A retrospective analysis of tuberculosis in livestock farmers in Lahore district, Pakistan.

INTRODUCTION: Mycobacterium tuberculosis is the main cause of tuberculosis in humans, accounting for numerous illnesses and thousands of fatalities globally. Data regarding the association of various risk factors and TB in livestock farmers in Pakistan is scarce. METHODOLOGY: A retrospective matched case-control study of TB cases was performed in Lahore, Pakistan to investigate the potential risk factors that lead to the development of TB in Pakistani livestock farmers. A total of 170 participants were included in the study. The case was matched with control based on neighborhood and the case-control ratio was kept 1:1. Data were statistically analyzed using R version 4.2.1. Conditional logistic regression was conducted to identify biologically and statistically plausible risk factors associated with the TB outcome among livestock farmers. RESULTS: In univariable analysis, 10 risk factors were identified (p < 0.05). Gender, age, being married, family type, living in a big family, BCG vaccination status, history of smoking, working at a cattle farm, co-housing with cattle at night, , consumption of raw milk. The multivariable model identified four risk factors i.e., consumption of raw milk (Odds Ratio [OR]: 7.7; 95% Confidence Interval [CI]: 1.95-30.68), living in big family (OR: 6.2; 95% CI: 1.25-30.82) and working at cattle farm (OR: 4.2; 95% CI: 1.08-16.56), while gender was found to be a protective factor with OR < 1 (OR: 0.06; 95% CI: 0.01-0.26). CONCLUSIONS: This study demonstrated that sociodemographic risk factors and exposure to infected cattle can influence the development of TB in farmers.

Deep feature fusion with computer vision driven fall detection approach for enhanced assisted living safety.

Assisted living facilities cater to the demands of the elderly population, providing assistance and support with day-to-day activities. Fall detection is fundamental to ensuring their well-being and safety. Falls are frequent among older persons and might cause severe injuries and complications. Incorporating computer vision techniques into assisted living environments is revolutionary for these issues. By leveraging cameras and complicated approaches, a computer vision (CV) system can monitor residents' movements continuously and identify any potential fall events in real time. CV, driven by deep learning (DL) techniques, allows continuous surveillance of people through cameras, investigating complicated visual information to detect potential fall risks or any instances of falls quickly. This system can learn from many visual data by leveraging DL, improving its capability to identify falls while minimalizing false alarms precisely. Incorporating CV and DL enhances the efficiency and reliability of fall detection and allows proactive intervention, considerably decreasing response times in emergencies. This study introduces a new Deep Feature Fusion with Computer Vision for Fall Detection and Classification (DFFCV-FDC) technique. The primary purpose of the DFFCV-FDC approach is to employ the CV concept for detecting fall events. Accordingly, the DFFCV-FDC approach uses the Gaussian filtering (GF) approach for noise eradication. Besides, a deep feature fusion process comprising MobileNet, DenseNet, and ResNet models is involved. To improve the performance of the DFFCV-FDC technique, improved pelican optimization algorithm (IPOA) based hyperparameter selection is performed. Finally, the detection of falls is identified using the denoising autoencoder (DAE) model. The performance analysis of the DFFCV-FDC methodology was examined on the benchmark fall database. A widespread comparative study reported the supremacy of the DFFCV-FDC approach with existing techniques.

Predominance of blaNDM- and blaIMP-Harboring Escherichia coli Belonging to Clonal Complexes 131 and 23 in a Major University Hospital.

