A two-month prospective study conducted at Hayatabad Medical Complex (HMC) Peshawar, Pakistan. In this study the pharmacotherapy patterns and drug-drug interaction (DDI) incidences were analyzed among 150 diabetic patients, of whom 50 presented with diabetic foot ulcer (DFU). Significant deviations from World Health Organization (WHO) core prescribing indicators were observed, particularly in the areas of polypharmacy and generic prescribing practices. The majority of DFU patients were from urban regions, with sedentary lifestyle factors identified as prominent contributors to DFU development. A higher incidence of DFU was noted among male patients with type 2 diabetes mellitus (T2DM) compared to female patients. Age distribution analysis revealed that patient ages ranged from 8 to 85 years, with 68% falling within the 41-60 age bracket, while only 2% were under 20 years of age. Among the all 391 pharmacotherapeutic agents prescribed, injectable medications constituted the majority (47.82%). Analysis of DDIs showed that 39.1% of prescribed medications were associated with drug interactions, with 72% of these classified as major interactions. The most frequently observed major DDIs involved combinations such as aspirin with Ramipril and Pregabalin with Losartan. These findings highlight the necessity for clinical pharmacists to review prescribing regimens to mitigate the risk of severe DDIs. The high prevalence of diabetes and DFU in this patient cohort is closely associated with lifestyle factors, insufficient health education, and lack of physical activity. These findings underline the urgent need for preventative strategies, including lifestyle modifications and public health education. Further investigation is recommended to enhance understanding of DFU risk factors and to develop improved prognostic and preventive frameworks.

Electromyographic (EMG) analysis was conducted to evaluate the functional characteristics of masticatory muscles in patients with myogenous temporomandibular disorders (TMD), aiming to enhance the clinical understanding of muscle activity in these conditions. Based on the Diagnostic Criteria for Temporomandibular Disorders (DC/TMD), 28 patients with myogenous TMD, characterized by persistent pain exceeding six months, were examined alongside a control group of 35 asymptomatic subjects. EMG assessments were performed on the masseter, temporalis, and suprahyoid muscles during resting states and maximum intercuspation clench. Quantitative parameters, including myoelectric indices in the amplitude domain and mean power frequency (MPF) in the frequency domain, were evaluated. Significant differences in muscle activity patterns between the TMD and control groups were observed. During maximum clenching, temporalis muscles (TA) in TMD patients exhibited a markedly higher asymmetry index and activity index, alongside a lower MPF, compared to the control group. Conversely, the MPF of the suprahyoid muscles was elevated, while masseter muscles (MM) displayed a reduction in MPF. In the resting state, the MPF of the TA was found to be higher than that of both the control group and the MM. These findings indicate that patients with myogenous TMD exhibit increased muscle activity asymmetry, reduced coordination, and altered frequency-domain characteristics of the masticatory muscles. The results suggest that the TA may play a more significant role in the compensatory mechanisms associated with myogenous TMD, potentially contributing to the observed dysfunction and pain. This study underscores the utility of EMG as a diagnostic tool for elucidating the pathophysiological changes in masticatory muscle function in TMD and highlights the potential for targeted therapeutic interventions based on these findings.

A bibliometric analysis was conducted to explore the research trends and emerging hotspots in the application of health monitoring technologies within nursing. Literature spanning from January 2021 to January 2025 was retrieved from the Web of Science Core Collection (WOSCC), and CiteSpace software was employed to analyze and visualize research outputs, institutional contributions, author collaborations, high-frequency keywords, and the evolution of keyword clusters over time. A total of 425 articles were identified, revealing a stable global publication output. The United States emerged as the leading contributor, with 138 articles, followed by China with 47. Prominent keywords such as "care," "management," and "remote patient monitoring (RPM)" were found to be indicative of current research foci. Analysis indicates a shift towards home-based care, smartphone integration, digital health solutions, and wearable devices, particularly in managing clinical conditions such as cardiovascular disease (CVD), cancer, and diabetes. The prevailing research trends highlight the importance of remote monitoring and nursing care within home settings, with an increasing emphasis on chronic diseases. Despite the growth in research activity, uneven international development and limited collaborative efforts, primarily within research teams, present challenges to the field’s progress. It is suggested that future research should focus on fostering international collaboration between academic, healthcare, and engineering sectors to ensure that monitoring technologies align with clinical needs. Moreover, the establishment of international regulations was recommended to standardize production processes, enhance product reliability, and facilitate the broader application of these technologies in nursing practice.

Heart disease remains a leading cause of mortality worldwide, necessitating early and accurate detection to improve clinical outcomes. Traditional diagnostic approaches relying on conventional clinical data analysis often encounter limitations in precision and efficiency. Machine learning (ML) techniques offer a promising solution by enhancing predictive accuracy and decision-making capabilities. This study evaluates the performance of a clinical support system (CSS) for heart disease prediction using a hybrid classification approach that integrates support vector machine (SVM) and k-nearest neighbor (KNN). Patient data were stratified by age group and gender to assess the model’s performance across diverse demographic profiles. Key performance metrics, including accuracy, recall, precision, F1-score, and area under the curve (AUC), were employed to quantify predictive efficacy. Experimental results demonstrated that the combined SVM-KNN model achieved superior classification performance, yielding an accuracy of 97.2%, recall of 97.6%, precision of 96.8%, AUC of 97.1%, and an F1-score of 98.2%. These findings indicate that the integration of SVM and KNN enhances heart disease prediction accuracy, thereby reinforcing the potential of CSS in improving early diagnosis and patient management.