Spectrum of Research and Reviews
type: channel
description: Spectrum of Research and Reviews (SRR) is a double-blind peer-reviewed academic journal published by THE LUMINARY LEARNING INSTITUTE (SMC-PRIVATE) LIMITED. The journal provides a platform for researchers, academics, and professionals to publish high-quality research across multiple disciplines. It emphasizes rigorous peer review, originality, and scholarly excellence. The journal is published semiannually. Scope The journal covers a multidisciplinary range of topics, including but not limited to: Physical and Life Sciences Medicine and Allied Health Sciences Clinical and Medical Sciences Environmental Science and Sustainability Health and Medical Sciences Business, Management, and Economics Interdisciplinary and Applied Research
publisher: THE LUMINARY LEARNING INSTITUTE (SMC-PRIVATE) LIMITED
language: en
publication name: Spectrum of Research and Reviews
issn: 3104-3844
items: FEDERATED ANOMALY DETECTION FOR IDENTIFYING ZERO-DAY INTRUSIONS IN IOT NETWORKS
items: EXPLAINABLE DEEP LEARNING FOR REDUCING FALSE NEGATIVES IN EARLY CANCER IMAGING DETECTION
items: IDENTIFICATION OF GENETIC MARKERS ASSOCIATED WITH DISEASE RESISTANCE IN LIVESTOCK
items: FACTORS AFFECTING MILK YIELD AND QUALITY IN COMMERCIAL DAIRY FARMS
items: PREDICTING REFRACTORY CANCER PAIN USING MACHINE LEARNING INTEGRATION OF CLINICAL, IMAGING, GENOMIC, AND OPIOID-RESPONSE PROFILES
items: ETHICAL AND REGULATORY CHALLENGES OF AI DEPLOYMENT IN AUTONOMOUS WEAPONS SYSTEMS
items: INVESTIGATING THE IMPACT OF ARTIFICIAL INTELLIGENCE ON JOB AUTOMATION AND EMPLOYMENT IN THE GLOBAL WORKFORCE
items: VETERINARY EDUCATION IN THE AGE OF DIGITAL LEARNING: EXPLORING THE INTEGRATION OF VIRTUAL REALITY AND SIMULATION IN TRAINING
items: DEVELOPING AI-POWERED DIAGNOSTIC TOOLS FOR EARLY DETECTION OF PATHOGENS IN DAIRY CATTLE TO IMPROVE HERD HEALTH MANAGEMENT
items: ASSESSMENT OF THE LONG-TERM IMPACT OF CLIMATE CHANGE ON LIVESTOCK PRODUCTIVITY AND BREEDING SYSTEMS IN ASIA
items: PHARMACOLOGICAL CHEMISTRY OF ANTIVIRAL DRUGS: MECHANISMS OF ACTION AND CHALLENGES IN TREATING EMERGING VIRAL DISEASES
items: MECHANISTIC STUDY OF METAL ION HOMEOSTASIS AND ITS DISRUPTION IN NEURODEGENERATIVE DISEASES
items: UNDERSTANDING THE ROLE OF INDIGENOUS FALLOW MANAGEMENT SYSTEMS IN SOIL FERTILITY RESTORATION AND PEST CONTROL
items: IMPACT OF AIR POLLUTION ON RESPIRATORY FUNCTION IN URBAN SCHOOL-AGED CHILDREN
items: CLINICAL AND GENETIC PREDICTORS OF STROKE RECURRENCE AFTER ISCHEMIC EVENTS
items: ADVANCEMENTS IN PERSONALIZED MEDICINE: TAILORING TREATMENT FOR GENETIC DIVERSITY
items: EXPLORING MICROBIAL GENOMICS AND METABOLIC PATHWAYS FOR SUSTAINABLE BIOTECHNOLOGICAL INNOVATIONS
items: COGNITIVE BEHAVIORAL THERAPY: TRANSFORMING NEGATIVE THINKING AND PROMOTING MENTAL HEALTH
items: THE ROLE OF PHARMACOVIGILANCE IN ENSURING DRUG SAFETY
items: THE ROLE OF PERSONALIZED MEDICINE IN CARDIOVASCULAR DISEASE MANAGEMENT
items: ANALYZING THE GUT-BRAIN AXIS IN ALZHEIMER'S DISEASE: A MICROBIOME-CENTRIC APPROACH
items: QUANTIFYING THE CONTRIBUTION OF ORGANIC AMENDMENTS TO LONG-TERM SOIL CARBON SEQUESTRATION AND FERTILITY
items: COLOSTRUM MANAGEMENT EFFECTS ON PASSIVE IMMUNITY TRANSFER IN CALVES
items: Chemical Modulation of Host-Pathogen Interactions: Investigating the Role of Secondary Metabolites in Bacterial Virulence and Host Immunity
items: MACHINE LEARNING MODELS FOR PREDICTING SEPSIS ONSET IN ICU PATIENTS BASED ON EHR DATA
items: VETERINARY BIOMECHANICS AND INJURY PREVENTION IN RACEHORSES
items: MOLECULAR PATHOGENESIS OF ZOONOSES IN URBAN WILDLIFE
items: HYBRID AQUATIC ANIMAL DISEASE MANAGEMENT STRATEGIES
items: HYBRID MODELING OF ECOSYSTEM-LEVEL METABOLISM
items: STEM CELL BIOLOGY AND REGENERATIVE TRANSCRIPTOMICS

FEDERATED ANOMALY DETECTION FOR IDENTIFYING ZERO-DAY INTRUSIONS IN IOT NETWORKS
type: item
description: The rapid expansion of Internet of Things (IoT) networks has increased the attack surface for cyber threats, particularly zero-day intrusions that remain undetected by traditional signature-based security systems. This paper presents a federated learning and anomaly detection framework for identifying zero-day intrusion patterns in distributed IoT environments without requiring raw data to be centralized. The proposed approach enables multiple IoT clients to collaboratively train an intrusion detection model while preserving data privacy and reducing communication overhead. An anomaly detection layer is integrated with the federated model to identify previously unseen attack behaviours by learning deviations from normal network traffic patterns. Experimental results demonstrate that the proposed framework improves detection accuracy, F1-score, and area under the ROC curve compared with centralized baseline and standalone anomaly detection models. The model also maintains stable performance across heterogeneous IoT clients, showing its suitability for real-world distributed network environments. The findings suggest that federated learning combined with anomaly detection can provide a scalable, privacy-preserving, and adaptive solution for zero-day intrusion detection in IoT networks.
subject: Articles
  topic: section
subject: Federated Learning
  topic: keywords
subject: Zero-Day Intrusion Detection
  topic: keywords
subject: Iot Security
  topic: keywords
subject: Anomaly Detection
  topic: keywords
subject: Network Traffic Classification
  topic: keywords
creator: Hina Rauf
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
date: 2026-06-30
publication date: 2026-06-30
starting page: 65
ending page: 77

EXPLAINABLE DEEP LEARNING FOR REDUCING FALSE NEGATIVES IN EARLY CANCER IMAGING DETECTION
type: item
description: Early cancer detection remains a major clinical challenge because false-negative predictions can delay diagnosis, reduce treatment effectiveness, and increase patient risk. This study presents an explainable deep learning framework designed to reduce false negatives in medical imaging-based cancer detection while maintaining strong diagnostic reliability. The proposed model integrates convolutional feature extraction with attention-based interpretability to improve the identification of subtle malignant patterns that are often missed by conventional classifiers. Experimental evaluation was conducted using medical imaging data divided into training, validation, and testing subsets. The results demonstrated improved sensitivity, recall, and overall diagnostic performance compared with baseline deep learning models. In particular, the explainable model reduced false-negative cases by emphasizing clinically relevant image regions through Grad-CAM-based visual explanations. The model achieved high classification accuracy, stronger area under the ROC curve, and better calibration across multiple validation folds. The findings show that explainable deep learning can support early cancer screening by improving transparency, strengthening clinician trust, and reducing missed cancer detections. Overall, this research highlights the potential of interpretable artificial intelligence as a decision-support tool for safer, more reliable, and clinically useful cancer diagnosis from medical imaging data.
subject: Articles
  topic: section
subject: Early Cancer Detection
  topic: keywords
subject: Explainable Deep Learning
  topic: keywords
subject: Medical Imaging
  topic: keywords
subject: False Negatives
  topic: keywords
subject: Grad-CAM
  topic: keywords
creator: Zainab Qamar
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
date: 2026-06-30
publication date: 2026-06-30
starting page: 52
ending page: 64

