Tag: pubmed

Interdiscip Sci. 2025 Sep 17. doi: 10.1007/s12539-025-00749-9. Online ahead of print.

ABSTRACT

Traditional disease risk prediction models predominantly rely on statistical algorithms and often focus on genetic factors or a limited set of lifestyle factors to estimate the risk of disease onset. Recently, more comprehensive approaches have emerged that integrate genetic factors with additional lifestyle factors (e.g., alcohol intake) and physical features (e.g., body mass index, age) to increase predictive accuracy. Since the onset of complex diseases is often accompanied by the occurrence of comorbidities, incorporating medical history records is a critical yet underexplored avenue for improving risk prediction. In this study, we propose a novel framework, MIDRP (Multi-source Integration for Disease Risk Prediction), which incorporates genetic variants, lifestyle factors, physical attributes, and medical history records to achieve more robust and accurate predictions. At the heart of our approach lies a causal Transformer architecture, specifically designed to extract and interpret nuanced patterns from medical history records. In the experiments, we compared MIDRP with several baselines, including LDPred2, random forest, multilayer perception, logistic regression, AdaBoost, DiseaseCapsule, EIR, and Med-Bert, on three complex diseases Coronary Artery Disease, Type 2 Diabetes, and Breast Cancer using data from the UK Biobank. Our method achieved state-of-the-art performance, AUROC scores of 0.783, 0.841, and 0.784, respectively, demonstrating its potential in the field of complex disease risk prediction.

PMID:40963070 | DOI:10.1007/s12539-025-00749-9

J Robot Surg. 2025 Sep 17;19(1):614. doi: 10.1007/s11701-025-02788-2.

ABSTRACT

Telesurgery refers to the surgical procedures performed by surgeons with the help of computer or satellite-linked robotic instruments when patients are in distant areas where a surgeon’s physical presence is challenging. This innovative technology has great potential, particularly in providing access to specialized surgical care in remote or underserved areas. This innovation integrates telecommunication networks with robotic surgery and offers enhanced precision, reduced invasiveness, and faster patient recovery. As telesurgery continues to evolve, understanding the knowledge, attitudes, and awareness of healthcare professionals is crucial for its successful integration into medical practice. This cross-sectional study was conducted among 1,053 medical students and health professionals aged 18 and above in Karachi, Pakistan. Participants were selected through convenience sampling and completed a self-administered questionnaire covering demographics, knowledge, attitudes, and awareness of telesurgery. The study reported that 93.9% of participants had heard about telesurgery. However, only 33.6% correctly identified the concept. Acceptance of telesurgery was reported by 44.9% of participants, while 12.1% remained uncertain. Significant associations were found between knowledge and factors such as gender, clinical status, and professional experience (p < 0.05). Participants identified high costs and reliance on technology as major barriers, while rural areas and emergency scenarios were seen as key contexts for telesurgery’s application. This study offers a thorough grasp of the knowledge, attitudes, and perceptions of medical students and professionals regarding the application of telesurgery, which will be heightened by initiating awareness programs and training sessions that will enlighten the importance of telesurgery.

PMID:40963059 | DOI:10.1007/s11701-025-02788-2

J Relig Health. 2025 Sep 17. doi: 10.1007/s10943-025-02446-9. Online ahead of print.

