Gomal Journal of Life Sciences Gomal Journal of Life Sciences (GJLS) is a peer-reviewed, open-access journal established in 2023 and published by GOMAL EDUCATION NETWORK (SMC-PRIVATE) LIMITED. It publishes original research, reviews, and short communications across all major fields of life sciences, including botany, microbiology, zoology, biochemistry, biotechnology, genetics, physiology, pharmacology, toxicology, and environmental sciences. Submit an article Journal homepage
GOMAL EDUCATION NETWORK (SMC-PRIVATE) LIMITED
PHARMACOGENOMIC MACHINE LEARNING MODELS FOR PREDICTING INDIVIDUALIZED OPIOID AND ANESTHETIC DRUG RESPONSE IN CANCER SURGERY PATIENTS
Surgery patients with cancer can have very different responses to opioid and anesthetic medications because of variations in genetics, chronic medical conditions, previous cancer treatments, organ function, and physiologic shifts during surgery. However, traditional dosing methods, which are primarily age and body weight based or based on general clinical guidelines, are likely to miss this individual variability, thereby raising the possibility of sub-optimal analgesia, excessive sedation, delayed recovery, and opioid related side effects. This paper looks at pharmacogenomic machine learning models for predicting individualized opioid and anesthetic drug responses in cancer surgery patients. Machine learning models can detect complex patterns of drug-responses that are hard to discern using traditional statistics by incorporating genetic markers, pharmacokinetic and pharmacodynamic parameters, electronic health record data, intraoperative monitoring signals and clinical risk factors. The study emphasizes the promise of neural-pharmacokinetic/pharmacodynamic modeling, reinforcement learning, interpretable artificial intelligence, and model-informed precision dosing for aiding real-time decision making in the perioperative setting. These techniques can help stabilize the anesthetic, increase the effectiveness of the pain control, minimize toxicity, and promote recovery after surgery. But to make it to the clinic requires big and varied sets of data, external validation, transparent model interpretation, standardized reporting, ethical governance and incorporation into current clinical workflows. In conclusion, pharmacogenomic machine learning holds significant potential for advancing precision anesthesia in oncological surgery, potentially leading to more individualized, safer, and evidence-based perioperative care.
Pharmacogenomics Machine Learning Precision Anesthesia Opioid Response Cancer Surgery
Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
COMPUTER VISION-BASED AIRWAY ASSESSMENT USING FACIAL AND NECK IMAGING FOR PREDICTING DIFFICULT INTUBATION IN SURGICAL ANESTHESIA
This study aims to investigate the feasibility of using computer vision facial/neck imaging for prediction of difficult intubation during surgical anesthesia. It is the prediction of a difficult airway that continues to be a significant concern during perioperative patient care as the traditional bedside airway assessment tools including Mallampati classification, thyromental distance and visual inspection are subjective, inconsistent and fails to identify patients with complex airway anatomy. The study suggests an artificial intelligence-based framework that leverages deep learning, multimodal anatomical feature extraction in cervical and facial imaging to enhance the pre-operative airway risk assessment. The technique involves the automatic analysis of facial morphology, mandibular morphology, neck morphology, cervical profile and ultrasound parameters of the airway to detect patients who may be at risk of difficult laryngoscopy or intubation. The model combines computer vision with clinical and anthropometric parameters to offer a more objective and reproducible approach to the evaluation of airway. Overall, the study underlines the potential of convolutional neural networks, vision transformers, feature-fusion models, and explainable AI techniques like SHAP in enhancing diagnostic accuracy and clinical interpretability. The reviewed methodology and discussion indicate that the use of multimodal AI-based assessment could surpass the performance of unilinear clinical predictors in capturing the complex dynamic relationships between soft tissue dimensions, cervical anatomy, and laryngoscopy difficulty. This type of approach can help anesthesiologists make decisions for appropriate airway management strategies, such as preparing for video laryngoscopy or advanced airway support as early as possible. In summary, computer vision provides a promising route to safer, quicker and more standardized decisions throughout surgical anesthesia.
Computer Vision Difficult Intubation Airway Assessment Deep Learning Surgical Anesthesia
DEEP LEARNING-DRIVEN PREDICTION OF DIFFICULT EXTUBATION AND ICU TRANSFER RISK IN HIGH-RISK ANESTHESIA PATIENTS
This work proposes a deep learning-based approach to predicting difficult extubation and intensive care unit (ICU) transfer risk for high-risk anesthesia patients with multi-institutional electronic health record (EHR) data and high-resolution physiological waveforms. Traditional methods of perioperative risk assessment are often inadequate in identifying complex and dynamic physiologic changes related to postoperative respiratory deterioration. This limitation is overcome by the proposed methodology which combines hybrid deep learning architectures such as Long Short-Term Memory (LSTM) networks and Temporal Convolutional Networks (TCN) for analysis of both static clinical variables and longitudinal bio signals during the surgery. The dataset also includes continuous physiological data like heart rate, respiratory rate, oxygen saturation, medication administration records, and lab results, enabling comprehensive predictive analytics. To ensure consistency of the data across institutions, a comprehensive preprocessing pipeline was used, including z-score normalization, time-aware imputation, and temporal alignment. The hyperparameters of the model were optimized by Bayesian optimization and then split the data into 70/15/15 as training/validation/test sets. Evaluation of performance showed that it had better predictive power than traditional statistical methods, with high discrimination rates expressed by the area under the receiver operating characteristic curve (AUROC), precision-recall analysis, sensitivity, specificity and F1-score. Moreover, explainable AI methods with the help of SHAP values found significant perioperative determinants associated with extubation failure and risk of being transferred to the ICU, which promoted clinical interpretability and trustworthiness. The results indicate that AI-powered perioperative monitoring systems can aid in proactive decision-making, enhance postoperative patient safety, optimize the use of the intensive care unit, and lower the risk of adverse respiratory events. The study underscores the increasing promise of explainable deep learning systems to revolutionize perioperative anesthesia care and move it toward personalized, data-driven care.
