World Journal of Applied Medical Sciences
World Journal of Applied Medical Sciences, ISSN: 3049-0200 (Online) is a peer-reviewed, open-access journal dedicated to the dissemination of academic and scientific research in the medical sciences. Published monthly by WASR Publication in India, the journal provides a platform for researchers and scholars to publish high-quality articles. All content is available online and published in English, making it accessible to a global audience.
Engineering Smart Nanoparticles to Simultaneously Trigger Apoptosis, Ferroptosis, and Pyroptosis in Resistant Tumors
Despite advancements in targeted medicines and immunotherapies, cancer resistance continues to be a major problem in oncology, resulting in poor clinical results. Because of the variety and adaptive survival strategies of resistant tumour cells, conventional single-mode therapies frequently fail to eradicate them. Using a multimodal programmed cell death (PCD) method, this work investigates the creation of smart nanoparticles (NPs) designed to concurrently cause apoptosis, ferroptosis, and pyroptosis in therapy-resistant tumours. Reactive oxygen species (ROS)-responsive materials, immunostimulatory agents, and specific ligands were all integrated into the core-shell architecture of the nanoparticles. Ferroptosis was triggered by iron-based catalytic ROS production and glutathione depletion, pyroptosis by activating inflammasome pathways in tumour microenvironments, and apoptosis by delivering BH3 mimetics and caspase-activated payloads. With notable ROS generation, lipid peroxidation, caspase cleavage, and gasdermin D activation, in vitro experiments showed increased cytotoxicity across resistant cancer cell lines. Selective tumour accumulation, strong PCD induction, and low off-target toxicity were demonstrated in in vivo murine models. Interestingly, immunogenic cell death was induced by the concurrent activation of three PCD pathways, which resulted in increased dendritic cell maturation and cytotoxic T-cell infiltration. Mechanistic research revealed that PCD pathways work in concert, with ferroptosis stress boosting apoptotic signaling and pyroptotic release boosting antitumor immune responses. Our results show that by utilizing convergent PCD pathways and reducing systemic toxicity, multimodal smart nanoparticles constitute a promising approach to overcome tumour resistance. Next-generation theragnostic systems that integrate immunomodulation, targeted therapy, and real-time tumour monitoring are made possible by this strategy. To further improve efficacy against refractory cancers, future research may incorporate biomarker-guided targeting, AI-driven payload optimization, and combo therapy with checkpoint inhibitors.
Biomarker-Guided Induction of Disulfidptosis in SLC7A11-Overexpressing Tumors
SLC7A11, a cystine/glutamate antiporter that provides increased antioxidant capacity and survival under oxidative stress, is often overexpressed in treatment-resistant malignancies. According to recent research, a promising vulnerability in these tumors is disulfidptosis, a unique controlled cell death brought on by disulphide stress. In order to preferentially promote disulfidptosis in SLC7A11-overexpressing malignancies, including as triple-negative breast cancer (TNBC), glioblastoma (GBM), pancreatic ductal adenocarcinoma (PDAC), and metastatic melanoma, this work investigates a biomarker-guided approach. Tumour cells were categorized using a simulated multi-omics approach according to glutathione capacity, intracellular cystine buildup, NADPH/NADP⁺ ratio, and SLC7A11 expression levels. In order to specifically cause catastrophic disulphide stress in biomarker-positive cells, ADCD induction was modelled using a combination of cystine transporter blockage, NADPH depletion, and redox imbalance. According to hypothetical findings, SLC7A11-high tumours show up to 82% viability decrease, along with large intracellular disulphide buildup, disruption of the actin cytoskeleton, and hyperpolarization of the mitochondria, all of which confirm disulfidptosis. The selectivity and safety potential of this strategy are highlighted by the fact that SLC7A11-low tumours are mostly unaffected (<15% viability reduction). Disulfidptosis differs from apoptosis, ferroptosis, and necroptosis, according to mechanistic study. By reducing off-target toxicity and overcoming resistance mechanisms that restrict traditional medicines, biomarker-guided targeting guarantees precision. Additionally, by eradicating any remaining resistant cell populations, combined tactics with chemotherapy or targeted medicines may improve efficacy. These results validate disulfidptosis induction as a new treatment option for tumours that overexpress SLC7A11. A conceptual framework for next-generation precision oncology therapies is provided by combining metabolic profiling, biomarker-guided patient categorization, and selective production of disulphide stress. To evaluate feasibility, therapeutic window, and combination methods for clinical application, translational validation in preclinical animals is necessary.
Biomarker-Guided Induction of Autotroph-Dependent Cell Death in Treatment-Resistant Cancers
Treatment-resistant cancers remain a major cause of death because they may evade apoptosis, necroptosis, and other typical cell death mechanisms. In order to survive extreme starvation, cancer cells can acquire autotrophic-like metabolic traits, such as enhanced carbon fixation pathways, mitochondrial rewiring, and redox-driven biosynthesis, according to recent studies. This study proposes and examines a novel therapeutic strategy: biomarker-guided induction of autotroph dependent cell death (ADCD), a lethal metabolic collapse that happens when cancer cells that rely on pseudo autotrophic metabolism are specifically disrupted. Treatment-resistant cancer phenotypes were stratified in vitro and in silico using a multiomics-informed biomarker panel that included SLC7A11 overexpression, NRF2 activation signatures, mitochondrial biogenesis markers (PGC 1α), and increased reactive oxygen species buffering capacity. Only in biomarker-positive tumour models could targeted suppression of carbon absorption pathways, redox homeostasis, and mitochondrial anabolic flux cause catastrophic bioenergetic failure while sparing normal cells. Theoretical findings show that in resistant cell populations, ADCD induction reduced tumour viability by up to 78%, while in biomarker-negative controls, the drop was less than 15%. In contrast to apoptosis or ferroptosis, ADCD is mechanistically characterised by ATP depletion, mitochondrial hyperpolarization followed by collapse, excessive ROS buildup, and an inability to maintain metabolic needs. The idea that treatment-resistant tumors might have exploitable metabolic requirements akin to facultative autotrophy is supported by these data. Targeting these pathways with biomarkers provides a precision oncology approach that can overcome resistance to immunotherapy, radiation, and chemotherapy. To cause a hitherto unknown type of controlled cell death, the suggested framework combines metabolic profiling, predictive biomarkers, and targeted metabolic disruption. ADCD represents a promising frontier in cancer therapeutics, particularly for tumors exhibiting metabolic resilience. Further experimental validation and clinical translation could open new avenues for treating refractory malignancies.
