Internet of Things (IoT) Based Real-Time Kitchen Monitoring System The emergence of Internet of Things (IoT) has undoubtedly transformed safety, automation, and monitoring systems across various sectors, particularly in smart home environments. This study presents the design and implementation of an Internet of Things (IoT)-based real-time kitchen monitoring and automation system aimed at enhancing safety, efficiency, and intelligent control within kitchen environments. The system was developed to address the limitations of manual monitoring processes, which are often labor-intensive, error-prone, and unreliable in emergency situations. The system integrates multiple sensors to enable real-time alerts and automated responses. Wireless communication is achieved through a Wi-Fi network connected to the Blynk IoT platform, allowing remote monitoring via smartphones and laptops. Upon detection of hazardous conditions such as gas leakage, fire outbreak, abnormal temperature levels, motion intrusion, or low water levels, the system triggers local alarms and transmits notifications to users through the IoT dashboard. Sensor fusion techniques were applied to enhance data accuracy and reduce false alarms. Experimental evaluation was conducted under simulated real-world kitchen scenarios to assess responsiveness, accuracy, and reliability. The results demonstrated an overall system accuracy of 91.8%, with high detection reliability across all integrated sensors and minimal false positives.

]]> Internet of Things (IoT), Real-Time Monitoring, Kitchen Automation, Smart Kitchen Research Article Mon, 24 Aug 2026 00:00:00 IST An Enhanced Oil Spill Detection System Using Explainable Ai (XAI) and Transfer Learning on Synthetic Aperture Radar (SAR) Imagery Marine petroleum spills caused serious ecological degradation. It is urgent for automation early oil spill detection and identification. Although SAR is an active microwave sensor providing continuous day-and-night operation in adverse conditions such as cloudy days or night, the difficulty for differentiating between oil slicks and natural ocean look-alike has increased since their SAR backscatter properties are quite similar. This paper presents an interpretable deep neural network model for pixel-level oil slick segmentation that leverages a U-Net with a ResNet50 (pre-trained) feature extractor and Spatial/Channel Squeeze-and-Excitation (SCSE) attention modules. Gradient-weighted Class Activation Mapping (Grad-CAM) was used in the meantime to improve the visual transparency of automatic decisions to find important input regions by showing where and why the decision was made (for example, a pixel-level region of an oil spill region was found because its internal convolutional layer recognized a particular pattern). Through utilizing an official Sentinel-1 SAR dataset for testing and validation, the model reached an overall accuracy of 98.65% and mean Intersection over Union (mIoU) of 0.894, showing that accurate and reliable remote sensing-based oil spill monitoring is improved by the combination of attention processes and transfer learning.

]]> ResNet50, Explainable Artificial Intelligence, Semantic Segmentation, Transfer Learning, Oil Spill Detection, Synthetic Aperture Radar (SAR) Research Article Thu, 20 Aug 2026 00:00:00 IST Hepatoprotective effect of high dose of vitamin A on the liver in toxic dose of methamphetamine induced adult male Wistar rats Methamphetamine (METH) is a potent psychostimulant that induces oxidative stress and hepatotoxicity following prolonged or high-dose exposure. Vitamin A, a fat-soluble antioxidant, has been reported to exert protective effects against oxidative tissue injury. This study evaluated the effect of high-dose vitamin A on methamphetamine-induced liver toxicity in adult male Wistar rats. Twenty adult male Wistar rats were randomly assigned to four experimental groups (n = 5 per group). Group A served as the control and received standard feed and water only; Group B received methamphetamine (10 mg/kg body weight) administered at 3-hour intervals daily; Group C received high-dose vitamin A (2.5 mg/kg body weight); and Group D received methamphetamine in combination with high-dose vitamin A. All experimental animals had free access to standard feed and water throughout the study. The administration was done orally by gavage for 28 consecutive days. Twenty-four hours after the final administration, the animals were anesthetized and sacrificed. The livers were excised, weighed, and fixed in 10% neutral buffered formalin for histopathological examination using haematoxylin and eosin (H&E) staining. Blood samples were collected for the determination of serum liver enzyme activities, including alanine aminotransferase (ALT), aspartate aminotransferase (AST), and alkaline phosphatase (ALP). Body weight results revealed reduction in body weight of the rats in METH treated group while co-administration with high-dose vitamin A mitigated the weight loss. The organ weight assessment revealed an increase in relative liver weight across the experimental groups compared with the control group. However, the methamphetamine-treated group (Group B) exhibited a significantly higher relative liver weight than the control group (P < 0.005). Histological observation revealed that methamphetamine caused liver damage such as hepatocellular degeneration and inflammatory infiltration. Co-treatment with high-dose vitamin A showed improved liver cytoarchitecture suggesting protective effect. In conclusion, methamphetamine caused hepatotoxicity in Wistar rats of the animal models studied while high-dose vitamin A offered hepatoprotection against methamphetamine-induced liver damage.

]]> Vitamin A, Wistar rat, Hepatoprotective, Toxic dose, Methamphetamine Research Article Tue, 30 Jun 2026 00:00:00 IST Nephroprotective effect of low dose of vitamin A on the kidney in toxic dose of methamphetamine induced adult male Wistar rats Methamphetamine (METH), a potent psychostimulant, is widely recognized for its neurotoxic and systemic toxic effects, including oxidative and inflammatory damage to peripheral organs such as the kidney. Excessive METH exposure induces nephrotoxicity characterized by oxidative stress, tubular degeneration, and impaired renal function. Vitamin A, in its active metabolite form (retinoic acid), exhibits strong antioxidant and cytoprotective properties that may counteract drug-induced organ injury. This study investigated the potential protective effect of low-dose of vitamin A on the kidneys of adult male Wistar rats exposed to toxic doses of methamphetamine. Twenty rats were randomly divided into four groups (n = 5): group A (control) feed and water, group B (METH-only; 20g/kg at 3-hour intervals within 12 hours in a day), group C (vitamin A-only; 0.7 mg/kg) while group D (combined METH + vitamin A). All the experimental groups were fed with feed and water. All treatments were administered orally via intubation for 28 consecutive days. After the final administration, animals were anesthetized and sacrificed; kidneys were harvested, fixed in 10% neutral formal-saline and processed for histological examination using Hematoxylin and Eosin (H&E) staining. Results of methamphetamine exposure group showed weight loss, oxidative stress, and notable renal architectural disruptions, including tubular necrosis, glomerular shrinkage, and inflammatory infiltration. However, co-administration of low-dose of vitamin A preserved renal morphology, reduced cellular degeneration, and stabilized biochemical markers of kidney function. The findings of this study suggest that vitamin A confers a nephroprotective effect against methamphetamine-induced renal toxicity, likely mediated through its antioxidant and anti-inflammatory mechanism.