ABSTRACT

This study aims to investigate the barriers that physicians encounter when incorporating prayer into patient care, and to identify factors influencing both their desire to offer prayer and their actual practice of offering to pray with patients. Between March and July 2023, a convenience sample of 203 physicians affiliated with faith-based networks was recruited, including 195 from the United States and eight internationally. An anonymous online survey assessed demographics, as well as attitudes, and practices related to prayer, both personally and in clinical practice. Bivariate analyses and multivariable logistic regression analyses identified factors associated with both the desire to pray and the actual practice of offering prayer. A subgroup analysis examined physicians who offered prayer less frequently than they desired. Participants were primarily Caucasian (64%), Protestant (79%), and 97% valued prayer in their personal lives. Of the 203 physicians, 195 were from the United States and eight were from other countries. Additionally, 53% were aged 46 years or older, 54% were specialists, 46% were in primary care, 65% had academic affiliations, 71% worked in non-faith-based settings, and 48% identified as female. Key factors associated with a lower frequency of desiring to offer prayer included having an academic rank of professor (OR = 3.29, 95% CI: 1.01-10.63) and lower religiosity scores (OR = 4.16, 95% CI: 1.15-15.05). Factors linked to a lower frequency of offering prayer included specialization (OR = 2.72, 95% CI: 1.12-6.56), lower religiosity scores (OR = 2.69, 95% CI: 1.12-6.43), and fear of institutional repercussions (OR = 6.48, 95% CI: 2.44-17.22). Subgroup analyses revealed that specialization (OR = 2.32, 95% CI: 1.06-5.06) and fear of institutional repercussions (OR = 4.78, 95% CI: 2.10-10.87) were significant barriers to offering prayer as often as desired. This study highlights the multifaceted barriers physicians face when incorporating prayer into clinical practice. The findings will inform the development of patient-centered strategies that adhere to ethical and professional healthcare standards. Addressing these challenges through patient-centered strategies and clear guidelines can enhance comprehensive care that supports spiritual well-being. Such efforts align with evidence-informed, whole-person-centered approaches, fostering a more holistic and compassionate healthcare experience.

PMID:40963056 | DOI:10.1007/s10943-025-02446-9

Support Care Cancer. 2025 Sep 18;33(10):859. doi: 10.1007/s00520-025-09935-3.

ABSTRACT

PURPOSE: Breast cancer-related fatigue is one of the most common and debilitating symptoms reported. Knowledge of its prevalence and associated factors may lead to the development of strategies to reduce its impact. This study aimed to identify the factors associated with fatigue and the functional profile of women diagnosed with breast cancer.

METHODS: Cross-sectional study with women aged ≥ 18 years, with breast cancer, recently admitted to the institution, who had their first consultation in the physiotherapy service between January and August 2023, before starting oncological treatment. Data were collected, as well as dynamometry, calf circumference, Timed Up and Go test and questionnaire the fatigue. Descriptive and logistic regression analyses were performed to determine the factors associated with the development of severe fatigue, using the Statistical Package for the Social Sciences program.

RESULTS: 292 women were included in the study, with a mean age of 57.62 (± 11.70) years. The factors associated with severe fatigue, patients who did not practice physical exercise (OR = 6.36, 95% IC 2.09-19.32; p = 0.001), with high body mass index (OR = 1.05; 95% IC 1.00-1.11; p = 0.036), the presence of pulmonary comorbidities (OR = 1.19; 95% IC 1.19-14.23; p = 0.025), pain (OR = 3.33; 95% IC 1.83-6.04; p < 0.001) and the report of subjective lymphedema in the upper limb (OR = 3.29; 95% IC 1.09-9.92; p = 0.034) increased the chance of presenting severe fatigue.

CONCLUSIONS: Patients who did not practice physical exercise, with high body mass index, pulmonary comorbidities, pain and subjective sensation of lymphedema in the upper limb had a greater chance of severe fatigue.

PMID:40963054 | DOI:10.1007/s00520-025-09935-3

Odontology. 2025 Sep 17. doi: 10.1007/s10266-025-01192-4. Online ahead of print.

ABSTRACT

Dental implants are continuously exposed to aggressive oral conditions that can trigger corrosion and compromise their long-term success. In the present work, findings highlight the potential of surfactant nanocomposities (NCs), sodium dodecyl sulfate (SDS) with the titanium oxide (TiO₂) nanoparticals (NPs), which were evaluated as corrosion inhibitors using mild steel as a model substrate in artificial saliva. Electrochemical impedance spectroscopy (EIS) and open circuit potential (OCP) measurements revealed that the SDS-TiO₂ system achieved markedly higher inhibition efficiency than SDS alone, owing to strong adsorption and stable inhibitor-surface interactions. Structural characterization confirmed nanoscale particle size and stability. Transmission electron microscopy (TEM) confirmed nanoscale dimensions (13.9-28.6 nm) and and zeta-sizer analysis revealed a single sharp peak with an approximate size of 17 nm with good stability, supporting its effective performance. The high E_ads value ( – 2585.50 kcal/mol) for the SDS-TiO₂ NCs system reflects the greater stability (inhibitor/surface interaction) and consequently increases their inhibition efficiencies. Statistical analysis (ANOVA, p < 0.05) further validated the significant improvement in resistance parameters with SDS-TiO₂. Computational modeling (DFT, Monte Carlo (MC), and Molecular Dynamics (MD) simulations) corroborated experimental findings by demonstrating the strong binding affinity of the inhibitor system adsorbed on the surface of Fe (110) by a horizontal orientation. While mild steel was employed as a surrogate, these results highlight the translational promise of SDS-TiO₂ NCs for enhancing corrosion resistance in dental implant environments. Future validation of the present findings on clinically relevant alloys (CP-Ti and Ti-6Al-4 V) is essential to confirm the translational potential of SDS-TiO₂ systems for real-world dental implant applications. This addition strengthens the clinical anchoring of the study by outlining a clear direction for future research.

