Hasil untuk "Computer applications to medicine. Medical informatics"

Amir Mohammad Dorosti, Amin Soheili, Hamed Gholizad Gougjehyaran et al.

Yuchen Zou, Menglong Li, Tuerxunayi Abudumijiti et al.

Shuming Du, Xiaolong Zheng, Yaxu Zhang et al.

Abstract Patients with unstable pelvic fractures combined with traumatic brain injury (TBI) present complex conditions, with pathophysiological contradictions in clinical treatment between limited fluid resuscitation and maintenance of cerebral perfusion pressure. A retrospective analysis was conducted on 204 patients with unstable pelvic fractures combined with TBI between January 1, 2010, and May 31, 2025. A Nomogram model was constructed via LASSO and multivariate logistic regression, with simultaneous development of an interpretable machine learning prediction model. Results identified shock, abnormal systolic blood pressure (SBP), non-surgical management, and prolonged coagulation time as independent poor prognosis risk factors. The Nomogram constructed using SBP, shock, pelvic fracture surgery, and prothrombin time (PT), showed good performance (AUC = 0.801, C-index = 0.7747). Poor prognosis risk was lowest at SBP 110–122 mmHg; a U-shaped correlation existed between SBP and prognosis. The Nomogram and interpretable XGBoost model provide a basis for individualized blood pressure management, coagulation monitoring, and surgical decision-making. ChiCTR2500103926, registered on May 9, 2025, PID: 275507

Jiao Qin, Liangjia Wei, Chunxing Tao et al.

BackgroundThe 23-valent pneumococcal polysaccharide vaccine reduces the risk of pneumonia among adults by 38% to 46%. However, only a few older adults in resource-limited areas of China have received the pneumococcal vaccination. Pay-it-forward is a social innovation that offers participants free or subsidized health services and a community-engaged message, with an opportunity to donate to support subsequent recipients. ObjectiveThis study aims to assess the effectiveness and cost-effectiveness of the pay-it-forward intervention in encouraging the uptake of the 23-valent pneumococcal polysaccharide vaccine in adults aged ≥60 years. MethodsA 2-arm, parallel randomized controlled trial will be conducted in 4 community health centers in Nanning city, Guangxi province, China. We will use a block randomization design. A total of 204 older adults will be randomly allocated in a 1:1 ratio to either the pay-it-forward group or the standard-of-care group. Each participant will complete a web-based questionnaire. The standard-of-care group will be required to pay for the vaccine themselves. In contrast, the pay-it-forward group will receive a 150 RMB (US $20.7) vaccination subsidy, postcards, and the opportunity to donate. The participants in both groups will be followed up in the second and fourth weeks after enrollment. The primary outcome will be uptake of the 23-valent pneumococcal polysaccharide vaccine, as determined by administrative data. Secondary outcomes include costs, pneumococcal vaccination knowledge, attitudes toward the vaccine, perceptions of gratitude, incidence of adverse reactions and adverse events, and the likelihood of recommending pneumococcal vaccination to others. ResultsParticipant recruitment and follow-up were conducted from January 2024 to September 2024. A total of 220 participants were enrolled. Finalized results are expected in June 2026. ConclusionsThis study will provide evidence on the effectiveness and economic costs of the pay-it-forward strategy for pneumonia vaccination among older adults. The findings could have implications for vaccination policy and offer a new approach for increasing vaccination in resource-limited areas. Trial RegistrationChinese Clinical Trial Registry ChiCTR2400079410; https://www.chictr.org.cn/showprojEN.html?proj=213999 International Registered Report Identifier (IRRID)DERR1-10.2196/70246

Iryna Manuilova, Jan Bossenz, Annemarie Bianka Weise et al.