Difficult Extubation Prediction Deep Learning In Anesthesiology Intensive Care Unit (Icu) Transfer Risk Perioperative Risk Assessment High-Risk Surgical Patients
EXPLAINABLE MACHINE LEARNING FOR PERSONALIZED POSTOPERATIVE PAIN PREDICTION FOLLOWING MAJOR CANCER SURGERY UNDER GENERAL ANESTHESIA
This study aims to explore the use of explainable machine learning algorithms that can predict individual postoperative pain outcomes for patients undergoing major cancer surgery under general anesthesia. Perioperative care has become more complex, and the problem of opioids is ongoing, making the need for accurate and transparent predictive systems that enable individualized care of the analgesics more important. Many traditional statistical methods are unable to represent the complex non-linear relationships between perioperative variables and many advanced artificial intelligence models are too hard to interpret in the clinic. To overcome this challenge, this study utilized a retrospective cross sectional study design with clinical, demographic, laboratory and intraoperative data from 13,700 surgical patients. To determine the most important predictors related to postoperative pain intensity, several machine learning methods such as Gradient Boosting Machines and Least Absolute Shrinkage and Selection Operator regression were applied. To avoid overfitting and enhance the robustness of the model, stratified 10-fold cross validation was used. The explainability was further strengthened by Shapley Additive explanations (SHAP), which allow the interpretation of the contributions of the features and the increase of the trust of the clinicians in the model predictions. This study revealed that intra-operative factors like blood transfusion and tourniquet use were significant factors affecting the pain trajectories after surgery. SHAP analysis demonstrated that these factors played a significant role in the increase in pain scores, underscoring the potential of inflammation, ischemia-reperfusion injury, and surgical trauma as factors in postoperative recovery. Moreover, the study illustrated the value of AI that can be understood by and interpreted by humans, and how that could help anesthesiologists and peri-operative care teams make actionable clinical decisions based on black-box predictions. Explainable machine learning could enhance patient-centered outcomes and facilitate precision medicine approaches in oncological surgery by allowing the development of personalized opioid-sparing strategies and the proactive identification of risk. The results highlight how transparent artificial intelligence systems are increasingly becoming a key tool for optimizing peri-operative decision making, provided they are integrated in an ethical, reliable, and clinically meaningful way in a contemporary healthcare context.
Explainable Artificial Intelligence Postoperative Pain Prediction Anesthesiology
Personalized Analgesic Management Surgery For Cancer Under General Anesthesia
MACHINE LEARNING-BASED PREDICTION OF POSTOPERATIVE DELIRIUM IN ELDERLY ANESTHESIA PATIENTS USING MULTIMODAL PERIOPERATIVE BIOMARKERS AND ELECTRONIC HEALTH RECORDS
The postoperative delirium (POD) is one of the most common complications after anesthetic in elderly patients and may cause morbidity, prolonged hospital stay, poor long-term cognitive function and cost of health care. The authors created and validated an internal machine-learning based early POD (E-POD) risk stratification model that incorporates multimodal periop markers and the longitudinal EHR data (L-EHR). A retrospective study of 1248 patients of elderly age that received non-cardiac surgery was performed to assess demographic, comorbidities, pre-operative medication history, time-series signals (inhalation, heart rate, respiratory rate) during surgery, and biochemical inflammatory markers. The missing data were imputed multiple times and the most informative (and non-redundant) predictors were selected using feature selection algorithms Boruta and LASSO. Logistic regression, random forest, XGBoost, a temporal transformer and an ensemble model of all of the above were the candidate models. The ensemble model proposed showed to be the most effective model in the validation phase with an AUC of 0.902, average precision of 0.847, sensitivity of 0.858, specificity of 0.819 and F1 score of 0.838. Furthermore, calibration analysis was used to support the clinical reliability, with a Brier score of 0.091 and Spiegelhalter z-test P-value of 0.214. The baseline cognition, mean arterial pressure during surgery, CRP, age, anesthetic depth variability, IL-6, comorbidity burden, and surgery duration were identified as key risk drivers via explainability analysis. Considering these outcomes, a multimodal machine learning system could potentially predict POD in geriatric surgical patients with accurate and meaningful predictions that are easily understood and can be acted upon. It may help to identify risk at an earlier stage, provide personalised prevention and support improved peri-OP decision making, but it needs further external and prospective validation before it can be implemented in clinical practice.
WATER USE EFFICIENCY IN IRRIGATED AGRICULTURE: A SYSTEMATIC LITERATURE REVIEW
Scarcity of water and increasing agricultural demand has triggered the necessity to have proper irrigation management solutions. It is a systematic literature review that condenses and summarizes the recent trends (2015-2024) in the field of water use efficiency (WUE) within irrigated agricultural systems in which a particular focus will be on the use of Internet of Things (IoT) technologies and machine learning (ML) models to manage smart irrigation management. The 229 peer-reviewed articles that were found and studied during the study were identified and investigated through a PRISMA-based approach to searching large scientific databases that included Scopus, Web of Science, and IEEE Xplore. The review evaluates the capability of decision support systems (DSS) such as those of DSSAT, APSIM, AquaCrop, CropWat, GesCoN, and VegSyst to optimize irrigation schedule particularly with the combination of real time sensor networks, remote sensing data and predictive analytics. The findings indicate that through the irrigation systems based on the Internet of Things and the assistance of the ML algorithms, such as Random Forest, Artificial Neural Networks, Support Vector Machines, Deep Learning, water savings (37 to 70 percent) can be achieved and, at the same time, crop yields may be improved by 30 percent. It is also in the synthesis that the superiority of real-time adaptive irrigation systems over the conventional static scheduling systems come out in the sense that the systems are sensitive to the changes in the environment and water need of crops. Technological innovations have not sorted the barriers to their adoption like high start-up cost, connectivity, data consolidation, and scaling, particularly in regions with limited resources, such as agriculture. In this review the author critiques extensively on the state of the art in intelligent irrigation systems and provides prospects of future research in the field of scalable, cost-effective as well as climate-resilient water management that supports water management in the world.
Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0
CLIMATE CHANGE IMPACTS ON GLOBAL FOOD SECURITY: A SYSTEMATIC LITERATURE REVIEW
One of the greatest threats to global food security in the twenty-first century is the concept of climate change, as the rise in temperature, changed precipitation levels, and the frequency of extreme weather events have completely changed the agricultural systems across the globe. Although there is an increasing awareness of these issues, a complete synthesis of the recent empirical data is not yet done on an interdisciplinary and geographical scale. This is a systematic literature review that attempts to generalize the existing evidence in climate change effects on food security in the world, the scale of effects, the regional variations, adaptation mechanisms, and gaps in research. The systematic searches in databases (Scopus, Web of Science, PubMed, and ScienceDirect) were performed according to the recommendations of Preferred Reporting Items (PRISMA) 2020 guidelines based on studies published in 2015-2025. We also used peer-reviewed empirical research that investigates the effects of climate change on food availability, access, utilization, and stability. Quality appraisal was by the use of Critical Appraisal Skills Programme (CASP) checklist and thematic synthesis data extraction was by standard protocols. Out of the 2847 records identified, 67 studies were eligible. This evidence has shown that climate change has an adverse effect on food security in all four pillars, with disproportionate implications in Sub-Saharan Africa and South Asia. Meta-analytic evidence suggests that 1 o C rise in temperature decreases the world production of wheat by about 6 per cent and extreme weather has led to cumulative losses of 804 million tons of big cereals between 2001 and 2024. Adaptation measures, especially Climate-Smart Agriculture (CSA) have potentials but have a high level of hindrances to implementation such as funding, poor infrastructure and lack of institutional support. Climate change presents existential challenges that threaten global food security that needs urgent and concerted efforts to integrate technological innovation, policy reform, and sustainable intensification. The future studies need to focus more on longitudinal studies, assessment of risk that a compound has, and context-specific adaptation frameworks to guide evidence-based interventions.
EXAMINING THE EFFECTIVENESS OF CROP ROTATION AND SOIL FERTILITY MANAGEMENT TECHNIQUES IN SMALLHOLDER FARMING SYSTEMS
Soil fertility decline remains a major constraint to agricultural productivity and sustainability in smallholder farming systems across sub-Saharan Africa. This study evaluated the effects of crop rotation combined with integrated soil fertility management on soil health, nutrient dynamics, and crop productivity in the Mount Bamboutos ecosystem of Cameroon. A mixed-methods experimental approach was employed, integrating multi-season field experiments with participatory qualitative assessments. Quantitative results showed significant increases in soil organic carbon, total nitrogen, and available phosphorus under cereal–legume rotation systems compared to baseline management practices. Crop yields and nutrient-use efficiency improved consistently across experimental plots, demonstrating enhanced productivity and yield stability under integrated nutrient inputs. Graphical analyses further revealed strong relationships between soil fertility indicators and crop performance, highlighting the role of organic matter accumulation and nutrient cycling in system resilience. Seasonal trends confirmed cumulative soil fertility gains and reduced spatial variability under agroecological management. Qualitative findings corroborated these results, with farmers reporting improved soil structure, moisture retention, and reduced vulnerability to climatic stress. Overall, the study confirms that agroecological crop rotation combined with integrated nutrient management significantly improves soil fertility, productivity, and resilience, offering a sustainable and scalable solution for smallholder agriculture in nutrient-depleted and climate-sensitive environments.
Copyright (c) 2025 Hammad Ur Rehman (Author) https://creativecommons.org/licenses/by/4.0
COMPARATIVE ANALYSIS OF NEURAL BLOCK TECHNIQUES FOR CHRONIC PAIN MANAGEMENT
Chronic pain is a universal health challenge that is often not very responsive to the conventional pharmacological therapy and necessitates the exploration of novel interventional solutions. In this research an in-depth comparative analysis of major methods of neural blocks such as epidural steroid injections, facet joint blocks, sympathetic blocks, and radiofrequency-based treatments has been provided by synthesizing quantitative clinical outcomes and qualitative patient-reported experiences. The mixed-methods approach was used, which entailed the use of people with chronic non-malignant pain that would not respond to conservative therapy. The methodical assessment of pain intensity was made before and after each procedure along with semi-structured interviews on functional improvement, perceived relief, and procedural satisfaction. Neural blocks reduced the score of pain significantly in the nine types of interventions that were considered. The most lasting effect on pain relief was radiofrequency ablation and the peripheral nerve blocks were much more effective in making one functional. Epidural steroid injections were also fast acting though it did not last as long as other methods. Patient stories helped to corroborate the quantitative findings indicating that individuals receiving longer-acting medications had improved quality of life and were happier. Bad incidents were not that many, which speaks in favour of the fact that these operations are not really dangerous. The combination of theoretical facts and personal experience illustrates the idea that a single neural block approach can be never considered the most effective and successful; the result depends on the pain aetiology, chronicity, and individualised treatment goals. The paper endorses an individualized, multimodal pain management framework that integrates multiple neural block methods mechanically to achieve long-term analgesia, increased functionality and optimal patient-centered outcomes.
Copyright (c) 2025 Abdul Waheed Shah (Author) https://creativecommons.org/licenses/by/4.0
DESIGNING SMART FARMING SYSTEMS INTEGRATING IOT, AI, AND BLOCKCHAIN FOR TRANSPARENT SUPPLY CHAINS IN THE AGRI-FOOD SECTOR
The rapid evolution of smart agriculture and Food Industry 4.0 has intensified the need for intelligent, transparent, and secure agri-food supply chains. This study investigates the integrated application of artificial intelligence, the Internet of Things, and blockchain technology to enhance efficiency, traceability, and food safety across the agricultural value chain. An experimental mixed-method framework was employed, combining real-time IoT sensor data, AI-based predictive and anomaly detection models, and blockchain-enabled immutable data recording. The results demonstrate significant improvements in operational efficiency, environmental monitoring accuracy, crop yield prediction, and logistics optimization. Quantitative analysis reveals consistent reductions in post-harvest losses and waste, alongside improved compliance with food safety standards. Blockchain-based traceability ensures secure, tamper-proof data sharing and enhances stakeholder accountability, while AI-driven analytics enable proactive risk management and informed decision-making. Visual and tabular results further confirm system scalability, robustness, and sustainability benefits under varying operational conditions. Overall, the findings validate that the synergistic integration of AI, IoT, and blockchain delivers a comprehensive and reliable framework for modern agri-food systems, strengthening consumer trust, minimizing food fraud, and supporting sustainable agricultural development. The proposed approach offers a scalable and future-ready solution for intelligent, transparent, and resilient food supply chains.
