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.
Students' Perceptions of Community Health Field Practices I
Introduction: Reflective analysis of the Primary Health Care (PHC) strategy and principles allows for a look back at the past, facilitating the identification of strengths and weaknesses, which is fundamental. Lafit et al. (2025). Objective: To examine students' perceptions of field placements before and after the Community Health I course in Mar del Plata, 2025. Materials and methods: This descriptive, non-experimental, longitudinal study was conducted with students in the Community Health I course of the Medicine program at a private university in Mar del Plata during the 2025 academic year. The sample consisted of 57 students, selected using non-probability convenience sampling. Results: Regarding the expectations students expressed for their pre- and post-course field placements in Community Health I, 95% reported being able to plan and implement health promotion and prevention strategies, 88% reported identifying factors that influence health, and 84% reported learning about the problems in different areas of the city. However, they acknowledged the need for a deeper understanding of how other institutions collaborate with health centers. Conclusions: The study of students' perceptions of the Community Health I course allowed for the identification of strengths and weaknesses in the data obtained through the pre- and post-test questionnaire. A strengthening of students' competencies during their initial training as health promotion and disease prevention agents was evident, although there are aspects that require further development.
Primary Health CarePercepción de estudiantes sobre las prácticas en terreno de Salud Comunitaria I
Self-Medication Practices with Antibiotics Among the General Population: A Questionnaire-Based Cross-Sectional Study
Background: Antibiotics are essential in the treatment of bacterial infections; however, their misuse, particularly through self-medication, poses a significant global public health challenge. Inappropriate use contributes to antimicrobial resistance (AMR), treatment failure, and increased healthcare burden. Young adults are especially prone to self-medication due to convenience and lack of awareness. Aim: To assess self-medication practices with antibiotics among individuals aged 18–35 years and evaluate their knowledge, attitudes, and practices using a questionnaire-based approach. Materials and Methods: A cross-sectional study was conducted over three months among 500 participants aged 18–35 years selected through convenience sampling. A structured and pre-validated questionnaire assessed demographics, knowledge, attitudes, and practices related to antibiotic use. Data were analyzed using descriptive statistics and Chi-square tests, with a p-value <0.05 considered statistically significant. Results: Self-medication with antibiotics was reported by 75.6% of participants. A large proportion (87.2%) lacked awareness about appropriate antibiotic use and AMR. Misconceptions were common, with 62% believing antibiotics are effective against viral infections. Common sources of antibiotics included pharmacies without prescription (58.2%) and leftover medications (29.1%). Convenience (47.6%) and prior experience (31.7%) were major reasons for self-medication. A significant association was found between education level and self-medication practices (p < 0.001), whereas gender showed no significant association. Conclusion: The study reveals a high prevalence of antibiotic self-medication among young adults, coupled with poor knowledge and inappropriate practices. There is an urgent need for public education, stricter enforcement of prescription regulations, and awareness programs to promote rational antibiotic use and combat antimicrobial resistance.
ANALYTICAL METHODOLOGY FOR PHARMACEUTICAL EXTRACTABLES AND LEACHABLES: A COMPREHENSIVE APPROACH FOR CONTAINERS AND CLOSURES
The control of extractables and leachables (E&L) from pharmaceutical packaging and delivery systems is paramount to ensuring patient safety and product quality. This manuscript provides a detailed, comprehensive analytical methodology for the identification, qualification, and quantification of E&L substances from containers and closures used across a wide range of pharmaceutical formulations, including oral liquids, nasal sprays, and injectables. The methodology is meticulously aligned with key international regulatory guidelines from bodies such as the International Council for Harmonization (ICH), the U.S. Food and Drug Administration (FDA), and the United States Pharmacopeia (USP). The core framework encompasses a robust risk assessment, systematic controlled extraction studies, and the application of highly sensitive and orthogonal analytical techniques, including Gas Chromatography-Mass Spectrometry (GC-MS), Liquid Chromatography-Mass Spectrometry (LC-MS), and Inductively Coupled Plasma-Mass Spectrometry (ICP-MS). A critical component of the methodology involves the toxicological evaluation of identified compounds and the establishment of science-based acceptance thresholds, such as the Safety Concern Threshold (SCT) and the Qualification Threshold (QT). A detailed case study on a nasal spray formulation highlights the practical application of this methodology from initial sample preparation to data interpretation and regulatory submission. The manuscript also addresses common challenges faced during implementation, such as the analysis of low-level impurities and the absence of commercial reference standards, and proposes practical solutions. Finally, it explores future perspectives for E&L analysis, including the integration of non-targeted screening and in silico toxicology.