BackgroundPatient similarity is a fundamental concept in precision oncology, offering a pathway to personalized medicine by identifying patterns and shared characteristics among patients. This concept enables stratification into clinically meaningful subgroups, prediction of treatment responses, and the tailoring of therapeutic interventions to individual needs. Despite its transformative potential, the definition, measurement, and clinical application of patient similarity remain inconsistently established, creating challenges in its integration into cancer research and clinical practice. ObjectiveThis study aimed to synthesize evidence on the multidimensional concept of patient similarity in cancer research by analyzing its application across different points of possible data types, methodological frameworks, biological contexts, and commonly studied cancer types. MethodsThis scoping review followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) framework and the Joanna Briggs Institute guidelines. A systematic search was conducted across PubMed, MEDLINE, LIVIVO, and Web of Science (covering the period from 1998 to February 2024) and was supplemented by snowball sampling and manual searches. Duplicate records were removed, and study selection was carried out in 3 phases: title and abstract screening, disagreement resolution, and full-text screening. Each step was independently performed by 2 reviewers in Rayyan, with conflicts resolved by a third reviewer. Data extraction was performed using a predefined template to capture methodological approaches, data types, cancer types, and research objectives related to similarity in patients with cancer. ResultsThis scoping review synthesized evidence from 137 studies, emphasizing the multidimensional concept of patient similarity in cancer research, which integrates diverse data types, methodological frameworks, research objectives, and cancer types. Transcriptomic data (92/137, 67.1%) and clinical data (65/137, 47.4%) were the most frequently used, often combined to enhance the comprehensiveness of similarity analyses. Machine learning (76/137, 55.5%) and network-based approaches (72/137, 52.5%) were prominent methods, reflecting their capacity to handle complex, high-dimensional data and uncover intricate relationships. Cancer subtype identification (70/137, 51.1%) and biomarker discovery (41/137, 29.9%) were the primary research objectives, underscoring the centrality of patient similarity in precision oncology. Breast, lung, and brain cancers were the most frequently studied, benefiting from established research frameworks and abundant datasets. Conversely, rare cancers were underrepresented, highlighting a critical gap in the generalizability of current methodologies. ConclusionsThis comprehensive scoping review examines the concept of patient similarity in cancer research and highlights the critical role of a multilayered perspective in capturing its complexity and identification to enhance understanding and application in precision oncology.

Yuanyuan Qin, Biheng Feng, Qingjiang Cai et al.

Abstract Background Financial resources beyond housing may influence survival in later life. Given China’s rapid population aging and high home ownership, focusing on non-housing assets can clarify wealth–health links. We therefore examined the association between total non-housing assets and all-cause mortality among Chinese middle-aged and older adults. Methods A nationwide cohort of 12,670 adults (aged 45–85) was established using the harmonized CHARLS dataset (2011–2018). All-cause mortality was ascertained through 2020 by supplementing harmonized data with vital status information from the raw CHARLS 2020 wave. The main exposure was total non-housing assets. In addition, non-housing assets were combined with household consumption (median split) to create four joint groups: Group 1 (low assets/low consumption), Group 2 (low assets/high consumption), Group 3 (high assets/low consumption), and Group 4 (high assets/high consumption). All-cause mortality was tracked. Baseline characteristics and mortality were presented by asset quartile and asset consumption group. Survival curves, Cox models (adjusted for confounders), and restricted cubic splines assessed associations. Subgroup and interaction analyses, especially for marital status, were visualized using forest and stratified plots. Results During a 9-year follow-up, 2,418 deaths occurred. Higher total non-housing assets were associated with lower mortality: Q4 (highest) vs. Q1 (lowest), adjusted HR = 0.79 (95% CI 0.68–0.91). In fully adjusted models, we also observed a graded inverse association across asset–consumption groups (P for trend < 0.001); high-consumption categories remained protective (Group 2: HR = 0.85, 95% CI 0.74–0.97; Group 4: HR = 0.75, 95% CI 0.65–0.85). Marital status showed a significant interaction with asset level (P‑interaction < 0.001). Conclusions Greater non-housing assets was associated with lower mortality. Marital status has a significant interacting effect on this association. Focus should be on vulnerable elderly groups with middle-low assets, low consumption, or those who are non-married.

Ashutosh P Raman, Tanner J Zachem, Sarah Plumlee et al.