]]> Toxic dose, Vitamin A, Wistar rat, Nephroprotective, Methamphetamine Research Article Tue, 30 Jun 2026 00:00:00 IST 3D Printed Ocular Drug Delivery Systems: Recent Progress and Future Perspectives The three-dimensional (3-D) printing model has been recognized as an ideal platform through which ocular drug delivery vehicles can bypass the natural anatomical and physiological barriers that limit the performance of traditional eye drops, suspensions, and injections. Through the ability to provide geometrical precision in regulating space and volume for drug loading, 3D printing can be used to create personalized, location-specific ocular dosage geometries capable of sustaining drug release, decreasing dosing frequency, and improving patient compliance and comfort. Rational design of 3D-printed devices has enabled the development of ocular inserts, microneedles, contact lenses, and microfluidic systems (Giri et al., 2024; Tan et al., 2022). Extrusion has been widely used in anterior segment therapy, in which ocular inserts capable of extending residence time and providing controlled drug release have been fabricated. More recently, sodium hyaluronate-derived hydrogel inserts loaded with liposomal moxifloxacin (SL:MOX) were printed as multilayered structures with consistent size and thickness. FTIR and SEM analyses demonstrated the successful production of liposomes (~150 nm) with an encapsulation efficiency of approximately 80%, while maintaining uniform drug content and avoiding destructive drug–polymer interactions. In vivo, 10-layer SL:MOX inserts achieved approximately 71% drug release with a near zero-order release profile and slower release than non-liposomal MOX inserts and conventional eye drop formulations, resulting in improved ocular retention and bioavailability (Duman et al., 2024; Giri et al., 2024; Alzahrani et al., 2023). The treatment challenges associated with posterior segment diseases have stimulated the development of 3D-printed intraocular implants capable of prolonged drug delivery. Homogeneous dispersion of triamcinolone acetonide (TA) within a polycaprolactone (PCL) matrix has been achieved with loading efficiencies approaching 100% through precise geometry control, while eliminating residual organic solvents that may irritate ocular tissues. Implants with higher surface-area-to-volume ratios demonstrated the greatest cumulative drug release over 180 days in vitro, and release kinetics followed the Korsmeyer–Peppas diffusion model. Cytocompatibility exceeded 90% cell viability, highlighting the potential of customizable implants to provide sustained steroid delivery while reducing the need for repeated intravitreal injections (Annuryanti et al., 2023; Ioannou et al., 2023).

]]> 3D Print, Ocular Drug Delivery, physiological barriers, traditional eye drops Editorial Article Tue, 30 Jun 2026 00:00:00 IST Green Synthesis of Nanoparticles from Musa paradisiaca: Comprehensive Review Nanotechnology, an interdisciplinary and fast-advancing field, deals largely with particles whose dimensions fall between 1 and 100 nm, valued for the distinctive optical, physicochemical, and biological behavior that emerges at this scale. Green synthesis routes rely on plant- and fruit-derived reagents that are inexpensive, renewable, and lower in toxicity than conventional chemical precursors, and such biologically produced nanoparticles tend to be more stable and quicker to form. Because they are highly biocompatible, green-synthesized nanoparticles find use across a wide span of biomedical and pharmaceutical settings. Metal oxide nanoparticles, in particular, display strong catalytic behavior and are routinely applied to neutralize toxic or hazardous substances, especially where environmental safety is a concern. Gold, aluminium, zinc, copper, titanium, iron, and silver are among the metals most frequently exploited for nanoparticle fabrication, with several standing out for their broad relevance in biomedicine; zinc oxide nanoparticles (ZnONPs), for instance, are extensively incorporated into food-packaging materials and into paint and varnish formulations. The plant at the centre of this review, Musa paradisiaca (locally called virupakshi), serves as a key raw material for producing several nanoparticle types, including those of silver, gold, iron oxide, copper oxide, and zinc oxide. Traditionally, this plant has been employed to manage ailments such as diabetes, diarrhoea, hypertension, ulcers, and inflammation, a therapeutic profile attributed to bioactive constituents that impart antioxidant, antibacterial, wound-healing, and anti-diarrhoeal effects. In this review, ZnONPs synthesized from Musa paradisiaca peel extract are examined for their relevance to biology, biomedicine, environmental remediation, industry, agriculture, and food science.

]]> green-synthesized, Musa paradisiaca, antioxidant, antibacterial, nanotechnology, sustainable Review Article Tue, 30 Jun 2026 00:00:00 IST Computational assessment of bioactives from Phyllanthus emblica leaves as potential anti-obesity agents Obesity is a chronic metabolic disorder associated with serious health complications, including cardiovascular diseases, type 2 diabetes mellitus, and dyslipidemia. The increasing prevalence of obesity has created a need for safer and more sustainable therapeutic alternatives. Plant-derived waste materials are rich sources of bioactive compounds and offer significant potential for drug discovery. The present study investigated the anti-obesity potential of phytoconstituents obtained from amla leaves (Phyllanthus emblica) using an in-silico approach. Obesity-related target proteins (PDB IDs: 9MIZ and 9VG4) involved in lipid metabolism and adipogenesis were selected for molecular docking studies. Ligand structures were retrieved from the PubChem database and docked using AutoDock Vina. Protein–ligand interactions were analyzed using Discovery Studio Visualizer. Drug-likeness and pharmacokinetic properties were evaluated through ADME analysis, while toxicity prediction was performed to assess safety profiles. The results demonstrated that several phytoconstituents exhibited favourable binding affinities toward both target proteins and formed stable interactions with key amino acid residues. Furthermore, selected compounds satisfied Lipinski’s Rule of Five and Ghose filter criteria, showed acceptable ADME characteristics, and exhibited low predicted toxicity. These findings suggest that phytoconstituents derived from plant waste materials possess promising anti-obesity potential and may serve as lead candidates for further development. However, experimental validation through in vitro and in vivo studies is required to confirm their efficacy and safety.

]]> obesity, molecular docking, plant waste, Phyllanthus emblica, ADME, toxicity prediction, anti-obesity activity Research Article Tue, 30 Jun 2026 00:00:00 IST An Offline Classroom Presentation System Via Local Area Networks for Real-Time Screen Sharing Traditional multimedia projectors in Nigerian classrooms are frequently affected by technical failures, poor maintenance, and outright unavailability, disrupting lessons and reducing student engagement. This study developed an offline classroom presentation system that uses WebRTC technology over a local wireless network as a practical alternative to traditional projectors. The system comprises a presenter device, a Node.js signalling server built with Express.js and Socket.io, a Mediasoup Selective Forwarding Unit (SFU), and multiple viewer devices, all connected over a classroom local area network (LAN) without internet access. The presenter uploads a single media stream to the SFU, which forwards it independently to each connected viewer, eliminating the bandwidth and CPU scaling constraints associated with a peer-to-peer topology. The frontend was built with HTML, CSS, and vanilla JavaScript, requiring only a modern browser for access, while the server enforced HTTPS using pre-generated self-signed certificates to satisfy the browser security requirements of the screen-capture API. System performance was evaluated using latency, CPU and RAM usage, scalability, and cross-browser compatibility metrics. Results showed stable, low-latency screen and audio sharing to up to twenty simultaneous viewers, with average latency remaining below 100 ms and consistent performance across Chrome, Edge, and Firefox. The findings confirm the technical feasibility of the system and its ability to replicate the core functionality of a projector without dependence on internet connectivity or costly hardware. The study concludes that WebRTC-based LAN systems represent a viable, scalable, and cost-effective alternative to traditional classroom projectors, with meaningful implications for educational technology deployment in resource-constrained environments.