ML Models Using Single-Cell Sequencing to Forecast Chemotherapy Resistance in Breast Cancer
One of the most common cancers in the world and a major contributor to cancer-related death in women is breast cancer. Chemotherapy is a common therapeutic approach, especially for aggressive and advanced breast cancer. Chemotherapeutic agent resistance, however, continues to be a major clinical problem that frequently leads to treatment failure, illness recurrence, and a bad prognosis for patients. Therefore, enhancing therapeutic outcomes and increasing precision oncology require an understanding of the ability to forecast chemotherapy resistance. Conventional bulk RNA sequencing techniques yield gene expression patterns that are averaged over substantial cell populations, which may mask significant biological differences within tumours. This restriction makes it impossible to accurately identify heterogeneous tumour cell groups and uncommon resistant clones that could withstand treatment and cause relapse. Single-cell sequencing technologies, on the other hand, provide high-resolution molecular profiling at the individual cell level. Tumour heterogeneity may be thoroughly examined and malignant cells and the tumour microenvironment, including immunological and stromal components, can be characterised using single-cell RNA sequencing (scRNA-seq). Different resistant subpopulations and dynamic cellular changes that lead to chemotherapy failure can be identified using this method. A potent computational method for deriving significant insights from intricate scRNA-seq datasets is machine learning (ML). ML models can distinguish between resistant and sensitive tumour states, find predictive biomarkers, and discover therapy-induced transcriptional changes by examining high-dimensional transcriptome profiles. Additionally, these models can reveal clonal evolution trajectories, cell state transitions, and circuit activation patterns linked to drug resistance. The integration of scRNA-seq with ML-based prediction frameworks for predicting chemotherapy resistance in breast cancer is the main focus of this study. It covers popular machine learning algorithms, feature engineering techniques, preprocessing pipelines, and validation tactics needed to guarantee reliable prediction. The study also identifies important biological processes that contribute to resistance, such as the epithelial–mesenchymal transition (EMT), drug efflux transporter activation, increased DNA repair capability, metabolic reprogramming, and immune microenvironment remodeling. All things considered, the combination of scRNA-seq and ML offers a promising approach for the early detection of resistant tumour clones, directing individualized treatment choices, maximizing chemotherapeutic selection, and enhancing long-term outcomes for patients with breast cancer.
FEATURES OF CEREBRAL BLOOD FLOW REACTIVITY IN PATIENTS WITH POST-TRAUMATIC EPILEPSY
Transcranial Doppler ultrasonography was used to examine 65 young patients with symptomatic posttraumatic partial epilepsy. Cerebral arterial blood flow velocity and reactivity to functional stress were assessed. Hyporeactivity to vasoconstrictor stress was observed in all clinical groups. Depletion of vasodilator responses was observed in patients with subcompensation and decompensation of the disease. Patients with frequent epileptic seizures showed a significant decrease in reactivity to orthostatic stress. Mild hyperreactivity to the antiorthostatic test was detected in the group of patients with clinical compensation. The hyporeactivity to functional stress in patients with epilepsy is due to the possible parasympathetic influence of the suprasegmental autonomic nervous system.
