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)

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.

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.

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 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%.

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.

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.

Demographic Characteristics, Comorbidities, and Length of Stay of COVID-19 Patients Admitted Into Intensive Care Units in Saudi Arabia: A Nationwide Retrospective Study.

Background: This study aimed to describe the demographic characteristics and determine the risk factors associated with disease severity and length of hospital and intensive care unit (ICU) stay in a cohort of COVID-19 patients admitted into ICU in Saudi Arabia. Methods: This was a national, multi-center, retrospective cross-sectional study of all COVID-19 cases admitted into different ICUs in Saudi Arabia between March 2020 and September 202l. Demographic, clinical features, comorbidities, and length of stay (LOS) data were retrieved from the national Health Electronic Surveillance Network (HESN) and Taqassi databases at the Saudi Ministry of Health (MOH) for subsequent analyses. We used multiple linear regression models to determine risk factors associated with critical outcomes (including LOS in ICU) among COVID-19 cases. Results: A total of 12,436 COVID-19 patients were included in this study, with a mean age of 59.57 ± 18.30 years and 7,679 (62%) were <65 years old. COVID-19 was more common in males (N = 7,686, 61.9%) and Saudi nationals (N = 8,516, 68.5%). The clinical characteristic findings showed that 36.3% of patients required invasive ventilation whilst 65.4% received tracheostomies for ventilation, and 4% were on dialysis. Our analysis revealed that 2,978 (23.9%) patients had one comorbidity, 4,977 (47.4%) had two or more comorbidities, and diabetes (48.2%) was the most prevalent comorbidity, followed by hypertension (44.2%), and chronic cardiovascular disease (10.5%). Thirteen variables emerged as significant predictors of LOS in ICU using multiple linear regression analyses, with invasive ventilation as the strongest predictor of LOS in the ICU (beta = -0.68, p = 0.001) and hospital admission (beta = -0.65, p = 0.001). Conclusions: COVID-19 continues to affect millions of people around the world, with a mortality rate of about 2-3% of all infected patients. Our analysis revealed that comorbidities such as chronic kidney disease, cardiovascular disease, diabetes, and older age were significant risk factors associated with a poorer prognosis and longer duration of stay in hospitals and ICU.

Attitudes toward Receiving COVID-19 Booster Dose in the Middle East and North Africa (MENA) Region: A Cross-Sectional Study of 3041 Fully Vaccinated Participants.

COVID-19 vaccines are crucial to control the pandemic and avoid COVID-19 severe infections. The rapid evolution of COVID-19 variants such as B.1.1.529 is alarming, especially with the gradual decrease in serum antibody levels in vaccinated individuals. Middle Eastern countries were less likely to accept the initial doses of vaccines. This study was directed to determine COVID-19 vaccine booster acceptance and its associated factors in the general population in the MENA region to attain public herd immunity. We conducted an online survey in five countries (Egypt, Iraq, Palestine, Saudi Arabia, and Sudan) in November and December 2021. The questionnaire included self-reported information about the vaccine type, side effects, fear level, and several demographic factors. Kruskal−Wallis ANOVA was used to associate the fear level with the type of COVID-19 vaccine. Logistic regression was performed to confirm the results and reported as odds ratios (ORs) and 95% confidence intervals. The final analysis included 3041 fully vaccinated participants. Overall, 60.2% of the respondents reported willingness to receive the COVID-19 booster dose, while 20.4% were hesitant. Safety uncertainties and opinions that the booster dose is not necessary were the primary reasons for refusing the booster dose. The willingness to receive the booster dose was in a triangular relationship with the side effects of first and second doses and the fear (p < 0.0001). Females, individuals with normal body mass index, history of COVID-19 infection, and influenza-unvaccinated individuals were significantly associated with declining the booster dose. Higher fear levels were observed in females, rural citizens, and chronic and immunosuppressed patients. Our results suggest that vaccine hesitancy and fear in several highlighted groups continue to be challenges for healthcare providers, necessitating public health intervention, prioritizing the need for targeted awareness campaigns, and facilitating the spread of evidence-based scientific communication.

Impact of ANN in Revealing of Viral Peptides.

All organisms contain antimicrobial peptides (AMPs), which are a critical component of the innate immune system. These chemicals have the ability to suppress the growth of a variety of fungi, bacteria, and viruses. Because AMPs interact with structural components of the microbial cell membrane and have a wide range of cellular targets, bacteria are unlikely to be able to develop resistance to them in the short term. The underlying structure of AMPs is critical in determining the selectivity with which they target their respective targets. As far as we know, peptides have not been tested in a lab to see if they can fight bacteria, fungus, and viruses in real life. In this paper, we develop an artificial neural network (ANN) using a back propagation neural network (BPNN) that enables optimal classification of tendency of a peptide sequence that involves the activities of antifungal, antibacterial, or antiviral. The BPNN is trained on the datasets collected across different repositories and then the overfitting is avoided using particle swarm optimization (PSO) algorithm. Hence, at the time of testing, the BPNN clearly finds the predicted samples belonging to the same classes and this avoids the problem of finding the false positives. The simulation is conducted to test the efficacy of the model against various metrics that includes accuracy, precision, recall, and f1-measure. The effectiveness of the BPNN-PSO model in classifying instances at a faster rate than other techniques is demonstrated by its performance. The principle is straightforward, it is not difficult to programme, it converges more quickly, and it generally offers a superior solution.

Deep Neural Networks for Optimal Selection of Features Related to Flu.

In recent times, humans who have been exposed to influenza A viruses (IAV) may not become hostile. Despite the fact that KLRD1 has been discovered as an influenza susceptibility biomarker, it remains to be seen if pre-exposure host gene expression can predict flu symptoms. In this paper, we enable the examination of flu using deep neural networks from input human gene expression datasets with various subtype viruses. This study enables the utilization of these datasets to forecast the spread of flu and can provide the necessary steps to eradicate the flu. The simulation is conducted to test the efficiency of the model in predicting the spread against various input datasets. The results of the simulation show that the proposed method offers a better prediction ability of 2.98% more than other existing methods in finding the spread of flu.

Recurrent Neural Networks for Feature Extraction from Dengue Fever.

Dengue fever modelling in endemic locations is critical to reducing outbreaks and improving vector-borne illness control. Early projections of dengue are a crucial tool for disease control because of the unavailability of treatments and universal vaccination. Neural networks have made significant contributions to public health in a variety of ways. In this paper, we develop a deep learning modelling using random forest (RF) that helps extract the features of the dengue fever from the text datasets. The proposed modelling involves the data collection, preprocessing of the input texts, and feature extraction. The extracted features are studied to test how well the feature extraction using RF is effective on dengue datasets. The simulation result shows that the proposed method achieves higher degree of accuracy that offers an improvement of more than 12% than the existing methods in extracting the features from the input datasets than the other feature extraction methods. Further, the study reduces the errors associated with feature extraction that is 10% lesser than the other existing methods, and this shows the efficacy of the model.