]]> Offline Classroom, Presentation System, Local Area, Networks, Real-Time Screen Sharing Research Article Tue, 30 Jun 2026 00:00:00 IST The Adoption of Digital Technologies and Service Delivery in Public Organizations in Bauchi State: A Case Study of Bauchi State Ministry of Science, Technology and Innovation Digital technology adoption in public sector organizations has become a strategic imperative for improving service delivery quality, administrative efficiency, and citizen satisfaction in developing economies. Despite Nigeria's National Digital Economy Policy 2022–2025 mandating digital transformation across all government ministries, departments, and agencies (MDAs), empirical evidence on the specific mechanisms through which digital technology dimensions translate into service delivery quality improvements particularly in North-Eastern state-level ministries remains limited. This study examined the adoption of digital technologies and their influence on service delivery quality at the Bauchi State Ministry of Science, Technology and Innovation (BMSTI), with digital adoption as a mediating variable. Grounded in the Technology-Organization-Environment (TOE) Framework and the New Public Management (NPM) theory, a quantitative cross-sectional survey design was adopted. A structured questionnaire was administered to 260 staff using stratified random sampling. IBM SPSS Statistics Version 29 was used for descriptive and reliability analysis. SmartPLS Version 4 was employed for PLS-SEM and mediation analysis. Python 3.11 (scikit-learn, matplotlib) was used for Decision Tree feature importance analysis and service delivery KPI visualization. Results showed that digital infrastructure (β = 0.334), e-service deployment (β = 0.278), staff digital capacity (β = 0.247), and leadership support (β = 0.221) each significantly influenced service delivery quality (all p ≤ 0.001), collectively explaining 63.8% of variance (R² = 0.638). Digital adoption mediated all four paths (VAF: 39.7%–42.6%). Python Decision Tree analysis identified digital infrastructure as the most important predictor (Gini importance = 0.312, Rank 1). Service delivery KPIs showed document retrieval time improved by 78.2% and digital service use rate grew by 265.8% between 2019 and 2023. All constructs demonstrated strong reliability (α > 0.82) and validity (AVE > 0.56).

]]> Digital Technologies, Service Delivery, Public Sector, PLS-SEM, TOE Framework, New Public Management, Bauchi State Research Article Tue, 30 Jun 2026 00:00:00 IST Predictive Maintenance of Oil and Gas Infrastructure Using AI Models: Enhancing Operational Efficiency and Economic Output Nigeria's oil and gas sector, the bedrock of the national economy accounting for over 87% of foreign exchange earnings, suffers chronic infrastructure deterioration resulting in annual production losses estimated at $3–5 billion. Conventional preventive and reactive maintenance paradigms have proven inadequate for the scale and operational hazards of Nigerian petroleum infrastructure. This study develops, validates, and compares five AI-based predictive maintenance (PdM) models – Long Short-Term Memory (LSTM) networks, Random Forest, Support Vector Machine (SVM), XGBoost, and Temporal Fusion Transformer (TFT) – for fault detection and Remaining Useful Life (RUL) prediction, using a dataset of 52,840 multi-sensor time-series observations collected from operational Nigerian oil and gas installations over 2019–2024. The stacked ensemble model achieved the highest classification accuracy of 98.1% (F1-Score: 0.977; AUC-ROC: 0.994), outperforming the TFT (97.3%), LSTM (96.7%), and XGBoost (95.1%) individually, and substantially exceeding the conventional preventive maintenance baseline (71.4%). Economic impact analysis reveals that AI PdM deployment reduced annual unplanned downtime by 68.2% (from 4,218 to 1,342 hours), decreased maintenance expenditure by 47.3% (from $521.4M to $274.8M), and recovered an estimated $5.64 billion in previously lost production revenue annually. Macroeconomic modelling projects a 1.5 percentage point uplift in the oil sector's contribution to Nigeria's GDP. These findings establish a compelling evidence base for the systematic adoption of AI predictive maintenance across Nigeria's petroleum infrastructure and provide actionable policy recommendations for NUPRC, NNPC Limited, and international oil company (IOC) operators.

]]> Predictive Maintenance; Artificial Intelligence; LSTM; XGBoost; Transformer; Oil and Gas Infrastructure; Nigeria; Operational Efficiency; Economic Output; Industry 4.0 Research Article Tue, 30 Jun 2026 00:00:00 IST Comparative Evaluation of OpenAI and Transformer Models for Twitter Sentiment Classification This paper presents a comparative analysis of five advanced Natural Language Processing (NLP) models: GPT-4, GPT-3.5, BERT, RoBERTa, and DistilBERT, specifically trained and evaluated for sentiment classification on Twitter data. The study emphasizes the development of these models and assesses their performance using standard metrics, including accuracy, precision, recall, and F1-score. The results indicate that BERT achieved the highest F1-score of 0.8% with a balanced focus on accuracy and efficiency, while DistilBERT delivered a competitive accuracy of 0.86% with significantly reduced inference times. Although GPT-based models excelled in contextual understanding, they exhibited higher latency. These findings highlight the trade-off between predictive accuracy and computational efficiency when deploying AI models for real-time sentiment analysis applications.

]]> Sentiment Analysis, Natural Language Processing, Transformer Models, OpenAI, GPT, Twitter Research Article Tue, 30 Jun 2026 00:00:00 IST A Behavioral Analytics Framework for Machine-Learned Insider Threat Detection The rapid expansion of modern digital environments and cyber-physical systems has significantly increased organizational exposure to insider threats. Unlike external actors, malicious insiders operate with legitimate credentials and system access, enabling them to easily bypass static perimeter controls and traditional intrusion detection systems. To address this challenge, this paper introduces a multi-tiered Behavioral Analytics Framework for Machine-Learned Insider Threat Detection. The proposed framework continuously ingests multi-modal telemetry spanning computer-mediated linguistic communications, system access logs, and physical badge records to construct dynamic user profiles and isolate subtle behavioral anomalies. While traditional linear classifiers struggle in this domain due to high false-positive rates, tree-based gradient boosting models provide exceptional discriminative capability. In particular, XGBoost achieves superior threat sensitivity with an F_1-score of 75.5% and an AUC of 98.5%, recording the fewest false negatives overall. By integrating these gradient boosting dynamics into an optimized voting ensemble core, the proposed framework achieves a peak classification accuracy of 98.3%, an AUC of 98.5%, and an F_1-score of 98.2%, while constraining False Acceptance (FAR) and False Rejection (FRR) rates to 3.1% and 2.8%, respectively. Operating with an average inference latency of 185 ms, predictive risk scores feed directly into an automated Zero-Trust enforcement engine. Augmented by Explainable AI (XAI) feature attribution modules and adversarial input defenses, the framework offers high operational transparency for analysts while maintaining resilience against insider manipulation and evasion.

]]> Insider Threats, Cyber security, Behavioural Analysis, Machine Learning, Anomaly Detection, Data Security Research Article Tue, 30 Jun 2026 00:00:00 IST IoT-Based Intruder Detection and Monitoring System of Transmission Tower The rapid growth of the Internet of Things (IoT) has significantly advanced its application across various sectors, particularly in safety and security systems. One of the critical applications of IoT-based solutions is in the development of an IoT-based intruder detection and monitoring system for transmission towers. This study aims to design and implement such a system to enhance security measures, enable real-time monitoring, and reduce the risk of vandalism, unauthorized access, and damage to transmission tower infrastructure. This study developed an IoT-based real-time intrusion detection system for transmission tower security, integrating PIR sensors, a microcontroller, wireless communication, and solar power for autonomous operation in remote areas. The system enables real-time monitoring and alerting, with performance evaluated using a confusion matrix across multiple test scenarios. Results show an accuracy of approximately 96% with fast response times (1.5–1.9 seconds) and minimal false alarms, though performance is occasionally affected by environmental and network factors. Overall, the system provides a reliable and sustainable solution for enhancing infrastructure security in off-grid environments with 96%.

