathogen and may ultimately help to reduce the impact of these strains on human health. Furthermore, the findings of this study will particularly help to reduce the prevalence of uncomplicated UTIs and the cost associated with their treatment in women belonging to low-income groups.

Uropathogenic bacteria and deductive genomics towards antimicrobial resistance, virulence, and potential drug targets / Bacterias uropatógenas y genómica deductiva hacia la resistencia, virulencia y posibles objetivos farmacológicos a los antimicrobianos

Urinary tract infections (UTIs) are among the most prevalent bacterial infections affecting people in inpatient and outpatient settings. The current study aimed to sequence the genome of uropathogenic Escherichia coli strain CUI-B1 resourced from a woman having uncomplicated cystitis and pyelonephritis. Followed by deductive genomics towards potential drug targets using E. coli strain CUI-B1, strain O25b: H4-ST131, Proteus mirabilis strain HI4320, Klebsiella pneumoniae strain 1721, and Staphylococcus saprophyticus strain ATCC 15305 uropathogenic strains. Comparative genome analysis revealed that genes related to the survival of E. coli, P. mirabilis, K. pneumoniae, and S. saprophyticus, such as genes of metal-requiring proteins, defense-associated genes, and genes associated with general physiology, were found to be highly conserved in the genomes including strain CUI-B1. However, the genes responsible for virulence and drug resistance, mainly those that are involved in bacterial secretion, fimbriae, adherence, and colonization, were found in various genomic regions and varied from one species to another or within the same species. Based on the genome sequence, virulence, and antimicrobial-resistant gene dataset, the subtractive proteomics approach revealed 22 proteins mapped to the pathogen’s unique pathways and among them, entB, clbH, chuV, and ybtS were supposed to be potential drug targets and the single drug could be utilized for all above-mentioned strains. These results may provide the foundation for the optimal target for future discovery of drugs for E. coli-, P. mirabilis-, K. pneumoniae-, and S. saprophyticus-based infections and could be investigated further to employ in personalized drug development.(AU)

Exposure to Imidacloprid under variable conditions disturbs the muscle fatty acid profile of a fresh water non target fish: Labeo rohita.

Economy of Pakistan is heavily dependent upon agriculture and extensive use of pesticide is quiet common to enhance the crop yield. Imidacloprid is among the first choice pesticides in Pakistan and it has been reported that through run off along with water it ends up in water bodies affecting non target aquatic fauna. Through the present investigation, we are reporting the effects of Imidacloprid on the fatty acids composition of a non-target, commercially important carp: Labeo rohita. Fish were exposed to sub lethal concentration of Imidacloprid (120 mgL1) for 2, 4 and 8 days (short term) as well as for 16, 32 and 64 days (long term experimental conditions). Pesticide untreated controls were also maintained for each treatment. Following the specific Imidacloprid exposure, fatty acid composition (%) was determined in the muscle of all experimental groups by using gas chromatography. Fish exposed to Imidacloprid for 8 days had reduced Palmitic acid (p = 0.02) and elevated muscle Arachidic acid (p < 0.001) than control group. Labeo rohita exposed to the pesticide for 32 days had elevated muscle Oleic (p = 0.02) and Linoleic acid (p = 0.02) while fish exposed to Imidacloprid to 64 days had reduced muscle Palmitic (p = 0.04) and Oleic acid (p = 0.03). In conclusion, we are reporting that the exposure to sub lethal concentration of Imidacloprid disturb the muscle fatty acid composition of Labeo rohita that may affect its food quality. The effects were more pronounced under long term experimental conditions and were probably due to potentiating lipid peroxidation and disturbed fish metabolism upon Imidacloprid exposure.

Exploring the Impact of Social Media Sites on Compulsive Shopping Behavior: The Mediating Role of Materialism.

Purpose: This study aims to examine the influence of social media sites and television advertisements on compulsive shopping behavior, and whether this influence is mediated by materialism in the university students of Saudi Arabia. Methods: Data was collected from 487 students at Saudi universities. AMOS and Structural equation modeling (SEM) were utilized to examine the data. The research supports the Hypothesis that adolescents who are more materialistic are more prone than less materialistic adolescents to engage in compulsive shopping behavior. Results: The findings were consistent with other research, suggesting that the same remains true in the culture of Saudi Arabia. The research's findings show that television advertisements and the use of social media sites positively related to compulsive shopping behavior among university students, and materialism mediated the relationship between television advertisements and social media sites. Conclusion: The research emphasizes the significance of comprehending the materialistic attitude and consumption choices of adolescents and offers crucial information for scholars, decision-makers, and management of top companies.