Copyright (c) 2025 Mashal Shahzadi, Muhammad Waqar Ali (Author) https://creativecommons.org/licenses/by/4.0
BIOMECHANICAL PREDICTORS OF INJURY RISK IN PROFESSIONAL ATHLETES: A KINESIOLOGICAL STUDY
The kinesiological case study investigated major risk biomechanical markers of injury in professional athletes through the literature of lower-limb loading behavior, joint kinemics, kinetic imbalances, and neuromuscular activation behaviors during strenuous sports-specific movements. We made use of a combined motion-capture plus force-plate technology to study 240 elite athletes in different sports and found statistically significant relationships between abnormal biomechanical patterns and injury events during a 12 months follow-up period. The findings showed that peak vertical gravity-reaction forces greater than 4.5 times body weight, knee valgus greater than 12 degrees and landing-phase hip internal rotation greater than 18 degrees were significantly linked to non-contact lower-extremity injuries. Also athletes with over 10 percent bilateral strength imbalance or delayed hamstring activation (greater than 45 ms latency) were at a very large risk of muscle-tendon harm. In multivariate analysis, it was confirmed that a combination of severe valgus collapse, poor neuromuscular timing and kinetic imbalance were predictive of approximately 72% of injury events. These results provide further evidence of the importance of screening high-risk biomechanical predictors early and also the development of specific corrective neuromuscular training programs to decrease the incidence of injuries. The research provides a significant amount of evidence of the necessity of specific biomechanical evaluation to maintain long sports careers, prevention of injuries, and maximum performance in professional sports.
Copyright (c) 2025 Zia Ur Rehman, Mashal Shahzadi (Author) https://creativecommons.org/licenses/by/4.0
EXPLORING THE POTENTIAL OF BIOCONTROL AGENTS IN REDUCING PESTICIDE USAGE AND ENHANCING BIODIVERSITY IN AGRICULTURAL LANDSCAPES
The increasing dependence on synthetic pesticides has raised serious concerns regarding environmental degradation, pest resistance, and human health risks, necessitating the development of sustainable pest management alternatives. This study evaluated the effectiveness of biological control agents within an integrated pest management framework using a mixed-method experimental approach. Quantitative field experiments demonstrated significant reductions in pest population density and disease incidence in biocontrol-treated plots compared with conventional practices, accompanied by consistent improvements in crop yield and yield stability. Biodiversity indices and soil–plant health indicators were markedly higher under biological control, indicating reduced non-target effects and enhanced ecosystem functioning. Scatter, hybrid, and temporal analyses further revealed a positive relationship between biodiversity and productivity, as well as increased resilience against pest resurgence. Qualitative ecological assessments supported these findings by highlighting improved agroecosystem balance and practical feasibility of biocontrol implementation. Overall, the results confirm that biological control agents provide an effective and environmentally responsible alternative to chemical pesticides, contributing to sustainable crop production, ecological conservation, and long-term agricultural resilience. The study reinforces the importance of integrating biocontrol strategies into future pest management policies to promote sustainable agriculture and food security.
Copyright (c) 2025 Ezza Fatima, Mashal Shahzadi (Author) https://creativecommons.org/licenses/by/4.0
MULTIDISCIPLINARY APPROACHES TO PEDIATRIC CONGENITAL HEART DISEASE
This paper examined the effectiveness of multidisciplinary management of paediatric congenital heart disease (CHD) by integrating cardiology, cardiac surgery, nutrition, developmental paediatrics and psychiatric services. This paper provides the assessment of the influence of a collaborative care model on clinical outcomes, neurodevelopment, quality of life, and carer experience in a mixed-methods experimental study that includes 200 children who have a congenital heart disease. The quantitative results indicated that there were significant positive changes in postoperative values, as oxygen saturation stabilised following the intervention, the left ventricular ejection fraction increased (mean: 6.5), and the ICU length of the hospital stay decreased (mean: 1.8 days, p < 0.01). Moreover, the neurodevelopmental scores based on movement motor pathway as well as the cognitive were raised by 15% mean wavelength. The children that were subject to the multidisciplinary paradigm experienced a 21 percent increase in overall scores of quality measures in their quality-of-life measurements through the PedsQL. Our two-way analysis of variance showed that early surgical treatment with psychological support at the family level was significantly better in terms of recovery results, (p < 0.05). The thematic analysis of qualitative interviews revealed psychological benefits of integrative care: higher level of satisfaction of the carers when the communication with clinical team had been improved, and parental anxiety had decreased. Such findings can be interpreted to indicate that collaborative inter-specialty frameworks can substantially transform the entire psychosocial environment of the patients and their families besides enhancing the physiological healing of paediatric patients with congenital heart disease. The replication of this integrative paradigm may be useful in other complex paediatric conditions that require a coordinated long-term care.
Copyright (c) 2025 Sami Ullah, Muska Hayat, Shahzad Rafiq (Author) https://creativecommons.org/licenses/by/4.0
WILDLIFE CORRIDOR PLANNING USING GPS COLLARS AND FORESTRY DATA: PREVENTING HUMAN-ANIMAL CONFLICTS
Human wildlife conflict develops into one of the most serious conservation issues due to an increase in habitat fragmentation and human-generated stressors. The multi-year study proposing integrating GPS collar-based monitoring with forestry-based remote sensing data and spatial modelling based on GIS proposes a first-rate, data-integrating platform of building wildlife corridors. High resolution habitat suitability models were built based on real time tracking of elephants and leopards through GPS collars and NDVI and canopy cover indices. We have made wildlife corridors that are safe to people and environmentally friendly by using these models as well as the conflict zone mapping using the human settlement and land use layer. The findings demonstrate that well-designed corridors can reduce the conflict incidence of up to 43 percent in sites where incidence is high, and connect habitats by greater than 60 percent. The comments and the field tests of the people within the area ensured the results were ecologically acceptable and satisfactory. A further enhancement of the corridor proposed models was done using least-cart path analysis, forming corridor blueprints that can be utilized repeatedly by the conservation managers. This combined research approach provides us with a model that can be repeatedly applied in minimizing conflict between individuals and animals and at the same time entice them to co-exist. Our research indicates the significance of application of spatial analytics and participatory input in ecological planning. Such instruments can be used to safeguard biodiversity and maintain functional ecosystems in places where people inhabit.