]]> IoT, Monitoring System, Transmission Tower, Integrated Sensors, Microcontroller, Wireless Communication Research Article Tue, 30 Jun 2026 00:00:00 IST Quality of Life and Visual Satisfaction with Progressive and Single-Vision Lenses in Myopic Presbyopes Background: Presbyopia superimposed on myopia creates a complex correction requirement because clear vision is needed across distance, intermediate, and near viewing distances. The functional impact may vary with myopia severity and the spectacle-correction modality used. This study evaluated vision-related quality of life (QOL) and visual satisfaction among myopic presbyopes using progressive addition lenses (PALs) and single-vision (SV) spectacles.

Methods: A cross-sectional, observational, questionnaire-based study was conducted from February to September 2025 among 60 presbyopic participants with myopia aged 41-75 years. Participants comprised 30 PAL users and 30 SV users, with each modality group containing 15 low-myopia and 15 high-myopia participants. Vision-related QOL was assessed using the Refractive Status and Vision Profile (RSVP) questionnaire, with lower scores indicating better QOL. Visual satisfaction was assessed at distance, intermediate, and near with and without habitual spectacle correction. Independent-samples t-tests were used for group comparisons, and paired t-tests were used for with-versus-without spectacle comparisons; p<0.05 was considered statistically significant.

Results: PAL users demonstrated lower RSVP scores and therefore better QOL than SV users in both myopia categories. Among low myopes, mean RSVP scores were 3.45 for PAL users and 23.59 for SV users, whereas among high myopes they were 27.62 and 44.87, respectively. The overall difference in QOL between correction modalities was statistically significant (p<0.001). The greatest modality-related difference in visual satisfaction occurred at intermediate vision: mean scores were 1.07 versus 2.67 among low myopes and 2.00 versus 4.13 among high myopes for PAL and SV users, respectively. Distance satisfaction was comparable between modalities, while near-vision differences were relatively small. High myopes demonstrated greater visual difficulty than low myopes despite spectacle correction. Overall, 50 participants (83.3%) were satisfied with their current spectacles; among the 10 participants wishing to change correction, 9 (90.0%) preferred switching from SV to PAL.

Conclusion: PAL use was associated with better vision-related QOL and greater intermediate-vision satisfaction than SV correction among myopic presbyopes. Higher myopia was associated with greater visual burden despite spectacle correction. These findings support individualized, patient-centered spectacle prescribing, particularly for patients with substantial intermediate and multi-distance visual demands.

]]> Myopia, Presbyopia, Progressive addition lenses, Single-vision lenses, Quality of life, Visual satisfaction Research Article Tue, 30 Jun 2026 00:00:00 IST Awareness, Knowledge, and Barriers to Low Vision Services Among Eye Care Practitioners Background: Low vision rehabilitation is an important component of comprehensive eye care for individuals whose visual function remains impaired despite conventional ophthalmic management. Eye care practitioners have a pivotal role in recognizing patients who may benefit from rehabilitation, providing appropriate low vision interventions, and facilitating timely referral. However, inadequate knowledge, training, resources, and awareness may limit effective service provision. This study assessed awareness and knowledge of low vision services among eye care practitioners and explored perceived barriers to service provision and patient uptake.

Methods: A cross-sectional survey was conducted among 100 eye care practitioners using a structured questionnaire. Information was collected regarding demographic and professional characteristics, frequency of low vision encounters, awareness and knowledge of World Health Organization (WHO) low vision criteria, provision of low vision devices, practitioner- and patient-level barriers, awareness of concession facilities, and strategies for improving low vision services. Descriptive statistics were used to summarize participant characteristics, knowledge, service provision, perceived barriers, and recommended interventions.

Results: All practitioners (100%) reported awareness of the WHO definition of low vision, and 98% recognized the relevance of visual acuity and visual-field assessment. However, only 60% reported using WHO-based criteria in clinical practice, while 59% and 60% correctly identified the specified visual-acuity and visual-field thresholds, respectively. This demonstrated an important gap between general awareness and application of specific clinical knowledge. Regarding service provision, 66% reported providing both optical and non-optical low vision devices, whereas 33% provided optical devices alone and 1% provided non-optical devices alone. Major practitioner-level barriers included inadequate awareness and training, limited motivation, time constraints, and device-related expense. Patient-level barriers prominently included inadequate awareness and motivation, service-related expense, and concerns regarding the cosmetic acceptability of devices. Awareness of concession facilities was reported by 82% of practitioners. A combined strategy incorporating public awareness, practitioner awareness, and professional training was the most frequently recommended approach (65%).

Conclusion: Although general awareness of low vision was high, substantial gaps remained in the recognition and clinical application of specific low vision criteria and in comprehensive service provision. Strengthening structured low vision education and continuing professional development, improving access to appropriate devices and referral pathways, and increasing public awareness may help overcome practitioner- and patient-level barriers and facilitate greater integration of low vision rehabilitation into routine eye care.

]]> Low vision, Low vision rehabilitation, Eye care practitioners, Optometry, Knowledge, Health services Research Article Tue, 30 Jun 2026 00:00:00 IST Occupational Ocular Surface Foreign-Body Injuries: Characterizing Workplace Exposures and Gaps in Protective Eyewear Use Background: Occupational ocular surface foreign-body (OSFB) injuries are important yet largely preventable workplace eye injuries, particularly in occupations involving metal processing and particulate-generating activities. This study characterized the demographic, occupational, and clinical profile of occupational OSFB injuries and evaluated protective-eyewear practices and factors associated with protective-eyewear non-use.

Methods: This hospital-based cross-sectional observational study included 130 participants presenting with occupational OSFB injuries. Demographic characteristics, occupation, work activity at injury, foreign-body material and anatomical location, presenting symptoms, protective-eyewear use, reasons for non-use, previous OSFB injury, self-removal attempts, and awareness of ocular risks were recorded. Categorical variables were summarized using frequencies and percentages. Associations were assessed using Pearson's χ² or Fisher's exact test, as appropriate. Multivariable binary logistic regression was performed to identify factors independently associated with protective-eyewear non-use, with adjusted odds ratios (ORs) and 95% confidence intervals (CIs).

Results: Participants were predominantly male (123/130, 94.62%), and 68 (52.31%) worked in the metal industry. Metal grinding/cutting was the most common activity at injury (73, 56.15%). Metallic foreign bodies were predominant (77, 59.23%), with the cornea being the most frequently affected site (92, 70.77%). Ocular irritation was the most frequently reported symptom (77, 59.23%), followed by pain (65, 50.00%), redness (61, 46.92%), and watering (41, 31.54%). Only 27 participants (20.77%) reported using protective eyewear, whereas 103 (79.23%) reported no protection. Among non-users, the most common reason was PPE non-availability (36/103, 34.95%), followed by other reasons (24, 23.30%) and forgetting to wear PPE (21, 20.39%). Awareness of potential harm from self-removal was reported by 84 (64.62%) participants. PPE use was significantly associated with this awareness in bivariate analysis (29.8% vs 4.3%; χ²=10.17, p=0.001). In multivariable analysis, high-risk work activity was independently associated with PPE non-use (adjusted OR 2.14, 95% CI 1.01–4.54, p=0.047), whereas awareness was not statistically significant after adjustment (adjusted OR 0.53, 95% CI 0.26–1.08, p=0.081).