IDENTIFICATION OF GENETIC MARKERS ASSOCIATED WITH DISEASE RESISTANCE IN LIVESTOCK
type: item
description: Food security is impacted by disease and it is vital to ensure genetic improvement in disease resistance is sustainable. This study was conducted to identify genetic markers for disease resistance in cattle (bovine tuberculosis) and pigs (porcine respiratory disease syndrome), by using a case Post quality control, 612,447 (cattle) and 62,103 (pigs) SNPs were analysed. Ensemble prediction was the most accurate, and significantly higher than GBLUP and Bayesian methods. A total of 46 genome wide significant SNPs were identified (27 in cattle, 19 in pigs), with the strongest association observed for rs80775688 mapping to a candidate gene involved in Toll like receptor signaling. Genomic heritabilities (on the liability scale) ranged between 0.381-0.453. Validation rate was 82%. Gene Ontology analysis identified over-represented immune pathways such as Toll Survival analysis also identified that high.
subject: Articles
  topic: section
subject: Genome Wide Association
  topic: keywords
subject: Disease Resistance
  topic: keywords
subject: Single Nucleotide Polymorphisms
  topic: keywords
subject: Genomic Selection
  topic: keywords
subject: Marker Assisted Selection
  topic: keywords
subject: Livestock Breeding
  topic: keywords
creator: Iqra Basharat
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
date: 2026-06-30
publication date: 2026-06-30
starting page: 19
ending page: 35

FACTORS AFFECTING MILK YIELD AND QUALITY IN COMMERCIAL DAIRY FARMS
type: item
description: The research question of this paper was to examine the multifactorial determinants of milk yield and quality in Holstein Friesian dairy cattle with special reference to the impacts of heat stress measured by the temperature-humidity index through a twelve-month study of three hundred lactating cows in three different agroecological areas. Longitudinal data was collected, comprising daily adjustment of milk yield, fortnightly analysis of milk composition (fat, protein, lactose, and somatic cell count), monthly analysis of blood biomarkers, and continuous climatic data. The findings showed that the milk yield decreased gradually from a high of 34.43 kilograms per day in thermoneutral conditions to a low of 19.07 kilograms per day in conditions of extreme heat stress, which is a 44.6 percent decrease, and parity occurred between three cows with the steepest rate of decrease. The percentages of milk fat and protein declined by 13.3 percent and 13.3 percent, respectively, during severe thermal loading, and the levels of lactose had diurnal variation, with afternoon milking being worst hit with a decline in the levels of over 7 percent under extreme conditions. The number of somatic cells increased more than 2 times in the summer months between 145,300 and 367,800 cells per milliliter with heat stress and high stocking density and exhibited synergistic effects on the health of the udder. Analysis of biomarkers showed that cortisol levels almost quintupled, starting at 12.34 to 56.78 nanograms per milliliter, and that the levels of heat shock protein seventy rose by five times, starting at 145.6 up to 789.2 picograms per milliliter as the temperature-humidity index went above eighty and under sixty-eight, respectively. The fatty acid analysis revealed that short-chain fatty acids such as butyric and caproic acid were decreased by 12 and 18 percent, respectively, and stearic acid was increased paradoxically by 10.24 percent during thermal stress, indicating a change in lipi…
subject: Articles
  topic: section
subject: Stress Heat
  topic: keywords
subject: Milk Production
  topic: keywords
subject: Temperature-Humidity Index
  topic: keywords
subject: Dairy
  topic: keywords
subject: Milk Composition
  topic: keywords
subject: SCC
  topic: keywords
subject: Fatty Acid Analysis
  topic: keywords
subject: Economic Losses
  topic: keywords
subject: Thermotolerance
  topic: keywords
subject: Holstein Friesian
  topic: keywords
creator: Rabail Shafique
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
date: 2026-06-30
publication date: 2026-06-30
starting page: 19
ending page: 35

PREDICTING REFRACTORY CANCER PAIN USING MACHINE LEARNING INTEGRATION OF CLINICAL, IMAGING, GENOMIC, AND OPIOID-RESPONSE PROFILES
type: item
description: The management of refractory cancer pain is a significant clinical problem, and there is a substantial biological, psychological, genomic and treatment-response heterogeneity that is not always addressed by traditional cancer pain management strategies. This study aims to delve into the potential of machine learning in predicting refractory cancer pain using multimodal clinical, imaging, genomic, opioid-response, and longitudinal patient-reported outcome data. Machine learning can incorporate structured EHRs, radiomic markers, pharmacogenomic markers, and temporal pain trajectories to help identify patients at a high risk for opioid resistance, breakthrough pain, and poor analgesic response. The proposed framework will focus on three aspects: multimodal learning, federated data integration and explainable artificial intelligence, as well as prospective clinical validation, to enhance predictive capabilities without compromising patient privacy and clinical interpretability. The results indicate that multimodal machine learning techniques could be better than traditional single-source evaluation methods in providing more accurate risk stratification and tailoring pain-management approaches. For clinical translation to be successful, however, these matters must be addressed: external validation, reporting of clinical results, reduction of bias, integration of the workflow, and the clinician-centered design of the decision support. Overall, the present study confirms the potential for the evolution of precision pain medicine approaches that shift cancer pain care from a reactive approach of symptom management to a proactive, personalized, and data-informed approach.
subject: Articles
  topic: section
subject: Machine-Learning
  topic: keywords
subject: Refractory Cancer
  topic: keywords
subject: Pain Multimodal
  topic: keywords
subject: AI Opioid-Response
  topic: keywords
subject: Prediction
  topic: keywords
subject: Precision Pain Medicine
  topic: keywords
creator: Areeba Siddiq
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
date: 2026-06-30
publication date: 2026-06-30
starting page: 1
ending page: 18

ETHICAL AND REGULATORY CHALLENGES OF AI DEPLOYMENT IN AUTONOMOUS WEAPONS SYSTEMS
type: item
description: This paper analyses the considerations of ethics and the regulation challenges, as applied to the application of artificial intelligence in autonomous weapons systems, which will be conducted using a mixed-method research methodology. To evaluate the accuracy of AI judgment, the probability of committing an ethical violation, the accumulation of risk, or the effectiveness of the human control in different cases of autonomous interactions, quantitative simulations were conducted. The findings demonstrate that AI systems are ethical in most instances but the more autonomous and the less stable environment the less ethical they become. It was observed that ethical risk was increasing over time, and the principles of proportionality and discrimination were regularly violated in the fully independent situations. Graphic presentations of experimental results reveal that there is a declining level of transparency and also the capacity of people to overrule is reducing, which poses some serious questions about accountability. Qualitative regulatory review indicates that existing international legal systems are weak in alleviating these factual risks, as they do not specify enforceable methods to use to enforce real-time compliance with ethics and post-deployment responsibility. The combination of the results highlights a growing gap between technological capability and ethical control, which reveals the urgent need to reinforce international controls, establish human surveillance as a mandatory requirement, and enforce ethical constraints with proven constraints in AI-controlled autonomous weapon systems.
subject: Articles
  topic: section
subject: Artificial Intelligence, Autonomous Weapons Systems, Ethical Governance, International Humanitarian Law, AI Regulation, Military AI
  topic: keywords
creator: Muneeba
creator: Zia Ur Rehman
rights: Copyright (c) 2025 Muneeba, Zia Ur Rehman (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-12-31
publication date: 2025-12-31
starting page: 80
ending page: 97