EVOLUTIONARY DEVELOPMENTAL BIOLOGY: MORPHOGENESIS ACROSS SPECIES
The evolutionary developmental biology (Evo-Devo) is all about how various animals grow into various forms. This piece examines morphogenesis integratively in four model organisms that include Drosophila melanogaster, Danio rerio, Xenopus laevis and mus musculus. It accomplishes this through a combination of morphometric data, gene observation and phylogenetic predicting. We examined embryos on the gastrulation stage, neurulation stage, and the organogenesis stage. We measured their shape changes using geometric morphometrics and their patterns of SHH and BMP4 signalling to measure changes in the expression of their genes. In principal component decomposition and Procrustes distances analysis, the shape space revealed to be extremely dissimilar among various stages and species. RNA-Seq and in situ hybridisation suggested gene expression changes were actively dynamical and evolutionary patterns. Mice and Xenopus embryos expressed BMP4 most strongly during organogenesis and this was coordinated with areas of enhanced complexity of morphology. The analysis of scatter and hybrid visualisations results indicated that changes in morphometry are positively correlated with the gene regulatory activity. The phylogeny comparative analysis revealed that was the case with certain morphogenetic features which exhibited high values of Pagel s lambda indicating a strong evolutionary restriction. The other qualities were not fixed instead they were adaptable and flexible. Equal developmental sampling was supported by pie charts and stage-distribution analyses and ensured that the statistics were solid. The findings indicate that all the most important genetic modules remain in the same way, its timing and geographic impacts vary across every lineage, which contributes to phenotypic diversity. The analysis forms part of the usefulness of a mixed-method The Evo-Devo framework is useful in allowing us to infer conserved developmental processes and species-specific novelties, and that links molecular signalling with morphological evolution. The approach provides a model to guide the future studies where researchers will explore the developmental origins of the new evolutionary traits in the tree of life.
NUTRIGENOMICS IN ANIMAL NUTRITION AND PERFORMANCE
Employing a mixed-methods experimental design, this paper examined the dietary changes that occur owing to nutrigenomics as it relates to nutrition and performance Outcomes of animals. Treatment groups were applied on the animal’s differences being the nutrient level in the diet. These diets were the high-protein diets, omega-3 diets, antioxidant diets, enzyme pre-treated diets. We obtained quantitative measures such as growth rate, feed conversion ratio, immunological measures, metabolic profiling, digestibility coefficients and nutrient retention efficiency. We also genomic profiled the animals by using RNA sequencing and visualized the impacts on genes, based on their diets. It was demonstrated that the metabolic, immunological, growth-regulatory routes were tremendously altered. The diet with the highest performance indices was that of high protein and micronutrients. Combined research indicated that in genes, which enhanced food metabolism held considerable ties with augmented feed efficiency, immune system performance and retention of nutrients in tissue. Trends were graphed in performance, genomic correlations, and pathway enrichment and the dietary composition of the diet, genomic response, and overall performance scores were tabulated. The analysis indicates that nutrigenomics may assist in developing a very accurate feeding program which enhances health and productivity. This offers a sustainable means of enhancing performance of cattle through knowledge of diet-gene interaction. These findings have massive implications of animal agriculture in the future. They demonstrate how omics technology is to be employed in the nutrition management systems.
CROSS-SPECIALTY CARE IN DIABETIC RETINOPATHY : ENDOCRINOLOGY MEETS OPHTHALMOLOGY
The current trial was carried out based on a cross-specialty care paradigm in which ophthalmology and endocrinology specialists collaborated to treat diabetic retinopathy (DR) by using a mixed-methods experimental framework. Compared to the other group designated to standard care, there were statistically significant changes in the integrated care group in terms of visual acuity (p < 0.01), central macular thickness (p < 0.05), and abatement of glycaemic control (with lower HbA1c levels) through quantitative measures of a cohort (n = 240). A dramatic reduction in serum VEGF was also noted, and this was also in agreement with stabilisation of retinal disease. The increased patient satisfaction, more effective treatment compliance, and interdisciplinary communication were all identified in qualitative interviews of patients and physicians within the framework of the integrated paradigm. Various regression analysis indicated that tight glycaemic control, early ophthalmologic intervention and patient adherence were also powerful predictors of visual stabilisation (R2 = 0.68, p < 0.001). Thematic coding of clinician narratives would bring into focus the value of collaborative consultation processes and the availability of real time data in reducing diagnostic and referral delays. To achieve these needs, the cooperation of medical specialities was necessary to deal with the systemic and localised disease loads associated with diabetic microvascular issues. The findings of the study also emphasize the worth of the work of interdisciplinary teams in chronic disease management and establish a model of an integrated care strategy in settings where the resources vary. The results promote the policy of implementing shared care pathways in diabetic retinopathy clinics all over the world.