Conclusion: Occupational OSFB injuries predominantly involved male workers engaged in metal-related, particulate-generating activities, with metallic foreign bodies and corneal involvement being most common. The high prevalence of PPE non-use highlights an important preventable safety gap. Prevention should combine reliable PPE availability, hazard-specific protective eyewear, worker adherence, and targeted occupational eye-health education.

]]> Occupational eye injury, ocular surface foreign body, protective eyewear, personal protective equipment, occupational safety, corneal foreign body, metal workers Research Article Tue, 30 Jun 2026 00:00:00 IST Short-Term Visual Outcomes and Associated Factors After Retinal Laser Photocoagulation in Diabetic Retinopathy Background: Retinal laser photocoagulation remains an established treatment for sight-threatening diabetic retinopathy (DR), with its principal benefit often being preservation of existing vision rather than substantial visual acuity gain. However, short-term visual trajectories may vary according to baseline ocular and systemic characteristics. This study evaluated visual outcomes following retinal laser photocoagulation over a 6-week follow-up period and assessed associations between visual outcomes and baseline visual acuity, DR severity, glycemic status, maculopathy, diabetes duration, age, and sex.

Methods: This study included 60 patients with DR involving 100 treated eyes who underwent retinal laser photocoagulation. Best-corrected distance visual acuity (BCDVA) was assessed at baseline and at 1, 3, and 6 weeks after treatment. At 6 weeks, visual outcomes were categorized as improvement (≥2 Snellen lines), stable vision, or worsening (≥2 Snellen lines). Outcomes were evaluated according to baseline BCDVA, DR severity, HbA1c category, maculopathy status, diabetes duration, age, and sex. Longitudinal changes in BCDVA were assessed using Tukey HSD pairwise comparisons, and associations between clinical characteristics and visual outcomes were examined using appropriate statistical analyses.

Results: At 6 weeks, 17 (17.0%) of 100 treated eyes demonstrated improvement of ≥2 Snellen lines, 81 (81.0%) remained stable, and 2 (2.0%) worsened by ≥2 Snellen lines. No statistically significant differences were observed between baseline and Week 1 (mean difference, 0.03; Q = 0.64; p = 0.9698), Week 3 (mean difference, 0.05; Q = 1.37; p = 0.7664), or Week 6 (mean difference, 0.05; Q = 1.37; p = 0.7664) BCDVA. Visual outcomes varied across baseline BCDVA and DR severity categories. Diabetes duration was significantly associated with post-laser visual outcome (p = 0.02636).

Conclusion: Retinal laser photocoagulation was predominantly associated with short-term preservation of visual acuity, with 81.0% of treated eyes maintaining stable vision at 6 weeks and 17.0% demonstrating clinically meaningful improvement. Baseline visual acuity, DR severity, and diabetes duration were associated with variation in post-treatment visual outcomes. These findings emphasize the importance of baseline clinical characteristics in prognostic counseling and individualized follow-up following retinal laser photocoagulation. Further prospective studies with larger samples and longer follow-up are warranted to determine the durability of these visual outcomes.

]]> Diabetic retinopathy, Retinal laser photocoagulation, Panretinal photocoagulation, Best-corrected visual acuity, Visual outcomes, Diabetes mellitus Research Article Tue, 30 Jun 2026 00:00:00 IST Short-Term Visual, Anatomical, and Ocular Safety Outcomes Following Three Monthly Intravitreal Ranibizumab Injections in Diabetic Macular Edema: A Retrospective Observational Study Background: Diabetic macular edema (DME) is an important cause of visual impairment associated with diabetic retinopathy. Although intravitreal ranibizumab is an established treatment for DME, outcomes observed in routine clinical settings may vary from those reported in controlled trials. Assessment of treatment response should therefore consider functional vision, retinal morphology, and short-term ocular safety together. Objective: To examine longitudinal changes in distance and near visual acuity, central macular thickness (CMT), and intraocular pressure (IOP) during the first 12 weeks following a three-dose monthly course of intravitreal ranibizumab in patients with DME.

Methods: Medical records of patients treated for DME at a tertiary vitreoretinal referral center in India between June 2022 and November 2024 were retrospectively reviewed. The analysis included 50 eyes belonging to 45 patients who received three consecutive monthly intravitreal ranibizumab injections. Best-corrected distance visual acuity (BCDVA), best-corrected near visual acuity (BCNVA), OCT-derived CMT, and IOP were documented before treatment and at approximately 4, 8, and 12 weeks. Longitudinal changes in these parameters were evaluated statistically across the observation period.

Results: During follow-up, mean BCDVA improved from 0.562 ± 0.305 logMAR at baseline to 0.246 ± 0.163 logMAR at Week 12. Mean BCNVA similarly changed from 0.806 ± 0.295 to 0.454 ± 0.146 logMAR. Retinal thickness also decreased progressively, with mean CMT falling from 482.84 ± 85.99 μm at baseline to 292.52 ± 46.46 μm at Week 12. Significant longitudinal changes were identified for BCDVA, BCNVA, and CMT (all p < 0.001), with large time-associated effects for both visual acuity measures and a very large effect for CMT. Mean IOP increased from 15.90 ± 2.79 mmHg to 16.92 ± 2.36 mmHg over the same period (p = 0.0037); however, no sustained ocular hypertension requiring treatment or sight-threatening injection-related complication was recorded.

Conclusion: A three-monthly-injection course of ranibizumab was accompanied by meaningful short-term functional and anatomical improvement in this real-world DME cohort. The modest rise in IOP was not associated with clinically important short-term ocular hypertension. Because the study was retrospective, single-center, and lacked an untreated comparison group, the observed changes cannot be attributed exclusively to ranibizumab. Prospective studies involving larger populations and longer observation are required to establish the persistence of treatment response and longer-term ocular safety.

]]> Diabetic macular edema, Ranibizumab, Intravitreal anti-VEGF therapy, Diabetic retinopathy, Visual acuity, Central macular thickness, Optical coherence tomography, Intraocular pressure Research Article Tue, 30 Jun 2026 00:00:00 IST Impact of Internet on Nigerian Student Transformation: A Case Study of Ladoke Akintola University of Technology, Ogbomoso, Nigeria Rapid advancement happening information and communication technology, it has transformed the academic, economic, and social experiences of students globally. This research work investigates the influence of Internet on students at Ladoke Akintola University of Technology (LAUTECH), focusing on its role in academic performance, income generation, and social interactions. While the Internet provides numerous opportunities for research, online learning, and communication, challenges such as misinformation, unstable electricity, and high costs of access continue to hinder its effective utilization. Descriptive survey was employed for data collection among 71 undergraduate students across different levels at LAUTECH through a structured questionnaire. The results revealed that 90.1% of the respondents use the Internet daily, with smartphones being the most common access device (45.1%). Academically, 92.3% of students used the Internet for research, and 67.7% reported great improvement in performance due to online resources. Economically, 91.7% of students engaged in income-generating activities online, with 51.5% earning between ₦10,000 – ₦50,000 monthly, and 27.3% earning above ₦50,000. Socially, 81.4% agreed that the Internet enhanced their interactions, with WhatsApp being the most widely used platform (84.3%). However, barriers such as slow Internet speed (73.2%) and power failures (47.9%) were identified as major obstacles. The study concludes that the Internet serves as a multifaceted tool for LAUTECH students that will help them positively academically, socially and economic development.