Manual surgical resection of soft tissue sarcoma tissue can involve many challenges, including the critical need for precise determination of tumor boundary with normal tissue and limitations of current surgical instrumentation, in addition to standard risks of infection or tissue healing difficulty. Substantial research has been conducted in the biomedical sensing landscape for development of non-human contact sensing devices. One such point-of-care platform, previously devised by our group, utilizes autofluorescence-based spectroscopic signatures to highlight important physiological differences in tumorous and healthy tissue. The following study builds on this work, implementing classification algorithms, including Artificial Neural Network, Support Vector Machine, Logistic Regression, and K-Nearest Neighbors, to diagnose freshly resected murine tissue as sarcoma or healthy. Classification accuracies of over 93% are achieved with Logistic Regression, and Area Under the Curve scores over 94% are achieved with Support Vector Machines, delineating a clear way to automate photonic diagnosis of ambiguous tissue in assistance of surgeons. These interpretable algorithms can also be linked to important physiological diagnostic indicators, unlike the black-box ANN architecture. This is the first known study to use machine learning to interpret data from a non-contact autofluorescence sensing device on sarcoma tissue, and has direct applications in rapid intraoperative sensing.

Georgios C Manikis, Nicholas J Simos, Konstantina Kourou et al.

BackgroundHealth professionals are often faced with the need to identify women at risk of manifesting poor psychological resilience following the diagnosis and treatment of breast cancer. Machine learning algorithms are increasingly used to support clinical decision support (CDS) tools in helping health professionals identify women who are at risk of adverse well-being outcomes and plan customized psychological interventions for women at risk. Clinical flexibility, cross-validated performance accuracy, and model explainability permitting person-specific identification of risk factors are highly desirable features of such tools. ObjectiveThis study aimed to develop and cross-validate machine learning models designed to identify breast cancer survivors at risk of poor overall mental health and global quality of life and identify potential targets of personalized psychological interventions according to an extensive set of clinical recommendations. MethodsA set of 12 alternative models was developed to improve the clinical flexibility of the CDS tool. All models were validated using longitudinal data from a prospective, multicenter clinical pilot at 5 major oncology centers in 4 countries (Italy, Finland, Israel, and Portugal; the Predicting Effective Adaptation to Breast Cancer to Help Women to BOUNCE Back [BOUNCE] project). A total of 706 patients with highly treatable breast cancer were enrolled shortly after diagnosis and before the onset of oncological treatments and were followed up for 18 months. An extensive set of demographic, lifestyle, clinical, psychological, and biological variables measured within 3 months after enrollment served as predictors. Rigorous feature selection isolated key psychological resilience outcomes that could be incorporated into future clinical practice. ResultsBalanced random forest classifiers were successful at predicting well-being outcomes, with accuracies ranging between 78% and 82% (for 12-month end points after diagnosis) and between 74% and 83% (for 18-month end points after diagnosis). Explainability and interpretability analyses built on the best-performing models were used to identify potentially modifiable psychological and lifestyle characteristics that, if addressed systematically in the context of personalized psychological interventions, would be most likely to promote resilience for a given patient. ConclusionsOur results highlight the clinical utility of the BOUNCE modeling approach by focusing on resilience predictors that can be readily available to practicing clinicians at major oncology centers. The BOUNCE CDS tool paves the way for personalized risk assessment methods to identify patients at high risk of adverse well-being outcomes and direct valuable resources toward those most in need of specialized psychological interventions.

Cesare Sala, Tiziano Lottini, Elena Lastraioli et al.

Simon B. Goldberg, Shufang Sun, Per Carlbring et al.

Abstract Hundreds of randomized controlled trials (RCTs) have tested the efficacy of mobile health (mHealth) tools for a wide range of mental and behavioral health outcomes. These RCTs have used a variety of control condition types which dramatically influence the scientific inferences that can be drawn from a given study. Unfortunately, nomenclature across mHealth RCTs is inconsistent and meta-analyses commonly combine control conditions that differ in potentially important ways. We propose a typology of control condition types in mHealth RCTs. We define 11 control condition types, discuss key dimensions on which they differ, provide a decision tree for selecting and identifying types, and describe the scientific inferences each comparison allows. We propose a five-tier comparison strength gradation along with four simplified categorization schemes. Lastly, we discuss unresolved definitional, ethical, and meta-analytic issues related to the categorization of control conditions in mHealth RCTs.

Stéphane Téletchéa, Jérémy Esque, Aurélie Urbain et al.