Background and Objectives: Carbapenem resistance is a growing global challenge for healthcare, and, therefore, monitoring its prevalence and patterns is crucial for implementing targeted interventions to mitigate its impact on patient outcomes and public health. This study aimed to determine the prevalence of carbapenem resistance among Escherichia coli (E. coli) strains in the largest tertiary care hospital of the capital territory of Pakistan and to characterize the isolates for the presence of antimicrobial resistance genes. Additionally, the most prevalent sequence types were analyzed. Materials and Methods: A total of 15,467 clinical samples were collected from November 2020 to May 2022, underwent antimicrobial susceptibility testing, and were analyzed for antimicrobial resistance genes through conventional PCR and sequence typing using MLST. Results: In carbapenem-resistant E. coli (CR-EC), 74.19% of isolates harbored the blaNDM gene, with blaNDM-1 (66.96%), blaNDM-5 (12.17%), and blaNDM-7 (20.87%) variants detected. Additionally, blaIMP was found in 25.81% and blaOXA-48 in 35.48% of isolates. The presence of blaCTX-M15 and blaTEM was identified in 83.87% and 73.55% of CR-EC isolates, respectively, while armA and rmtB were detected in 40% and 65.16% of isolates, respectively. Colistin and tigecycline were the most effective drugs against CR-EC isolates, with both showing an MIC50 of 0.5 µg/mL. The MIC90 for colistin was 1 µg/mL, while for tigecycline, it was 2 µg/mL. MLST analysis revealed that the CR-EC isolates belonged to ST131 (24.52%), ST2279 (23.87%), ST3499 (16.13%), ST8051 (15.48%), ST8900 (9.68%), ST3329 (7.10%), ST88 (1.94%), and ST6293 (1.29%). The ST131 complex (70.97%) was the most prevalent, harboring 95.65% of the blaNDM gene, while the ST23 complex (18.06%) harbored 62.50% of the blaIMP gene. Conclusions: Implementing large-scale surveillance studies to monitor the spread of specific pathogens, along with active infection control policies, is crucial for the effective containment and prevention of future epidemics.

Pharmacies and use of antibiotics: a cross sectional study in 19 Arab countries.

BACKGROUND: The rise of antimicrobial resistance, which is partially attributed to the overuse and/or misuse of antibiotics in health care, is one of the world's largest public health challenges. The distribution of antibiotics in absence of a prescription in pharmacies is a significant contributor to the growing global public health crisis of antibiotic resistance. A pharmacist's clinical and lawful knowledge of antibiotic provide has an impact on the proper way to dispense medication. There are few novel studies assessing pharmacists comprehension and experience in prescribing antibiotics in low- and middle-income countries, including those in the Arabian region. OBJECTIVES: (I) assess pharmacy team members Knowledge about antibiotics as reported by individuals themselves and their behavior in dispensing antimicrobial without a prescription and (ii) find potential influences on this behavior. SETTING: Pharmacists were chosen from various regions in Algeria, Bahrain, Egypt, Iraq, Jordan, Kuwait, Lebanon, Libya, Morocco, Oman, Palestine, Qatar, Saudi Arabia, Somalia, Sudan, Syria, Tunisia, the United Arab Emirates, and Yemen, based on their convenience and ease of access. METHODS: A descriptive cross-sectional assessment among a random sample (n = 2833) of community pharmacists was conducted Utilizing a structured, validated, and questionnaire that underwent pilot testing, a comprehensive survey with four distinct sections covering biography, knowledge, practice, and attitude domains was employed. THE MAIN OUTCOME: Measures were knowledge, attitude, and practice toward dispensing antibiotics without prescription. RESULTS: Of the 3100 pharmacists reached, 2833 completed and return the questionnaires (response rate 91.3%). Most of the respondents were male (57.4%). Aged between 19 and 31 years old (76.2%). Most of them held a B.Sc. Degree (78.5%). Worked as staff pharmacists (73.2%). During the survey, it was discovered that there were gaps in their knowledge regarding antibiotic usage. A total of 45.7% of the respondents were unaware that antibiotics can be used as prophylaxis, while 33.3% did not recognize the consequences of making incorrect antibiotic choices. Regarding their practice patterns, 53.8% of the pharmacists admitted that they did not consistently adhere to guidelines when dispensing antibiotics. In terms of attitudes toward antibiotic usage, 36.8% disagreed with the guidelines of not supply antibiotics without a prescription, suggesting some variation in opinions among pharmacists on this matter. Additionally, a significant percentage (75%) believed that community pharmacists had qualifications to prescribe antibiotics for infections. CONCLUSION: The recent survey has shed light on the differences among pharmacists in regard to dispensing antibiotics without prescriptions and their understanding of resistance. The findings are concerning, indicating a deficient in of knowledge as regards the use of antibiotics. It is crucial to implement regulations and enhance education efforts to tackle the growing problem of resistance. Collaboration between healthcare professionals and awareness campaigns is essential in addressing this issue.

Exploring Neurocognitive and Emotional Outcomes of Long COVID: A Study Among Pakistani Patients.

