Search Results (1,564)

Objectives: This study aimed to assess the knowledge, attitudes, and self-confidence of physicians and dentists in Croatia regarding the relationship between oral and systemic health, focusing on periodontal disease and oral manifestations of systemic diseases. Methods: A cross-sectional, web-based survey was conducted among physicians and dentists in Croatian primary healthcare. The questionnaire addressed six thematic domains, including demographic information, knowledge, self-assessment, and clinical practice. Descriptive and comparative statistical analyses were performed. Results: A total of 529 respondents were included (291 physicians and 238 dentists). The mean knowledge score for the association between periodontitis and systemic diseases was 6.8 ± 3.6 out of 15, indicating limited knowledge. For oral manifestations of systemic diseases, the mean score was 10.0 ± 3.8 out of 16, reflecting moderate proficiency. Dentists scored higher than physicians in both domains, though not significantly (p > 0.05). Routine oral mucosal examinations were reported by 89.5% of dentists and 43.0% of physicians (p ≤ 0.001). Only 21.3% of physicians correctly identified the link between periodontitis and adverse pregnancy outcomes, compared to 58.8% of dentists. The primary barriers to effective clinical management were a lack of experience (52.7%) and inadequate education. While 68.3% of dentists felt adequately educated on oral–systemic links, only 22.7% of physicians reported the same. Conclusions: Significant gaps in knowledge and confidence were observed, particularly among physicians. These findings underscore the need to integrate oral–systemic health topics into medical education and to promote interprofessional collaboration to improve patient outcomes. Full article

Background/Objectives: Chronic cervical myofascial pain syndrome (CMPS) is often diagnosed in the current population by doctors of various specialties. One method of treating spinal pathology is mesotherapy. The purpose of this study is to evaluate the efficacy and safety of collagen mesotherapy, as well as to assess the frequency of pain medication after mesotherapy in chronic CMPS. Methods: Patients were diagnosed and treated by an orthopedist in three different offices between 1 January 2018 and 31 December 2024. The patients were diagnosed with chronic CMPS. Patients were qualified for cervical spine mesotherapy, which was performed weekly, in five repetitions. Retrospectively, based on medical records and in accordance with inclusion and exclusion criteria, two groups were created: group I (n = 65) with injectable type I collagen and group II (n = 65) with 1% lignocaine. Patients were evaluated using the VAS and Laitinen scale before the start of therapy, 1 week after the end of therapy, and at 3-month follow-up. In addition, the frequency of taking analgesic medications after mesotherapy was assessed. Results: After mesotherapy of the cervical spine with both injectable collagen type I and lignocaine 1%, statistically significant improvements were observed in terms of a decrease in pain on the scales used (p < 0.001), as well as a decrease in analgesic medication intake (p < 0.001). Collagen treatment yielded better results after 3 months of follow-up. No mesotherapy-related side effects were observed during the treatment or follow-up periods. Conclusions: Cervical spine mesotherapy using injectable type I collagen and lignocaine 1% is an effective and safe method for chronic CMPS. At a 3-month follow-up, injectable type I collagen appears to be more effective. After mesotherapy and at the 3-month follow-up, both groups reported less pain medication intake compared to before the intervention. Full article

The incorporation of digital health into the medical curricula is becoming more important to better prepare doctors in the future. Digital health comprises a wide range of tools such as electronic health records, health information technology, telemedicine, telehealth, mobile health applications, wearable devices, artificial intelligence, and virtual reality. The present study aimed to explore the medical and biomedical students’ perspectives on the integration of digital health in medical curricula. A cross-sectional study was conducted on the medical and biomedical undergraduate students at the College of Medicine and Health Sciences at Sultan Qaboos University. Data was collected using a self-administered questionnaire. The response rate was 37%. The majority of respondents were in the MD (Doctor of Medicine) program (84.4%), while 29 students (15.6%) were from the BMS (Biomedical Sciences) program. A total of 55.38% agreed that they were familiar with the term ‘e-Health’. Additionally, 143 individuals (76.88%) reported being aware of the definition of e-Health. Specifically, 69 individuals (37.10%) utilize e-Health technologies every other week, 20 individuals (10.75%) reported using them daily, while 44 individuals (23.66%) indicated that they never used such technologies. Despite having several benefits, challenges exist in integrating digital health into the medical curriculum. There is a need to overcome the lack of infrastructure, existing educational materials, and digital health topics. In conclusion, embedding digital health into medical curricula is certainly beneficial for creating a digitally competent healthcare workforce that could help in better data storage, help in diagnosis, aid in patient consultation from a distance, and advise on medications, thereby leading to improved patient care which is a key public health priority. Full article

