Left ventricular (LV) dysfunction is a major contributor to morbidity and mortality in chronic kidney disease (CKD). Therefore, it is of interest to determine the prevalence of LV Dysfunction among 195 patients with Stage 3A–5 CKD attending a secondary healthcare centre in South India using clinical evaluation and 2D echocardiography. The majority of patients were in CKD stages 4 and 5 and worsening renal function was significantly correlated with a decline in left ventricular ejection fraction, grades of diastolic dysfunction, E/A and E/e′ ratios, deceleration time, haemoglobin levels and serum creatinine levels (p<0.05). Serum sodium and hypertension were not significantly different across stages, whereas diabetes mellitus, oliguria and worsening NYHA functional class were significantly associated with advanced CKD. Thus, data shows the progressive relationship between renal dysfunction and cardiac impairment and underscore the importance of routine echocardiographic screening for early diagnosis and management of LV dysfunction in subjects with CKD.
Missed root canals are a major cause of endodontic treatment failure due to limitations in conventional diagnostic methods. Therefore, it is of interest to evaluate the diagnostic performance of deep learning–assisted CBCT analysis in detecting additional canals in mandibular molars and compared it with endodontic specialists. A total of 60 extracted teeth were analyzed using AI-based software and expert evaluation, with findings validated against a reference standard. AI-assisted analysis showed higher sensitivity and comparable specificity, with improved consistency in detecting complex canal anatomy. Thus, data shows the potential of deep learning–assisted CBCT analysis as a reliable adjunct for detecting additional root canals with improved sensitivity and consistency.
Long-term motor and functional disability following cerebral dysfunction remains a major global rehabilitation challenge despite advances in conventional physiotherapy approaches. Therefore, it is of interest to synthesise existing evidence on neuroplasticity and technology- based physiotherapy interventions for cerebral dysfunction and to evaluate their mechanistic foundations, clinical effectiveness and translational potential. Technology-based modalities such as robotic exoskeletons, VR systems, BCIs and non-invasive neuro-stimulation enhance cortical reorganisation, motor relearning and functional independence compared with traditional physiotherapy. Neuroplasticity- driven and technology-assisted physiotherapy represent a paradigm shift in the management of cerebral dysfunction, as evidenced by large- scale randomised controlled trials. Technology-assisted physiotherapy increases the neurorehabilitation outcomes regarding stroke, traumatic brain injury and cerebral palsy.
Metabolic dysfunction-associated steatotic liver disease is an important cause of chronic liver disease in patients with Type 2 diabetes mellitus, with advanced hepatic fibrosis substantially increasing liver-related morbidity and mortality. Therefore, it is of interest to evaluate the 120 patients with Type 2 diabetes mellitus and metabolic dysfunction-associated steatotic liver disease to determine the prevalence of advanced hepatic fibrosis and assess the diagnostic performance of non-invasive fibrosis scores. Hepatic fibrosis was assessed using transient elastography and compared with the NAFLD Fibrosis Score and Fibrosis-4 index, alongside clinical, biochemical and imaging parameters. The study was designed to assess whether these non-invasive scores can reliably identify advanced hepatic fibrosis in routine diabetic care, particularly where liver biopsy is impractical. Thus, data shows that evaluating accessible non-invasive strategies for identifying advanced hepatic fibrosis in patients with Type 2 Diabetes Mellitus and metabolic dysfunction-associated steatotic liver disease.
Restoring periodontal attachment in intrabony defects remains unpredictable with conventional therapy. Mesenchymal stem cells, including PDLSCs, DPSCs and GMSCs, aid repair through differentiation, paracrine signaling and immune regulation. Transcriptomic and single-cell RNA sequencing analyses show osteogenic and angiogenic drivers, including RUNX2, BMP2 and PDGFRA-positive lineages. Extracellular vesicle profiling and multi-omics integration, aided by machine learning, are refining biomarkers and cell-free protocols. Thus, molecular signatures with clinical outcomes through standardized pipelines points toward personalized periodontal regeneration.
