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 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.

Depression, a prevalent and severe medical condition, significantly impairs emotional well-being, cognitive functions, and behavior, often leading to substantial challenges in daily functioning and, in severe cases, an increased risk of suicide. Affecting approximately 264 million individuals worldwide across diverse age groups, depression necessitates effective and timely detection for intervention. In primary healthcare, the Patient Health Questionnaire-9 (PHQ-9) serves as a crucial tool for screening depression. This study leverages the PHQ-9 dataset, comprising 12 features and 534 samples, to evaluate depression levels using advanced machine learning (ML) techniques. A comparative analysis of the Support Vector Classifier (SVC) and AdaBoost Classifier (ABC) was conducted to determine their efficacy in classifying depression severity on a scale from 0 to 4. The SVC emerged as the superior model, achieving an accuracy of 94%. This research contributes to the early detection and prevention of depression by proposing an interactive interface designed to enhance user engagement. Future work will focus on expanding the dataset to improve model generalization and robustness, thereby facilitating more accurate and widespread applications in clinical settings.

This comprehensive review investigates the ethical implications of artificial intelligence (AI)-driven predictive analytics in healthcare, with a focus on patient privacy, algorithmic bias, equitable access, and transparency. The study further explores the integration of these ethical considerations into educational frameworks to enhance the training and preparedness of healthcare professionals in the responsible use of AI technologies. A systematic literature review was conducted using databases such as PubMed, Scopus, and Google Scholar, employing keywords related to AI, predictive analytics, healthcare, education, and ethics. Articles published from 2017 onwards, discussing the ethical challenges and applications of AI in healthcare and educational settings, were included. Thematic analysis of selected articles revealed significant ethical concerns, including patient privacy, algorithmic bias, and equitable access to AI technologies. Findings underscored the necessity for robust data protection mechanisms, transparent algorithm development, and equitable access policies. The study also highlighted the importance of incorporating AI literacy and ethical training in medical education. An ethical framework was proposed, outlining strategies to address these challenges in both healthcare practice and educational curricula. This framework aims to ensure the responsible use of AI technologies, promote transparency, and mitigate biases in healthcare settings. By addressing a critical gap in understanding the ethical implications of AI-driven predictive analytics in healthcare and its integration into education, the study contributes to the development of guidelines and policies for the equitable and transparent deployment of AI. The proposed ethical framework provides actionable recommendations for stakeholders, aiming to enhance medical education and improve patient outcomes while upholding essential ethical principles.

In this cross-sectional study, the prevalence and characteristics of adverse effects following the administration of the third dose of the coronavirus disease 2019 (COVID-19) vaccines were compared between recipients in Palestine and Jordan. Data were collected via an online survey targeting random samples from both countries. In Palestine, the primary factors predisposing individuals to side effects after the third dose were prior adverse reactions to earlier vaccinations and a history of COVID-19 infection before vaccination. Minor contributing factors included food sensitivities, weight, and drug sensitivities. In Jordan, gender, smoking, and food sensitivities emerged as the most significant variables influencing the development of side effects, with age being a secondary factor. Weight, COVID-19 infection post-vaccination, and prior adverse reactions to earlier doses were less significant. In Palestine, individuals with diabetes and respiratory diseases were more prone to adverse effects, followed by those who are obese, and those with cardiovascular diseases, osteoporosis, thyroid disorders, immune diseases, cancer, arthritis, and hypertension. In Jordan, participants with arthritis were the most likely to develop side effects, followed by those who are obese, and those with respiratory conditions and thyroid disorders. These findings confirm that COVID-19 vaccines authorized for use are generally safe, and vaccination remains a crucial tool in curbing the spread of the virus. Acceptance of the third dose has been notable in both Palestine and Jordan, underscoring the value of booster doses in enhancing immunity.

A retrospective analysis was conducted to assess potential drug-drug interactions (pDDIs) in the management of cardiovascular diseases, evaluating 500 prescriptions from hospitalized patients between January 1 and April 1, 2023. Using Medscape online software for the identification of drug-drug interactions (DDIs) and SPSS version 21 for statistical analysis, the study documented a 93% occurrence rate of pDDIs across the prescriptions. These interactions were categorized as serious (15% of cases, n=760, maximum per encounter: 4, mean: 1.52 ± 1.064), significant (75.6% of cases, n=3855, maximum per encounter: 30, mean: 7.71 ± 4.583), and minor (9.5% of cases, n=485, maximum per encounter: 4, mean: 0.95 ± 1.025). On average, 9.5 medications were prescribed per patient. Factors significantly associated with the incidence of pDDIs included age (r= 0.921, P < 0.01), presence of concurrent diseases (r= 0.782, P < 0.01), length of hospital stay (r= 0.559, P < 0.01), and the number of prescribed drugs (r= 0.472, P < 0.01). The most frequent interacting combinations were identified, with Clopidogrel + Enoxaparin (38.15%, n=290) and Enoxaparin + Aspirin (26.92%, n=210) being the most common, followed by other notable combinations. The study recorded adverse drug reactions in 15 patients. This investigation highlights a significant prevalence of pDDIs, particularly in cases of polypharmacy among cardiovascular patients. It underscores the critical need for systematic analysis and vigilant monitoring of prescriptions prior to drug administration by healthcare professionals.