Background and objective Coronavirus disease 2019 (COVID-19), primarily a respiratory illness, also significantly impacts neurocognitive and emotional health, particularly in its long-term manifestation known as long COVID. This study aimed to investigate the neurocognitive and emotional outcomes of long-term COVID-19 in Pakistani patients, to address the persisting symptoms and their effects on mental health and cognitive function. Methods A cross-sectional study involving 100 adult participants who had been COVID-19-free was conducted in Islamabad between March 2022 and March 2023. Participants were assessed using the Mini-Mental State Examination (MMSE), attention-deficit/hyperactivity disorder (ADHD) Self-Report Questionnaire, Satisfaction with Life Scale (SWLS), and Punishing Allah Reappraisal Scale. Data were analyzed using SPSS Statistics v26 (IBM Corp., Armonk, NY), employing chi-square tests, t-tests, and ANOVA. Results The study revealed significant correlations between COVID-19 symptoms and psychological variables. COVID-19 symptoms showed a negative correlation with MMSE scores (r = -0.04, p<0.01) and positive correlations with ADHD (r = 0.13, p<0.05), depression (r = 0.14, p<0.05), and anxiety (r = 0.25, p<0.05). Females reported higher levels of depression [mean: 1.21, standard deviation (SD): 0.83] and anxiety (mean: 1.33, SD: 0.86) compared to males. Conclusions Our findings highlight the extensive impact of long-term COVID-19 on neurocognitive and emotional health, with significant gender differences observed in emotional outcomes. These results emphasize the need for integrated mental health services in post-COVID-19 care plans, as well as gender-sensitive interventions.

Identification and characterization of human GDF15 knockouts.

Growth differentiation factor 15 (GDF15) is a secreted protein that regulates food intake, body weight and stress responses in pre-clinical models1. The physiological function of GDF15 in humans remains unclear. Pharmacologically, GDF15 agonism in humans causes nausea without accompanying weight loss2, and GDF15 antagonism is being tested in clinical trials to treat cachexia and anorexia. Human genetics point to a role for GDF15 in hyperemesis gravidarum, but the safety or impact of complete GDF15 loss, particularly during pregnancy, is unknown3-7. Here we show the absence of an overt phenotype in human GDF15 loss-of-function carriers, including stop gains, frameshifts and the fully inactivating missense variant C211G3. These individuals were identified from 75,018 whole-exome/genome-sequenced participants in the Pakistan Genomic Resource8,9 and recall-by-genotype studies with family-based recruitment of variant carrier probands. We describe 8 homozygous ('knockouts') and 227 heterozygous carriers of loss-of-function alleles, including C211G. GDF15 knockouts range in age from 31 to 75 years, are fertile, have multiple children and show no consistent overt phenotypes, including metabolic dysfunction. Our data support the hypothesis that GDF15 is not required for fertility, healthy pregnancy, foetal development or survival into adulthood. These observations support the safety of therapeutics that block GDF15.

Synthesis and in vitro evaluation of cross-linked tragacanthin nanofibers as implants for delivery of cisplatin to hepatocellular carcinoma.

There is growing interest in the use of electrospun polymeric nanofibers in drug delivery systems due to their remarkable surface-to-volume ratio, which enhances the processes of drug loading, specific cell binding and proliferation. The preferred polymers for drug delivery must be biocompatible and biodegradable. Gum tragacanth is one of the materials of choice for drug delivery. This work aimed at cross-linking the tragacanthin, the water-soluble fraction of gum tragacanth, with glutaraldehyde, synthesis of the cross-linked nanofibers and evaluating their properties to encapsulate and deliver a drug using caffeine as a model drug in the first place. The nanofibers were then loaded with cisplatin and evaluated against HepG2 cell line. The drug-loaded nanofibers (dia. 0.841 µm) were prepared by electrospinning using glutaraldehyde as the cross-linker and glycerol as a plasticizer and characterized by scanning electron microscopy, Fourier transform-infrared spectroscopy, electronic spectroscopy, 1HNMR, powder X-ray diffraction analysis, and thermogravimetric analysis. They released the encapsulated drugs in a sustained manner at pH 7.4 over 4.5 days (∼275 h with ∼80 % release) following Higuchi (cisplatin) and Hixon-Crowell (caffeine) kinetics. In a cytotoxicity assay against HepG2 cell line the cisplatin-loaded nanofibers exhibited enhanced activity compared to that with the standard cisplatin and in the caspase activity assay it activated caspase 3 to a higher extent and 8 and 9 to double the extent (4-fold) of cisplatin, suggesting a higher apoptotic activity by the nanoformulation than the standard cisplatin. Thus, nanoformulation appeared to be a potential candidate for treating hepatocellular carcinoma as an implant.