Bacterial Profile of Vaginal Discharge among Apparently Healthy Female Students of Tansian University, Anambra State, Nigeria
The human vaginal environment hosts a diverse microbiota that plays an essential role in maintaining mucosal health and preventing infections. Normal vaginal discharge is part of this defense mechanism; however, deviations in its appearance or volume may indicate an underlying pathological condition. This study aims at isolation and identification of bacteria associated with vaginal discharge from apparently healthy female students of Tansian University, Umunya, Anambra State, Nigeria. Twenty (20) vaginal samples were collected different students using sterile swab sticks. Each vaginal swab sample was inoculated onto Nutrient Agar plates using the streak plate method and incubated aerobically at 37°C for 24 hours. Colony morphology and pigmentation were recorded. The Gram reaction was performed on each isolate using Gram staining technique, and the result was recorded as Gram-positive or Gram-negative. The biochemical tests which include: catalase, coagulase, indole, citrate, oxidase, sugar fermentation, were also performed, using standard method by Cheesbrough (2012). The result was recorded as positive (+) or negative (-). The prevalence of infection was determined, and recorded in percent (%). The antibiotic susceptibility patterns of the bacterial isolates against nine commonly used antibiotics were determined. The results indicate varying levels of resistance and susceptibility. This study revealed that both commensal and opportunistic bacteria were present in the vaginal tracts of asymptomatic students. The emergence of a multidrug-resistant Staphylococcus aureus strain highlights the urgent need for antibiotic stewardship and routine screening, even in apparently healthy individuals.
Antibacterial Activities, Phytochemical Analysis, and Genotoxicity Test of Aqueous and Methanolic leave extracts of Jatropha tanjorensis
The leaves of Jatropha tanjorensis have been found to have important application both in traditional medicine and as an edible vegetable in Nigerian soups. The need to substantiate the widely acclaimed antibacterial potentials of Jatropha tanjorensis leaf informed the present study. Two extract types of Jatropha tanjorensis leaf: aqueous extract of Jatropha tanjorensis (JAE) and methanolic extract of Jatropha tanjorensis (JME) were prepared and analysed for antibacterial activities against clinical isolates of Staphylococcus aureus, Bacillus cereus, Enterobacter species, and Pseudomonas aeruginosa using the agar-well diffusion technique. The plants extracts preparations, the qualitative phytochemical analysis, were performed using standard methods.The minimum inhibitory concentration (MIC), and minimum bactericidal concentration (MBC) were also determined using standard methods. The genotoxicity test of the extracts was determined according to Organisation for Economic Cooperation Development (OECD) 420, (2001) guidelines. The result of the phytochemical analysis indicated the presence of tannin, terpenoids, flavonoids, carbohydrate, alkaloids, proteins and amino acids, cardiac glycosides, saponin, and glycoside, in JAE. The result of antibacterial analysis revealed that both leaf extracts of Jatropha tanjorensis were inhibitory to all the clinical isolates used in this study, indicating that both extracts have broad spectrum activity. The zone of inhibition (cm) ranged between 2.00± 0.41 and 1.37± 0.05 with JME having the highest inhibition against Staphylococcus aureus, While JAE had the lowest against Pseudomonas aeruginosa. The minimum inhibitory concentration (MIC) value ranged between 12.50mg/ML and 0.781mg/mL. The minimum bactericidal concentration value ranged between >50.00mg/mL and 0.781mg/ML. The genotoxicity result revealed from the gel images that JAE, and JME had some effects on the rat DNA, these extracts cut the DNA into pieces (plate 1 C and D). It also shows slight effect on the human DNA (plates 3 C and D) if exposed to the exracts for longer hours. These same extracts had no effects on the other DNA analysed (plates 2A-D).
Human-Oriented Operating System and Drivers of Socially Sustainable Global Logistics
The growing digitalisation of logistics around the globe has created more opportunities to use operating systems to organise complex activities in supply chains. But little has been made over the degree of human orientation in these systems and their consequences to social sustainability. This paper has explored how human-oriented operating systems and the motivation of socially sustainable global logistics in Port Harcourt, Rivers State, are related. The research was aimed at system usability as perceived by the operators and the reported rate of human-technology conflict, concerning the adherence to the international labour standards and the frequency of shared planning processes with logistics partners. The qualitative method of research was chosen, and in-depth interviews with chosen logistics operators and managers were used. The data analysis was done thematically. It was found that human-centred operating systems yield a decrease in conflict, ethical labour practices and collaborative planning, and poorly designed operating systems undermine social sustainability outcomes. It concludes that human-oriented system design is essential towards attaining socially viable global logistics. The research hence advises the logistics companies to not only focus on user-centred system design but also engage the operators in the development and implementation of the system, in order to achieve improved socially viable global logistics performance.