Malaria remains a formidable challenge to global public health, with an estimated 241 million cases reported across 85 endemic countries in 2020. Within this context, Indonesia, and particularly the Central Maluku Regency, has reported a notable burden of the disease, evidenced by 102 confirmed cases in 2022 as per the annual parasite incidence (API) data, highlighting indigenous transmissions in specific locales. This research was conducted to assess the susceptibility to malaria within the operational area of the Hila Perawatan Primary Healthcare Centre (Puskesmas), situated in the Leihitu sub-district of Ambon Island, through an examination of $Anopheles$ mosquito breeding sites, larval densities, and habitat indices. Employing a descriptive research design, this cross-sectional observational study was carried out on October 26-27, 2023, to meticulously document the ecological footprint of the $Anopheles$ mosquito, particularly $Anopheles$ $farauti$. Findings reveal a habitat index (HI) of 33% in Kaitetu village with a larval density of 20%, indicating a significant presence of Anopheles farauti larvae. These findings suggest that environmental and behavioral factors within households, such as the use of gauze and ceilings, nocturnal activities, application of mosquito repellents, wearing of long-sleeved clothing, and utilization of mosquito nets, are pivotal in influencing malaria transmission dynamics. This study underscores the imperative of integrating environmental management with community engagement strategies to mitigate malaria transmission in endemic regions. The results not only provide a nuanced understanding of the $Anopheles$ mosquito's breeding patterns and its implications for malaria transmission but also offer a foundational basis for tailoring targeted interventions aimed at reducing the malaria burden in the Central Maluku District.

Tuberculosis (TB), an airborne disease caused by Mycobacterium, poses a significant global health challenge due to its rapid transmission through air and interaction with infected individuals. This study presents a comprehensive dynamic model to assess the impact of TB treatment and vaccination strategies in Nigeria, focusing on the comparative analysis of untreated and treated populations, as well as evaluating mortality and recovery outcomes. Through simulations conducted using the Berkeley Madonna Software, it was observed that the populations of latent and susceptible individuals exhibit a near-equivalence, yet the cohort undergoing treatment markedly surpasses other groups. Interestingly, the infected demographic aligns closely with the average values across all compartments. An alarming trend was noted in chronic patients, whose numbers initially increase, followed by a decline over a six-year period, and then a subsequent rise, while the count of treated individuals demonstrates a continuous decrease. The study further reveals a pressing need for treatment among vaccinated individuals, highlighting a nuanced interplay between vaccination and therapeutic interventions. By employing stability and sensitivity analyses, this research underscores the critical importance of treatment in managing TB infection, advocating for enhanced strategies to mitigate the spread of this infectious disease. The findings contribute valuable insights into the dynamics of TB infection and the pivotal role of treatment, underscoring the necessity for integrated approaches in addressing the TB epidemic, particularly in regions burdened by high infection rates.

Background: Lung cancer poses a great threat to human life and health. Although the density differences between lesions and normal tissues shown on enhanced CT images is very helpful for doctors to characterize and detect lesions, contrast agents and radiation may cause harm to the health of patients with lung cancer. By learning the mapping relationship between plain CT image and enhanced CT image through deep learning methods, high quality synthetic CECT image results can be generated based on plain scan CT image. It has great potential to help save treatment time and cost of lung cancer patients, reduce radiation dose and expand the medical image dataset in the field of deep learning. Methods: In this study, plain and enhanced CT images of 71 lung cancer patients were retrospectively collected. The data from 58 lung cancer patients were randomly assigned to the training set, and the other 13 cases formed the test set. The Convolution Vison Transformer structure and PixelShuffle operation were combined with CycleGAN, respectively, to help generate clearer images. After random erasing, image scaling and flipping to enhance the training data, paired plain and enhanced CT slices of each patient are input into the network as input and labeled, respectively, for model training. Finally, the peak signal-to-noise ratio, structural similarity and mean square error are used to evaluate the image quality and similarity. Results: The performance of our proposed method is compared with CycleGAN and Pix2Pix on the test set, respectively. The results show that the SSIM value of the enhanced CT images generated by the proposed method improve by 2.00% and 1.39%, the PSNR values improve by 2.05% and 1.71%, and the MSE decreases by 12.50% and 8.53%, respectively, compared with Pix2Pix and CycleGAN. Conclusions: The experimental results show that the improved algorithm based on CylceGAN proposed in this paper can synthesize high-quality lung cancer synthetic enhanced CT images, which is helpful to expand the lung cancer image data set in the deep learning research. More importantly, this method can help lung cancer patients save medical treatment time and cost.