Uropathogenic bacteria and deductive genomics towards antimicrobial resistance, virulence, and potential drug targets.

Urinary tract infections (UTIs) are among the most prevalent bacterial infections affecting people in inpatient and outpatient settings. The current study aimed to sequence the genome of uropathogenic Escherichia coli strain CUI-B1 resourced from a woman having uncomplicated cystitis and pyelonephritis. Followed by deductive genomics towards potential drug targets using E. coli strain CUI-B1, strain O25b: H4-ST131, Proteus mirabilis strain HI4320, Klebsiella pneumoniae strain 1721, and Staphylococcus saprophyticus strain ATCC 15305 uropathogenic strains. Comparative genome analysis revealed that genes related to the survival of E. coli, P. mirabilis, K. pneumoniae, and S. saprophyticus, such as genes of metal-requiring proteins, defense-associated genes, and genes associated with general physiology, were found to be highly conserved in the genomes including strain CUI-B1. However, the genes responsible for virulence and drug resistance, mainly those that are involved in bacterial secretion, fimbriae, adherence, and colonization, were found in various genomic regions and varied from one species to another or within the same species. Based on the genome sequence, virulence, and antimicrobial-resistant gene dataset, the subtractive proteomics approach revealed 22 proteins mapped to the pathogen's unique pathways and among them, entB, clbH, chuV, and ybtS were supposed to be potential drug targets and the single drug could be utilized for all above-mentioned strains. These results may provide the foundation for the optimal target for future discovery of drugs for E. coli-, P. mirabilis-, K. pneumoniae-, and S. saprophyticus-based infections and could be investigated further to employ in personalized drug development.

Examining the informal urban growth trends in a Port city.

Rapid urban developmental growth is a heated debate worldwide due to environmental challenges. This research has examined the spatiotemporal trend of informal built-up growth in Karachi city. Using a geo-information system, the past twenty years (2000-2020) trends of informal built-up growth are examined. For attaining the research objectives, geo-referenced high-resolution maps and satellite images are used for accuracy based spatial data. Karachi is divided into five different land use and land cover (LULC): formal built-up, informal built-up, vacant, water bodies, and green spaces. Spatial data of informal built-up growth change of five different years, 2000, 2005, 2010, 2015, and 2020 are generated through acquired maps digitization using ArcMap. Subsequently, the gains and transfers of Karachi's informal built-up growth based on five years 2000-2005, 2005-2010, 2010-2015, and 2015-2020 are analyzed using the Land Change Modeler (LCM) in IDRISI software. Also, land use land cover changes (LULCC) are predicted for the next 40 years (2020-2060) using the integrated Cellular Automata Markov (CA-Markov) simulation model in IDRISI. The results revealed that Karachi's built-up is expanding rapidly. Land conversion into the informal built-up area is alarming, as it has changed from 144.31 km2 to 217.19 km2 with 72.88 km2 in the past twenty years (2000-2020) and has occupied green and agricultural land. Most informal built-up areas have transitioned from vacant (71.01 km2) land use land cover (LULC). The informal built-up area could expand from 217.19 km2 to 317.63 km2, with about 100.44 km2 up to 2060. The planned and unplanned development will be towards the city's East (E) direction and will convert and ruin agriculture and vacant land. The present study provides suggestions to urban planners, administrative authorities, and policymakers to control informal growth and achieve sustainable development goals in developing countries.

Computational exploration of novel ROCK2 inhibitors for cardiovascular disease management; insights from high-throughput virtual screening, molecular docking, DFT and MD simulation.