Topical Therapies in Psoriasis Management: A Comprehensive Review of External Medications and Clinical Outcomes
Background: Psoriasis is a chronic, immune-mediated dermatological disorder characterized by erythematous, scaly plaques resulting from hyperproliferation of keratinocytes and dysregulated immune responses. It significantly affects patients’ quality of life and is associated with comorbidities such as metabolic syndrome and cardiovascular disease. Topical therapies remain the cornerstone of management, particularly in mild to moderate cases, due to their targeted action and relatively favorable safety profile (1–3). Objective: This review aims to comprehensively evaluate the role of topical (external) therapies in psoriasis management, focusing on their mechanisms of action, clinical efficacy, safety, and therapeutic outcomes. Methods: A narrative review was conducted using published literature from standard dermatology textbooks and peer-reviewed journals. Various classes of topical agents, including corticosteroids, vitamin D analogues, retinoids, calcineurin inhibitors, keratolytics, and emerging therapies, were analyzed. Clinical outcomes such as reduction in Psoriasis Area and Severity Index (PASI), improvement in lesion characteristics, and patient adherence were assessed. Results: Topical corticosteroids remain the first-line therapy due to their rapid anti-inflammatory and immunosuppressive effects, though long-term use is limited by adverse effects such as skin atrophy. Vitamin D analogues regulate keratinocyte proliferation and differentiation and are commonly used in combination with corticosteroids for enhanced efficacy and safety. Retinoids such as tazarotene normalize epidermal differentiation but may cause irritation. Calcineurin inhibitors provide a safer alternative for sensitive areas, while keratolytics and coal tar act as adjuncts by improving drug penetration and reducing scaling. Emerging therapies, including phosphodiesterase-4 inhibitors and nanotechnology-based formulations, show promising results in improving drug delivery and clinical outcomes. Overall, combination therapies demonstrate superior efficacy compared to monotherapy, with significant reductions in PASI scores and lesion severity (4–8). Conclusion: Topical therapies play a pivotal role in psoriasis management, offering effective and localized treatment with minimal systemic involvement. While conventional agents remain the mainstay, newer therapeutic approaches provide opportunities for improved efficacy and patient adherence. A personalized treatment strategy that balances efficacy, safety, and patient preferences is essential for optimal clinical outcomes.
Comparative Evaluation of Siddha Neer Kuri and Modern Urine Analysis: Diagnostic Perspectives and Clinical Relevance – A Narrative Review
Background: Urine analysis is one of the oldest and most widely used diagnostic tools in medical practice, offering valuable insights into physiological and pathological states. Traditional systems such as Siddha medicine utilize qualitative approaches like Neer Kuri and Neikuri to evaluate urine characteristics based on humoral imbalances, while modern medicine employs standardized laboratory techniques for physical, chemical, and microscopic examination. Despite differences in methodology, both systems aim to facilitate early diagnosis and monitoring of diseases (1–4). Objective: This narrative review aims to comparatively evaluate Siddha Neer Kuri and modern urine analysis with respect to their principles, diagnostic parameters, clinical applications, and limitations, and to explore the potential for integrative diagnostic approaches. Methods: A comprehensive literature review was conducted using classical Siddha texts, standard medical textbooks, and peer-reviewed scientific articles. Key aspects analyzed include Siddha diagnostic principles such as Envagai Thervu, Neer Kuri, and Neikuri, alongside modern urinalysis techniques including dipstick testing, microscopy, and automated analysis. Correlations between traditional observations and modern biochemical parameters were also examined. Results: Siddha Neer Kuri evaluates urine based on sensory parameters such as color, odor, froth, and quantity, which are interpreted in relation to Vali, Azhal, and Iyyam imbalances. Neikuri further enhances diagnostic interpretation through oil drop pattern analysis, offering prognostic insights. Modern urinalysis provides quantitative and reproducible data on parameters such as pH, protein, glucose, ketones, bilirubin, and microscopic elements. Comparative analysis reveals that certain Siddha observations, such as frothy urine and discoloration, correspond to modern findings like proteinuria and bilirubinuria. While Siddha methods are cost-effective and accessible, they are subjective and lack standardization. Modern techniques, though accurate and reliable, require infrastructure and trained personnel (5–10). Conclusion: Both Siddha and modern urine analysis systems offer valuable diagnostic insights, with distinct advantages and limitations. Integrating traditional qualitative methods with modern quantitative techniques may enhance diagnostic accuracy and accessibility, particularly in resource-limited settings. Further research focusing on standardization and scientific validation of Siddha diagnostic methods is essential to promote their incorporation into evidence-based healthcare.