Prairie Dog Optimization Algorithm with deep learning assisted based Aerial Image Classification on UAV imagery.

This study presents a Prairie Dog Optimization Algorithm with a Deep learning-assisted Aerial Image Classification Approach (PDODL-AICA) on UAV images. The PDODL-AICA technique exploits the optimal DL model for classifying aerial images into numerous classes. In the presented PDODL-AICA technique, the feature extraction procedure is executed using the EfficientNetB7 model. Besides, the hyperparameter tuning of the EfficientNetB7 technique uses the PDO model. The PDODL-AICA technique uses a convolutional variational autoencoder (CVAE) model to detect and classify aerial images. The performance study of the PDODL-AICA model is implemented on a benchmark UAV image dataset. The experimental values inferred the authority of the PDODL-AICA approach over recent models in terms of dissimilar measures.

VacSol-ML(ESKAPE): Machine learning empowering vaccine antigen prediction for ESKAPE pathogens.

The ESKAPE family, comprising Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter spp., poses a significant global threat due to their heightened virulence and extensive antibiotic resistance. These pathogens contribute largely to the prevalence of nosocomial or hospital-acquired infections, resulting in high morbidity and mortality rates. To tackle this healthcare problem urgent measures are needed, including development of innovative vaccines and therapeutic strategies. Designing vaccines involves a complex and resource-intensive process of identifying protective antigens and potential vaccine candidates (PVCs) from pathogens. Reverse vaccinology (RV), an approach based on genomics, made this process more efficient by leveraging bioinformatics tools to identify potential vaccine candidates. In recent years, artificial intelligence and machine learning (ML) techniques has shown promise in enhancing the accuracy and efficiency of reverse vaccinology. This study introduces a supervised ML classification framework, to predict potential vaccine candidates specifically against ESKAPE pathogens. The model's training utilized biological and physicochemical properties from a dataset containing protective antigens and non-protective proteins of ESKAPE pathogens. Conventional autoencoders based strategy was employed for feature encoding and selection. During the training process, seven machine learning algorithms were trained and subjected to Stratified 5-fold Cross Validation. Random Forest and Logistic Regression exhibited best performance in various metrics including accuracy, precision, recall, WF1 score, and Area under the curve. An ensemble model was developed, to take collective strengths of both the algorithms. To assess efficacy of our final ensemble model, a high-quality benchmark dataset was employed. VacSol-ML(ESKAPE) demonstrated outstanding discrimination between protective vaccine candidates (PVCs) and non-protective antigens. VacSol-ML(ESKAPE), proves to be an invaluable tool in expediting vaccine development for these pathogens. Accessible to the public through both a web server and standalone version, it encourages collaborative research. The web-based and standalone tools are available at http://vacsolml.mgbio.tech/.

Aberrant HIF1- α and SIX-1 Expression is Associated with Poor Prognosis in Acute Myeloid Leukemia Patients with Isocitrate Dehydrogenase 1 Mutations.

BACKGROUND: IDH1 mutations are common in many cancers, however, their role in promoting the Warburg effect remains elusive. This study elucidates the putative involvement of mutant-IDH1 in regulating hypoxia-inducible factor (HIF1-α) and Sine-Oculis Homeobox-1 (SIX-1) expression. METHODOLOGY: Genetic screening was performed using the ARMS-PCR in acute myeloid leukemia (AML), brain, and breast cancer (BC) cohorts, while transcript expression was determined using qPCR. Further, a meta-analysis of risk factors associated with the R132 mutation was performed. RESULTS: Approximately 32% of AML and ∼60% of glioma cases were mutants, while no mutation was found in the BC cohort. 'AA' and TT' were associated with higher disease risk (OR = 12.18 & 4.68) in AML and had significantly upregulated IDH1 expression. Moreover, downregulated HIF1-α and upregulated SIX-1 expression was also observed in these patients, suggesting that mutant-IDH1 may alter glucose metabolism. Perturbed IDH1 and HIF-α levels exhibited poor prognosis in univariate and multivariate analysis, while age and gender were found to be contributory factors as well. Based on the ROC model, these had a good potential to be used as prognostic markers. A significant variation in frequencies of R132 mutations in AML among different populations was observed. Cytogenesis (R2 = 12.2%), NMP1 mutation status (R2 = 18.5%), and ethnic contributions (R2 = 73.21%) were critical moderators underlying these mutations. Women had a higher risk of R132 mutation (HR = 1.3, P < 0.04). The pooled prevalence was calculated to be 0.29 (95% CI 0.26-0.33, P < 0.01), indicating that IDH1 mutations are a significant prognostic factor in AML. CONCLUSION: IDH1 and HIF1-α profiles are linked to poor survival and prognosis, while high SIX-1 expression in IDH1 mutants suggests a role in leukemic transformation and therapy response in AML.