Background/Objectives: The demand for accessible and reliable digital health services has increased significantly in recent years, particularly in regions facing physician shortages. HomeDOCtor, a conversational AI platform developed in Slovenia, addresses this need with a nationally adapted architecture that combines retrieval-augmented generation (RAG) and a Redis-based vector database of curated medical guidelines. The objective of this study was to assess the performance and impact of HomeDOCtor in providing AI-powered healthcare assistance. Methods: HomeDOCtor is designed for human-centered communication and clinical relevance, supporting multilingual and multimedia citizen inputs while being available 24/7. It was tested using a set of 100 international clinical vignettes and 150 internal medicine exam questions from the University of Ljubljana to validate its clinical performance. Results: During its six-month nationwide deployment, HomeDOCtor received overwhelmingly positive user feedback with minimal criticism, and exceeded initial expectations, especially in light of widespread media narratives warning about the risks of AI. HomeDOCtor autonomously delivered localized, evidence-based guidance, including self-care instructions and referral suggestions, with average response times under three seconds. On international benchmarks, the system achieved ≥95% Top-1 diagnostic accuracy, comparable to leading medical AI platforms, and significantly outperformed stand-alone ChatGPT-4o in the national context (90.7% vs. 80.7%, p = 0.0135). Conclusions: Practically, HomeDOCtor eases the burden on healthcare professionals by providing citizens with 24/7 autonomous, personalized triage and self-care guidance for less complex medical issues, ensuring that these cases are self-managed efficiently. The system also identifies more serious cases that might otherwise be neglected, directing them to professionals for appropriate care. Theoretically, HomeDOCtor demonstrates that domain-specific, nationally adapted large language models can outperform general-purpose models. Methodologically, it offers a framework for integrating GDPR-compliant AI solutions in healthcare. These findings emphasize the value of localization in conversational AI and telemedicine solutions across diverse national contexts. Full article

Background/Objective: Frontline healthcare staff who contend diseases and mitigate their transmission were repeatedly exposed to high-risk conditions during the COVID-19 pandemic. They were at risk of mental health issues, in particular, psychological stress, depression, anxiety, financial stress, and/or burnout. This study aimed to investigate and evaluate the occupational stress of medical doctors, nurses, pharmacists, physiotherapists, and other hospital support crew during the COVID-19 pandemic in Saudi Arabia. Methods: We collected both qualitative and quantitative data from a survey given to public and private hospitals using methods like correspondence analysis, cluster analysis, and structural equation models to investigate the work-related stress (WRS) and anxiety of the staff. Since health-related factors are unclear and uncertain, a fuzzy association rule mining (FARM) method was created to address these problems and find out the levels of work-related stress (WRS) and anxiety. The statistical results and K-means clustering method were used to find the best number of fuzzy rules and the level of fuzziness in clusters to create the FARM approach and to predict the work-related stress and anxiety of healthcare staff. This innovative approach allows for a more nuanced appraisal of the factors contributing to work-related stress and anxiety, ultimately enabling healthcare organizations to implement targeted interventions. By leveraging these insights, management can foster a healthier work environment that supports staff well-being and enhances overall productivity. This study also aimed to identify the relevant health factors that are the root causes of work-related stress and anxiety to facilitate better preparation and motivation of the staff for reorganizing resources and equipment. Results: The results and findings show that when the financial burden (FIN) of healthcare staff increased, WRS and anxiety increased. Similarly, a rise in psychological stress caused an increase in WRS and anxiety. The psychological impact (PCG) ratio and financial impact (FIN) were the most influential factors for the staff’s anxiety. The FARM results and findings revealed that improving the financial situation of healthcare staff alone was not sufficient during the COVID-19 pandemic. Conclusions: This study found that while the impact of PCG was significant, its combined effect with FIN was more influential on staff’s work-related stress and anxiety. This difference was due to the mutual effects of PCG and FIN on the staff’s motivation. The findings will help healthcare managers make decisions to reduce or eliminate the WRS and anxiety experienced by healthcare staff in the future. Full article