PMID:40963041 | DOI:10.1007/s10266-025-01192-4

Biol Trace Elem Res. 2025 Sep 18. doi: 10.1007/s12011-025-04828-2. Online ahead of print.

ABSTRACT

Fluoride (F), an environmental contaminant, is known to induce cardiotoxicity, although the precise molecular mechanisms remain unclear. This study aimed to investigate the role of the SIRT1/PI3K/AKT signaling pathway in F-induced cardiotoxicity and explore the potential protective effects of SIRT1 activation. Human AC16 cardiomyocytes and zebrafish embryos were exposed to increasing concentrations of sodium NaF. Cellular assays were used to assess viability, apoptosis, cell cycle distribution, and oxidative stress. Expression levels of oxidative and inflammatory markers, as well as components of the SIRT1/PI3K/AKT pathway, were analyzed by western blotting, immunofluorescence, and real-time PCR. Zebrafish were evaluated for cardiac developmental abnormalities, apoptosis, and oxidative stress. The SIRT1 agonist SRT1720 was used to evaluate the protective effects of SIRT1 activation. Statistical analysis was performed using SPSS 23 Software and GraphPad Prism7 software, with significant differences evaluated by one-way analysis of variance (ANOVA) and Dunnett’s test (p < 0.05). NaF exposure significantly inhibited AC16 cell proliferation, induced G1 phase arrest, and increased apoptosis in a dose-dependent manner. Reactive oxygen species levels were elevated, accompanied by downregulation of antioxidant proteins and upregulation of inflammatory cytokines. NaF markedly suppressed SIRT1, PI3K, and AKT expression while activating FOXO1a. Zebrafish embryos exhibited dose-dependent cardiac malformations, increased apoptosis, and elevated oxidative stress markers. Treatment with SRT1720 restored SIRT1/PI3K/AKT pathway activity, enhanced cell proliferation, reduced apoptosis, and alleviated oxidative and inflammatory responses in both cell and zebrafish models. This study demonstrates that F induces cardiotoxicity by disrupting the SIRT1/PI3K/AKT signaling pathway, leading to increased oxidative stress, inflammation, and apoptosis. Activation of SIRT1 by SRT1720 mitigates these effects, highlighting the protective role of this pathway in F-related cardiac injury. These findings provide mechanistic insights and identify potential molecular targets for the prevention and treatment of fluorosis-associated cardiovascular toxicity.

PMID:40963040 | DOI:10.1007/s12011-025-04828-2

Biol Trace Elem Res. 2025 Sep 17. doi: 10.1007/s12011-025-04829-1. Online ahead of print.

ABSTRACT

This study investigated the Bioaccumulation of 14 trace elements (Li, Ti, V, Cr, Mn, Co, Ni, Cu, Zn, Sr, Cd, Ba, Pb, Bi) in six tissues types (muscle, gill, liver, intestine, heart and gallbladder) of five wild fish species (Schizothorax waltoni, Schizothorax o’connori, Schizothorax macropogon, Schizopygopsis younghusbandi, and Oxygymnocypris stewartii) inhabiting Nam Co Lake, Tibet. Elemental concentrations were determined using inductively coupled plasma mass spectrometry (ICP-MS). The results showed that Zn and Cu were the most enriched elements across all tissues. Significant differences in trace element concentrations were observed among different fish species and tissues (p < 0.05), with the omnivorous species S. macropogon exhibiting the highest trace element concentrations. Among the examined tissues, gills and liver were identified as the primary accumulation sites, while muscle exhibited the lowest concentrations. Correlation analysis and Hierarchical Cluster Analysis (HCA) revealed that fish body size had no significant impact on the accumulation of most trace elements. However, strong correlations were observed between Ti, Sr, and Ba, as well as between Cu, Zn, and Cd, suggesting common environmental or physiological factors influencing their accumulation. Based on the risk assessment results, it was demonstrated that the fish were not affected by significant trace element contamination, and that the consumption of these fish would not pose a health risk (Metal Pollution Index (MPI) < 2, the Hazard Quotient (THQ) < 1). These findings provide a scientific basis for future studies on long-term trace element accumulation trends and contribute to the ecological protection and management of alpine lake ecosystems.