INVESTIGATING THE IMPACT OF ARTIFICIAL INTELLIGENCE ON JOB AUTOMATION AND EMPLOYMENT IN THE GLOBAL WORKFORCE
type: item
description: <span style="font-size: 10.0pt; mso-bidi-font-size: 11.0pt; line-height: 107%; font-family: 'Times New Roman',serif; mso-ascii-theme-font: major-bidi; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: major-bidi; mso-bidi-theme-font: major-bidi; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA; mso-bidi-font-weight: bold; mso-no-proof: yes;">The rapid diffusion of artificial intelligence has intensified global debate regarding its implications for employment, job displacement, and workforce transformation. This study investigates the multifaceted relationship between AI adoption and labour-market outcomes using a mixed-methods experimental approach that integrates quantitative econometric analysis with qualitative thematic assessment. Sectoral and regional data reveal that AI adoption is associated with substantial productivity growth and the emergence of new job categories, particularly in high-skill and technology-intensive sectors. At the same time, significant job displacement is observed in routine and low-skill occupations, confirming the presence of skill-biased technological change. The results further indicate widening wage differentials and increased income inequality in regions experiencing rapid AI diffusion. However, empirical evidence shows that reskilling initiatives and active labour-market policies play a crucial role in mitigating unemployment risks and facilitating workforce transitions. Graphical and tabular analyses consistently demonstrate that sectors investing in human capital adaptation experience more balanced employment outcomes. Overall, the findings suggest that artificial intelligence reshapes rather than eradicates employment, with its net impact contingent upon policy responses, education systems, and institutional capacity. The study contributes to ongoing discourse by providing empirical evidence that supports a nuanced understanding of AI as both a disruptive…
subject: Articles
  topic: section
subject: Artificial intelligence, employment dynamics, job displacement, workforce reskilling, labour market transformation, automation economics
  topic: keywords
creator: Saad Abdullah
creator: Rizwan Ullah
rights: Copyright (c) 2025 Saad Abdullah, Rizwan Ullah (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-12-31
publication date: 2025-12-31
starting page: 62
ending page: 79

VETERINARY EDUCATION IN THE AGE OF DIGITAL LEARNING: EXPLORING THE INTEGRATION OF VIRTUAL REALITY AND SIMULATION IN TRAINING
type: item
description: <span style="font-size: 10.0pt; mso-bidi-font-size: 11.0pt; line-height: 107%; font-family: 'Times New Roman',serif; mso-ascii-theme-font: major-bidi; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: major-bidi; mso-bidi-theme-font: major-bidi; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA; mso-bidi-font-weight: bold; mso-no-proof: yes;">The rapid advancement of digital technologies has reshaped health sciences education, with virtual reality (VR) and simulation emerging as powerful tools in veterinary anatomy training. This study employed an experimental mixed-methods design to evaluate the educational effectiveness of VR-enhanced anatomy instruction compared with conventional teaching approaches. Quantitative analyses revealed significant improvements in post-test knowledge scores, spatial visualization accuracy, and normalized learning gains among students exposed to immersive VR modules. Enhanced engagement levels and efficient task performance were also observed, indicating strong learner involvement and improved procedural understanding. Cognitive load measures remained within optimal ranges, suggesting that immersive learning supported comprehension without inducing excessive mental strain. Qualitative findings further corroborated these results, with students reporting increased motivation, improved conceptual clarity, and greater confidence in applying anatomical knowledge to clinical contexts. The integration of interactive three-dimensional models enabled repeated, risk-free exploration of complex anatomical structures, addressing ethical, logistical, and accessibility challenges associated with cadaver-based instruction. Overall, the results demonstrate that VR-based anatomy education offers a pedagogically robust, cost-effective, and ethically sound alternative to traditional methods. The study concludes that immersive technologies can play a central role in modernizing veteri…
subject: Articles
  topic: section
subject: Virtual reality, Veterinary anatomy education, Simulation-based learning, Extended reality, Spatial cognition, Digital pedagogy
  topic: keywords
creator: Sami Ullah
creator: Rabia Nasir
rights: Copyright (c) 2025 Sami Ullah, Rabia Nasir (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-12-31
publication date: 2025-12-31
starting page: 41
ending page: 61

DEVELOPING AI-POWERED DIAGNOSTIC TOOLS FOR EARLY DETECTION OF PATHOGENS IN DAIRY CATTLE TO IMPROVE HERD HEALTH MANAGEMENT
type: item
description: <span style="font-size: 10.0pt; mso-bidi-font-size: 11.0pt; line-height: 107%; font-family: 'Times New Roman',serif; mso-ascii-theme-font: major-bidi; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: major-bidi; mso-bidi-theme-font: major-bidi; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA; mso-bidi-font-weight: bold; mso-no-proof: yes;">The increasing adoption of digital technologies in the dairy sector has created new opportunities for precision health management through artificial intelligence–based analytics. This study evaluated an experimental, mixed-methods framework for early pathogen detection in dairy cattle using AI-driven analysis of multi-source data, including physiological measurements, behavioral indicators, production metrics, and environmental variables. Quantitative results demonstrated that AI-derived risk scores were strongly associated with early changes in body temperature, activity levels, feed intake, and milk yield, enabling the identification of disease risk prior to clinical manifestation. Visual and statistical analyses confirmed the consistency, robustness, and generalizability of predictive models across herds and conditions, while herd-level aggregation revealed clustering patterns critical for outbreak prevention. Qualitative validation by veterinary experts further supported the clinical relevance and practical utility of AI-based predictions. Overall, the findings indicate that AI-powered diagnostic systems significantly enhance early disease detection, support proactive interventions, and improve decision-making at both individual and herd levels. The integration of continuous monitoring and advanced analytics reduces diagnostic delays, lowers dependence on broad-spectrum antibiotics, and promotes sustainable dairy farming practices. This study highlights the potential of artificial intelligence to fundamentally reshape livestock health management by imp…
subject: Articles
  topic: section
subject: Artificial intelligence, precision livestock farming, dairy cattle health, early pathogen detection, machine learning diagnostics, herd health management
  topic: keywords
creator: Muhammad Ali Khan
creator: Fatima Noor
rights: Copyright (c) 2025 Muhammad Ali Khan, Fatima Noor (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-12-31
publication date: 2025-12-31
starting page: 20
ending page: 40

ASSESSMENT OF THE LONG-TERM IMPACT OF CLIMATE CHANGE ON LIVESTOCK PRODUCTIVITY AND BREEDING SYSTEMS IN ASIA
type: item
description: Climate change poses a growing threat to global livestock systems by intensifying thermal stress and disrupting physiological, productive, and reproductive functions in cattle. This study experimentally investigated the effects of temperature and humidity variability on cattle productivity and breeding performance across multiple Asian agro-climatic zones using a mixed-method research design. Quantitative field experiments assessed environmental conditions, Temperature–Humidity Index (THI), physiological responses, and productivity indicators, while qualitative analyses captured farmer and expert perspectives on adaptive capacity. The results demonstrate a strong negative relationship between increasing THI and milk yield, accompanied by significant increases in respiration rate and body temperature, confirming the severity of climate-induced thermal stress. Indigenous cattle consistently exhibited greater tolerance to heat stress compared with crossbred animals, maintaining relatively stable productivity under high THI conditions. Seasonal analyses revealed that humidity amplifies thermal stress effects, accelerating productivity losses during hotter periods. Evaluation of adaptation interventions showed that improved housing, nutritional adjustments, and climate-resilient breeding strategies significantly reduced stress impacts and improved performance. The study highlights the importance of integrating genetic resilience, management innovations, and precision monitoring to enhance livestock sustainability. These findings provide critical evidence to inform climate-smart livestock policies and breeding programs aimed at safeguarding productivity, animal welfare, and food security under future climate change scenarios.
subject: Articles
  topic: section
subject: Climate change
  topic: keywords
subject: cattle productivity
  topic: keywords
subject: thermal stress
  topic: keywords
subject: Temperature–Humidity Index
  topic: keywords
subject: climate-resilient breeding
  topic: keywords
subject: livestock adaptation
  topic: keywords
creator: Sajjad Mehdi
creator: Shazia Khalid
rights: Copyright (c) 2025 Sajjad Mehdi, Shazia Khalid (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-12-31
publication date: 2025-12-31
starting page: 1
ending page: 19