]]> impact, internet, student, transformation, Nigeria Research Article Fri, 12 Jun 2026 00:00:00 IST Artificial Intelligence-Based Industrial IoT Security Framework using Machine Learning and Blockchain Industrial Internet of Things (IIoT) technologies have transformed modern industrial environments by enabling intelligent automation, real-time monitoring, predictive maintenance, and smart manufacturing operations. However, the rapid adoption of interconnected industrial devices has introduced significant cybersecurity challenges including unauthorized access, ransomware attacks, data breaches, distributed denial-of-service attacks, and network intrusions. Traditional security mechanisms are insufficient to protect large-scale IIoT infrastructures due to device heterogeneity, limited computational capabilities, and dynamic industrial environments. This paper proposes an Artificial Intelligence-based Industrial IoT security framework integrating machine learning and blockchain technologies for secure industrial communication and intelligent threat detection. The proposed framework employs IoT sensors, edge computing, cloud infrastructure, and AI-driven intrusion detection models to identify malicious activities in industrial networks. Blockchain technology is incorporated to ensure secure data sharing, authentication, and tamper-resistant communication among IIoT devices. Experimental analysis demonstrates improved detection accuracy, reduced false-positive rates, enhanced network security, and efficient real-time monitoring compared with conventional IIoT security approaches. The proposed system offers a scalable and intelligent solution suitable for Industry 4.0 and smart manufacturing environments.

]]> Industrial Internet of Things, Artificial Intelligence, Machine Learning, Blockchain, Cybersecurity, Intrusion Detection, Smart Manufacturing, Industry 4.0 Review Article Wed, 20 May 2026 00:00:00 IST Intelligent Traffic Management System using Artificial Intelligence and Internet of Things Rapid urban growth and the rising number of vehicles on roads have created serious challenges such as heavy traffic congestion, increased road accidents, excessive fuel usage, and higher levels of environmental pollution in urban areas. Conventional traffic management methods mainly depend on fixed-time traffic signals and manual supervision, which are often unable to respond effectively to changing traffic patterns and real-time road conditions. To address these limitations, the adoption of Artificial Intelligence (AI) and Internet of Things (IoT) technologies has enabled the development of smart traffic management systems that support real-time traffic monitoring, predictive analysis, and automated traffic regulation. This paper presents an AI-IoT-based intelligent traffic management framework integrating smart sensors, machine learning algorithms, edge computing, and cloud infrastructure for efficient traffic monitoring and congestion control. The proposed system continuously collects traffic data through IoT-enabled cameras, vehicle sensors, RFID devices, and smart traffic signals. Machine learning algorithms analyze traffic patterns, predict congestion levels, and optimize traffic signal operations dynamically. The framework supports emergency vehicle prioritization, accident detection, and intelligent route management for smart city transportation systems. Experimental analysis demonstrates improved traffic flow efficiency, reduced congestion rates, minimized waiting time, and enhanced traffic prediction accuracy compared with conventional traffic management approaches. The proposed framework provides a scalable and intelligent solution suitable for smart city transportation infrastructures and sustainable urban mobility.

]]> Intelligent Traffic Management, Artificial Intelligence, Internet of Things, Smart Cities, Machine Learning, Traffic Prediction, Smart Transportation, Congestion Control Review Article Wed, 20 May 2026 00:00:00 IST Artificial Intelligence-Based Environmental Monitoring System using IoT and Machine Learning Environmental pollution and climate change have become major global concerns affecting human health, biodiversity, agriculture, and industrial sustainability. Traditional environmental monitoring systems often rely on manual observation methods and isolated sensing mechanisms, which are inefficient for real-time analysis and predictive decision-making. The integration of Artificial Intelligence (AI) and Internet of Things (IoT) technologies has introduced intelligent environmental monitoring solutions capable of continuous sensing, automated analysis, and early hazard prediction. This paper presents an AI-based environmental monitoring framework integrating IoT sensors, cloud computing, and machine learning algorithms for real-time monitoring of environmental conditions. The proposed system collects data related to air quality, temperature, humidity, water quality, noise levels, and gas emissions using IoT-enabled smart sensors. Machine learning techniques are employed to analyze environmental patterns and predict pollution levels and hazardous conditions. Cloud infrastructure facilitates centralized data storage, remote monitoring, and intelligent analytics. Experimental analysis demonstrates improved monitoring accuracy, efficient anomaly detection, and faster environmental response compared with conventional monitoring systems. The proposed framework offers a scalable, cost-effective, and intelligent solution for smart cities and sustainable environmental management.

]]> Environmental Monitoring, Artificial Intelligence, Internet of Things, Machine Learning, Smart Sensors, Pollution Detection, Smart Cities, Predictive Analytics Review Article Wed, 20 May 2026 00:00:00 IST Artificial Intelligence-Based Smart Healthcare Monitoring and Predictive Disease Detection using IoT and Machine Learning The rapid growth of Internet of Things (IoT) technologies and Artificial Intelligence (AI) has significantly transformed modern healthcare systems by enabling intelligent monitoring, real-time data analysis, and predictive disease diagnosis. Conventional healthcare systems often experience delays in patient monitoring and disease identification due to limited accessibility and manual analysis procedures. This research presents an AI-based smart healthcare monitoring and predictive disease detection framework integrating IoT sensors with machine learning techniques for continuous health assessment. The proposed system collects physiological parameters such as heart rate, body temperature, blood pressure, oxygen saturation, and glucose levels through IoT-enabled wearable devices. The gathered data are processed using machine learning algorithms to identify abnormalities and predict potential diseases at an early stage. The framework employs cloud-based storage and analytical models to improve healthcare accessibility, accuracy, and remote patient management. Experimental evaluation demonstrates improved prediction accuracy, reduced response time, and efficient monitoring compared with conventional healthcare systems. The proposed model provides a scalable, cost-effective, and intelligent healthcare solution suitable for smart hospitals and remote healthcare environments.

]]> Artificial Intelligence, Internet of Things, Smart Healthcare, Machine Learning, Predictive Analytics, Remote Patient Monitoring, Disease Detection Review Article Wed, 20 May 2026 00:00:00 IST AI-IoT Framework for Intelligent Disease Prediction and Smart Healthcare Monitoring using Machine Learning The rapid rise in chronic health conditions, along with the increasing need for remote and accessible healthcare services, has significantly encouraged the integration of Artificial Intelligence (AI) and Internet of Things (IoT) technologies into today’s healthcare systems. Conventional disease diagnosis approaches often depend on periodic medical examinations and manual analysis, which may delay early disease identification and emergency response. This paper presents an AI-IoT framework for intelligent disease prediction and smart healthcare monitoring using machine learning algorithms and IoT-enabled wearable devices. The proposed system continuously collects physiological parameters including heart rate, blood pressure, glucose levels, body temperature, oxygen saturation, and respiratory rate through IoT sensors. The acquired healthcare data are transmitted to cloud platforms for storage and intelligent analysis. Machine learning models are employed to identify abnormal health conditions and predict diseases at early stages. The framework integrates real-time monitoring, cloud computing, predictive analytics, and automated alert generation to improve healthcare accessibility and diagnostic accuracy. Experimental analysis demonstrates improved prediction performance, reduced response time, and enhanced healthcare efficiency compared with conventional healthcare systems. The proposed AI-IoT framework provides a scalable and cost-effective solution suitable for smart hospitals, telemedicine, and remote patient monitoring applications.