Transmembrane proteins (TMPs) are a class of essential proteins for biological and therapeutic purposes. Despite an increasing number of structures, the gap with the number of available sequences remains impressive. The choice of a dedicated function to select the most probable/relevant model among hundreds is a specific problem of TMPs. Indeed, the majority of approaches are mostly focused on globular proteins. We developed an alternative methodology to evaluate the quality of TMP structural models. HPMScore took into account sequence and local structural information using the unsupervised learning approach called hybrid protein model. The methodology was extensively evaluated on very different TMP all-α proteins. Structural models with different qualities were generated, from good to bad quality. HPMScore performed better than DOPE in recognizing good comparative models over more degenerated models, with a Top 1 of 46.9% against DOPE 40.1%, both giving the same result in 13.0%. When the alignments used are higher than 35%, HPM is the best for 52%, against 36% for DOPE (12% for both). These encouraging results need further improvement particularly when the sequence identity falls below 35%. An area of enhancement would be to train on a larger training set. A dedicated web server has been implemented and provided to the scientific community. It can be used with structural models generated from comparative modeling to deep learning approaches.

Terusha Chetty, Yages Singh, Willem Odendaal et al.

BackgroundThe COVID-19 pandemic undermined gains in reducing maternal and perinatal mortality in South Africa. The Mphatlalatsane Initiative is a health system intervention to reduce mortality and morbidity in women and newborns to desired levels. ObjectiveOur evaluation aims to determine the effect of various exposures, including the COVID-19 pandemic, and a system-level, complex, patient-centered quality improvement (QI) intervention (the Mphatlalatsane Initiative) on maternal and neonatal health services at 21 selected South African facilities. The objectives are to determine whether Mphatlalatsane reduces the institutional maternal mortality ratio, neonatal mortality rate, and stillbirth rate (objective 1) and improves patients’ experiences (objective 2) and quality of care (objective 3). Objective 4 assesses the contextual and implementation process factors, including the COVID-19 pandemic, that shape Mphatlalatsane uptake and variation. MethodsThis study is an implementation science type 2 hybrid effectiveness, controlled before-and-after design with quantitative and qualitative components. The Mphatlalatsane intervention commenced at the end of 2019. For objective 1, intervention and control facility-level data from the District Health Information System are compared for changes in institutional maternal and neonatal mortality and stillbirth rates and associations with QI, the COVID-19 pandemic, and both. This first analysis includes data from 18 facilities, regardless of their allocation to intervention or comparison, to obtain a general idea of the effect of the COVID-19 pandemic. For objectives 2 to 3, data collectors abstract data from maternal and neonatal records, interview participants, and conduct neonatal facility assessments. For objective 4, interviews, program documentation, surveys, and observations are used to assess how contextual factors at the macro-, meso-, and microlevels explain variation in intervention uptake and outcome. The intervention dose is measured at the microlevel only in the intervention facilities. The study assesses the Mphatlalatsane Initiative from 2020 to 2022. ResultsFrom preliminary analysis, across the 3 provinces, maternal and neonatal deaths increased during the COVID-19 pandemic, whereas stillbirths remained unchanged. Maternal satisfaction with quality of care was >90%. The COVID-19 pandemic severely disrupted the QI teams functioning. However, the QI teams regained their pre–COVID-19 momentum by adapting the QI model, with advisers providing mentoring and support. Variation in adoption at the mesolevel was related to stable and motivated leadership (particularly at the facility level), poor integration into routine processes, and buy-in from senior district managers who were affected by competing priorities. Varying referral and specialist outreach systems, staff availability and development, and service delivery infrastructure are plausible factors in variable outcomes. ConclusionsFew evaluations rigorously evaluated the effect of health system interventions on improving health services and outcomes. Results will inform the scaling up of successful intervention components and strategies to mitigate the effects of the COVID-19 pandemic or similar emerging epidemics on maternal and neonatal mortality. International Registered Report Identifier (IRRID)DERR1-10.2196/42041

Malak A. Al-Shammari, Moataza M. Abdel Wahab, Nouf A. AlShamlan et al.