Advances in Forensic Laboratory Diagnosis: Techniques, Applications, and Challenges – A Narrative Review
Background: Forensic laboratory diagnosis has become a cornerstone of modern criminal investigations, providing objective and scientifically validated evidence for the administration of justice. With the increasing complexity of crimes, traditional investigative approaches are often insufficient, necessitating the integration of advanced laboratory techniques. The evolution of forensic science from conventional serological methods to sophisticated molecular and instrumental techniques has significantly enhanced the accuracy, sensitivity, and reliability of evidence analysis (1,2). Objective: This narrative review aims to comprehensively evaluate the various techniques employed in forensic laboratory diagnosis, including biological, chemical, and molecular approaches, and to highlight their applications, advancements, and associated challenges in the field of forensic science. Methods: A systematic literature-based approach was adopted, reviewing published articles, textbooks, and official forensic guidelines. Emphasis was placed on key domains such as forensic biology, toxicology, chemistry, and pathology. Advanced diagnostic tools including DNA profiling, chromatographic methods, spectroscopic techniques, and emerging technologies like artificial intelligence and rapid DNA systems were critically analyzed. Results: Forensic laboratory diagnosis encompasses diverse methodologies, with DNA profiling using PCR and STR analysis serving as the gold standard for human identification. Toxicological analysis using gas chromatography–mass spectrometry enables precise detection of drugs and poisons, while serological techniques assist in the identification of biological fluids. Recent advancements such as next-generation sequencing, biosensors, and digital forensics have further expanded the scope of forensic investigations. These technologies have improved case resolution rates by enabling rapid, accurate, and high-throughput analysis. However, challenges such as sample contamination, degraded DNA, ethical concerns, and limitations in infrastructure remain significant barriers to optimal implementation (3–6). Conclusion: Forensic laboratory diagnosis continues to evolve as a multidisciplinary and technology-driven field, playing a critical role in strengthening the criminal justice system. While modern diagnostic tools offer unparalleled accuracy and efficiency, addressing existing challenges through standardization, quality control, and technological integration is essential. Future advancements in automation, artificial intelligence, and omics-based approaches are expected to further enhance the capabilities of forensic laboratories, ensuring more reliable and timely delivery of justice.
Role of Yogic Asanas in the Management of Abdominal Obesity and Non-Alcoholic Fatty Liver Disease: A Narrative Review
Background: Abdominal obesity and Non-Alcoholic Fatty Liver Disease are increasingly prevalent metabolic disorders associated with sedentary lifestyles and unhealthy dietary habits. Visceral fat accumulation plays a central role in the development of insulin resistance, dyslipidemia, and hepatic steatosis. Conventional management strategies often face limitations in long-term adherence, highlighting the need for sustainable lifestyle interventions. Objective: This narrative review aims to evaluate the role of yogic asanas in reducing abdominal obesity and improving fatty liver disease, with a focus on underlying mechanisms and available clinical evidence. Methods: A comprehensive literature review was conducted using published studies on yoga, obesity, and fatty liver disease. Relevant clinical trials, observational studies, and review articles were analyzed to assess the effects of specific yogic asanas and integrated yoga practices on metabolic and hepatic parameters. Results: Yogic asanas such as Pavanamuktasana, Bhujangasana, Dhanurasana, and Naukasana demonstrate significant potential in reducing abdominal fat by enhancing core muscle activity, improving digestion, and increasing metabolic rate. These practices also contribute to improved insulin sensitivity, reduced stress hormone levels, and better lipid metabolism. Clinical studies indicate reductions in body mass index, waist circumference, and liver enzyme levels (ALT, AST) following regular yoga practice. Additionally, integrated approaches including pranayama and meditation further enhance these benefits through modulation of the autonomic nervous system and reduction of oxidative stress. Conclusion: Yoga offers a holistic and effective complementary approach for the management of abdominal obesity and fatty liver disease. Its combined effects on physical, metabolic, and psychological parameters make it a promising non-pharmacological intervention. However, further large-scale, well-designed clinical trials are needed to establish standardized protocols and strengthen the evidence base.