PHARMACOLOGICAL CHEMISTRY OF ANTIVIRAL DRUGS: MECHANISMS OF ACTION AND CHALLENGES IN TREATING EMERGING VIRAL DISEASES
type: item
description: Viral diseases transgress the engraved phenomenon of modern day disease a pandemic, that calls for the advent of proper antiviral therapeutics; for a medicine or state of medicine that functions by targeting some parts or components of the virus, inhibiting its replication, and modulating immune responses of the host. They have serious drawbacks such as drug resistant ones, alongside being toxic and mutation induction by virus; all of which can challenge the use of antiviral agents in the long term. This study investigates the science related to the pharmacological chemistry of antiviral drugs, their mechanisms in action, and hurdles in treating emerging viral diseases.By utilizing in vitro and in vivo models, we will demonstrate with antiviral agents - polymerase inhibitors (remdesivir), protease inhibitors (lopinavir/ritonavir), and fusion inhibitors (enfuvirtide) - the results show that these agents can highly reduce viral replication; however, their effect is diminished due to mutations on viral proteins. Also, looking on the new approaches, CRISPR-based antivirals and RNA interference therapies, could be investigated to counteract resistance. This pens a big avenue in chemical-modification field for improvement of drug stability as well as reduced toxicity. One major outcome is increased efficacy for therapy via association of drugs by minimal resistance mechanisms. Future thrusts should be directed toward structure-based drug design and host-targeting therapies to improve effectiveness of antivirals. The study indicates how much need there is for novel antiviral drug development for emerging and re-emerging threats emanating from viral organisms.
subject: Articles
  topic: section
subject: Antiviral Drugs
  topic: keywords
subject: Pharmacological Chemistry
  topic: keywords
subject: Emerging Viruses
  topic: keywords
subject: Drug Resistance
  topic: keywords
creator: Saad Abdullah
creator: Rizwan Ullah
creator: Muhammad Fahid Ramzan
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0
date: 2024-12-31
publication date: 2024-12-31
starting page: 58
ending page: 67

MECHANISTIC STUDY OF METAL ION HOMEOSTASIS AND ITS DISRUPTION IN NEURODEGENERATIVE DISEASES
type: item
description: Disruption in metal ion homeostasis has emerged as a critical factor in the onset and progression of neurodegenerative diseases such as Alzheimer’s disease (AD), Parkinson’s disease (PD), and Huntington’s disease (HD). This study presents a comprehensive mechanistic investigation into the role of dysregulated iron (Fe), copper (Cu), zinc (Zn), and manganese (Mn) in neuronal degeneration. Using post-mortem brain tissues from affected and control individuals, we quantified region-specific metal ion concentrations and observed significant iron and manganese accumulation in the substantia nigra and hippocampus of diseased brains. Gene expression profiling revealed that DMT1 and ferroportin were significantly upregulated in AD and PD, whereas ATP7A and ATP7B were downregulated, indicating impaired copper trafficking. Oxidative stress markers—including reactive oxygen species (ROS), malondialdehyde (MDA), and 4-hydroxynonenal (4-HNE)—were markedly elevated in all disease groups, with ROS levels nearly doubling in AD compared to controls. Immunohistochemical analysis confirmed the overexpression of metal transporters in disease tissues. Furthermore, binding assays demonstrated that neurotoxic protein aggregates such as amyloid-β, tau, and α-synuclein exhibited strong affinity for Fe and Zn, suggesting a role in catalyzing oxidative damage and protein misfolding. Computational pathway modeling further highlighted the disruption of key metal-regulatory networks. Collectively, these findings establish a causal link between altered metal ion regulation, oxidative injury, and protein aggregation in neurodegeneration. The study not only elucidates the molecular underpinnings of metal-induced neuronal toxicity but also identifies potential targets for therapeutic intervention, including metal chelators and transporter regulators. These insights underscore the urgent need to develop disease-modifying strategies that restore metal ion balance in the brain.
subject: Articles
  topic: section
subject: Metal Ion Homeostasis
  topic: keywords
subject: Neurodegenerative Diseases
  topic: keywords
subject: Oxidative Stress
  topic: keywords
subject: Amyloid Aggregation
  topic: keywords
subject: Metal Transporters
  topic: keywords
subject: Neuronal Toxicity
  topic: keywords
creator: Zia Ur Rehman
creator: Muhammad Qasim
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0
date: 2024-12-31
publication date: 2024-12-31
starting page: 42
ending page: 57

UNDERSTANDING THE ROLE OF INDIGENOUS FALLOW MANAGEMENT SYSTEMS IN SOIL FERTILITY RESTORATION AND PEST CONTROL
type: item
description: This research investigates the ecological functionality and sustainability potential of indigenous fallow management systems through a combination of field experiments and participatory farmer interviews. The findings reveal that fallow practices—especially those involving legumes, shrubs, and mixed vegetation—significantly improve soil organic matter, nitrogen content, microbial biomass, and biodiversity. Table-based results highlight that shrub-integrated systems yielded the highest nutrient availability, while legume-based systems showed superior pest suppression. Figure analyses further underscore the ecological benefits of diversified fallow vegetation in supporting predator populations and stabilizing soil ecosystems. Natural pest control was shown to be highly effective through life cycle disruption and the enhancement of predator habitats, minimizing dependence on synthetic agrochemicals. The study also emphasizes the critical role of indigenous knowledge in tailoring fallow management to local environmental and cultural contexts, thus reinforcing its relevance for agroecological transitions. By demonstrating the multifunctional benefits of fallow systems—ranging from soil fertility restoration to biodiversity enhancement—this research supports the integration of indigenous practices into modern regenerative agricultural strategies. It concludes that the revival and formal inclusion of indigenous fallow systems in agricultural planning can significantly advance the goals of sustainability, resilience, and food security in ecologically challenged farming regions.
subject: Articles
  topic: section
subject: Indigenous Fallow Systems
  topic: keywords
subject: Soil Fertility
  topic: keywords
subject: Pest Control
  topic: keywords
subject: Agroecology
  topic: keywords
subject: Biodiversity
  topic: keywords
subject: Regenerative Agriculture
  topic: keywords
creator: Muneeba
creator: Muhammad Suleman Aziz
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0
date: 2024-12-31
publication date: 2024-12-31
starting page: 28
ending page: 41

IMPACT OF AIR POLLUTION ON RESPIRATORY FUNCTION IN URBAN SCHOOL-AGED CHILDREN
type: item
description: Chronic exposure to urban air pollution poses a significant risk to pediatric respiratory health, yet microenvironmental exposure–response relationships remain underexplored. In this cross‐sectional study of 200 schoolchildren (mean age 9.1 ± 1.8 years; 51% male), we quantified ambient PM₂.₅, NO₂, and O₃ at five high‐traffic urban schools over four weeks and assessed lung function via spirometric FEV₁ and FVC z-scores. Mean pollutant concentrations varied markedly by site—PM₂.₅ ranged from 28 to 50 µg/m³, NO₂ from 35 to 55 ppb, and O₃ from 25 to 32 ppb. Multivariable linear regression, adjusted for age, sex, BMI, and passive smoking, revealed that each 1 µg/m³ increase in PM₂.₅ was associated with a 0.012 decrease in FEV₁ z-score (95% CI: –0.015 to –0.009; p < 0.001), while NO₂ and O₃ increases of 1 ppb corresponded to FEV₁ z-score reductions of 0.010 (95% CI: –0.013 to –0.007; p < 0.001) and 0.008 (95% CI: –0.011 to –0.005; p = 0.002), respectively. Similar inverse associations were observed for FVC. Sensitivity analyses across alternative exposure windows and model specifications confirmed the robustness of these findings. These results underscore the vulnerability of developing lungs to pollutant exposures in school environments and highlight the need for targeted mitigation—such as traffic management, green buffers, and stricter emission controls—around schools. Routine lung‐function screening and educational interventions may further protect at-risk children. Future longitudinal studies should evaluate long-term respiratory trajectories and intervention efficacy to inform policy and safeguard urban pediatric populations.
subject: Articles
  topic: section
subject: Air Pollution
  topic: keywords
subject: Respiratory Function
  topic: keywords
subject: School-Aged Children
  topic: keywords
subject: PM₂.₅
  topic: keywords
subject: NO₂
  topic: keywords
subject: O₃
  topic: keywords
creator: Sami Ullah
creator: Rabia Nasir
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0
date: 2024-12-31
publication date: 2024-12-31
starting page: 15
ending page: 27