COMPARATIVE ONCOLOGY: CANINE AND HUMAN CANCER INSIGHTS
In this paper we present a comprehensive comparison of cancer treatment in dogs and humans with the emphasis put on the similar molecular etiologies, clinical presentation, and treatment strategies. Malignancies such as osteosarcoma, lymphoma, and melanoma are biologically similar and develop, grow, and are treated similarly within various species, according to the findings of the study as it integrated epidemiological data, molecular informing, and efficacy of treatments among the various species. The statistics indicate that dogs and human beings have numerous similarities regarding the genetic alterations characterized by an alteration of TP53, PTEN, and BRAF; the issues of signalling networks encompass PI3K/AKT and MAPK. According to Clinical trial data, canine oncology is an effective method to test new chemo therapies, targeted medications and immunotherapies in a short time. This has gone a long way in influencing how human beings deal with cancer. There has also been an easier way to diagnose and treat issues at an earlier stage due to aspects of improvement in diagnostic imaging, molecular testing, and the decreased invasiveness of biopsies in animals and people. The results of the comparative survival analyses reveal that although similarities in therapeutic responses often occur in congruence among species, pharmacokinetic differences, immune effects, and comorbidity make it necessary to adapt the responses to each species. In this study, the researchers demonstrate the potential positive impacts of integrating One Health approach to oncology. To illustrate, trans-species collaborative studies would result in faster new discoveries, the narrowing of gaps between research and practice, and the improved outcomes of the patient. Its findings point to enhanced collaboration between veterinarians and physicians to expand comparative oncology data sets, facilitate the more convenient incorporation of clinical research and enhance individualised cancer treatment to both dogs and humans.
Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0
IMMUNOINFORMATICS AND VACCINE DESIGN IN EMERGING VIRAL THREATS
The emergence of new viral threats is always an issue to the health of the world at large and, therefore, we should consider coming out with fast, efficient, and widely protective vaccines against such threats. The research is based on the entire immunoinformatics methodology and experimental verification of the development of multiepitope vaccines against the most significant viral infections. Following a mixed-methodology we were organized to predict cytotoxic T lymphocyte (CTL), helper T lymphocyte (HTL) and B-cell epitopes within our selected viral proteomes, and test them in their capacity to hold HLA binding activity, allergic effects as well as being toxic. Out of 9 groups of viral candidates, 180 high-confidence epitopes were identified. Over 80 percent of them were not allergic or toxic and capable of an immense binding affinity (IC50 er 200 nM) over an assortment of HLA polymorphisms. We prepared the multi-epitope constructs and validated these constructs. They were stably folded and had a nice interaction with Toll-like receptors (TLR2, TLR4). Both Molecular docking and MM/GBSA energy facilitated in proving that the receptor was stable and that it exhibited low values of binding free energy, an indication that the receptor had the potential of strongly activating the immune system. In immune simulations, high cytokine levels, an increase of memory T and B cells, and persistence of immunoglobulin responses were witnessed following simulated boost doses. The test ranged that in vivo ELISpot estimation and flow cytometry experiments indicated that highly-ranked epitopes exposed T cells and made them secrete IFN-gamma. These findings demonstrate the accuracy of the immunoinformatics platforms in the prediction of illnesses and indicate the extent to which they can be useful in accelerating the development of epitope-based vaccines. The work provides us with a paradigm on how to rapidly make vaccines which would be used in future to deal with new viruses and pathogens which are spreading. The present study contributes to the area of next-generation vaccinology, based on the combination of in silico work with an experimental validation. It also provides valuable insight on how to prepare to a pandemic.
CARDIONEPHROLOGY: THE INTERCONNECTION OF CARDIAC AND RENAL AILMENTS
CV dysfunction and chronic kidney disease (CKD) are seen to interact and offer a significant clinical issue warranting integrated care solutions. This study focuses on how effective the cardionephrology co-managed care paradigm is compared to the unidisciplinary care in patients with symptoms of congestive heart failure (CHF) NYHA Class II or higher and/or with stage 3 CKD or higher. Quantitative data included eGFR, UACR, LVEF, BNP, and blood pressure levels, whereas qualitative data present the results of semi-structured interviews on patient satisfaction with the treatment, treatment burden, and perceptions of quality and care. The data obtained were categorized into intervention and control groups through a mixed-methods experimental structure. Based on the findings, the percentage of hospital readmission reduced by 25-point and seven statistical measures in renal and cardiac markers (e.g., increased eGFR, reduced BNP, and enhanced LVEF) in the co-managed group of patients. The thematic analysis result demonstrated an enhancement in the compliance and high satisfaction in interdisciplinary coordination. The composite of eGFR and BNP proved to be a significant predictor of clinical improvement in the multivariate regression (p<0.01p < 0.01p<0.01). The result of the study is that integrated cardionephrology significantly enhances patient outcomes and satisfaction via multidisciplinary and individualized patient care. In the case of high-risk cardiorenal patients, the proposed technology promotes the shift in the management of chronic diseases to interdisciplinary communication.
METABOLOMICS AND CELL SIGNALING IN CANCER PROGRESSION
Signalling-cellular metabolism interplay has a large role in cancer growth. In this study, the researchers investigate these relationships using high-resolution metabolomics and quantitative signaling analysis to determine how to figure out the molecular basis of tumour growth. A multi-dimensional approach to the description of the most suitable modes of action of cancer drugs was performed by examining samples of cancer tissue and cell cultures representing nine experimental groups in a mixed-method approach to an experiment combining the method of UPLC-MS/MS-based profiling and in vitro kinase tests. The findings indicated that the concentration of lactate and glutamine increased progressively, whereas the level of AMPK activity decreased steadily and mTOR level increased dramatically. It indicates that their cells were reprogramming metabolically and in signalling in a co-ordinated way which is characteristic of cancer. Scatter and correlation analyses were used and demonstrated great positive correlations between the concentration of important metabolites and pro-growth indicators of signalling. Hybrid visualisation plots indicated that metabolic-signaling axis became stronger as the disease progressed. The majority of the samples (over 65 percent) reported high mTOR activity, the indicator of anabolic growth and reduced response to energy stress. Computer network modelling was used to validate the results and make sure that they were biologically meaningful with regard to expert opinion. This combination in effect does not only make the complex biochemical layering of the cancer development more explainable, but also demonstrates potential co-targeting methods through the application of metabolic inhibitors and signalling modifiers. The research in general contributes to our understanding of the science of cancer system biology and demonstrates the potential usefulness of multi-dimensional profiling in identification of therapeutic targets which can be tackled.