Accurate detection of fractures in X-ray images is important to initiate appropriate medical treatment in time—in this study, an advanced combined attention CNN model with multiple attention mechanisms was developed to improve fracture detection by deeply representing features. Specifically, our model incorporates squeeze blocks and convolutional block attention module (CBAM) blocks to improve the model’s ability to focus on relevant features in X-ray images. Using computed tomography X-ray images, this study assesses the diagnostic efficacy of the artificial intelligence (AI) model before and after optimization and compares its performance in detecting fractures or not. The training and evaluation dataset consists of fractured and non-fractured X-rays from various anatomical locations, including the hips, knees, lumbar region, lower limb, and upper limb. This gives an extremely high training accuracy of 99.98 and a validation accuracy 96.72. The attention-based CNN thus showcases its role in medical image analysis. This aspect further complements a point we highlighted through our research to establish the role of attention in CNN architecture-based models to achieve the desired score for fracture in a medical image, allowing the model to generalize. This study represents the first steps to improve fracture detection automatically. It also brings solid support to doctors addressing the continued time to examination, which also increases accuracy in diagnosing fractures, improving patients’ outcomes significantly. Full article

Background/Objectives: Extracting meaningful medical information from chest X-ray images and transcribing it into text is a complex task that requires a high level of expertise and directly affects clinical decision-making processes. Automatic reporting systems for this field in Turkish represent an important gap in scientific research, as they have not been sufficiently addressed in the existing literature. Methods: A deep learning-based approach called Model-SEY was developed with the aim of automatically generating Turkish medical reports from chest X-ray images. The Swin Transformer structure was used in the encoder part of the model to extract image features, while the text generation process was carried out using the cosmosGPT architecture, which was adapted specifically for the Turkish language. Results: With the permission of the ethics committee, a new dataset was created using image–report pairs obtained from Elazıg Fethi Sekin City Hospital and Indiana University Chest X-Ray dataset and experiments were conducted on this new dataset. In the tests conducted within the scope of the study, scores of 0.6412, 0.5335, 0.4395, 0.4395, 0.3716, and 0.2240 were obtained in BLEU-1, BLEU-2, BLEU-3, BLEU-4, and ROUGE word overlap evaluation metrics, respectively. Conclusions: Quantitative and qualitative analyses of medical reports autonomously generated by the proposed model have shown that they are meaningful and consistent. The proposed model is one of the first studies in the field of autonomous reporting using deep learning architectures specific to the Turkish language, representing an important step forward in this field. It will also reduce potential human errors during diagnosis by supporting doctors in their decision-making. Full article

Phytohormones, representing a diverse group of molecules, are essential in orchestrating plant growth and development, ensuring the smooth progression of the entire life cycle from germination to senescence. Emerging research reveals that these compounds also exert biological effects in non-plant systems, including animals. Although some phytohormones can be harmful, their health-promoting potential is rapidly gaining attention. This has sparked a growing interest in exploring plant hormones as novel therapeutic agents, particularly in precision medicine. This review brings together a multidisciplinary team—plant physiologists, a pharmacist, and a medical doctor—to delve into the latest insight surrounding the health-related impacts of plant hormones on animal systems, with a particular emphasis on human health. We comprehensively analyze their effects, weighing both the benefits and potential risks. Key phytohormones—auxin, abscisic acid, cytokinins, jasmonates, ethylene, strigolactones, and gibberellins—are highlighted for their remarkable regulatory roles in animal physiology, with a special focus on their implications for human health. Our discussion reveals how phytohormones may help address critical health challenges, particularly those related to aging populations, including neurodegenerative diseases, diabetes, and cancers. These plant-derived molecules are emerging as promising candidates for future drug development and nutritional therapies. Hence, a deeper understanding of phytohormone action may not just revolutionize agriculture but also open new frontiers in medicine and human health. Full article

Mitotic figures in tumor tissues are an important criterion for diagnosing malignant lesions, and physicians often search for the presence of mitosis in whole slide imaging (WSI). However, prolonged visual inspection by doctors may increase the likelihood of human error. With the advancement of deep learning, AI-based automatic cytopathological diagnosis has been increasingly applied in clinical settings. Nevertheless, existing diagnostic models often suffer from high computational costs and suboptimal detection accuracy. More importantly, when assessing cellular abnormalities, doctors frequently compare target cells with their surrounding cells—an aspect that current models fail to capture due to their lack of intercellular information modeling, leading to the loss of critical medical insights. To address these limitations, we conducted an in-depth analysis of existing models and propose an Inter–Intra Hypergraph Neural Network (II-HGNN). Our model introduces a block-based feature extraction mechanism to efficiently capture deep representations. Additionally, we leverage hypergraph convolutional networks to process both intracellular and intercellular information, leading to more precise diagnostic outcomes. We evaluate our model on publicly available datasets under varying imaging conditions, and experimental results demonstrate that our approach consistently outperforms baseline models in terms of accuracy. Full article