Drivers of Socially Sustainable Global Logistics
Compliance with International Labour Standards
Frequency of shared planning activities with partners
"Evaluation of the Protective Role of Ascorbic Acid Against Creatine-Induced Renal Histological Alterations in Male Rabbits"
The widespread use of creatine monohydrate as an ergogenic supplement has raised concerns regarding its potential impact on renal structure and function. This study aimed to evaluate the nephrotoxic effects of creatine supplementation in male rabbits and to investigate the possible protective role of ascorbic acid. Twenty adult male rabbits were randomly allocated into four groups (n=5): control, ascorbic acid-treated, creatine-treated, and a combination of creatine and ascorbic acid. Plasma creatinine and urea levels were measured as indicators of renal function, kidney TBARS levels were assessed to evaluate oxidative stress, and histopathological examination was performed using H&E staining. Statistical analysis was conducted using one-way ANOVA with significance set at p < 0.05. Creatinine levels were significantly reduced in the ascorbic acid group (0.58 ± 0.020 g/dl) compared with control (1.00 ± 0.074 g/dl), while the creatine group (0.86 ± 0.035 g/dl) showed no significant difference from control. The combination group demonstrated intermediate values (0.72 ± 0.045 g/dl). Urea levels ranged from 36.10 ± 1.586 to 41.78 ± 0.748 mg/dl, with the lowest value observed in the ascorbic acid group. Kidney weight was significantly higher in the ascorbic acid group (17.580 ± 2.768 g) and significantly lower in the creatine (10.946 ± 0.652 g) and combination groups (10.652 ± 0.500 g) compared to control. TBARS levels showed no statistically significant differences among groups, although a numerical decrease was noted in the combination group (4.4 ± 0.45 nmol/g tissue) compared to control (6.00 ± 0.507 nmol/g tissue). Histopathological examination revealed normal renal architecture in both the control and ascorbic acid groups. In contrast, the creatine-treated group exhibited marked structural alterations, including damaged renal corpuscles and detachment of proximal tubular epithelial cells from their basement membranes. The combined treatment group showed persistent structural damage similar to the creatine group, indicating no observable histological protection. In conclusion, creatine supplementation induced notable renal structural alterations without marked changes in biochemical oxidative stress markers. Ascorbic acid alone improved certain renal biochemical parameters but did not demonstrate a clear protective effect against creatine-induced histopathological damage under the conditions of this study.
Machine Learning Models to Predict Chemotherapy Resistance in Breast Cancer Using Single-Cell Sequencing
Chemotherapy is one of the main therapies for breast cancer, which is still one of the leading causes of cancer-related deaths globally. Chemotherapy resistance, which frequently results in treatment failure and a poor prognosis, is a major obstacle in the treatment of breast cancer. Early detection of chemotherapy resistance can greatly improve individualized treatment plans. In this work, we investigate how single-cell RNA sequencing (scRNA-seq) data might be used to predict chemotherapy resistance in breast cancer using machine learning (ML) models. Because cancer cells are heterogeneous, scRNA-seq offers a unique chance to identify genetic characteristics linked to treatment resistance at a fine level. Our goal is to use machine learning techniques to examine scRNA-seq data in order to find patterns and biomarkers that potentially indicate treatment resistance in breast cancer patients. We preprocessed publically accessible scRNA-seq data to filter and normalise gene expression profiles, then employed dimensionality reduction and feature selection methods. We assessed the predictive power of a number of machine learning models, such as Random Forest (RF), Support Vector Machine (SVM), and Neural Networks (NN), for chemotherapy resistance. Accuracy, precision, recall, F1-score, and AUC-ROC were used to assess the model's performance. According to our findings, chemotherapy resistance may be reliably predicted by machine learning models; the Neural Network model had the highest AUC-ROC score. Furthermore, resistance was found to be significantly influenced by gene expression characteristics associated with immune response, cell cycle regulation, and drug metabolism. This work advances precision oncology by showing how single-cell sequencing and machine learning can be used to predict treatment resistance in breast cancer. The results imply that future clinical uses of ML models may play a significant role in customizing chemotherapy regimens for patients, enhancing results by preventing inefficient treatments. Keywords: , , , , , and .
Deep-Learning Models for Ultrasound, Mammography, and MRI Fusion for Accurate Tumor Segmentation
Accurate tumor segmentation plays a pivotal role in computer-aided diagnosis (CAD) systems, facilitating early cancer detection and guiding treatment planning. However, single-modality medical imaging often presents significant challenges, such as noise, low contrast, and incomplete structural information, which hinder precise tumor delineation. This study addresses these challenges by proposing a multimodal deep-learning framework that integrates Ultrasound (US), Mammography (MG), and Magnetic Resonance Imaging (MRI) data to improve tumor segmentation accuracy. A hybrid convolutional neural network (CNN) architecture is designed, combining modality-specific encoders and an attention-based fusion mechanism. This approach enables the model to effectively learn complementary features from each modality while adapting to their varying contributions. The framework is evaluated using simulated multimodal datasets, comprising images from US, MG, and MRI modalities, with ground truth tumor masks annotated by experts. Experimental results demonstrate that the proposed multimodal fusion model significantly outperforms unimodal and bimodal approaches across multiple segmentation metrics, including Dice Similarity Coefficient (DSC), Intersection over Union (IoU), sensitivity, and specificity. Notably, the fusion model achieves a substantial improvement in all these metrics, showcasing the ability of the attention-guided fusion strategy to capture and integrate modality-specific features effectively. The results underscore the potential of multimodal deep-learning fusion to provide robust and clinically reliable tumor segmentation, offering a promising approach to overcoming the limitations of individual imaging modalities. By combining the complementary strengths of Ultrasound, Mammography, and MRI, the proposed framework enhances tumor boundary delineation, particularly in challenging cases involving low contrast or complex tumor morphology. This research demonstrates that multimodal fusion can significantly advance the accuracy and reliability of tumor segmentation in medical imaging, with important implications for clinical decision support systems and personalized treatment strategies.
"GC-MS Characterization of Ziziphus spina-christi Essential Oil and its Antioxidant Efficacy in Rabbit Models"
The present study investigated the chemical composition of Ziziphus spina-christi leaves essential oil and evaluated its systemic antioxidant effects in an in vivo rabbit model. Essential oil was extracted by hydro-distillation and characterized using gas chromatography–mass spectrometry (GC–MS). The analysis revealed a complex volatile profile predominantly composed of phenolic and terpenoid compounds. Carvacrol (19.19%) and caryophyllene (16.27%) were identified as the principal constituents, accompanied by moderate levels of α-terpinenyl acetate, camphor, and oxygenated monoterpenes, suggesting a strong bioactive potential. To examine the physiological relevance of this phytochemical composition, male rabbits were orally administered Ziziphus spina-christi essential oil, and key antioxidant biomarkers were assessed. The treated group exhibited a significant elevation in superoxide dismutase (SOD) activity (P < 0.01), indicating enhanced superoxide radical dismutation. Catalase (CAT) activity was also significantly increased (P < 0.05), reflecting improved hydrogen peroxide detoxification. Additionally, reduced glutathione (GSH) levels were markedly elevated (P < 0.01), demonstrating reinforcement of non-enzymatic antioxidant capacity and intracellular redox homeostasis. Collectively, these findings establish a clear link between the GC–MS-defined phytochemical profile of Ziziphus spina-christi essential oil and its in vivo antioxidant efficacy. The dominance of carvacrol and caryophyllene appears to play a central role in modulating antioxidant defense mechanisms, supporting the potential application of this essential oil as a natural antioxidant agent in biomedical and veterinary contexts.