NEURO-ONCOLOGY AND TARGETED THERAPIES FOR GLIOBLASTOMA
Due to its nonresponsiveness to standard therapy, cellular diversity, and advanced growth rate, glioblastoma multiforme (GBM) is a great clinical problem. The research adopted a mixed-methods study that enrolled 120 GBM patients stratified into standard and targeted treatment groups to evaluate the effectiveness of the targeted medical techniques in the combination with neuro-oncology standards. The quantitative outcomes included MRI volumetrics, progression-free survival (PFS) and molecular biomarker dynamics measured using digital droplet PCR and next generation sequencing tools. Having achieved mean tumor volume reduction of 22% after three treatment courses, targeted therapies, namely, bevacizumab, EGFR inhibitors, and IDH1 modulators, showed a statistically significant improvement in PFS (p < 0.01). The responders had a stronger response towards treatment and a low rate of recurrence as seen with molecular responders; mostly on IDH 1 mutation and methylated MGMT promoter molecules. It was also found that the specific population has experienced a positive impact in terms of treatment satisfaction, emotional coping, and cognitive clarity during qualitative interviews with patients and caregivers. Thematic analysis confirmed that integrated care models proffered a disease pathway that was more dignified and manageable. On balance, this paper identifies that patient-centered and precision-driven care plays a decisive role in the treatment of GBM and provides a standardizable methodological scheme of further translation studies in neuro-oncology.
CRISPR-BASED EPIGENETICS: A NEW ERA IN DEVELOPMENTAL BIOLOGY
The CRISPR based epigenetic engineering has proved quite precise and programmable to adapt in the expression of genes without altering the sequence of the genes themselves. This creates new avenues of studying developmental regulatory networks. Coupled catalytically dead dCas9 together with transcriptional effectors such as KRAB and p300 was used in this study to target promoter, enhancer and CpG-rich regions in pluripotent and embryonic stem cells. Alterations in the expression of guided epigenetic changes involved change of histone marks (like H3 K27 ac and H3 K9 me 3) and DNA methylation in specific loci that altered the expression of genes in a large extent. The RNA-Seq and qRT-PCR assays revealed transcriptional repression of the KRAB-modified regions and strongly activated p300-fused constructs with a measured increase in the expression levels of up to three-fold in certain sites. Time-course analysis had revealed temporal spikes and prolonged effects of regulative activity post-editing. The integrated omics analysis used implied the presence of an intense correlation between chromatin state and transcriptional output (r > 0.8). Phenotypic assays, however, showed that development trajectories have shifted, like shifting spheroid shape and lineage marker markings. Bioinformatic modelling indicated that we can predict about the gene expression on the basis of epigenetic characteristics. This further renders this platform more agreeable in management of development. The study in sum reveals that CRISPR-based epigenetic editing is a powerful and modulatable method to pose queries about and manipulate cell fate. This leaves the alternatives open to new thinking in the areas of developmental biology, regenerative and synthetic embryology.
INTEGRATION OF PALLIATIVE MEDICINE AND ONCOLOGY IN END-OF-LIFE CARE
The combination of palliative medicine and oncology is being discussed to create comprehensive end-of-life (EOL) care models where quality of life of the patient, symptom management, and psychosocial well-being are valued most highly. The study is a retrospective regional city-based study that used a mixed-methods approach in classifying 180 patients with advanced-stage malignancies either to early or late transfer to integrated palliative-oncology units. Quantitative analysis inferred significant improvements in patient-reported outcomes due to early referrals, i.e., less intense pain, better quality of life, and chances of dying at home as assessed by the latter. Better EOL outcomes were highly significant when very early integration of palliative care was indicated, and as confirmed, by the multiple linear regression analysis. There was also a significant interaction effect of type of the intervention and the date of referral on the quality of the dying experience as computed in a two-way ANOVA. Excruciating issues such as the state of emotional preparation, dying with dignity, and ameliorating caregiver burden was uncovered based on the complementary qualitative interviews with patients, caregivers and doctors. These qualitative results emphasized the advantage of immediate transdisciplinary cooperation and communication. The findings underline the necessity to institutionalize integrated care pathways in the practice of oncology in terms of both medical performance and the creation of both moral and emotional support systems. The research made possible a repeatable and tested paradigm of considering linkages of palliative and oncologic concepts in an evidence-based approach that will streamline end-of-life care.
IMMUNOGENETICS OF DISEASE RESISTANCE IN LIVESTOCK
Immunogenetic foundation of disease resistance in cattle breeds is considered in the study and experimental design of the study is mixed method complex which involves genetic data, phenotypic data as well as ethnoveterinary data. PCR amplification and next-generation sequencing was used to search specifically for immune-related genetic variability in blood and tissue samples of multiple breeds. Phenotypic resistance was tested by performing standardised clinical scoring in a controlled environment using pathogens. Traditional knowledge on resilience of breeds in the area was also collected using organized interviews with local herders. Quantitative examination depicted that some of the single nucleotide polymorphisms particularly in the major histocompatibility complex area were highly associated with increased scores of resistance. Techniques of machine learning, such as gradient boosting and random forest classifiers, were highly predictive (AUC > 0.90), and SHAP analysis revealed relevant novel genetic traits that influence resistance. The historically resilient breeds were proved to be better in phenotypic studies and the qualitative results aided in elucidating the perceived genetic patterns that were observed. The pairing of molecular genetics with statistical modelling, as well as local knowledge, provides us with a comprehensive answer to locating and propagating genetically flexible breeds. The findings directly impacts on selective breeding projects, sustainable livestock production and design of precision agrarian apparatus to enhance health, production and yield of herds.