CLINICAL AND GENETIC PREDICTORS OF STROKE RECURRENCE AFTER ISCHEMIC EVENTS
type: item
description: Stroke recurrence remains a leading cause of disability and death among ischemic stroke survivors. In a prospective cohort of 500 adults (aged 45–85 years) with first-ever ischemic stroke, we evaluated the combined utility of clinical risk factors and a genome-wide polygenic risk score (PRS) for predicting recurrent events over 12 months. Baseline prevalence of hypertension (68%), diabetes (24%), dyslipidemia (57%), and atrial fibrillation (18%) was consistent with high-risk profiles. In univariable Cox analyses, hypertension (HR 1.80, 95% CI 1.50–2.15), atrial fibrillation (HR 2.10, 95% CI 1.65–2.65), diabetes (HR 1.45, 95% CI 1.10–1.90), and each SD increase in PRS (HR 1.25, 95% CI 1.10–1.42) were all significant predictors (p < 0.01). Patients in the highest PRS tertile experienced a 25.1% one-year recurrence rate versus 9.0% in the lowest tertile (HR 2.90, 95% CI 1.70–4.95; p < 0.001). A combined clinical-genetic Cox model achieved a C-index of 0.81 and demonstrated superior calibration compared with clinical-only (C-index 0.72) or genetic-only (C-index 0.68) models. Random forest analyses corroborated the primacy of atrial fibrillation and hypertension but also ranked PRS, age, and diabetes among the top five importance metrics. Semi-structured interviews (n = 40) identified medication adherence barriers and lifestyle modification challenges as prevalent patient-reported obstacles to effective secondary prevention. Our findings support an integrative risk-stratification paradigm that merges genomic profiling with traditional clinical assessment to identify high-risk survivors for targeted interventions—such as intensified blood pressure control, novel antithrombotic therapy, and adherence support—to reduce recurrent stroke burden.
subject: Articles
  topic: section
subject: Stroke Recurrence
  topic: keywords
subject: Polygenic Risk Score
  topic: keywords
subject: Hypertension
  topic: keywords
subject: Predictive Modeling
  topic: keywords
subject: Cohort Study
  topic: keywords
subject: Precision Prevention
  topic: keywords
creator: Sajjad Mehdi
creator: Shazia Khalid
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0
date: 2024-12-31
publication date: 2024-12-31
starting page: 1
ending page: 14

ADVANCEMENTS IN PERSONALIZED MEDICINE: TAILORING TREATMENT FOR GENETIC DIVERSITY
type: item
description: Individualized medicine has become an innovative trend in the health sector, and it provides custom treatment plans depending on individuals genetic, environmental, and lifestyle variations. With the developing pace of improvements in the field of genomics and molecular diagnostics, molecular data regarding individual patients are changing the process of clinical decision-making and approaches to achieving optimal therapeutic effects. In this study, the use of genomic technologies, including next-generation sequencing (NGS), CRISPR-Cas9 gene therapy, and high-throughput genomic screening in the process of developing precision treatment approaches to complex diseases is discussed.Evidence based on genetic variants, pharmacogenomic information and actual clinical application were reviewed systematically to assess the effect of genetic-diversity on susceptibility to disease and reactions to various drugs. The research synthesizes the evidence in precision oncology, cardiovascular pharmacogenomics, management of metabolic disorders, which allows defining actionable genetic markers and demonstrating their practicality in therapy stratification.Based on such mutations as EGFR, BRCA1, and CYP2C19, the outcomes show a high rate of variability in treatment response. Interventions matched to genotype were more effective and associated with fewer adverse drug reactions generally, and when both clinical decisions and pharmacogenomic profiles were used in decision. The adoption of individualized genomic technologies has grown immensely during the last 10 years, which affirms their clinical importance. Moreover, predictive analytics based on AI showed great promise in increasing the accuracy of diagnosis and designing optimal treatment plans.To sum up, personalized medicine is the shift in a paradigm toward more preventive, predictive, and participatory medicine. Although its application is hampered by its ethicalities, integrity of data, and healthcare inequity problems, the adv…
subject: Articles
  topic: section
subject: Personalized Medicine
  topic: keywords
subject: Genetic Diversity
  topic: keywords
subject: Genomic Data
  topic: keywords
subject: Pharmacogenomics
  topic: keywords
creator: Usama Raza
creator: Uzair Nisar
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0
date: 2024-06-30
publication date: 2024-06-30
starting page: 70
ending page: 78

EXPLORING MICROBIAL GENOMICS AND METABOLIC PATHWAYS FOR SUSTAINABLE BIOTECHNOLOGICAL INNOVATIONS
type: item
description: The rapid expansion of microbial biotechnology has transformed approaches to environmental sustainability, industrial processing, and bioremediation. Central to this transformation is microbial genomics, which enables the comprehensive analysis of microbial DNA to uncover functional traits, metabolic pathways, and pollutant-degrading capabilities. In this study, an integrated framework leveraging genomic sequencing, metabolic pathway engineering, and multi-omics analysis was implemented to explore the biotechnological applications of microbial systems.Our methodology involved sequencing microbial genomes, annotating functional genes, and applying CRISPR and synthetic biology techniques to engineer improved strains. We also utilized transcriptomic, proteomic, and metabolomic datasets to assess metabolic activity and optimize processes such as biofuel production, pollutant degradation, and microbial fuel cell efficiency. A series of statistical evaluations and visualizations were conducted to benchmark the engineered strains against conventional systems in terms of metabolic yield, stress response, and pollutant removal efficacy.The results demonstrated a significant increase in biofuel yield (up to 42%) in modified strains and enhanced pollutant degradation efficiency by over 60% in controlled environments. Omics-driven optimization showed consistent metabolic stability across diverse conditions, with protein expression profiles supporting enhanced enzyme activity and metabolite turnover. These findings affirm the value of genome-informed microbial design in advancing sustainable technologies.In conclusion, this study highlights the transformative potential of microbial genomics and multi-omics integration in reshaping industrial and environmental biotechnology. The engineered microbial systems not only exhibit high efficiency and adaptability but also offer scalable and eco-friendly alternatives for energy production, waste treatment, and ecological restoration. As …
subject: Articles
  topic: section
subject: Microbial Genomics
  topic: keywords
subject: Metabolic Pathways
  topic: keywords
subject: Biotechnological Innovations
  topic: keywords
subject: Sustainability
  topic: keywords
creator: Abdul Waheed Shah
creator: Ezza Fatima
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0
date: 2024-06-30
publication date: 2024-06-30
starting page: 51
ending page: 69

COGNITIVE BEHAVIORAL THERAPY: TRANSFORMING NEGATIVE THINKING AND PROMOTING MENTAL HEALTH
type: item
description: One of the most common psychotherapeutic approaches is the Cognitive Behavioral Therapy (CBT), which has been proven to treat different psychological disorders due to its focus on the relationship that exists between mind, feelings, and actions. Based on the cognitive models constructed by Beck and Ellis, the idea behind CBT is to help patients take control of their unconstructive thoughts and substitute them with balanced, objective ideas.This paper examines the underlying theoretical concepts, underpinning principles of CBT and the emerging practices such as cognitive restructuring, behavioural activation, exposure treatment and mindfulness-based cognitive treatment (MBCT). In the methodology, it will be used both traditional and digital methods of conducting the CBT and more emphasis will be placed on following the structural methods of interventions, goal-setting strategies and techniques of building skill to tackle anxiety, depression, phobias, and any other kind of disorder at different age levels.The results with CBT show a high decrease in anxiety and depression measures and increase in engagement and adherence via digital systems as well as positive functional outcomes were observed in clinical populations. It is of note that the involvement of patients in the CBT was linked to elevated resiliency, enhanced emotional control, and reductions in recurrent depressive setting relapse. Moreover, the flexibility of CBT in children, adolescents, and older adults was also exhibited, and, in each of the groupings, progress in cognitive restructuring and behavioral results was measurable.To summarize, CBT is one of the pillars of evidence-based psychological therapy as it can be considered profound and broadly applicable to the treatment of emotional disorders. In spite of the problems of lack of efficacy in the treatment of complex or chronic psychopathologies, persistent innovations, especially through digital CBT and the inclusion of mindfulness, have extended its…
subject: Articles
  topic: section
subject: Cognitive Behavioral Therapy
  topic: keywords
subject: Negative Thinking
  topic: keywords
subject: Maladaptive Behaviors
  topic: keywords
subject: Anxiety
  topic: keywords
subject: Depression
  topic: keywords
subject: CBT Techniques
  topic: keywords
subject: Thought Patterns
  topic: keywords
subject: Psychological Disorders
  topic: keywords
subject: Mindfulness-Based CBT
  topic: keywords
subject: Digital Therapy
  topic: keywords
creator: shaz
creator: Muska Hayat
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0
date: 2024-06-30
publication date: 2024-06-30
starting page: 34
ending page: 50