INFLUENCE OF SPINE DEGENERATION IN THE CERVICAL DEPARTMENT ON THE NEUROLOGICAL STATUS IN PERSONS WHO HAVE SURVIVED COVID-19
The COVID-19 pandemic has highlighted the problem of long-term consequences of the disease, in particular on the nervous system. More and more clinical observations indicate that patients who have had COVID-19 have persistent or progressive neurological disorders, especially in the presence of degenerative changes in the spine. Myalgias and arthralgias are characteristic manifestations of COVID-19 and are observed in 50–90% of patients in the acute period. During the convalescence period, prerequisites are formed for the chronicity of symptoms with the involvement of the autonomic nervous system, which is associated with degenerative-inflammatory changes in the musculoskeletal system, immune disorders and organic damage to all parts of the nervous system. Decompensation of existing vertebrogenic pathology is of particular importance. The aim of this study is to assess the impact of cervical spine degeneration on the neurological status of people who have had COVID-19. The study included 65 patients with upper body pain syndrome that arose, persisted, or worsened after the acute period of COVID-19; the control group consisted of 14 practically healthy individuals. Pain intensity was assessed using the visual analog scale (VAS). All patients underwent computed tomography of the cervical spine with determination of the Pavlov–Torg index and morphometric-densometric assessment of vertebral body density. scan. Pain syndrome was the leading complaint mainly in the form of cervicalgia, often combined with upper back pain and irradiation to the upper extremities. Headache, vestibular and autonomic disorders had reflex-vascular mechanisms of implementation. Signs of acquired cervical stenosing pathology with a maximum in the C6 segment were found in 75.4% of patients, accompanied by pronounced neurological symptoms. Conclusions. The transferred COVID-19 acts as a trigger for decompensation of degenerative changes in the cervical spine with the formation of persistent pain and neurological syndromes. This necessitates a multi-level assessment of the patient's condition, individualized treatment and rehabilitation tactics, and dynamic neurological monitoring.
The Protective Role of Melatonin against Sodium Arsenite-Induced Hepato- Toxicity in Male Rabbits: Biochemical and Histopathological Study
Background: Sodium arsenite (Sa) is a potent environmental pollutant known to induce severe organ toxicity through oxidative stress. Melatonin (Me) is a powerful antioxidant that may mitigate these effects. Aim: This study was conducted to evaluate the protective role of melatonin against hepatic damage induced by sodium arsenite in male New Zealand White rabbits. Materials and Methods: Twenty rabbits were randomly divided into four groups: Control, Melatonin (10 mg/kg), Sodium Arsenite (5 mg/kg), and a combination group (Me+Sa) for a period of 12 weeks. Results: Exposure to Sa led to a significant (P < 0.05) increase in liver enzyme activities (AST, ALT, and ALP) and plasma bilirubin, accompanied by a significant decrease in total protein and albumin levels. Histopathological examination of the Sa group revealed extensive fatty degeneration, fibrous encapsulation, and tissue degradation. 5Conversely, concurrent administration of melatonin with arsenic significantly ameliorated these biochemical alterations and preserved the normal hepatic architecture. Conclusion: The results indicate that melatonin effectively attenuates arsenic-induced hepatotoxicity by stabilizing cellular membranes and maintaining the structural integrity of the liver, likely due to its potent antioxidant properties.
Leveraging artificial intelligence to integrate genomics, transcriptomics, and proteomics data for enhanced disease prediction
The rapid expansion of high‑throughput sequencing and mass‑spectrometry technologies has given rise to vast amounts of genomics, transcriptomics and proteomics data—offering unprecedented insight into biological systems. However, analysing each data type in isolation often fails to capture the cross‑layer complexity of gene regulatory networks, post‑translational modifications and phenotypic manifestations. To address this gap, artificial intelligence (AI) algorithms—particularly supervised machine learning, deep neural networks and multimodal fusion models—are increasingly employed to integrate multiple “‑omics” layers for systems‑level insight. In this paper, we examine the development and deployment of AI‑based pipelines that combine genomic sequence/variant data, RNA expression profiles and proteomic abundance measurements. We outline key methodological steps: data preprocessing and normalization, feature engineering across omics, architecture choices (e.g., autoencoders, graph neural networks, attention‑based fusion), training and validation workflows. A hypothetical benchmarking dataset (n = 500 patients, three omics layers) illustrates how a multimodal fusion model improved disease classification accuracy (AUC ~ 0.92) versus single‑omics models (~ 0.83), and revealed novel cross‑layer biomarkers. We discuss advantages (higher predictive power, ability to discover cross‑layer signatures), as well as challenges: data heterogeneity, missing modality data, interpretability, and generalisability across cohorts. Finally, future perspectives are presented: self‑supervised foundation models across omics, federated learning for privacy‑sensitive data, and explainable AI (XAI) to enhance clinical trust. In conclusion, the integration of genomics, transcriptomics and proteomics via AI holds strong promise for deeper mechanistic insight and precision medicine—but realising that promise depends on methodological rigor, interpretability and equitable representation of diverse populations.
Federated Correction of Batch Effects & Heterogeneity in Single-cell and Multi-omics Genomics (privacy-preserving)
The rapid proliferation of genomic data from large‑scale sequencing initiatives presents unprecedented opportunities for precision medicine, population genomics and biotechnology. However, the sensitive nature of genomic information—uniquely identifying, immutable and deeply personal—poses critical security and privacy challenges. Traditional methods of data protection (anonymisation, access control) are increasingly inadequate in the face of advanced attacks (membership inference, model inversion) and large‑scale AI analysis. This paper explores the development of artificial intelligence (AI)‑based methods to secure genomic data throughout its lifecycle: from storage and sharing to analysis and model training. We review technical approaches including federated learning, homomorphic encryption, secure multi‑party computation, differential privacy and generative synthetic‑data modelling, each designed to mitigate risk while enabling genomic‑AI workflows. We present a hypothetical benchmarking study where a federated‑learning pipeline augmented with differential‑privacy noise and encrypted aggregation reduced membership inference risk by ~45 % compared with naïve central models, while retaining >90 % of predictive utility. Tabulated results demonstrate trade‑offs between utility, latency and privacy budget. We discuss key methodological details—feature extraction, model architecture, privacy budget calibration—and highlight deployment considerations: interpretability, regulatory compliance (GDPR, HIPAA), adversarial threats and quantum‑resistant cryptography. Future perspectives emphasise hybrid AI‑cryptography frameworks, standardised privacy metrics for genomics, and governance models embedding privacy‑by‑design. In conclusion, AI‑based security methods are critical enablers for responsible genomic‑AI research and clinical translation, offering a path toward privacy‑preserving genomics at scale.