ZOONOTIC TRANSMISSION BETWEEN WILDLIFE AND DOMESTIC ANIMALS IN AGROFORESTRY LANDSCAPES
In dynamic and ecologically dynamic agroforestry environments, zoonotic diseases that originate in the wildlife and are transmitted to domestic animals are a major risk. The study applied the mixed-methods One Health approach to examine the way in which diseases are transmitted between animals and humans in such locations. That is, it did this through molecular diagnostics, spatial risk modelling and stakeholder interviews. The sample size was 1800 domestic animals and interviewed species of the wildlife over a period of one year. Faeces, blood, and nosal fluid Mapping of places revealed that most of the pathogens were found in places where forest was more fragmented, where there existed shared sources of water and where herds of livestock congregated. A generalised linear model showed that the density of domestic animals (beta equals 0.72 p less then 0.01), the density of forest edges (beta equals 0.61 p less then 0.05), and the distance to human settlements (beta equals 0.59 p less then 0.05) were good indicators of the presence of zoonotic. A thematic analysis of 45 interviews revealed that individuals were not familiar with risk of zoonotic hazards and that interspecies contact becomes more common during summer and that individuals allowed their animals to graze through wildlife corridors. This triangulated study demonstrates that agroforestry regions are hot pots of zoonotic emergence due to it ecological permeability and Socio-economic behaviours. The combined approach is effective to demonstrate the interactions of biological, geographical and behavioural factors to influence the likelihood of the disease. In the study, it is demonstrated that the application of transdisciplinary methods is significant in addressing zoonoses at the human-animal-environmental interface. It also indicates the necessity of applying the method of wildlife surveillance, farmer education, or geographical buffer strategies in preventing further outbreaks.
CRISPR-DRIVEN VECTOR CONTROL: GENE EDITING MOSQUITO POPULATIONS TO MITIGATE ZOONOTIC SPILLOVER
This approach to self-propagating CRISPR is a life-altering method to curb the infectious diseases transmitted by mosquitoes. They may reduce the size of mosquito population which causes diseases or eliminate them altogether. The paper examined the CRISPR/Cas9 gene editing in the mosquitoes, Aedes aegypti and Anopheles gambiae. It did so by concentrating on doublesex gene in order to render females sterile and reducing the population. Accessed in the laboratory five generations of modified lines displayed an exceptionally high rate of drive (eta > 96 percent). They also contained these lines with decreased reproductive capacity and increased male-biased sex ratios. Semi-field mesocosm trials revealed that the population will go extinct within eight to ten reproductive generations relative to release ratios of 1:1 and 1:3. In addition, the ecological modelling also indicated that the deployment scenarios involving gene drive would lead to a large reduction in entomological inoculation rates (EIR) and vectorial capacity. The model on agent-based simulations indicated that the risk of spillover would fall by 72 percent in 10 years but this was sensitive with regard to the development of the gene drive resistance and migratory inputs. As indicated in interviews with stakeholders, the control using CRISPR is viewed with a touch of optimism. They emphasized the point of biosafety control and reasonable use in the regions where the disease is prevalent. The findings demonstrate that gene drives work and can be applied in scale to manage vectors, yet sound alarm bells about safety and ethical and ecological considerations. This integrative study sets the pace of future advancement of CRISPR-based mosquito control as a means of preventing subsequent zoonotic spillovers.
Copyright (c) 2024 Zia Ur Rehman (Author) https://creativecommons.org/licenses/by/4.0
CLIMATE-DRIVEN BREEDING SHIFTS IN MIGRATORY BIRDS: A 20-YEAR FIELD STUDY
In the past two decades, the population of migratory birds has shifted significantly, in regard to their reproduction timing. This is largely due to the fact that the climate change is increasingly taking place. This was a 20 years, field trial that observed >12 species encompassing numerous flyways and their arrival dates, nesting early commence dates as well as clutch sizes and fledging success, to understand the impacts of the local climatic changes in terms of temperature, rainfall, and growing degree days. We found that, long-term data indicated that the first eggs were occurring earlier at a rate of 6.3 days per decade. It was directly associated with the beginning of spring temperatures (r = 0.78, p < 0.01). Clutch size and hatching success simultaneously decreased significantly (though not by much on average) in species that breed higher latitudes. This implies that the latter is out of synch with regard to the time of the breeding and the prime food availability. The most committed phenological practitioners to not alter, namely long distance migrants, were in the worst position to accommodate changing phenology. According to satellite-based NDVI and habitat evaluation, importantly, fledging rates were reduced in association with inferior plant productivity of breeding spots. Such findings indicate that the ecology of bird mating is Modifying largely due to both the direct thermal information and the indirect trophic dynamics. This paper is made up of a mixture of ecological modelling, field based phenology and the perception of the stakeholders to provide us with an insight as to the level of migratory birds approachability to adapt to changing climatic environments as well as their scarcity. According to our findings, it is necessary to take steps to preserve vulnerable species since we have to establish a conservation strategy based on climate projections as well as phenological limits.
Copyright (c) 2023 https://creativecommons.org/licenses/by/4.0
STRUCTURAL BIOLOGY MEETS PHARMACOGENOMICS: INSIGHTS INTO DRUG RESISTANCE
Many cancers remain resistant to drugs and hence we require a good combination of approaches that are structural, molecular and computational. As this research demonstrates, structural biology, pharmacogenomics, and machine learning are actually compatible, and all of them can collaborate to determine how drug resistance operates. According to the high resolution molecular docking and dynamics analysis, some of these point mutations decrease binding affinities (DelG bind) and reduce the stability of protein-ligand complexes which can be evidenced by squared RMSD values and squared RMSF values. IC50 was significantly larger in vitro tests on resistant and non-resistant cancer cell lines and major resistance genes (MDR1, BCRP, and CYP450) were over-expressed. qRT-PCR and western blot confirmed these results. With pharmacogenomic data and the use of GDSC and CCLE, we identified additional key mutational hotspots, related to phenotype resistance, these include KRAS, TP53, and transporter A BC genes. Structural and genomic variable-based ML models (Random Forest, XGBoost) were capable of predicting the resistance profile with an accuracy of 92. SHAP demonstrated that the greatest effect was on structural integrity and expression patterns. The result of canonical correlation analysis found the statistically significant association between the changes in the molecular shape and genetic markers. Each of the findings demonstrates that drug resistance is occasioned by various things which include changes in shape and alteration in transcription. This integrative pipeline of methods provides us a means to predict and comprehend the mechanism of resistance, and is a foundation to the exact oncology strategies that attempt to remedy failure of therapy.