Background: Clinical imaging is an important part of health care providing physicians with great assistance in patients treatment. In fact, segmentation and grading of tumors can help doctors assess the severity of the cancer at an early stage and increase the chances of cure. Despite that Deep Learning for cancer diagnosis has achieved clinically acceptable accuracy, there still remains challenging tasks, especially in the context of insufficient labeled data and the subsequent need for expensive computational ressources. Objective: This paper presents a lightweight classification and segmentation deep learning model to assist in the identification of cancerous tumors with high accuracy despite the scarcity of medical data. Methods: We propose a multi-task architecture for classification and segmentation of cancerous tumors in the Brain, Skin, Prostate and lungs. The model is based on the UNet architecture with different pre-trained deep learning models (VGG 16 and MobileNetv2) as a backbone. The multi-task model is validated on relatively small datasets (slightly exceed 1200 images) that are diverse in terms of modalities (IRM, X-Ray, Dermoscopic and Digital Histopathology), number of classes, shapes, and sizes of cancer pathologies using the accuracy and dice coefficient as statistical metrics. Results: Experiments show that the multi-task approach improve the learning efficiency and the prediction accuracy for the segmentation and classification tasks, compared to training the individual models separately. The multi-task architecture reached a classification accuracy of 86%, 90%, 88%, and 87% respectively for Skin Lesion, Brain Tumor, Prostate Cancer and Pneumothorax. For the segmentation tasks we were able to achieve high precisions respectively 95%, 98% for the Skin Lesion and Brain Tumor segmentation and a 99% precise segmentation for both Prostate cancer and Pneumothorax. Proving that the multi-task solution is more efficient than single-task networks. Full article

Infectious disease outbreaks amplify the influence of stressors on psychological conditions. The purpose of this study was to analyze the disturbing influence of COVID-19 outbreak-related information and the influence of trust on the Serbian healthcare system and COVID-19 preventive measures on anxiety and depression. An anonymous online questionnaire assessing the demographic information, disturbance level and causes, and levels of anxiety and depression has been distributed to the participants, divided into student and non-student groups. The non-student group was further divided into healthcare, military, and education workers. Anxiety and depression levels, as well as the level of decreased trust in COVID-19-related preventive measures, were higher among students compared to non-students (p = 0.011). Higher anxiety and depression levels, and higher influence of the COVID-19 outbreak on those levels, were observed in education and healthcare workers, compared to military personnel. Medical doctors reported a higher level of trust in the healthcare system compared to nurses (p = 0.023). Trust in the healthcare system increased more frequently compared to the pre-vaccination period among medical doctors, compared to nurses (p = 0.040). Higher anxiety and depression and lower public trust levels in students and workers in education and the healthcare sector indicate a need to focus on these important society members during public health emergencies. Full article

Introduction: Access of transgender and gender diverse (TGD) youth to gender-affirming medical care (GAMC) has become a contentious topic in the West, with many members of the general population, politicians, and even some experts and academic researchers voicing concerns about possible adverse effects of GAMC on the mental and physical health of TGD youth. Due to these concerns, recent years have seen a significant rise in legislation restricting TGD youth from accessing GAMC in countries such as the United States, the United Kingdom, and Canada. However, in this critical review of the literature on the psychological (e.g., anxiety, depression, suicide, and body satisfaction) and physical (e.g., bone health, cognitive function, and fertility) health outcomes associated with GAMC among TGD youth, we argue that, given the state of current research, youth should not be restricted from accessing GAMC. Conclusions: Our findings reinforce the importance of close monitoring by doctors, counselling for TGD youth with respect to potential risks, and increased studies on the topic, especially those focusing on reproductive health. Full article

Maternal mortality and morbidity are critical health challenges in the U.S., and building the perinatal workforce is a key to providing high-quality maternal medical care and services. Texas Southern University (TSU), home to a Doctor of Pharmacy program, launched the first Maternal Health Education and Research Training (MHERT) program to educate a cohort of high school, pharmacy, and health sciences students. Aiming to raise awareness of maternal health issues, build research skills, and promote action-based solutions. MHERT integrated online self-paced interactive lessons with hands-on research or community projects. Topics included maternal health epidemiology, causes of morbidity and mortality, research methods, literature reviews, and the development of action plans addressing maternal health challenges. Assessment tools included quizzes, open-ended reflection responses, training surveys, and course evaluations. Running from 3 June to 26 July 2024, the program enrolled 22 students. All participants completed both course components. Course evaluations showed strong and consistent satisfaction with the program, with teaching effectiveness rated at 95% and 96% for mid-program and final evaluations, respectively. MHERT enhanced participants’ understanding of maternal health, improved research skills, and encouraged community engagement and interdisciplinary collaboration. It offers a scalable model to strengthen public health education among high school, pharmacy, and health sciences students. Full article