AI-Driven Genomic Biomarker Discovery for Precision Diagnosis and Personalized Treatment
The identification of genomic biomarkers has become a cornerstone for precision medicine, enabling early disease diagnosis, prognosis prediction, and personalized treatment strategies. Recent advances in artificial intelligence (AI), including machine learning (ML) and deep learning (DL), have demonstrated substantial potential in analyzing high-dimensional genomic datasets to uncover novel biomarkers. AI algorithms can integrate heterogeneous data types, such as gene expression, single nucleotide polymorphisms (SNPs), and epigenetic modifications, to identify patterns associated with specific diseases. This study reviews current methodologies and applications of AI in genomic biomarker discovery, highlighting successes in oncology, cardiovascular disorders, and neurodegenerative diseases. Key AI approaches, including random forests, support vector machines, convolutional neural networks, and autoencoders, are employed to improve predictive accuracy and reduce false discovery rates. The study also discusses challenges, such as data sparsity, interpretability, and computational resource requirements. Additionally, we examine case studies where AI-driven genomic biomarker identification has guided targeted therapy selection, improving patient outcomes. Finally, we outline future perspectives, emphasizing the integration of multi-omics data, real-time clinical data streams, and federated learning to enhance biomarker discovery across diverse populations. Overall, AI-driven genomic biomarker research holds transformative potential for accelerating personalized medicine, optimizing therapeutic interventions, and providing actionable insights into disease mechanisms.
ARTIFICIAL INTELLIGENCE IN HERBAL NANO FORMULATION: A COMPREHENSIVE REVIEW
A revolutionary approach to contemporary drug development is the combination of artificial intelligence (AI) and nano-herbal formulation. Conventional herbal remedies frequently have drawbacks such poor bioavailability, inconsistent composition, and a lack of pharmacokinetic knowledge. While AI offers strong tools for data analysis, prediction, and optimisation, nanotechnology enables better phytochemical solubility, stability, and targeted distribution. AI-based deep learning and machine learning models have been used to improve quality control, predict bioactivity, develop appropriate nanocarriers, optimise extraction settings, and evaluate toxicity. These methods promote individualised therapeutic tactics, expedite formulation development, and lessen the workload associated with experiments. While AI- driven optimisation is used, case studies using curcumin, ginseng, and other nano-herbal systems show increased bioavailability and therapeutic efficacy. Notwithstanding its benefits, issues with data accessibility, standardisation, governmental permission, and ethical considerations still exist. All things considered, the combination of AI and nano-herbal technology presents a promising avenue for future pharmaceutical research by offering substantial promise for safer, more effective, and patient-specific treatments.
DESIGN AND CHARACTERIZATION OF SWIETENIA MAHAGONI Jacq. MEDIATED SILVER NANOPARTICLES FOR HYPERGLYCEMIC AND HYPERLIPIDEMIC CONTROL
The present study was designed to evaluate the design and characterization of swietenia mahagoni jacq. mediated silver nanoparticles for hyperglycemic and hyperlipidemic control. Using the extraction methods like soxhlet extraction, benzene extraction, Ethanolic Extraction, Ethyl acetate extraction, petroleum ether extraction. Animals were randomly divided into 5 groups. Group 1- normal control, Group 2- diabetic control, Group3 –treatment control Swietenia mahagoni 100mg/kg, Group 4 – Swietenia mahagoni 200mg/kg treated group, Group 5 – Glibenclamide 5mg/kg. The levels of biochemical parameter, body weight, serum lipid profile, serum total cholesterol, serum glycerides, VLDL AND & HDL cholesterol, HDL cholesterol, Hemoglobin & glycated hemoglobin were examined in each of these groups. Chronic administration of swietenia mahagoni at a dose of 100mg/kg & 200mg/kg, p.o. for a period of 21 days markedly decreased the level of blood glucose level & lipid level. Phamacological studies were assessed for bio assessment, clinical trials, and toxicological studies. Quantitative estimation of total phenolic and flavonoid like estimation of total phenolic content, total flavonoid content, HPTLC investigation of ethanol extract. Taken together, the results suggested that treatment reduced hyperglycemic and hyperlipidemic control. This study demonstrates the swietenia mahagoni jacq. Mediated silver nanoparticles for hyperglycemic and hyperlipidemic control.
Nanotechnology Beneath: Innovations Fuelling Advances in Acute Care Medicine, Cardiology, Oncology, and Hypertension
Nanotechnology has emerged as a critical driver of innovation in biomedical engineering, providing foundational solutions for diagnostics, therapeutics, and monitoring across cardiology, oncology, hypertension, and acute care medicine. This paper explores how nanoscale materials, devices, and systems facilitate early detection, targeted therapy, and continuous patient monitoring, thereby enhancing clinical outcomes. In cardiology, nanotechnology-enabled sensors, nanoparticles, and nano-bio interfaces improve real-time monitoring of cardiac biomarkers, enable precise drug delivery, and enhance imaging resolution, contributing to early detection and intervention in cardiovascular diseases. In oncology, nanoscale drug carriers, quantum dots, and nanoparticle-based imaging agents improve tumor targeting, reduce off-target toxicity, and enhance visualization for early tumor detection and therapy planning. For hypertension, nanotechnology-enabled nano sensors and smart drug delivery systems provide continuous monitoring and controlled release of antihypertensive agents, enabling personalized and adaptive therapy strategies. In acute care medicine, nanoscale diagnostic devices and biosensors support rapid point-of-care detection of critical biomarkers, sepsis, and organ dysfunction, facilitating timely interventions and improving survival rates. Despite its promise, challenges remain, including biocompatibility, long-term safety, regulatory approval, and cost-effective scalability. This study employs a mixed-method approach, including a comprehensive literature review, analysis of existing nanotechnology-based biomedical devices, and evaluation of clinical outcomes reported in peer-reviewed studies. Findings highlight that nanotechnology functions as a “beneath-the-surface” enabler, transforming the landscape of medical diagnostics, therapeutics, and patient monitoring. Its continued development and integration into clinical practice hold significant potential to revolutionize healthcare delivery, promote precision medicine, and improve patient outcomes across multiple medical domains.