Cardiovascular disorders are the world's major cause of death nowadays. To treat cardiovascular diseases especially coronary artery diseases and hypertension, researchers found potential ROCK2 (Rho-associated coiled-coil-containing protein kinase 2) target due to its substantial role in NO-cGMP and RhoA/ROCK pathway. Available drugs for ROCK2 are less effective and some of them depict side effects. Therefore, a set of novel compounds were screened that can potentially inhibit the activity of ROCK2 and help to treat cardiovascular diseases by employing In-silico techniques. In this study, we undertook ligand based virtual screening of 50 million compound's library, to that purpose shape and features (contain functional groups) based pharmacophore query was modelled and validated by Area Under Curve graph (AUC). 2000 best hits were screened for Lipinski's rule of 5 compliance. Subsequently, these selected compounds were docked into the binding site of ROCK2 to gain insights into the interactions between hit compounds and the target protein. Based on binding affinity and RMSD scores, a final cohort of 15 compounds were chosen which were further refined by pharmacokinetics, ADMET and bioactivity scores. 2 potential hits were screened using density functional theory, revealing remarkable biological and chemical activity. Potential inhibitors (F847-0007 and 9543495) underwent rigorous examination through MD Simulations and MMGBSA analysis, elucidating their stability and strong binding affinities. Results of current study unveil the potential of identified novel hits as promising lead compounds for ROCK2 associated with cardiovascular diseases. These findings will further investigate via In-vitro and In-vivo studies to develop novel druglike molecules against ROCK2.

Identification, Genome Sequencing, and Characterizations of Helicobacter pylori Sourced from Pakistan.

The stomach's colonization by Helicobacter pylori (H. pylori) results in gastritis, ulcers, and stomach cancer. Frequently, pain is treated with medication, but resistant H. pylori infections are not. Therefore, it is important to find pharmacological targets and improved treatments for resistant H. pylori strains. The aim of the current study was sampling, identification, drug susceptibility testing following genome sequencing and comparative genome-wide analysis of selected H. pylori strains from Pakistan with three representative strains for virulence and drug-resistant characteristics. Based on culture, biochemistry, and molecular biology, 84 strains of H. pylori were identified, which made up 47% of the enrolled cases. Among all H. pylori strains, the highest resistance was reported for metronidazole with 82 H. pylori strains (98%), followed by clarithromycin with 62 resistant strains (74%). Among metronidazole-resistant strains, 38 strains (46%) were also resistant to clarithromycin, contributing 61% of clarithromycin resistant cases. Two strains, HPA1 and HPA2, isolated from 'gastritis' and 'gastric ulcer' patients, respectively, were further processed for WGS. The draft genome sequences of H. pylori strains HPA1 and HPA2 encode 1.66 Mbp and 1.67 Mbp genome size, 24 and 4 contiguous DNA sequences, and 1650 and 1625 coding sequences, respectively. Both the genomes showed greater than 90% similarity with the reference strain H. pylori ATCC 43504/PMSS1. The antibiotic-resistant genes were identified among all the strains with overall similarity above 95%, with minor differences in the sequence similarity. Using the virulent gene data obtained from the Virulence Factor Database, 75 to 85 virulent genes were identified in the five genome assemblies with various key genes such as cytolethal distending toxin (cdt), type IV secretion system, cag PAI, plasticity region, cell-motility- and flagellar-associated genes, neutrophil-activating protein (HP-NAP), T4SS effector cytotoxin-associated gene A (cagA), and urease-associated genes ureA and ureB, etc. Sequence similarity between the virulence factors found in this study and reference genes was at least 90%. In summary, the results of our study showed the relationship between clinical results and specific H. pylori strains' (HPA1 and HPA2) genetics such as antibiotic resistance and specific virulence factors. These findings provide valued understanding of the epidemiology of H. pylori-associated diseases. Moreover, identification and genomics analysis have provided insights into the epidemiology, genetic diversity, pathogenicity, and potential drug resistance genes of H. pylori strains, offering a foundation for developing more targeted and effective medical interventions, including anti-virulent medications.

Measuring Performance of Rural Mental Healthcare Services in Saudi Arabia.

Introduction: Recently, the Saudi government has proposed several initiatives to promote mental health, including the national program named Wazen. The objective of this study was to observe the performance of mental hospitals using a balanced scorecard through this federal program. Methods: Secondary quantitative analysis was implemented utilizing the Wazen report program data in 2022. The report adopted a balanced scorecard (BSC) concept. The study focused on 19 mental health facilities (Eraddah Hospitals) in the Ministry of Health (MOH) regions. The MOH's annual statistical report for 2018 and 2022 was reviewed to explore more about beds, staff, and the number of new mental disorder cases. Data were analyzed using Microsoft Excel 365 and the Statistical Package for Social Sciences (SPSS Version 25) software. Mental health hospitals were classified into three categories. Results: Most rural hospitals had lower performance in the yellow threshold value that might need improvement. The data shows that the mean of all hospital performance in some domains ranged, yielding 70% staff engagement and 77% continued educational activity, indicating unsatisfactory performance across public mental health services. The means score of access to care was 97.0% and 94.7%, marking the better mental health services provided. Between 2018 and 2022, there was a significant rise in the prevalence of mental disorders, as evidenced by the number of new patients and outpatients indicated by specific mental diseases, including conditions of psychological development (F80-F98). Discussion: The high quality of mental healthcare is manifested by therapeutic ethos with a high degree of interaction between professional careers and service users. The former is enhanced by highly educated, competent, compassionate, self-aware, and specialized healthcare professionals in mental health. When assessing mental healthcare services, we recommend considering providers' and professionals' conditions for successful implementation in alignment with patient experience.