Introduction/Objectives: The prevalence of thyroid disorders is high in Saudi Arabia. Among the disorders, goiter and thyroiditis are the most common and have unique ultrasound (US) features, underscoring the need for US screening for thyroid pathologies. This study aimed to determine the prevalence of thyroiditis and thyroid nodules in patients attending the Family and Community Medicine Center of Imam Abdulrahman Bin Faisal University. Methods: This registry-based cross-sectional study analyzed laboratory and US data from 240 patients who attended the Family and Community Medicine Center of Imam Abdulrahman Bin Faisal University from January 2020 to December 2021. Abnormalities of the thyroid gland were categorized according to laboratory and US data. Associations between different types of thyroid pathology and clinical and laboratory findings were assessed using appropriate statistical methods. Results: The majority of participants were Saudi women. The prevalence of thyroiditis in the study population was 43%. Approximately 25% of these patients had more than 1 nodule, and fine-needle aspiration biopsy showed that most nodules were benign. Most nodules were found in clinically euthyroid patients. Thyroiditis might be associated with abnormal thyroid function. Conclusions: Thyroiditis and thyroid nodules were common in our cohort. Vitamin D deficiency, other autoimmune diseases, and a family history of thyroid disorders were associated with thyroiditis and thyroid nodules. US is useful for identifying the type of thyroid disease.

Anastasia A. Dinakrisma, Purwita Wijaya Laksmi, Teofilus Abdiel et al.

Background Technology, including information and communication technology (ICT), plays a significant role in the quality of health services. However, its application in elderly health services is still lacking. The aim of this study was to determine the knowledge of, attitudes toward, and practices of cell phone and mobile application use for elderly health care among Indonesian health care workers. Methods This was a cross-sectional study conducted with health care workers in Jakarta, Indonesia. The potential subjects were contacted through instant messenger application and/or through conventional short message service or telephone calls from August through November 2020. Results There were 134 subjects. All the subjects had used various health applications to assist with their daily work, including telemedicine (64.2%), guidelines (60.4%), and medical calculators (46.3%). However, 96.3% of the subjects were not aware of the existence of a health application for geriatric assessment. Furthermore, 98.5% of subjects thought that it is important to use ICT to manage geriatric patients, and 94.8% felt that comprehensive geriatric assessment (CGA) in digital form would help them manage geriatric patients better. Nevertheless, 35.10% of subjects had never applied CGA to their geriatric patients. Conclusions The current health care system has begun to enter a period of using ICT in performing health services for geriatric patients. Nevertheless, only a few Indonesian health care workers were aware of or were using the geriatric mobile application. It is essential to improve the dissemination of geriatric health care and e-health literacy among them to improve the quality of elderly health care.

Laura Maaß, Chen-Chia Pan, Merle Freye

BackgroundRapid developments and implementation of digital technologies in public health domains throughout the last decades have changed the landscape of health delivery and disease prevention globally. A growing number of countries are introducing interventions such as online consultations, electronic health records, or telemedicine to their health systems to improve their populations’ health and improve access to health care. Despite multiple definitions for digital public health and the development of different digital interventions, no study has analyzed whether the utilized technologies fit the definition or the core characteristics of digital public health interventions. A scoping review is therefore needed to explore the extent of the literature on this topic. ObjectiveThe main aim of this scoping review is to outline real-world digital public health interventions on all levels of health care, prevention, and health. The second objective will be the mapping of reported intervention characteristics. These will include nontechnical elements and the technical features of an intervention. MethodsWe searched for relevant literature in the following databases: PubMed, Web of Science, CENTRAL (Cochrane Central Register of Controlled Trials), IEEE (Institute of Electrical and Electronics Engineers) Xplore, and the Association for Computing Machinery (ACM) Full-Text Collection. All original study types (observational studies, experimental trials, qualitative studies, and health-economic analyses), as well as governmental reports, books, book chapters, or peer-reviewed full-text conference papers were included when the evaluation and description of a digital health intervention was the primary intervention component. Two authors screened the articles independently in three stages (title, abstract, and full text). Two independent authors will also perform the data charting. We will report our results following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) checklist. ResultsAn additional systematic search in IEEE Xplore and ACM, performed on December 1, 2021, identified another 491 titles. We identified a total of 13,869 papers after deduplication. As of March 2022, the abstract screening state is complete, and we are in the state of screening the 1417 selected full texts for final inclusion. We estimate completing the review in April 2022. ConclusionsTo our knowledge, this will be the first scoping review to fill the theoretical definitions of digital public health with concrete interventions and their characteristics. Our scoping review will display the landscape of worldwide existing digital public health interventions that use information and communication technologies. The results of this review will be published in a peer-reviewed journal in early 2022, which can serve as a blueprint for the development of future digital public health interventions. International Registered Report Identifier (IRRID)DERR1-10.2196/33404

Yu‐Xi Zhu, Run Yang, Xin‐Yu Wang et al.