Application of Point-of-Care Testing in Viral, Bacterial, and Fungal Infections: A Comprehensive Review
Point-of-care testing (POCT) has emerged as a transformative approach in the diagnosis and management of infectious diseases, offering rapid, accurate, and decentralized detection of pathogens at or near the site of patient care. This review evaluates the applications of POCT in viral, bacterial, fungal, and parasitic infections, highlighting its role in accelerating clinical decision-making and improving patient outcomes. POCT platforms utilize diverse principles, including immunoassays, nucleic acid amplification, and biosensor-based detection, enabling detection from minimal sample volumes with minimal technical expertise. In viral infections, POCT facilitates early detection and outbreak control, exemplified by COVID-19, influenza, dengue, and HIV diagnostics. In bacterial infections, rapid antigen and molecular assays support early pathogen identification, detect drug-resistant infections and critical conditions such as bacteriemia, Septicemia and its management . Fungal and parasitic POCT provide timely bedside diagnosis in immunocompromised patients and enhancing therapeutic interventions and reducing morbidity. Despite advantages such as rapid turnaround time, portability, and ease of use, POCT faces challenges in sensitivity, specificity, cost, and standardization. Ongoing technological innovations, including multiplex assays, microfluidics, CRISPR-based detection, and integrated biosensors, are expanding its diagnostic capabilities and accessibility. Overall, POCT represents a dynamic and evolving tool in modern clinical microbiology, with significant potential to improve infectious disease management across diverse healthcare settings.
Seroprevalence of Cytomegalovirus Among Children Born Hiv Positive at the Yaounde University Teaching Hospital, After 12 Months of Follow-up: A Cross Sectional Study
Background: Cameroon is a country located in Sub-Saharan Africa, an endemic area for the Herpesviridae family. There is very little data on the epidemiology of herpes virus infections, particularly those associated with Human Immunodeficiency Virus (HIV) infection in people living with HIV(PLHIV), HIV-positive infants, and those with undetectable viral loads. The objective of our study was to determine the seroprevalence of four herpes viruses: cytomegalovirus (CMV) in children living with HIV who were born HIV-positive in Yaoundé. Methods: This was a prospective cross-sectional study conducted at Yaounde University Teaching Hospital after 12 months of follow-up among children living with HIV who were born HIV-positive, under antiretroviral treatment, and whose medical records were complete and available at the Approved Treatment Center. IgG/IgM antibodies against CMV were qualitatively determined using rapid diagnostic tests for the detection of these pathogens. Data entry and analysis were performed using the Statistical Package for Social Sciences (SPSS) v 22.0, with Fisher's exact test, Chi-square test, and Mann-Whitney test. P-values less than 0.05 were considered statistically significant. Results: Seventy-four participants were included in the study, with a female predominance of 68.92% (n=51/74). The mean age of our cohort was 9.05±5.09 years, and most participants were under 10 years old (56.76%, n=42/74). The seroprevalence of CMV was 95.95%, and the prevalence of alcohol and tobacco use was 22.97% and 24.32%, respectively. Other parameters such as sex, age, disease stage, smoking, and alcohol consumption were significantly associated with seropositivity for these herpesviruses. Conclusion: Despite the absence of most clinical manifestations related to CMV, HSV-1, and HSV-2, it is important to note a high circulation of these viruses among HIV-infected patients, mainly in bi- and tri-infections.
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.