Design and Assessment of a Novel In Silico Approach for Developing a Next-Generation Multi-Epitope Universal Vaccine Targeting Coronaviruses.

In the past two decades, there have been three coronavirus outbreaks that have caused significant economic and health crises. Biologists predict that more coronaviruses may emerge in the near future. Therefore, it is crucial to develop preventive vaccines that can effectively combat multiple coronaviruses. In this study, we employed computational approaches to analyze genetically related coronaviruses, including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its variants, focusing on the spike glycoprotein as a potential vaccine candidate. By predicting common epitopes, we identified the top epitopes and combined them to create a multi-epitope candidate vaccine. The overall quality of the candidate vaccine was validated through in silico analyses, confirming its antigenicity, immunogenicity, and stability. In silico docking and simulation studies suggested a stable interaction between the multi-epitope candidate vaccine and human toll-like receptor 2 (TLR2). In silico codon optimization and cloning were used to further explore the successful expression of the designed candidate vaccine in a prokaryotic expression system. Based on computational analysis, the designed candidate vaccine was found to be stable and non-allergenic in the human body. The efficiency of the multi-epitope vaccine in triggering effective cellular and humoral immune responses was assessed through immune stimulation, demonstrating that the designed candidate vaccine can elicit specific immune responses against multiple coronaviruses. Therefore, it holds promise as a potential candidate vaccine against existing and future coronaviruses.

Identification and genome-wide analysis provide insights into the genetic diversity and biotechnological potentials of novel cold-adapted Acinetobacter strain.

Extreme cold environments, such as polar regions or high-altitude mountains, are known for their challenging conditions including low temperatures, high salinity, and limited nutrient availability. Microbes that thrive in these environments have evolved specialized strategies to survive and function under such harsh conditions. The study aims to identify, sequence the genome, perform genome assembly, and conduct a comparative genome-wide analysis of Acinetobacter sp. strain P1, which was isolated from the Batura glacier regions of Pakistan. A basic local alignment search tool of NCBI using 16 s RNA gene sequence confirmed the strain Acinetobacter following phylogenetic analysis revealed that strain P1 clustered with Acinetobacter sp. strain AcBz01. The high-throughput Genome sequencing was done by the NovaSeq 6000 sequencing system following de novo genome assembly reported 23 contigs, a genome size of 3,732,502 bp containing approximately 3489 genes and 63 RNAs (60 tRNA, 3 rRNA). The comparative genome analysis revealed that Acinetobacter sp. strain P1 exhibited the highest homology with the Acinetobacter baumannii ATCC 17978 genome and encompassed 1668 indispensable genes, 1280 conserved genes 1821 specific genes suggesting high genomic plasticity and evolutionary diversity. The genes with functional assignments include exopolysaccharide phosphotransferase enzyme, cold-shock proteins, T6SS, membrane modifications, antibiotic resistance, and set of genes related to a wide range of metabolic characteristics such as exopolysaccharides were also present. Moreover, the structural prediction analysis of EPS proteins reveals that structural flexibility allows for conformational modifications during catalysis, which boosts or increases the catalytic effectiveness at lower temperatures. Overall, the identification of Acinetobacter, a cold-adapted bacterium, offers promising applications in bioremediation, enzyme production, food preservation, pharmaceutical development, and astrobiology. Further research and exploration of these microorganisms can unlock their full biotechnological potential and contribute to various industries and scientific endeavors.

Diverse Repertoire and Relationship of Exopolysaccharide Genes in Cold-Adapted Acinetobacter sp. CUI-P1 Revealed by Comparative Genome Analysis.