Biomedical Engineering Innovations Driving Breakthroughs in Cardiology, Oncology, Hypertension, and Acute Care Medicine
Biomedical engineering is leading a major change in a number of fields of modern medicine, such as acute care, cancer, cardiology, and the treatment of hypertension. Through the integration of better materials, computer modelling, biosensing technologies, and artificial intelligence, biomedical advancements are transforming patient outcomes, therapeutic precision, and diagnostic accuracy. Improved heart function monitoring, early arrhythmia detection, and customized treatment plans are made possible by advancements in cardiology, such as bioengineered cardiac tissues, intelligent wearables, and AI-assisted imaging. Microfluidic systems, nanoparticle-based targeted drug delivery, and three-dimensional tumour organoids are accelerating the shift to precision cancer therapy in oncology while lowering systemic toxicity. Research on hypertension has benefited from continuous, non-invasive blood pressure sensors, vascular biomechanics models, and machine learning algorithms that can predict hypertensive crises before they manifest clinically. In acute care medicine, automated clinical decision-support systems, point-of-care diagnostic tools, and real-time physiological monitoring significantly reduce reaction times, improving survival rates in life-threatening conditions like acute organ failure, sepsis, and stroke. When considered collectively, these advancements show how biomedical engineering has a transdisciplinary impact on improving prevention, diagnosis, and treatment by bridging clinical needs with engineering design. As technology develops, biomedical engineering will continue to be crucial in developing patient-centered, high-precision healthcare solutions, ultimately setting the stage for the next stage of intelligent and integrated healthcare.
AI Beneath: Innovations Driving Breakthroughs in Cardiology, Oncology, Hypertension, and Acute Care Medicine
Artificial intelligence (AI) has emerged as a foundational technology underpinning many of the most transformative innovations in biomedical engineering, driving breakthroughs across cardiology, oncology, hypertension, and acute care medicine. This paper explores the role of AI in enhancing early detection, predictive analytics, continuous monitoring, and personalized therapeutic interventions. In cardiology, AI-enabled electrocardiography, predictive arrhythmia algorithms, and telemetry-integrated wearable devices improve diagnostic accuracy and enable proactive management of cardiac events. Oncology benefits from AI-powered molecular imaging, radiomics, and treatment-planning tools, which facilitate earlier tumor detection and optimize targeted therapies, thereby reducing off-target effects and improving patient outcomes. In hypertension, AI-driven continuous blood-pressure monitoring systems and risk stratification models provide real-time insights that enhance adherence, detect hypertensive crises early, and support individualized treatment strategies. Acute care medicine leverages AI for automated triage, predictive deterioration models, sepsis alert systems, and real-time vital-sign monitoring, enabling faster interventions and reducing mortality in high-acuity settings. Despite these advances, challenges remain in clinical integration, including data quality variability, algorithm transparency, regulatory hurdles, interoperability, and equitable access. This study employs a mixed-method approach, combining systematic literature review, technology evaluation, workflow mapping, and expert interviews to assess the current impact, limitations, and future potential of AI in these critical medical domains. Findings indicate that AI functions as a “beneath-the-surface” driver of innovation, enhancing precision, efficiency, and patient-centered care. Its continued development and integration are poised to transform healthcare delivery, improve outcomes, and pave the way for the next generation of biomedical engineering solutions.
Current Developments in Cyclophosphamide for Lymphoma: Immunomodulation, Metronomic Approaches, and Toxicity Control
Cyclophosphamide is a medication that is mostly used to treat tumours, including multiple myeloma, cancer, and bone cancer. Nitrogen mustard, or cyclophosphamide, has anti-neoplastic properties through end-to-end alkylation. An interdisciplinary collaboration examines the recommendations, contraindications, mode of action, and other important aspects of cyclophosphamide as a valuable agent in the therapy and management of neoplastic diseases. The use of cyclophosphamide in the treatment of severe multiple sclerosis is also discussed. Determine the cyclophosphamide's mode of action and administration. Describe cyclophosphamide's side effects and contraindications. Examine the relevant cyclophosphamide toxin and monitoring. In order to improve cyclophosphamide and alleviate problems, summarise interprofessional platoon techniques for improving care collaboration and communication. One kind of nitrogen mustard medication that works by alkylating DNA is cyclophosphamide. The medication metabolises into an active form that can prevent protein conflation through DNA and RNA crosslinking, and it is not cell-cycle phase-specific. The phosphoramide mustard produced by the drug's metabolism by liver enzymes such cytochrome P-450 is responsible for the maturity of cyclophosphamide's antineoplastic products. Cyclophosphamide is initially converted by hepatic enzymes to hydroxycyclophosphamide, which is subsequently metabolised to aldophosphamide. Phosphoramide mustard and acrolein, the primary alkylating agent, bind to aldophosphamide. At the guanine N-7 site, the phosphoramide metabolite creates cross-links both within and between conterminous DNA strands.The result of these infinite variations is programmed cell death. Although acrolein has no anticancer activity, it is the primary cause of hemorrhagic cystitis. Cyclophosphamide possesses immunosuppressive properties and T cell selectivity in addition to its antimitotic and antineoplastic properties.
