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Muhammad Usman

Publications and source records attributed to Muhammad Usman.

2 recordsLinked to original sources
PubMed2026

Molecular Epidemiology of Non-Polio Enterovirus: Insights From L20B Cell Line Adaptation From Children With Acute Flaccid Paralysis in Pakistan.

BACKGROUND: Non-polio enteroviruses (NPEVs) are increasingly implicated in acute flaccid paralysis (AFP), often resembling poliomyelitis and complicating eradication efforts. In Pakistan, limited molecular surveillance has hindered comprehensive characterization. The L20B cell line, designed for poliovirus detection, occasionally supports NPEV replication, challenging AFP case interpretation. METHODS: Between January 2021 and December 2022, 4615 stool samples from AFP cases in children ≤15 years were analyzed. Of these, 435 were identified as NPEVs via L20B cytopathic effects and intertypic differentiation reverse transcription-polymerase chain reaction. VP1 sequencing was performed on 218 representative isolates, yielding 153 high-quality sequences (70.2%). The 224/222 primer set showed superior amplification. Phylogenetic analysis used MUSCLE alignment and the Neighbor-Joining method in MEGA X, with statistical evaluation of epidemiological data. RESULTS: NPEVs were frequently found in L20B-positive AFP cases, highlighting the cell line's limited specificity. Most cases involved children under 5, with a slight male bias. Enterovirus B was predominant (98.0%), especially Echovirus 7 (20.3%) and Echovirus 11 (10.5%), followed by Coxsackievirus B1 and Echovirus 33 (5.9% each). Geographic clustering was noted in Punjab (45.1%), Khyber Pakhtunkhwa (30.7%) and Sindh (20.3%), with seasonal peaks in late summer and early autumn. Phylogenetic data revealed localized Enterovirus B circulation with minimal genetic variation. CONCLUSIONS: The detection of diverse NPEVs in L20B-positive AFP cases emphasizes their relevance in post-polio surveillance. Incorporating routine VP1 sequencing, optimized primer use, and targeted seasonal and regional monitoring is vital to reduce diagnostic uncertainty and inform public health strategies.

Humans↗
PubMed2026

Artificial Intelligence and Machine Learning Applications in Fibromuscular Dysplasia: Transforming Diagnosis, Risk Stratification, and Clinical Decision-Making.

Fibromuscular dysplasia (FMD) is a non-atherosclerotic vascular disorder with heterogeneous presentations, making diagnosis and management highly dependent on imaging and clinical expertise. This narrative review examines how artificial intelligence (AI) and machine learning (ML) are transforming FMD care. AI-enhanced imaging, particularly convolutional neural network-based analysis, improves detection of the characteristic "string-of-beads" pattern on CT angiography, magnetic resonance angiography, and ultrasound, although FMD-specific validation remains limited. ML models facilitate risk stratification, prediction of disease progression, and early identification of complications such as aneurysms and stroke by integrating clinical, imaging, and genomic data. AI-driven clinical decision support systems further enable personalized treatment selection through pharmacogenomic insights and robot-assisted interventions. Despite promising real-world applications, challenges persist, including limited large-scale datasets, workflow integration, regulatory barriers, and algorithmic bias affecting underrepresented populations. Future advances in explainable AI, federated learning, and digital health integration may enable a shift toward predictive, patient-centered FMD management.

Humans↗