IDH1 mutations are common in many types of cancer, but scientists have not fully understood how they contribute to the Warburg effect - a process that alters glucose metabolism in cells. In this study, we evaluate the association between mutant-IDH1 and HIF1 as well as SIX-1 gene expression. We analyzed genetic data from patients with brain cancer, breast cancer, and acute myeloid leukemia (AML), and found that roughly 32% of AML cases and 60% of glioma cases had IDH1 mutations, while no mutations were found in breast cancer. Patients with mutant genotypes had a higher risk of disease and showed upregulated IDH1 expression. They also had downregulated HIF1 and upregulated SIX-1 expression, suggesting that mutant-IDH1 can change glucose metabolism in cancer cells. Patients with abnormal IDH1 and HIF1 levels were more likely to have a poor prognosis. Further, we identified several risk factors that can influence IDH1 mutations, including cytogenesis, NMP1 mutation status, and ethnicity. The researchers calculated that IDH1 mutations are a significant factor in predicting outcomes for AML.

Gingerol nanoparticles attenuate complete Freund adjuvant-induced arthritis in rats via targeting the RANKL/OPG signaling pathway.

Rheumatoid arthritis (RA) is characterized by inflammatory joint pathology leading to the degradation of articular bone and cartilage, primarily triggered by synovial inflammation, resulting in joint discomfort. The metacarpophalangeal and proximal interphalangeal joints are predominantly affected. Treatment typically involves a combination of biological and synthetic disease-modifying antirheumatic drugs (DAMARDs) alongside steroid therapy. The application of nanomedicine has been instrumental in enhancing treatment efficacy by facilitating controlled release of pharmacologically active compounds, thus augmenting bioavailability and enabling targeted drug delivery. Gingerol, a constituent of ginger, possesses multifaceted properties. including anti-inflammatory, anti-oxidant, antidiabetic, and antipyretic effects. In this study, gingerol-loaded poly(lactic-co-glycolic acid) (PLGA) nanoparticles (NPs), coated with chitosan, were administered orally to rats over a period of 21 days to address RA induced by complete Freund adjuvant (CFA). The rats were segregated into four experimental groups. Upon completion of the treatment regimen, blood samples were collected for the assessment of cyclooxygenase-2 (COX-2), RA factor, interleukin-6 (IL-6), and tumor necrosis factor-alpha (TNF-α). Subsequent gene expression analysis was conducted to evaluate the levels of interleukin-4 (IL-4), interleukin-17a (IL-17a), IL-6, interferon-gamma (INF-γ), TNF-α, interleukin-1 beta (IL-1ß), osteoprotegerin (OPG), and receptor activator of nuclear factor kappa-B ligand (RANKL). Statistical analyses utilizing one-way ANOVA followed by Tukey tests were applied to the data. The gene expression profiling revealed significant disparities in mRNA levels of IL-1ß, IL-6, IL-4, IL-17a, RANKL, INF-γ, and TNF-α between the CFA-induced arthritis group and the control group. Consequently, it was inferred that gingerol-loaded PLGA NPs coated with chitosan exhibited heightened therapeutic efficacy in addressing CFA-induced arthritis in rats.

The migraine epidemic among medical students: a call for action.