This study focused on the exploration of microbial communities inhabiting extreme cold environments, such as the Passu and Pisan glaciers of Pakistan, and their potential utilization in industrial applications. Among the 25 initially screened strains, five were found to be suitable candidates for exopolysaccharide (EPS) production, with strain CUI-P1 displaying the highest yield of 7230.5 mg/L compared to the other four strains. The purified EPS from CUI-P1 was tested for its ability to protect probiotic bacteria and E. coli expressing green fluorescence protein (HriGFP) against extreme cold temperatures, and it exhibited excellent cryoprotectant and emulsification activity, highlighting its potential use in the biotechnological industry. Furthermore, the genome of Acinetobacter sp., CUI-P1 comprised 199 contigs, with a genome size of 10,493,143bp and a G + C content of 42%, and showed 98.197% nucleotide identity to the type genome of Acinetobacter baumannii ATCC 17978. These findings offer promising avenues for the application of EPS as a cryoprotectant, an essential tool in modern biotechnology.

An Artificial Intelligence-Based Bio-Medical Stroke Prediction and Analytical System Using a Machine Learning Approach.

Stroke-related disabilities can have a major negative effect on the economic well-being of the person. When left untreated, a stroke can be fatal. According to the findings of this study, people who have had strokes generally have abnormal biosignals. Patients will be able to obtain prompt therapy in this manner if they are carefully monitored; their biosignals will be precisely assessed and real-time analysis will be performed. On the contrary, most stroke diagnosis and prediction systems rely on image analysis technologies such as CT or MRI, which are not only expensive but also hard to use. In this study, we develop a machine learning algorithm for the prediction of stroke in the brain, and this prediction is carried out from the real-time samples of electromyography (EMG) data. The study uses synthetic samples for training the support vector machine (SVM) classifier and then the testing is conducted in real-time samples. To improve the accuracy of prediction, the samples are generated using the data augmentation principle, which supports training with vast data. The simulation is conducted to test the efficacy of the model, and the results show that the proposed classifier achieves a higher rate of classification accuracy than the existing methods. Furthermore, it is seen that the rate of precision, recall, and f-measure is higher in the proposed SVM than in other methods.

An Efficient Signal Processing Algorithm for Detecting Abnormalities in EEG Signal Using CNN.

Electroencephalography (EEG) is crucial for epilepsy detection; however, detecting abnormalities takes experience and knowledge. The electroencephalogram (EEG) is a technology that measures brain motion and represents the brain's function. EEG is an effective instrument for deciphering the brain's complicated activity. The information contained in the EEG signal pertains to the electric functioning of the brain. Neurologists have typically used direct visual inspection to detect epileptogenic abnormalities. This method is time-consuming, restricted by technical artifacts, produces varying findings depending on the reader's level of experience, and is ineffective at detecting irregularities. As a result, developing automated algorithms for detecting anomalies in EEGs associated with epilepsy is critical. The construction of a novel class of convolutional neural networks (CNNs) for detecting aberrant waveforms and sensors in epilepsy EEGs is described in this research. In this study, EEG signals are analyzed using a convolutional neural network (CNN). For the automatic detection of abnormal and normal EEG indications, a novel deep one-dimensional convolutional neural network (1D CNN) model is suggested in this paper. The regular, pre-ictal, and seizure categories are detected using this approach. The proposed model achieves an accuracy of 85.48% and a reduced categorization error rate of 14.5%.

A retrospective analysis of substance use among female psychiatric patients in Saudi Arabia.

Objective: This study investigated the prevalence of substance use (SU), and its risk factors, among women attending psychiatric outpatients center in Saudi Arabia. Design: A retrospective cross-sectional design. Materials and methods: We reviewed outpatients' records of 200 female patients with a history of SU from a psychiatric unit in Jeddah, Saudi Arabia from December 2018 to February 2019. The researchers developed the pro forma, and 2 psychiatrists and a family medicine physician validated the form. Results: The most common and widely used were psychoactive substances (58%), followed by central nervous system (CNS) depressants (22%), and finally cannabinols (9.5%). Overall, the highest substance use was the amphetamine-cannabis-nicotine (ACN) representing nearly half of the illicit items (46.6%), followed by heroine-alcohol-benzodiazepine (16.4%), and with the lowest being benzodiazepine-nicotine (1.7%). There was a significant difference between the single substance and multiple substance use in terms of age (p = 0.001), smoking behavior (p = 0.001), patients past history (p = 0.005), and age of the patient at the start of drug use (p = 0.005). Conclusion: Although the prevalence of substance use among women is low in Saudi Arabia, screening of substance use disorders risks and building a rehabilitation program to control drug dependence are needed.