PMID:40963037 | DOI:10.1007/s12011-025-04829-1

JMIR Res Protoc. 2025 Sep 17;14:e67059. doi: 10.2196/67059.

ABSTRACT

BACKGROUND: Self-harm is a major public health concern, with prevalence increasing worldwide, particularly after the COVID-19 pandemic and associated lockdown restrictions. Health care workers (HCWs) face various challenges, such as pressures of social and familial responsibilities, a lack of integration within the profession, heavier workload, bullying at the workplace, and limited support in the workplace, that impact their mental health and often lead to self-harm.

OBJECTIVE: We aim to synthesize the evidence on the pooled prevalence of self-harm worldwide and identify risk factors for self-harm among HCWs.

METHODS: We will conduct a systematic review of observational and experimental studies that investigated the overall prevalence of self-harm among HCWs. We will search the PubMed, PsycINFO, Embase, and CINAHL databases for eligible articles from inception until March 2025 using specific search terms developed using the population, exposure, comparison, and outcome framework. Study selection and reporting will follow the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) and the Meta-Analysis of Observational Studies in Epidemiology guidelines. We will contact the corresponding author via email if the required data are not available in the article. After completing the article search, duplicate records will be removed. Titles and abstracts will then be screened according to the inclusion and exclusion criteria, followed by retrieval of the full texts for detailed screening. All the required data for the review, such as names of authors, publication year, prevalence of self-harm, type of profession, associated risk factors to self-harm, and others, will be extracted using a standardized data extraction form. The quality of the studies will be assessed using the Joanna Briggs Institute guidelines based on the study design. Random-effects meta-analysis will be used to derive the pooled prevalence using Stata (version 17.0) software. We will conduct a subgroup meta-analysis on sex, regions, and the type of profession (physicians or nurses). We will also examine the association of risk factors of self-harm with sociodemographic factors to observe their relationship. Both analyses will be performed using RevMan software. Publication bias will be examined using the funnel plot and Egger test.

RESULTS: Data analysis is expected to be completed by August 2025, and manuscript preparation is expected to be completed by October 2025. This review is expected to be completed and published by January 2026.

CONCLUSIONS: We will provide a comprehensive synthesis of the overall prevalence of self-harm among HCWs. We will also provide important information to develop effective strategies for preventing and managing self-harm among HCWs.

TRIAL REGISTRATION: PROSPERO CRD42024581791; https://www.crd.york.ac.uk/PROSPERO/view/CRD42024581791.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/67059.

PMID:40961495 | DOI:10.2196/67059

J Med Internet Res. 2025 Sep 17;27:e73391. doi: 10.2196/73391.

ABSTRACT

BACKGROUND: Among the alternative solutions being tested to improve access to genetic services, chatbots (or conversational agents) are being increasingly used for service delivery. Despite the growing number of studies on the accessibility and feasibility of chatbot genetic service delivery, limited attention has been paid to user interactions with chatbots in a real-world health care context.

OBJECTIVE: We examined users’ interaction patterns with a pretest cancer genetics education chatbot as well as the associations between users’ clinical and sociodemographic characteristics, chatbot interaction patterns, and genetic testing decisions.

METHODS: We analyzed data from the experimental arm of Broadening the Reach, Impact, and Delivery of Genetic Services, a multisite genetic services pragmatic trial in which participants eligible for hereditary cancer genetic testing based on family history were randomized to receive a chatbot intervention or standard care. In the experimental chatbot arm, participants were offered access to core educational content delivered by the chatbot with the option to select up to 9 supplementary informational prompts and ask open-ended questions. We computed descriptive statistics for the following interaction patterns: prompt selections, open-ended questions, completion status, dropout points, and postchat decisions regarding genetic testing. Logistic regression models were used to examine the relationships between clinical and sociodemographic factors and chatbot interaction variables, examining how these factors affected genetic testing decisions.