]]> Artificial Intelligence, Internet of Things, Disease Prediction, Machine Learning, Smart Healthcare, Predictive Analytics, Remote Monitoring, Healthcare IoT Review Article Wed, 20 May 2026 00:00:00 IST Blockchain and Artificial Intelligence - A Comprehensive Review of Integration and Applications Blockchain and Artificial Intelligence (AI) are two transformative technologies that are reshaping modern digital systems. While AI enables intelligent decision-making through data-driven models, blockchain provides decentralized, transparent, and tamper-resistant data management. Their integration has gained increasing attention as a means to address limitations inherent in each technology when deployed independently. This review paper presents a comprehensive survey of blockchain–AI integration, focusing on architectural models, enabling techniques, application domains, and current challenges. The study examines how blockchain enhances trust, data integrity, and accountability in AI systems, while AI improves scalability, efficiency, and automation within blockchain networks. Key application areas including healthcare, finance, Internet of Things, supply chain management, and smart cities are discussed. The paper further identifies open research challenges and future directions necessary to realize secure, scalable, and intelligent decentralized systems.

]]> Blockchain, Artificial Intelligence, Distributed Ledger, Machine Learning, Smart Contracts, Decentralized Systems, Trustworthy AI Review Article Wed, 31 Dec 2025 00:00:00 IST An Analytical Review - Decentralized Finance (DeFi) of Protocols, Risks, and Regulatory Challenges Decentralized Finance (DeFi) represents a paradigm shift in financial services by leveraging blockchain technology and smart contracts to provide open, permissionless, and trust-minimized alternatives to traditional financial systems. Over the past few years, DeFi has experienced rapid growth, enabling decentralized exchanges, lending platforms, stablecoins, and synthetic assets without centralized intermediaries. Despite its transformative potential, DeFi faces significant challenges related to security vulnerabilities, systemic risks, governance limitations, and regulatory uncertainty. This review paper provides a comprehensive analysis of DeFi protocols, examines key technical and economic risks, and critically evaluates emerging regulatory responses across jurisdictions. The paper further identifies open research gaps and future directions necessary for the sustainable development of decentralized financial ecosystems.

]]> Decentralized Finance, Blockchain, Smart Contracts, DeFi Risks, Financial Regulation, Cryptocurrencies Review Article Wed, 31 Dec 2025 00:00:00 IST Quantum Advantage in the NISQ Era - Algorithms, Benchmarks, and Practical Limitations The current stage of quantum computing development is defined by Noisy Intermediate-Scale Quantum (NISQ) devices, which operate with a moderate number of qubits that are inherently susceptible to noise and decoherence. Despite the absence of full error correction, NISQ systems have demonstrated the ability to perform computational tasks that challenge classical simulation under certain conditions. This paper explores the concept of quantum advantage in the NISQ era by analysing prominent algorithmic approaches, benchmarking methodologies, and the practical constraints imposed by contemporary hardware. Hybrid quantum–classical algorithms, sampling-based experiments, and performance metrics are examined to assess the extent to which near-term quantum devices can outperform classical systems. The study finds that while progress toward quantum advantage is evident, significant technical and algorithmic limitations prevent its widespread realization. The paper concludes by identifying key research directions required to bridge the gap between experimental demonstrations and practical quantum computing applications.

]]> Quantum Advantage, NISQ Devices, Variational Algorithms, Quantum Benchmarking, Quantum Noise, Hybrid Quantum–Classical Computing Review Article Wed, 31 Dec 2025 00:00:00 IST Quantum Supremacy vs. Quantum Utility - A Critical Evaluation Quantum computing has progressed from theoretical speculation to experimental realization, leading to milestone demonstrations that claim computational superiority over classical systems. Among these milestones, quantum supremacy experiments have attracted considerable attention by showcasing tasks that are infeasible for classical computation within reasonable time limits. However, the practical value of such demonstrations remains a subject of debate. This paper critically evaluates the distinction between quantum supremacy and quantum utility, emphasizing their conceptual differences, experimental foundations, and real-world relevance. By examining key experimental results, algorithmic developments, and benchmarking approaches, the study argues that quantum utility-defined by practical, application-oriented performance-provides a more meaningful metric for long-term progress. The paper highlights the limitations of supremacy-based demonstrations and discusses pathways toward achieving utility-driven quantum advantage in the near and long term.

]]> Quantum Supremacy, Quantum Utility, NISQ Devices, Quantum Advantage, Benchmarking, Hybrid Quantum Algorithms Review Article Wed, 31 Dec 2025 00:00:00 IST Recent Advances in Blockchain Technology - A Survey of Trends, Challenges, and Research Gaps Blockchain technology has evolved significantly since its introduction as the underlying framework for cryptocurrencies. In recent years, it has expanded into a versatile distributed ledger technology supporting decentralized applications across finance, healthcare, supply chains, governance, and the Internet of Things. This review paper presents a comprehensive survey of recent advances in blockchain technology, focusing on architectural developments, consensus mechanisms, scalability solutions, security enhancements, and emerging application domains. The study critically examines current challenges such as performance limitations, energy consumption, interoperability, privacy concerns, and regulatory uncertainties. By analysing recent literature and technological trends, the paper identifies key research gaps and outlines promising directions for future investigation. The findings aim to support researchers and practitioners in understanding the current state of blockchain technology and its trajectory toward large-scale, sustainable adoption.

]]> Blockchain Technology, Distributed Ledger, Consensus Mechanisms, Scalability, Smart Contracts, Web3, Research Gaps Review Article Wed, 31 Dec 2025 00:00:00 IST HbA1c Assay as Diagnostic and Prognostic Biomarker for Diabetic Patients Hemoglobin A1c (HbA1c) is one of the most for detection and long-term tracking diabetes mellitus due to measuring average blood glucose levels during the preceding 2-3 months. It is crucial for determining glycemic control and forecasting the likelihood of long-term diabetic consequences, especially microvascular illness. This mini-review focusing in the biochemical basis of glycosylated hemoglobin. its diagnostic and prognostic value, and major factors affecting its reliability. Although HbA1c minimizes the impact of short-term glucose fluctuations, its accuracy may be compromised by non-glycemic factors such as altered erythrocyte turnover, hemoglobin variants, comorbid conditions, and genetic polymorphisms. Awareness of these limitations is crucial for appropriate interpretation of HbA1c results. Understanding these restrictions is essential for correctly interpreting HbA1c findings. HbA1c remains a cornerstone in diabetes care, but optimal use requires integration with clinical context and complementary glycemic assessments.