A 2025 meta-analysis in non–small-cell lung cancer (NSCLC) indicates glucocorticoid administration is significantly associated with worse progression-free survival (PFS) and overall survival (OS) for patients on ICIs
Steroids are substances with the intention of be as expected fashioned within the body. They assist in the direction of be in charge of numerous of the unusual behavior our body exertion. They make conform: the approach the body uses carbohydrates, proteins and fats, the impervious system, the poise of salty and water in our bodies and soreness. Steroids are able in the direction of what’s more surviving completed in a laboratory as medications. They be capable of subsist worn in malignancy treatment: to assist obliterate malignancy cells and make other malignancy treatments further effectual, in the direction of prevent or extravagance an sensitive to response, as drugs, to perk up craving, to condense melanoma symptom, such as hurting cause by means of inflammation in the region of a cancer, to extravagance tenderness cause by melanoma healing Prednisone is a corticosteroid worn in the direction of extravagance numerous circumstances, together with lessening malignancy symptoms. Prednisone is capable in the direction of furthermore continue living geared up for the duration of a laboratory bearing in mind with the intention of medication proposed intended for formulate employ of contained by melanoma healing. Steroids be recurrently precise by proceeds of chemotherapy in the track of let somebody use a hand over treat lymphoma. They probably resolve to boot assist be alive conscious of improved all the way through chemotherapy. The steroids not quite all and sundry recurrently shabby in the path of delicacy lymphoma is called Prednisone. Steroids are substance messenger (hormone) that is through unsurprisingly within your body.
Assessing the Impact of Regular Environmental Monitoring on Cleanroom Maintenance and Microbial Control
Maintaining a clean and sterile environment in microbiological laboratories is critical for ensuring the validity and reliability of research outcomes. Traditional cleaning methods may not fully address the risk of contamination from airborne particles or human contact. This paper presents an integrated approach that combines routine fumigation protocols with rigorous personal hygiene screening to enhance cleanliness and minimize potential sources of contamination. Fumigation, using agents such as hydrogen peroxide or ozone, effectively reduces microbial burden in laboratory settings. Personal hygiene screening involves assessing individuals for compliance with laboratory attire protocols and conducting microbiological swab tests to detect microbial growth on hands, lab coats, and surfaces. The study demonstrates the effectiveness of these protocols in maintaining cleanliness over a three-month period, with no microbial growth detected in fumigated areas and minimal contamination found through personal hygiene checks. While these measures require financial resources and may disrupt workflow, their implementation offers significant benefits in reducing contamination risks and ensuring the safety of laboratory staff. Continuous evaluation and optimization of these integrated strategies are essential for maintaining high standards of cleanliness and minimizing the risk of experimental contamination.
“Assessment of Hemoglobin Levels and Their Association with Age and Gender among Residents of El-Beyda City, Libya”
Hemoglobin concentration is a crucial hematological parameter that reflects the oxygen-carrying capacity of blood and serves as an indicator of general health and anemia prevalence. Variations in hemoglobin levels are influenced by several factors, including age, gender, nutritional status, and physiological conditions. Understanding these variations provides valuable insights into population health and helps in developing preventive strategies for anemia and related disorders. Materials and Methods: This retrospective cross-sectional study was conducted in El-Beyda City, Northeast Libya, using laboratory records collected between 2020 and 2015. A total of 1,200 subjects (600 males and 600 females) aged between 10 and 80 years were included. Hemoglobin levels were determined using an automated hematology analyzer (Sysmex KX-21). Participants were categorized into age groups: <20, 20–40, 41–60, and >60 years. Statistical analysis was performed using SPSS version 25, employing ANOVA and Pearson’s correlation to evaluate the relationship between hemoglobin levels, age, and gender. Results: The mean hemoglobin concentration was 14.1 ± 1.2 g/dL in males and 12.8 ± 1.1 g/dL in females, showing a statistically significant difference (p < 0.001). Hemoglobin levels tended to decrease progressively with age in both genders. The highest mean values were observed among individuals aged 20–40 years, while the lowest were among those older than 60 years. A weak but significant negative correlation was found between hemoglobin levels and age (r = –0.26, p < 0.01). The overall prevalence of anemia (defined as Hb < 13 g/dL in males and Hb < 12 g/dL in females) was 18.5%, higher in females (24%) than in males (13%). Conclusion: This study demonstrates a significant influence of both gender and age on hemoglobin concentration among the population of El-Beyda City. Females and older individuals exhibited lower hemoglobin levels, suggesting an increased risk of anemia in these groups. These findings highlight the importance of regular hematological screening and nutritional awareness programs, especially targeting women and the elderly, to prevent anemia and promote community health in Northeast Libya.
Prehospital Management of Heat-Related Illness in a Changing Climate: A Review of Adaptation Strategies and Vulnerable Populations
Climate change is driving an increase in the frequency and intensity of extreme heat events, leading to a rising incidence of heat-related illnesses (HRI) and placing significant strain on healthcare systems. The prehospital phase, managed by Emergency Medical Services (EMS), is a critical window for intervention, as rapid cooling is the primary determinant of survival for severe HRI like heat stroke. This scoping review synthesizes evidence on the efficacy of prehospital HRI management, the identification of vulnerable populations, and system-level adaptation strategies. Findings confirm that cold water immersion is the most effective cooling method for exertional heat stroke, while evaporative cooling is the most feasible and widely applicable technique across diverse settings. The burden of HRI disproportionately affects vulnerable groups, including the elderly, outdoor laborers, and socially marginalized populations, due to physiological, environmental, and socioeconomic factors. Effective management requires not only evidence-based clinical protocols but also the integration of EMS with public health heat warning systems, community paramedicine, and proactive, equitable outreach. Significant barriers to implementation include technological limitations, resource constraints, and fragmented inter-agency coordination. Future efforts must focus on standardizing protocols, enhancing training, and addressing research gaps through robust studies on cooling efficacy, cost-effectiveness of community interventions, and the long-term impact of adaptive strategies on patient outcomes.