Migraine is characterized by recurrent headaches of moderate-to-severe intensity and poses a significant challenge for medical students. This is a narrative literature review using PubMed and Scopus databases. This study examines how common migraine is in this group and suggests working together to address how it affects students' well-being and chances of succeeding as a medical professional in the future. Early diagnosis by licensed medical specialists is essential for effective management of migraine. To address this, the authors propose a multifaceted strategy. By including direct education on migraines in medical school curricula, future doctors will be better prepared to treat patients with comparable problems and manage their own migraines. Students with migraines can also benefit greatly from creating a supportive learning environment through staff training, accommodating academic policies, and providing easily available healthcare resources. In addition, this technology may be helpful. Apps for relaxation and migraine tracking can help students better manage their condition. Long-term success requires cooperation among all parties. By promoting cooperation among medical schools, student associations, healthcare practitioners, and governmental organizations, the authors can raise public awareness of migraine, make pertinent resources easier to access, and create evidence-based solutions specially designed to meet the needs of medical students who experience migraine. In the end, putting student well-being first and working together to put these solutions into practice can enable aspiring doctors to succeed at both personal and professional levels.

Revolutionizing anemia detection: integrative machine learning models and advanced attention mechanisms.

This study addresses the critical issue of anemia detection using machine learning (ML) techniques. Although a widespread blood disorder with significant health implications, anemia often remains undetected. This necessitates timely and efficient diagnostic methods, as traditional approaches that rely on manual assessment are time-consuming and subjective. The present study explored the application of ML - particularly classification models, such as logistic regression, decision trees, random forest, support vector machines, Naïve Bayes, and k-nearest neighbors - in conjunction with innovative models incorporating attention modules and spatial attention to detect anemia. The proposed models demonstrated promising results, achieving high accuracy, precision, recall, and F1 scores for both textual and image datasets. In addition, an integrated approach that combines textual and image data was found to outperform the individual modalities. Specifically, the proposed AlexNet Multiple Spatial Attention model achieved an exceptional accuracy of 99.58%, emphasizing its potential to revolutionize automated anemia detection. The results of ablation studies confirm the significance of key components - including the blue-green-red, multiple, and spatial attentions - in enhancing model performance. Overall, this study presents a comprehensive and innovative framework for noninvasive anemia detection, contributing valuable insights to the field.

Natural products can be potential inhibitors of metalloproteinase II from Bacteroides fragilis to intervene colorectal cancer.

Bacteroides fragilis, a gram negative and obligate anaerobe bacterium, is a member of normal gut microbiota and facilitates many essential roles being performed in human body in normal circumstances specifically in Gastrointestinal or GI tract. Sometimes, due to genetics, epigenetics, and environmental factors, Bacteroides fragilis and their protein(s) start interacting with intestinal epithelium thus damaging the lining leading to colorectal cancers (CRC). To identify these protein(s), we incorporated a novel subtractive proteomics approach in the study. Metalloproteinase II (MPII), a Bacteroides fragilis toxin (bft), was investigated for its virulence and unique pathways to demonstrate its specificity and uniqueness in pathogenicity followed by molecular docking against a set of small drug-like natural molecules to discover potential inhibitors against the toxin. All these identified inhibitor-like molecules were analyzed for their ADMET calculations and detailed physiochemical properties to predict their druggability, GI absorption, blood brain barrier and skin permeation, and others. Resultantly, a total of ten compounds with the least binding energies were obtained and were subjected to protein-compound interaction analysis. Interaction analysis revealed the most common ligand-interacting residues in MPII are His 345, Glu 346, His 339, Gly 310, Tyr 341, Pro 340, Asp 187, Phe 309, Lys 307, Ile 185, Thr 308, and Pro 184. Therefore, top three compounds complexed with MPII having best binding energies were selected in order to analyze their trajectories. RMSD, RMSF, Rg and MMPBSA analysis revealed that all compounds showed good binding and keeping the complex stable and compact throughout the simulation time in addition to all properties and qualities of being a potential inhibitor against MPII.

Molecular Modification Strategies of Nitrilase for Its Potential Application in Agriculture.

Some feed source plants will produce secondary metabolites such as cyanogenic glycosides during metabolism, which will produce some poisonous nitrile compounds after hydrolysis and remain in plant tissues. The consumption of feed-source plants without proper treatment affect the health of the animals' bodies. Nitrilases can convert nitriles and have been used in industry as green biocatalysts. However, due to their bottleneck problems, their application in agriculture is still facing challenges. Acid-resistant nitrilase preparations, high-temperature resistance, antiprotease activity, strong activity, and strict reaction specificity urgently need to be developed. In this paper, the application potential of nitrilase in agriculture, especially in feed processing industry was explored, the source properties and catalytic mechanism of nitrilase were reviewed, and modification strategies for nitrilase application in agriculture were proposed to provide references for future research and application of nitrilase in agricultural and especially in the biological feed scene.