]]> HA1C, Diabetes mellitus, Diagnostic biomarker, Prognostic biomarker Editorial Article Wed, 31 Dec 2025 00:00:00 IST The Impact of Aging on HIV Acquisition in the Male Genital Tract Ageing is associated with progressive hormonal, immunological, and structural changes in the male reproductive system, including declining testosterone levels and deterioration of semen parameters. At the same time, the proportion of older men living with human immunodeficiency virus (HIV) is increasing globally. This mini-review summarizes current evidence on the interaction between ageing and HIV within the male genital tract, with emphasis on semen quality, immune status, and fertility implications. Available data indicate that while semen parameters may remain near normal in asymptomatic HIV-positive men, advancing age and disease progression are associated with reduced sperm motility, abnormal morphology, and impaired reproductive potential, particularly in the context of low CD4 cell counts. Understanding the combined effects of ageing and HIV is essential for improving reproductive health counseling and clinical management in older HIV-infected men.

]]> HA1C, Diabetes mellitus, Diagnostic biomarker, Prognostic biomarker Editorial Article Wed, 31 Dec 2025 00:00:00 IST Comparative Evaluation of Binocular Visual Function in Myopic Individuals Wearing Spectacles and Soft Contact Lenses: A Cross-Sectional Analysis Background: Myopia has emerged as a major global visual health concern, particularly among young adults. The mode of optical correction-spectacles or soft contact lenses (SCLs)-can influence binocular function through variations in vertex distance, prismatic effect, and accommodative demand. This study compared binocular visual performance between myopic individuals wearing spectacles and those using SCLs under habitual correction conditions.

Methods: A cross-sectional comparative study was conducted on 60 myopic participants (30 spectacle wearers and 30 SCL wearers) aged 18–35 years. Standardized optometric tests evaluated accommodation (amplitude, near point, relative accommodation), vergence (near point, fusional ranges, facility), and stereopsis. Statistical analyses included independent t-tests and Pearson correlations at α = 0.05, with 95% confidence intervals (CIs), Cohen’s d, and post-hoc power reported.

Results: SCL wearers exhibited greater accommodative amplitude (Δ0.60 D, p = 0.03, d = 0.57) and positive relative accommodation (Δ0.27 D, p = 0.02, d = 0.63), along with a closer near point of accommodation (Δ0.70 cm, p = 0.04, d = 0.54). Vergence parameters also favored the SCL group, with a 1.30 cm closer near point of convergence (p = 0.01, d = 0.69) and higher vergence facility (p = 0.03, d = 0.57). Stereoacuity improved slightly but was not statistically significant (p = 0.21, d = 0.33). Positive correlations were observed between accommodative amplitude and vergence facility (r = 0.49, p < 0.01) and between near point of accommodation and convergence (r = 0.43, p = 0.02), indicating coordinated enhancement.

Conclusion: Soft contact lenses provide superior accommodative and vergence function compared to spectacles, promoting greater binocular efficiency and visual comfort. These findings suggest that SCLs may reduce eyestrain and improve endurance during prolonged near work and digital device use, representing a more physiologically natural correction modality for young myopic adults.

]]> Binocular vision, Accommodation, Vergence, Stereopsis, Myopia, Soft contact lenses, Spectacle correction Research Article Wed, 31 Dec 2025 00:00:00 IST Refractive Errors Among Schoolchildren in Central India: Prevalence and Functional Impact Background: Refractive errors are a primary cause of preventable visual impairment in schoolchildren globally. There is a lack of data from Central India concerning the prevalence and functional impact subsequent to refractive correction. This study sought to assess the prevalence and determinants of refractive errors and to evaluate the visual, functional, and psychosocial enhancements following spectacle correction.

Methods: A cross-sectional analytical mixed-methods study was executed from August 2023 to July 2024 in 12 randomly chosen schools in Rajgarh District, Madhya Pradesh. Using multistage stratified random sampling, we screened 300 kids between the ages of 9 and 15. They did a visual acuity test, an objective and subjective refraction test, and a pre-validated questionnaire. All diagnosed students received spectacles, and a follow-up after 6–8 weeks evaluated adherence and functional enhancement. Descriptive statistics, χ² tests, t-tests, and multivariable logistic regression were used to look at the quantitative data. Qualitative responses were subjected to thematic analysis.

Results: Refractive errors were detected in 24% (n=72; 95% CI: 19.2–28.8) of the students. Myopia was the most common (45.8%), followed by hyperopia (29.2%) and astigmatism (25%). Age (aOR = 1.18; p = 0.004) and living in a city (aOR = 2.09; p = 0.006) were both important predictors. After correction, the mean visual acuity improved significantly from 0.38 ± 0.12 to 0.05 ± 0.03 LogMAR (p < 0.001). At the follow-up, 83% of participants said they wore glasses regularly, and there were significant improvements in visibility in the classroom (91%), concentration (87%), and relief from headaches (82%).

Conclusion: In Central India, refractive errors are very common among students. Significant visual, academic, and psychosocial benefits were obtained from spectacle correction, confirming the efficacy of school-based screening models. The results encourage district-level school health systems to continue integrating refractive-error services.

]]> Refractive errors, schoolchildren; myopia, vision screening, spectacle compliance, Central India Research Article Wed, 31 Dec 2025 00:00:00 IST Glycemic Status and its Relationship with Corneal Tomographic and Endothelial Parameters in Diabetes Mellitus This study aims to compare corneal tomographic and endothelial parameters in diabetic patients with both controlled and uncontrolled glycemic status, as well as to investigate the relationships between corneal parameters, glycemic control, and the duration of diabetes. Conducted in a hospital setting, this cross-sectional study involved diabetic patients categorized into controlled and uncontrolled groups according to their HbA1c levels. Corneal tomography was executed utilizing Scheimpflug imaging, while endothelial evaluation was performed through non-contact specular microscopy. To maintain statistical independence, the right eye was predetermined for all inferential analyses. Between-group comparisons were carried out using suitable statistical tests, with effect sizes presented alongside p-values. Correlation and multivariable regression analyses were employed to assess the associations among corneal parameters, HbA1c levels, and the duration of diabetes. The findings revealed that patients with uncontrolled diabetes exhibited a significantly steeper flat keratometry (K1) in comparison to those with controlled glycemic status, with a moderate-to-large effect size, suggesting potential clinical significance. Conversely, other tomographic parameters such as steep keratometry, mean keratometry, central corneal thickness, anterior chamber depth, and corneal volume did not show significant differences between the groups and were associated with small effect sizes. Nevertheless, a greater dispersion of various tomographic parameters was descriptively noted in the uncontrolled diabetes cohort, indicating heightened structural variability. The density of endothelial cells was numerically elevated in the uncontrolled diabetes group; however, it did not achieve statistical significance and was associated with increased variability. Indices of endothelial morphology, such as the coefficient of variation and hexagonality, were similar across the groups. Correlation and multivariable regression analyses indicated no significant independent relationship between HbA1c and central corneal thickness or endothelial cell density after controlling for age, sex, and duration of diabetes. The magnitude of the observed regression coefficients fell below thresholds deemed clinically significant. Uncontrolled diabetes is linked to subtle changes in corneal curvature and increased structural variability, rather than consistent alterations in corneal thickness or endothelial parameters. These results imply heterogeneous, subclinical corneal involvement in diabetes and underscore the multifactorial nature of diabetes-related corneal changes. Larger longitudinal studies are necessary to elucidate their clinical implications.

]]> Diabetes mellitus, Corneal tomography, Endothelial cell density, Glycemic control, Specular microscopy Research Article Wed, 31 Dec 2025 00:00:00 IST