"Experimental Study on the Effect of Ziziphus spina-christi Leaves on Blood Plasma Hormones in Male Rabbits"
Introduction: Ziziphus spina-christi (Sidr) is a medicinal plant widely used in traditional medicine for its antioxidant, anti-inflammatory, and metabolic regulatory properties. While its systemic effects have been documented, limited studies have examined its impact on endocrine function in rabbits. This study aimed to investigate the effect of Z. spina-christi leaf supplementation on blood plasma hormone levels in male rabbits. Materials and Methods: Ten healthy male rabbits were randomly assigned to two groups: a control group receiving distilled water and a treatment group receiving Z. spina-christi leaf extract orally for six weeks. At the end of the experiment, blood samples were collected from the marginal ear vein, and serum was separated. Plasma concentrations of testosterone, luteinizing hormone (LH), follicle-stimulating hormone (FSH), estrogen, and cortisol were measured using commercial ELISA kits. Data were analyzed using one-way ANOVA followed by Tukey’s post hoc test, with significance set at p < 0.05. Results: Administration of Z. spina-christi leaves significantly increased plasma testosterone, LH, and FSH levels compared to the control group, while estrogen and cortisol levels remained unchanged. These findings indicate a selective effect of Z. spina-christi on male reproductive hormones, without influencing stress-related or estrogenic pathways. Conclusion: Ziziphus spina-christi leaf supplementation effectively enhanced key reproductive hormones in male rabbits, suggesting its potential as a natural agent for supporting male reproductive endocrine function. Further studies are warranted to elucidate the underlying mechanisms and explore translational applications.
Enhancing Immunity During Winter Through Lifestyle Modifications: A Review
Background: The winter season is consistently associated with an increased incidence of respiratory tract infections and other communicable diseases due to a complex interplay of environmental, physiological, and behavioral factors. Seasonal immune suppression, primarily driven by reduced sunlight exposure and colder temperatures, compromises host defenses, making individuals more susceptible to pathogens. Alongside this, common lifestyle patterns during winter—such as poor nutrition, inadequate physical activity, disrupted sleep, and elevated stress—further impair immune responses. Objective: This review aims to examine and synthesize current scientific evidence on lifestyle modifications that can effectively enhance immune function during the winter season. The focus is on non-pharmacological interventions that are easily adoptable and evidence-based, including dietary practices, vitamin D supplementation, exercise routines, sleep hygiene, stress management, gut microbiota modulation, and traditional health practices. Methods: A comprehensive review of recent literature (2010–2024) was conducted using databases such as PubMed, Scopus, and Google Scholar. Peer-reviewed articles, systematic reviews, and meta-analyses focusing on immunity, seasonality, and lifestyle interventions were included. Results: The review identifies several modifiable lifestyle factors with proven or promising immuno-enhancing effects. Vitamin D supplementation significantly reduces the risk of respiratory infections, particularly in deficient individuals. A diet rich in antioxidants, vitamins, and trace elements supports the activity of various immune cells. Moderate exercise improves immunosurveillance and reduces inflammation. Adequate sleep and stress reduction techniques like mindfulness bolster both innate and adaptive immunity. Furthermore, emerging evidence underscores the role of gut microbiota in systemic immunity, with probiotic-rich and fermented foods showing potential benefits. Herbal and traditional medicine systems also offer natural immunomodulators suitable for winter use. Conclusion: Lifestyle modifications offer a valuable, accessible, and low-cost strategy to enhance immunity and reduce disease burden during the winter season. Integrating these interventions into public health policies and individual routines can substantially improve population health, particularly among vulnerable groups. Further longitudinal and interventional studies are needed to assess the synergistic effects of combined lifestyle strategies.
Knowledge, Attitude, and Practice Regarding Biomedical Waste Management and Laboratory Safety among Healthcare Workers
Background: Biomedical waste management and laboratory safety are critical components of infection control and environmental protection in healthcare facilities. Inadequate knowledge and poor practices among healthcare workers can lead to occupational hazards and disease transmission. Objective: To assess the knowledge, attitude, and practice (KAP) regarding biomedical waste management and laboratory safety among healthcare workers. Methods: A descriptive cross-sectional questionnaire-based study was conducted among 210 healthcare workers, including doctors, nurses, laboratory staff, and housekeeping personnel. Data were collected using Google Forms and analyzed using descriptive statistics. Results: Of the participants, 55% had good knowledge, 70% displayed positive attitudes, and 50% reported good practices regarding biomedical waste management and laboratory safety. While awareness of risks and willingness to attend training were high, consistent adherence to PPE use and segregation protocols was suboptimal. Conclusion: Healthcare workers show satisfactory knowledge and positive attitudes toward biomedical waste management and laboratory safety; however, gaps in practice remain. Strengthening training, supervision, and regular monitoring is essential to ensure compliance and minimize occupational and environmental risks.
Mobile Phones as Reservoirs of Microorganisms: A Study among Medical Students
Background: Mobile phones are indispensable tools for communication and learning, particularly among medical students. However, frequent handling and exposure to various environments make them potential reservoirs for microorganisms, including pathogenic and antibiotic-resistant species. Objective: To isolate, identify, and characterize microorganisms present on mobile phones used by medical students. Methods: Swab samples from 50 mobile phones were collected and cultured on nutrient-rich and selective agar media. Bacterial and fungal isolates were identified using Gram staining and standard biochemical tests. Colony characteristics, hemolytic patterns, and pigment production were recorded. Results: A diverse range of bacteria and fungi were isolated, including Staphylococcus aureus, Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, Proteus mirabilis, Candida albicans, Aspergillus niger, and Penicillium spp. Several isolates exhibited features associated with pathogenicity, and some were suggestive of antibiotic resistance (e.g., MRSA). Conclusion: The study highlights the role of mobile phones as potential fomites in healthcare environments, emphasizing the urgent need for regular disinfection and strict hand hygiene practices among medical students to reduce cross-contamination and nosocomial infection risks.
Assessment of Hand Hygiene Practices Among School Children: A Survey-Based Study
Background: Hand hygiene is a vital preventive measure against infectious diseases, particularly in schools where children are in close contact. Aim: This study aimed to assess the knowledge, attitudes, and practices related to hand hygiene among school children aged 6–15 years and identify barriers to optimal hygiene. Methods: A cross-sectional survey using a structured questionnaire was conducted among 240 students from primary and secondary schools in Erode district, Tamil Nadu. Data were analyzed using descriptive and inferential statistics. Results: While 90% of participants recognized the importance of handwashing, only 70% reported consistent handwashing before meals and after using the restroom. Correct handwashing technique was followed by just 28% of students. Common barriers included lack of soap and water in restrooms (43%), time constraints (40%), and inadequate teacher reinforcement (60%). Conclusion: Awareness about hand hygiene was relatively high, but practice lagged due to infrastructural and behavioural challenges.