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Volume 3, Issue 3, 2025
Open Access
Research article
Prevalence and Independent Predictors of Macrocytosis among Metformin-Treated Patients with Type 2 Diabetes Mellitus
Hamad Ali ,
ebad ali ,
qaisar ali ,
nadir ahmad ,
hammad khan ,
idrees khan
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Available online: 09-29-2025

Abstract

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Metformin remains the cornerstone of pharmacological management for Type 2 diabetes mellitus (T2DM); however, its long-term use has been associated with impaired vitamin B12 absorption and macrocytosis. The prevalence and independent determinants of macrocytosis in metformin-treated populations remain insufficiently characterized. A cross-sectional observational study was conducted at Saidu Teaching Hospital, Swat, between July 2024 and August 2025. A total of 236 adults with T2DM receiving metformin therapy for at least six months were enrolled. Macrocytosis was defined as a mean corpuscular volume (MCV) exceeding 100 fL. Demographic characteristics, clinical parameters, and concomitant medication use were systematically recorded. Univariate analyses were initially performed, followed by multivariate logistic regression to identify independent predictors of macrocytosis. The overall prevalence of macrocytosis was 16.1% (95% confidence interval (CI): 11.7–21.4%). Multivariate analysis demonstrated that metformin therapy duration >4 years (odds ratio (OR): 3.42, p = 0.002), daily metformin dose ≥1500 mg (OR: 2.89, p = 0.006), concomitant proton pump inhibitor (PPI) use (OR: 4.15, p < 0.001), and age ≥60 years (OR: 2.26, p = 0.030) were independently associated with macrocytosis, with concurrent PPI use emerging as the strongest predictor. These findings indicate that macrocytosis is a relatively common hematologic abnormality in metformin-treated adults with T2DM and is strongly influenced by treatment duration, dosage intensity, age, and concurrent PPI therapy. Risk-stratified surveillance strategies incorporating periodic assessment of MCV and serum vitamin B12 levels may therefore be warranted, particularly in high-risk patients, to enhance patient safety and optimize the long-term clinical management of T2DM.

Open Access
Research article
Hospital-Specific Automated Waste Segregation for High-Accuracy Real-Time Classification
sultan akinola atanda ,
shuqroh opeyemi abdulrasaq ,
olusola kunle akinde ,
sunday adeola ajagbe ,
abraham kehinde aworinde
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Available online: 09-29-2025

Abstract

Full Text|PDF|XML
Healthcare facilities generate heterogeneous waste streams that must be accurately segregated at the point of disposal to mitigate occupational exposure risks, reduce downstream treatment costs, and ensure compliance with stringent biomedical waste regulations. However, most existing automated waste segregation systems have been developed for domestic or general-purpose scenarios and are poorly adapted to the operational complexity and safety requirements of hospital environments. In this study, a hospital-specific automated waste segregation system was designed, implemented, and experimentally evaluated for real-time classification of five clinically relevant waste categories: infectious waste, sharps, pharmaceutical waste, recyclable waste, and general waste. The proposed system integrates an ultrasonic sensor with a Raspberry Pi 4B platform executing a lightweight MobileNetV2 model, coupled with a motorised mechanical sorting mechanism. A curated dataset comprising 6,868 labelled hospital-waste images was constructed and used to fine-tune the model to ensure robustness under embedded deployment constraints. Experimental validation under simulated hospital disposal scenarios demonstrated an overall classification accuracy of 97%, with end-to-end segregation cycle times ranging from 8 to 12 seconds per item across repeated trials. These results indicate that high-accuracy, real-time waste classification can be achieved using low-cost embedded hardware and compact deep learning architectures. The proposed approach establishes a practical and scalable foundation for intelligent healthcare waste management at the point of disposal, offering a viable pathway toward safer clinical environments, improved operational efficiency, and the broader adoption of edge AI solutions in resource-constrained healthcare settings.
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