Abstract Clarifying the mechanisms underlying microbial community assembly from regional microbial pools is a central issue of microbial ecology, but remains largely unexplored. Here, we investigated the gut bacterial and fungal microbiome assembly processes and potential sources in Drosophila simulans and Dicranocephalus wallichii bowringi, two wild, sympatric insect species that share a common diet of waxberry. While some convergence was observed, the diversity, composition, and network structure of the gut microbiota significantly differed between these two host species. Null model analyses revealed that stochastic processes (e.g., drift, dispersal limitation) play a principal role in determining gut microbiota from both hosts. However, the strength of each ecological process varied with the host species. Furthermore, the source‐tracking analysis showed that only a minority of gut microbiota within D. simulans and D. wallichii bowringi are drawn from a regional microbial pool from waxberries, leaves, or soil. Results from function prediction implied that host species‐specific gut microbiota might arise partly through host functional requirement and specific selection across host–microbiota coevolution. In conclusion, our findings uncover the importance of community assembly processes over regional microbial pools in shaping sympatric insect gut microbiome structure and function.

Wilson A. Tárraga, Horacio A. Garda, Lisandro J. Falomir Lockhart et al.

This article contains data for the self-association of pyrene-labelled single Cys-mutants of apolipoprotein A-I (apoA-I). Mathematical models were developed to characterise the self-association events at different cysteine positions on apoA-I obtained as a function of protein concentration based on the multi-parametric spectrum of pyrene, particularly P-value and excimer emissions. The present work complements data related to the article entitled “Analysis of pyrene-labelled apolipoprotein A-I oligomerisation in solution: Spectra deconvolution and changes in P-value and excimer formation” Tárraga et al. [1].

Bruce Bartholow Duncan, Ewerton Cousin, Mohsen Naghavi et al.

Abstract Background The Global Burden of Diseases (GBD) 2017 database permits an up-to-date evaluation of the frequency and burden of diabetes at the state level in Brazil and by type of diabetes. The objective of this report is to describe, using these updated GBD data, the current and projected future burden of diabetes and hyperglycemia in Brazil, as well as its variation over time and space. Methods We derived all estimates using the GBD 2016 and 2017 databases to characterize disease burden related to diabetes and hyperglycemia in Brazil, from 1990 to 2040, using standard GBD methodologies. Results The overall estimated prevalence of diabetes in Brazil in 2017 was 4.4% (95%UI 4.0–4.9%), with 4.0% of those with diabetes being identified as having type 1 disease. While the crude prevalence of type 1 disease has remained relatively stable from 1990, type 2 prevalence has increased 30% for males and 26% for females. In 2017, approximately 3.3% of all disability-adjusted life years lost were due to diabetes and 5.9% to hyperglycemia. Diabetes prevalence and mortality were highest in the Northeast region and growing fastest in the North, Northeast, and Center-West regions. Over this period, despite a slight decrease in age-standardized incidence of type 2 diabetes, crude overall burden due to hyperglycemia has increased 19%, with population aging being a main cause for this rise. Cardiovascular diseases, responsible for 38.3% of this burden in 1990, caused only 25.9% of it in 2017, with premature mortality attributed directly to diabetes causing 31.6% of the 2017 burden. Future projections suggest that the diabetes mortality burden will increase 144% by 2040, more than twice the expected increase in crude disease burden overall (54%). By 2040, diabetes is projected to be Brazil’s third leading cause of death and hyperglycemia its third leading risk factor, in terms of deaths. Conclusions The disease burden in Brazil attributable to diabetes and hyperglycemia, already large, is predicted by GBD estimates to more than double to 2040. Strong actions by the Ministry of Health are necessary to counterbalance the major deleterious effects of population aging.