A Compact Linear Microstrip Patch Beamformer Antenna Array for Millimeter-Wave Future Communication.

5/6G is anticipated to address challenges such as low data speed and high latency in current cellular networks, particularly as the number of users overwhelms 4G and LTE capabilities. This paper proposes a microstrip patch antenna array comprising six radiating patches and utilizing a microstrip line feeding technique to facilitate the compact design crucial for 5G implementation. ROGER 3003, chosen for its advanced and environmentally friendly features, serves as the dielectric material, ensuring suitability for 5G and B5G applications. The designed antenna, evaluated at a resonating frequency of 28.8 GHz with a -10 dB impedance bandwidth of 1 GHz, offers a high gain of 9.19 dBi. Its compact array, cost-effectiveness, and broad impedance and radiation coverage position it as a viable candidate for 5G and future communication applications.

Heavy Metal Concentrations in Water, Sediment, and Fish Species in Chashma Barrage, Indus River: A Comprehensive Health Risk Assessment.

The increasing levels of heavy metals in aquatic environments, driven by human activities, pose a critical threat to ecosystems' overall health and sustainability. This study investigates the bioaccumulation of heavy metals (Pb, Cu, Cr, and Cd) in water, sediment, and three fish species (Catla catla, Labeo rohita, Cirrhinus mrigala) of different feeding zones within Chashma Barrage, located in the Mianwali district of Punjab, Pakistan, on the Indus River. A comprehensive analysis, including an assessment of associated human health risks, was conducted. Thirty samples from all three sites for each fish species, with an average body weight of 160 ± 32 g, were collected from Chashma Barrage. Water quality parameters indicated suitability for fish growth and health. Heavy metal concentrations were determined using an atomic absorption spectrometer. Results indicated elevated levels of Cd, Cr, and Cu in sediment and Pb and Cd in water, surpassing WHO standard limits. Among the fish species, bottom feeder (C. mrigala) exhibited significantly (P < 0.05) higher heavy metal levels in its tissues (gills, liver, and muscle) compared to column feeder (L. rohita) and surface feeder (C. catla). Liver tissues across all species showed higher heavy metal bioaccumulation, followed by gills. Principal component analysis (PCA) revealed strong correlations among heavy metals in sediment, gills, muscle, and water in every fish species. However, the vector direction suggests that Cr was not correlated with other heavy metals in the system, indicating a different source. The human health risk analysis revealed lower EDI, THQ, and HI values (< 1) for the fish species, indicating no adverse health effects for the exposed population. The study emphasizes the bioaccumulation differences among fish species, underscoring the higher heavy metal concentrations in bottom feeder fish within Chashma Barrage.

Burden and factors associated with occupational tuberculosis infection among high-risk workers in Lahore District, Pakistan.

INTRODUCTION: The main objective of the study was to estimate the burden of occupational tuberculosis infection in high-risk occupational workers and to identify risk factors associated with the prevalence of Mycobacterium tuberculosis complex (MTBC). METHODOLOGY: An analytical cross-sectional study was conducted among high-risk occupational workers including veterinarians, abattoir workers, animal handlers, livestock farmers, and microbiology laboratory workers. Sputum samples were collected from 100 participants and polymerase chain reaction (PCR) tests were done to diagnose tuberculosis (TB) infection. Data on potential risk factors was collected in a pre-designed questionnaire. The MTBC prevalence ratio was estimated. Logistic regression analysis was conducted to identify risk factors and the crude odds ratio (OR) was calculated. RESULTS: Among the 100 enrolled high risk occupational workers, the prevalence of MTBC was 46% (95% CI: 35.98-56.25). Living in a joint family (OR 3.85, 95% CI: 1.58-9.37), and use of unpasteurized milk (OR 3.42, 95% CI: 1.4-8.39), were significantly associated with MTBC infection. CONCLUSIONS: Tuberculosis is a significant health burden in high-risk occupational groups, especially animal handlers and laboratory workers, in Lahore, Pakistan. The study also emphasized the need for formal work-related training, and enhanced zoonotic TB awareness among occupational workers.