THE ROLE OF PHARMACOVIGILANCE IN ENSURING DRUG SAFETY
type: item
description: Pharmacovigilance has become an essential component of the policy on the state of the population since the increasing pharmaceutical complexity and the development of extensive pharmaceutical use by people. Because adverse drug reactions (ADRs) remain one of the main causes of morbidity and a healthcare burden in most countries, effective post-marketing surveillance provisions are necessary to identify the occurrence of a variety of risks that may accompany the use of medicinal products.The study has a multi-pronged methodology through the spontaneous reporting part, cohort follow-up and case control assessments, which were complemented with the help of advanced statistics and artificial intelligence-backed algorithms. An organized framework to recognize the trends, demographic correlations and measure ADR severity both using traditional approaches and real-world data mix was created.Findings demonstrated that there was a significant growth in the number of ADR reports with time and that the rates of incidences varied between different classes of drugs like antibiotics and antidepressants. The disparity occurred in terms of age and gender and the results varied between mild and severe and a disproportionate risk of certain populations. The AI-assisted systems proved to be more effective than traditional ones in the process of creating a signal or a predictive model in the early discovery of the potential safety issues and allowed finding them quicker. The change of reporting behaviors also differed considerably within the healthcare professionals and patients which denotes using more diverse and accessible pharmacovigilance platforms.To summarize, the research emphasizes the current change of pharmacovigilance role in the contemporary healthcare environment and the necessity to provide international unification of safety requirements. The advancements in technologies provide the unprecedented opportunities to improve ADR monitoring, and the systemic issues like unde…
subject: Articles
  topic: section
subject: Pharmacovigilance
  topic: keywords
subject: Drug Safety
  topic: keywords
subject: Adverse Drug Reactions (Adrs)
  topic: keywords
subject: Post-Marketing Surveillance
  topic: keywords
subject: Regulatory Oversight
  topic: keywords
subject: Big Data
  topic: keywords
subject: AI In Pharmacovigilance
  topic: keywords
subject: Drug-Related Problems
  topic: keywords
creator: Saad Abdullah
creator: Sumaiya Sundus
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0
date: 2024-06-30
publication date: 2024-06-30
starting page: 19
ending page: 33

THE ROLE OF PERSONALIZED MEDICINE IN CARDIOVASCULAR DISEASE MANAGEMENT
type: item
description: Cardiovascular diseases (CVDs) remain the leading cause of mortality globally, imposing substantial health and economic burdens. Traditional treatment approaches often fail to account for individual variability in genetics, environment, and lifestyle, leading to suboptimal outcomes and adverse drug reactions. Personalized medicine, also referred to as precision medicine, offers a transformative alternative by tailoring treatment strategies to the unique biological profile of each patient.This study employs a multidisciplinary methodology integrating genetic testing, pharmacogenomics, and biomarker profiling to assess the impact of personalized interventions in cardiovascular care. Data were synthesized from recent clinical trials, real-world case studies, and computational modeling to evaluate therapeutic efficacy, adverse event reduction, and cost-effectiveness in genetically stratified patient populations.The results demonstrate that personalized medicine significantly improves clinical outcomes in CVD management. Genotype-guided drug prescriptions enhanced treatment responses and reduced incidences of drug-related side effects, particularly in therapies involving statins and warfarin. Biomarker-guided interventions further optimized heart failure treatment by enabling early detection and targeted therapy adjustments. Additionally, the integration of machine learning models effectively stratified patients by predicted drug response, supporting data-driven decision-making in clinical practice.In conclusion, personalized medicine enhances the precision, safety, and efficiency of cardiovascular disease management. By leveraging advancements in genomics, molecular diagnostics, and computational analytics, this approach enables clinicians to deliver tailored treatments that improve patient outcomes while minimizing risks. Despite existing challenges related to cost, regulatory frameworks, and clinical adoption, the continued integration of personalized strategies holds…
subject: Articles
  topic: section
subject: Cardiovascular Disease
  topic: keywords
subject: Personalized Medicine
  topic: keywords
subject: Pharmacogenomics
  topic: keywords
subject: Genetic Testing
  topic: keywords
creator: Muhammad Rehan
creator: Muhammad Inam Farooq
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0
date: 2024-06-30
publication date: 2024-06-30
starting page: 1
ending page: 18

ANALYZING THE GUT-BRAIN AXIS IN ALZHEIMER'S DISEASE: A MICROBIOME-CENTRIC APPROACH
type: item
description: The gut-brain axis has emerged as a crucial biological network influencing the onset and progression of Alzheimer’s disease (AD). This study explored the relationship between gut microbiota dysbiosis and AD-related neurodegeneration by synthesizing evidence from recent literature. The findings revealed that individuals with AD demonstrated significantly lower microbial diversity, higher systemic inflammation (IL-6, TNF-α, CRP), and disrupted metabolite levels (e.g., SCFAs, tryptophan) compared to healthy controls. Additionally, neurotransmitters critical for mood and cognition, including serotonin, dopamine, and GABA, were markedly diminished in the AD group. These alterations correlated strongly with cognitive deficits, as measured by standardized tests such as MMSE, MoCA, and ADAS-Cog. Interventional strategies targeting the microbiome—such as probiotics, prebiotics, and dietary modifications (notably the Mediterranean diet)—were associated with improved gut composition and better cognitive performance. Probiotic treatments like Lactobacillus and Bifidobacterium improved both gut diversity and MMSE scores. Dietary interventions reduced CRP levels and enhanced cognitive outcomes. Figures and tables presented in the study support the hypothesis that gut-brain axis modulation may serve as a viable adjunctive or preventive strategy in AD treatment. This research highlights the need for a paradigm shift from a purely neurocentric model to one that integrates microbial ecosystems as key regulators of brain health.
subject: Articles
  topic: section
subject: Gut-Brain Axis
  topic: keywords
subject: Alzheimer’s Disease
  topic: keywords
subject: Microbiota Dysbiosis
  topic: keywords
subject: Neuroinflammation
  topic: keywords
subject: Probiotics
  topic: keywords
subject: Cognitive Decline
  topic: keywords
creator: Hassan Yar Mahsood
creator: Abdul Ghaffar
creator: Roohan Ahmad
creator: Muhammad Rehan
creator: Muhammad Inam Farooq
rights: Copyright (c) 2025 Hassan Yar Mahsood , Abdul Ghaffar, Roohan Ahmad, Muhammad Rehan, Muhammad Inam Farooq (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-06-30
publication date: 2025-06-30
starting page: 60
ending page: 72

QUANTIFYING THE CONTRIBUTION OF ORGANIC AMENDMENTS TO LONG-TERM SOIL CARBON SEQUESTRATION AND FERTILITY
type: item
description: This study evaluated the effects of various organic amendments—including compost, manure, biochar, and chitin—on soil health, microbial activity, and crop performance under field conditions. A mixed-methods approach combining quantitative soil analysis and plant growth measurements with qualitative stakeholder input was employed. Results showed that organic amendments significantly improved soil pH, microbial biomass carbon, nitrogen mineralization, and enzymatic activities. Among all treatments, chitin-based amendments consistently outperformed others, resulting in the highest increases in microbial biomass (300 mg/kg), nitrogen mineralization (5.2 mg/kg/day), and plant biomass yield. Enhanced nutrient uptake and enzymatic activity were also observed, particularly in urease and dehydrogenase assays. The application of microbial inoculants alongside organic amendments promoted further improvements in nutrient absorption, root development, and disease resistance. Notably, disease incidence was reduced by up to 45% under chitin treatment, indicating strong biocontrol potential. Visual analyses reinforced these findings, with significant differences in soil and plant parameters across treatments. The results support the integration of organic amendments as a sustainable solution for enhancing soil fertility and crop yield while minimizing dependence on synthetic inputs. This study highlights the ecological and agronomic benefits of organic matter incorporation and advocates for biologically based soil management strategies in sustainable agriculture.
subject: Articles
  topic: section
subject: Organic Amendments
  topic: keywords
subject: Soil Fertility
  topic: keywords
subject: Microbial Activity
  topic: keywords
subject: Crop Productivity
  topic: keywords
subject: Nutrient Uptake
  topic: keywords
subject: Sustainable Agriculture
  topic: keywords
creator: Irfan Ahmad
creator: Muneeba
creator: Muhammad Suleman Aziz
rights: Copyright (c) 2025 Irfan Ahmad , Muneeba, Muhammad Suleman Aziz (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-06-30
publication date: 2025-06-30
starting page: 45
ending page: 59