RESULTS: Of the 468 participants who initiated a chat, 391 (83.5%) completed it, with 315 (80.6%) of the completers expressing a willingness to pursue genetic testing. Of the 391 completers, 336 (85.9%) selected at least one informational prompt, 41 (10.5%) asked open-ended questions, and 3 (0.8%) opted for extra examples of risk information. Of the 77 noncompleters, 57 (74%) dropped out before accessing any informational content. Interaction patterns were not associated with clinical and sociodemographic factors except for prompt selection (varied by study site) and completion status (varied by family cancer history type). Participants who selected ≥3 prompts (odds ratio 0.33, 95% CI 0.12-0.91; P=.03) or asked open-ended questions (odds ratio 0.46, 95% CI 0.22-0.96; P=.04) were less likely to opt for genetic testing.

CONCLUSIONS: Findings highlight the chatbot’s effectiveness in engaging users and its high acceptability, with most participants completing the chat, opting for additional information, and showing a high willingness to pursue genetic testing. Sociodemographic factors were not associated with interaction patterns, potentially indicating the chatbot’s scalability across diverse populations provided they have internet access. Future efforts should address the concerns of users with high information needs and integrate them into chatbot design to better support informed genetic decision-making.

PMID:40961494 | DOI:10.2196/73391

JMIR Med Inform. 2025 Sep 17;13:e73960. doi: 10.2196/73960.

ABSTRACT

BACKGROUND: Emergency department (ED) overcrowding remains a critical challenge, leading to delays in patient care and increased operational strain. Current hospital management strategies often rely on reactive decision-making, addressing congestion only after it occurs. However, effective patient flow management requires early identification of overcrowding risks to allow timely interventions. Machine learning (ML)-based predictive modeling offers a solution by forecasting key patient flow measures, such as waiting count, enabling proactive resource allocation and improved hospital efficiency.

OBJECTIVE: The aim of this study is to develop ML models that predict ED waiting room occupancy (waiting count) at 2 temporal resolutions. The first approach is the hourly prediction model, which estimates the waiting count exactly 6 hours ahead at each prediction time (eg, a 1 PM prediction forecasts 7 PM). The second approach is the daily prediction model, which forecasts the average waiting count for the next 24-hour period (eg, a 5 PM prediction estimates the following day’s average). These predictive tools support resource allocation and help mitigate overcrowding by enabling proactive interventions before congestion occurs.

METHODS: Data from a partner hospital’s ED in the southeastern United States were used, integrating internal and external sources. Eleven different ML algorithms, ranging from traditional approaches to deep learning architectures, were systematically trained and evaluated on both hourly and daily predictions to determine the models that achieved the lowest prediction error. Experiments optimized feature combinations, and the best models were tested under high patient volume and across different hours to assess temporal accuracy.

RESULTS: The best hourly prediction performance was achieved by time series vision transformer plus (TSiTPlus) with a mean absolute error (MAE) of 4.19 and a mean squared error (MSE) of 29.36. The overall hourly waiting count had a mean of 18.11 and a SD (σ) of 9.77. Prediction accuracy varied by time of day, with the lowest MAE at 11 PM (2.45) and the highest at 8 PM (5.45). Extreme case analysis at (mean + 1σ), (mean + 2σ), and (mean + 3σ) resulted in MAEs of 6.16, 10.16, and 15.59, respectively. For daily predictions, an explainable convolutional neural network plus (XCMPlus) achieved the best results with an MAE of 2.00 and a MSE of 6.64. The daily waiting count had a mean of 18.11 and a SD of 4.51. Both models outperformed traditional forecasting approaches across multiple evaluation metrics.

CONCLUSIONS: The proposed prediction models effectively forecast ED waiting count at both hourly and daily intervals. The results demonstrate the value of integrating diverse data sources and applying advanced modeling techniques to support proactive resource allocation decisions. The implementation of these forecasting tools within hospital management systems has the potential to improve patient flow and reduce overcrowding in emergency care settings. The code is available in our GitHub repository.

PMID:40961493 | DOI:10.2196/73960