Background: Occupational burnout and fatigue are critical issues affecting the health and performance of healthcare workers (HCWs) globally. These outcomes are often driven by complex and overlapping work-related stressors, which remain insufficiently understood in combination. Objective: To investigate the associations of multiple work-related stressors with occupational burnout and fatigue, and to identify distinct stress patterns and critical stressors among HCWs. Method: A cross-sectional survey was conducted using a self-administered electronic questionnaire among 2695 HCWs in Shanghai, China. Validated questionnaire scales were used to assess work-related stress (self-developed occupational stress scale for medical staff, CSSM), occupational burnout (Maslach Burnout Inventory–General Survey, MBI-GS), and fatigue (Fatigue Scale-14, FS-14). Latent profile analysis (LPA) was employed to identify distinct work-related stress patterns. Generalized linear models (GLMs) were used to explore the associations between individual stressors, stress patterns, and occupational burnout and fatigue. Additionally, weighted quantile sum (WQS) models were utilized to evaluate the combined effects of multiple stressors and identify the main contributors. Results: In this study, 77.0% and 71.2% of participants were classified as experiencing occupational burnout and fatigue, respectively. A strained doctor–patient relationship was the highest-rated work-related stressor. All work-related stressors, including career development, interpersonal relationships, work–life imbalance, physical environment, doctor–patient relationship, social environment, and workload, were significantly associated with burnout (β: 0.444~0.956, p < 0.001) and fatigue (β: 1.384~3.404, p < 0.001). WQS assigned higher weights to career development and workload for burnout, and to workload and work–life imbalance for fatigue. LPA identified two distinct occupational stress patterns. HCWs characterized by higher stress levels in physical environment, career development, workload, and interpersonal relationships exhibited significantly higher burnout scores (β = 0.325, 95% CI: 0.122, 0.528), particularly in the reduced personal accomplishment (PA) dimension (β = 1.003, 95% CI: 0.746, 1.259). Conclusions: This study highlighted the high prevalence of occupational burnout and fatigue among HCWs in Shanghai, China. Occupational stressors were associated with both burnout and fatigue, with higher workload, work–life imbalance, and poorer career development showing particularly significant contributions. These findings emphasized the urgent need for targeted interventions, including workload management, career development programs, and mental health support, to reduce occupational stress and mitigate its adverse effects on HCWs. Full article

The type of non-professional or professional support received affects the quality of life of the patient and their caregivers. Social support is the type of interaction that is taken by the patient and his caregivers in a problematic, difficult, stressful, or critical situation. Aim: The aim of the study was to assess the impact of social support on the functioning of patients under nursing home care. Material and methods: The study included 148 chronically ill patients under home nursing care. The study used the diagnostic survey method; the research technique was a questionnaire containing basic data about the respondent and the Social Support Scale (SWS) by Krystyna Kmiecik-Baran. Results: The need to continue the causal treatment at home means that the main source of support for care beneficiaries are nurses who provide medical services at the patient’s home, supported by doctors and family members of the patient. According to patients’ subjective assessment of the support they received from nurses, patients rated the informational support provided by nurses highest at 14.3 points and emotional support at 13.3 points (SD 1.776). on a scale where the maximum score was 16 points. In the opinion of the surveyed patients, the value-added support provided was the lowest-rated category by patients, 9.74 points (SD 2.505). Instrumental support was also rated very poorly by the respondents (10.17 points (SD 2.069). In each category, there was no statistically significant difference at the p < 0.05 level in the respondents’ evaluation, which means that the expressed opinion on each type of support from the highest to the lowest evaluation: informational, emotional, instrumental, and evaluative—overlapped in the patient group and the family group. Conclusions: Patients under home care highly appreciated the support provided to them by the nursing staff. Social support for a chronically ill person who requires constant care and care by the nursing staff is a form of direct impact that relieves stress and tension, minimizes the effects of the disease, directly affects the course of treatment and care, and prevents stigmatization. Full article