COLOSTRUM MANAGEMENT EFFECTS ON PASSIVE IMMUNITY TRANSFER IN CALVES
type: item
description: This paper discusses how methods of colostrum management might influence the passive immunity transfer in neonatal calves with particular focus on the variables; colostrum quality, time of feeding, hygiene and subsequent health outcomes in the calves. Determining the IgG levels in 120 colostrum samples and the corresponding calf serum samples was done by conventional ELISA methods. The results revealed a significant difference in the quality of colostrum between breeds and parities with multiparous Holstein cows having the highest mean concentrations of IgG. The timeliness of colostrum provision was quite crucial. Passive immunity transmission (serum IgG >10 g/L) success rate was 92 percent in calves fed within two hours of birth and only 48 percent in calves fed after six hours. Also, high bacterial levels in colostrum (>100,000 CFU/mL) were associated with a significantly low absorption efficiency of IgG, which explains the importance of hygienic treatment. Calves that acquired adequate passive immunity well had significantly lower morbidity rates, approximately 14 percent experienced diarrhea and 9 percent respiratory infection as compared to 33 percent and 28 percent in calves that had failed transfer. What the results of weight gain during the initial 30 days showed, was that even more, the growth is associated with elevated blood IgG levels. These findings demonstrate the significance of managing the colostrum properly that involves feeding the calves at the correct age, ensuring that the colostrum quality is high, and maintaining cleanliness. This matters in the wellbeing of the calves and farm production. The research gives credence to the use of practical, science-based colostrum policies as a cornerstone of herd health programs
subject: Articles
  topic: section
subject: Colostrum Quality
  topic: keywords
subject: Passive Immunity
  topic: keywords
subject: Igg Absorption
  topic: keywords
subject: Neonatal Calves
  topic: keywords
subject: Calf Health
  topic: keywords
subject: Colostrum Hygiene
  topic: keywords
creator: Fawad Ahmad
creator: Muhammad Asif
rights: Copyright (c) 2025 Fawad Ahmad, Muhammad Asif (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-06-30
publication date: 2025-06-30
starting page: 29
ending page: 44

Chemical Modulation of Host-Pathogen Interactions: Investigating the Role of Secondary Metabolites in Bacterial Virulence and Host Immunity
type: item
description: The intricate chemical communication between bacterial pathogens and host immune systems plays a pivotal role in determining infection outcomes. This study investigates the immunomodulatory functions of bacterial secondary metabolites—specifically pyocyanin, staphyloxanthin, and enterobactin—by integrating metabolite profiling, host immune response assays, and in vivo infection models. Metabolites were isolated and characterized using LC-MS and NMR, followed by functional assays on THP-1 macrophages and murine splenocytes. Cytokine quantification revealed significantly elevated IL-6 and TNF-α levels in pyocyanin-treated cells, with IL-6 reaching 320 pg/mL and TNF-α 420 pg/mL, compared to 45 pg/mL and 60 pg/mL in controls, respectively. Gene expression analyses indicated substantial fold changes for immune markers IL6 (5.4×), TNFA (6.1×), and CD80/CD86 in response to pyocyanin, suggesting strong immune activation. In vivo survival assays demonstrated that Galleria mellonella and BALB/c mice had the lowest survival rates post-exposure to pyocyanin (35% and 40%, respectively), highlighting its virulence-enhancing potential. Additionally, ROS generation and phagocytosis assays revealed a sharp rise in oxidative stress (820 MFI) alongside decreased phagocytic efficiency (28%) in pyocyanin-treated groups. Enterobactin and staphyloxanthin elicited moderate effects, underlining metabolite-specific immune modulation. These findings confirm that secondary metabolites actively modulate host immunity, not merely as virulence enhancers but as key biochemical modulators in infection dynamics. This research provides novel insights into how targeting these chemical agents may serve as an effective anti-virulence strategy in the fight against antibiotic-resistant infections.
subject: Articles
  topic: section
subject: Secondary Metabolites
  topic: keywords
subject: Bacterial Virulence
  topic: keywords
subject: Host Immunity
  topic: keywords
subject: Pyocyanin
  topic: keywords
subject: Immune Modulation
  topic: keywords
subject: Anti-Virulence Therapy
  topic: keywords
creator: Mashal Shahzadi
creator: Rooh Ullah
rights: Copyright (c) 2025 Mashal Shahzadi , Rooh Ullah (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-06-30
publication date: 2025-06-30
starting page: 15
ending page: 28

MACHINE LEARNING MODELS FOR PREDICTING SEPSIS ONSET IN ICU PATIENTS BASED ON EHR DATA
type: item
description: Early detection of sepsis remains a clinical priority in intensive care units due to its association with high morbidity and mortality rates. This study presents a machine learning-based framework for predicting sepsis onset using electronic health record data, with the goal of enabling timely clinical intervention. Multiple algorithms—including ensemble models, support vector machines, and linear classifiers—were trained on a dataset comprising vital signs, lab values, medications, and demographics. Results demonstrated that ensemble models achieved superior predictive performance, with significant gains in accuracy, precision, recall, and F1-score after hyperparameter tuning. Key predictive features identified included lactate concentration, respiratory rate, and white blood cell count. Visualization tools such as SHAP values, temporal risk plots, and feature importance charts enhanced the interpretability of the models. Comparative analysis with conventional clinical scoring systems revealed that the machine learning models offered improved sensitivity and earlier detection. The study also highlights critical challenges such as data heterogeneity, algorithmic transparency, and clinician adoption barriers. Despite these challenges, the findings underscore the transformative potential of machine learning in critical care, particularly in establishing real-time early warning systems for sepsis. The integration of interpretable and accurate models into clinical workflows could significantly reduce diagnostic delays and improve patient outcomes. This research contributes to the advancement of precision medicine by leveraging artificial intelligence for real-world clinical decision support.
subject: Articles
  topic: section
subject: Sepsis Prediction
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Electronic Health Records
  topic: keywords
subject: ICU
  topic: keywords
subject: Early Warning System
  topic: keywords
subject: Explainable AI
  topic: keywords
creator: Muhammad Rehan
creator: Muhammad Danial Ahmad Qureshi
rights: Copyright (c) 2025 Muhammad Rehan , Muhammad Danial Ahmad Qureshi (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-06-30
publication date: 2025-06-30
starting page: 1
ending page: 14

VETERINARY BIOMECHANICS AND INJURY PREVENTION IN RACEHORSES
type: item
description: This paper examines biomechanical factors that expose racehorses to the risk of being injured and etal analyses of how to ensure that they are not injured. We investigated biomechanics of locomotion locomotor mechanics and ground reaction force (GRF) at high speed among Thoroughbreds in training in various environments with the use of high-speed kinematic analysis and GRF platforms. The high vGRF and irregular limbs greatly increased the probability of horses in having joint and tendon issues. Indeed, wearable sensors allowed constantly monitoring gait and detecting biomechanical alterations that occur when one is fatigued at their earliest stages. It was found by statistical study, that measures such as surface modulation and physiotherapy, which are specifically tailored to the individual, reduced injury levels by 42%. A model based on limb loads, stride variability and angular joint kinematics in predicting injury risk was 85 percent accurate. Such findings indicate the value of real-time biomechanical profiling as a way of enhancing performance of racing horses and preventing major injuries.
subject: Articles
  topic: section
subject: Equine Biomechanics
  topic: keywords
subject: Racehorse Injuries
  topic: keywords
subject: Gait Analysis
  topic: keywords
subject: Injury Prevention
  topic: keywords
subject: Ground Reaction Force
  topic: keywords
subject: Predictive Modeling
  topic: keywords
creator: Abdul Wadood Jan
creator: Shafiq Ur Rehman
rights: Copyright (c) 2023 Abdul Wadood Jan, Shafiq Ur Rehman (Author) https://creativecommons.org/licenses/by/4.0
date: 2023-12-31
publication date: 2023-12-31
starting page: 90
ending page: 112

MOLECULAR PATHOGENESIS OF ZOONOSES IN URBAN WILDLIFE
type: item
description: Due to growing human-wildlife contacts and disturbance in the environment, wildlife-borne zoonotic diseases originated in urban areas are proving to be of a more concern to the population health. They used 300 specimens of rodents, birds, and mesocarnivores in the urban setting to investigate the molecular pathogenesis of major zoonotic agents such as Leptospira spp., Salmonella enterica, Bartonella henselae, and urban-adapted coronaviruses. We have found the encoders of genes [lipL32, invA, S1 spike protein], which play a role in virulence, to be abundant using the methods of PCR, ELISA, and metagenomic sequencing. According to the comparison between the urban isolates and their rural counterparts, the former had greater host adaptation and greater gene expression, implying that the evolution of the pathogen might proceed even faster under urban stressors. Host immune profile identified asymptomatic carriage in wildlife, which also means the competence of a reservoir lacking overt disease. Spatial mapping featured a likelihood of spill over, leading to the association of densely populated regions where the pathogen was highly loaded. Due to these results, a One Health molecular surveillance paradigm where wildlife disease surveillance, urban ecology and human public health preparedness are integrated is necessary.
subject: Articles
  topic: section
subject: Zoonosis
  topic: keywords
subject: Urban Wildlife
  topic: keywords
subject: Molecular Diagnostics
  topic: keywords
subject: Host-Pathogen Interaction
  topic: keywords
subject: One Health
  topic: keywords
subject: Virulence Genes
  topic: keywords
creator: Aftab Ahmed
creator: Mukhtar Ahmad
creator: Shahid Iqbal
creator: Muhammad Usman
rights: Copyright (c) 2023 Aftab Ahmed, Mukhtar Ahmad, Shahid Iqbal, Muhammad Usman (Author) https://creativecommons.org/licenses/by/4.0
date: 2023-12-31
publication date: 2023-12-31
starting page: 66
ending page: 89

HYBRID AQUATIC ANIMAL DISEASE MANAGEMENT STRATEGIES
type: item
description: This paper potential researches into the management of diseases in aquatic animals through mixed methods. It mixes experimental therapies with both quantitative and subjective evaluation to look at the most suitable methods of facilitating health and maintaining operations. There were three treatments among fish and crustacean in a controlled aquaculture conditions: Treatment A, Treatment B, and Treatment C. The baseline measurements included quality of the water, the level of pathogens, the immunological markers and growth rates. The Treatment A showed the highest survival rates, the fastest recovery and had the most significant growth performance within the 30-day follow up period. Treatment B, however, performed best in enhancing the immune response and maintaining the level of water quality steady. Feeding conversion efficiency of C treatment was improved, and this is advantageous to large organizations since money is saved. By performing ANOVA and regression analysis of the data a clear difference between the treatments was indicated which was substantial. Important correlations between the environmental variables and health indicators were also found in graphical visualisations. The professionals working in aquaculture wrote qualitative feedback that assisted us to comprehend the effectiveness of each type of therapy. The integration of such data favours a hybrid management approach which involves the utilisation of the most effective elements of the three therapies to facilitate survival, resilience, and efficiency in the operative process. These findings demonstrate that many aspects should be implemented in aquaculture to control diseases. The tactic is also useful in enhancing productivity, profits, and environment compatibility in the long run.
subject: Articles
  topic: section
subject: Aquaculture
  topic: keywords
subject: Disease Management
  topic: keywords
subject: Hybrid Strategies
  topic: keywords
subject: Pathogen Control
  topic: keywords
subject: Immune Response
  topic: keywords
subject: Sustainability
  topic: keywords
creator: Muhammad Waqar Ali
rights: Copyright (c) 2023 Muhammad Waqar Ali (Author) https://creativecommons.org/licenses/by/4.0
date: 2023-12-31
publication date: 2023-12-31
starting page: 44
ending page: 65

HYBRID MODELING OF ECOSYSTEM-LEVEL METABOLISM
type: item
description: Metabolic prediction and management has significant funding to manage change at the ecosystem level because it involves management of the environment. The proposed research will present a hybrid modeling design where field flux measurements can be coupled with remote sensing indices and machine learning to characterize and support predictions of biogeochemical processes in diverse ecosystems. We received information on forest, wetland, estuarine and grassland biomes including GPP, NEP, ER and nutrient fluxes. Non-linear relationships between environmental variables were determined using Random Forest and XGBoost algorithms and mechanistic carbon modeling. The model outperformed R 2 = 0.85 at every location and the SHAP analysis revealed that the most significant predictors were NDVI, temperature, and soil moisture. These findings are supported by 9 big tables and 12 high quality images that demonstrate the way by which carbon, and nutrients flow space- and time-wise. Experts, interviewed, confirmed that the outcomes of the models can be applied in real life practices in the monitoring of the environment and policy formulation. Combining mechanistic and data-driven approaches with respect to obtaining a complete picture of the metabolism of ecosystems: this work demonstrates just how useful such an integrated method can be. This may be of use when it comes to future climate-resilient management plans.
subject: Articles
  topic: section
subject: Ecosystem Metabolism
  topic: keywords
subject: Biogeochemical Modeling
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subject: Remote Sensing
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subject: Machine Learning
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subject: Carbon Flux
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subject: Predictive Ecology
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creator: Zia Ur Rehman
creator: Mashal Shahzadi
rights: Copyright (c) 2023 Zia Ur Rehman, Mashal Shahzadi (Author) https://creativecommons.org/licenses/by/4.0
date: 2023-12-31
publication date: 2023-12-31
starting page: 24
ending page: 43

STEM CELL BIOLOGY AND REGENERATIVE TRANSCRIPTOMICS
type: item
description: <span style="font-size: 10.0pt; mso-bidi-font-size: 11.0pt; line-height: 107%; font-family: 'Calibri',sans-serif; mso-ascii-theme-font: minor-latin; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: Arial; mso-bidi-theme-font: minor-bidi; color: black; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA; mso-bidi-font-weight: bold;"> <span style="font-size: 9.0pt; mso-bidi-font-size: 10.0pt; line-height: 107%; font-family: 'Times New Roman',serif; mso-ascii-theme-font: major-bidi; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: major-bidi; mso-bidi-theme-font: major-bidi; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA; mso-bidi-font-weight: bold; mso-no-proof: yes;">There is increasingly the reliance on a fusion of stem cell biology and transcriptomics to find out how the cells and tissues heal themselves, and how they can be induced to be reprogrammed using regenerative medicine. This experiment adopts a mixed design, whereby a thorough high-throughput transcriptome profiling, practical functional regenerative assays, and single-cell resolution will be used to explore how stem-cell derived lineages may be regenerated. Mesenchymal, neuronal, and epithelial has been generated when scientists converted pluripotent stem cells to other cells. Then they placed the cells in circumstances that appeared to be harmed, such as an experience of oxidative pressure and the activation of inflammation. The results of bulk RNA-Seq and single-cell RNA-Seq experiments demonstrated that key regenerative markers such as FGF2, IGF1, VEGF-A, and BMP7 were also being activated in all cases. This was highly associated with increased rates of wound healing and migration. Tables revealed expression stability, differentiation scores and regeneration index between various lineages and stres…
subject: Articles
  topic: section
subject: Stem Cells
  topic: keywords
subject: Regenerative Transcriptomics
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subject: Rna Sequencing
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subject: Wound Healing
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subject: Differentiation
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subject: Gene Expression.
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creator: Muhammad Asadullah Usman
creator: Irum Habib
rights: Copyright (c) 2023 Muhammad Asadullah Usman, Irum Habib (Author) https://creativecommons.org/licenses/by/4.0
date: 2023-12-31
publication date: 2023-12-31
starting page: 1
ending page: 23