Tag: pubmed

JMIR Form Res. 2025 Nov 24;9:e66931. doi: 10.2196/66931.

ABSTRACT

BACKGROUND: Breast cancer remains the most common cancer among women globally. Mammography is a key diagnostic modality; however, interpretation is increasingly challenged by rising imaging volumes, a global shortage of breast radiologists, and variability in reader experience. Artificial intelligence (AI) has been proposed as a potential adjunct to address these issues, particularly in settings with high breast density, such as Asian populations. This study aimed to evaluate the impact of AI assistance on mammographic diagnostic performance among resident and consultant radiologists in Singapore.

OBJECTIVE: To assess whether AI assistance improves diagnostic accuracy in mammographic breast cancer detection across radiologists with varying levels of experience.

METHODS: A multi-reader, multi-case study was conducted at the National University Hospital, Singapore, from May to August 2023. De-identified digital mammograms from 500 women (250 with cancer and 250 normal or benign) were interpreted by 17 radiologists (4 consultants, 4 senior residents, and 9 junior residents). Each radiologist read all cases over 2 reading sessions: one without AI assistance and another with AI assistance, separated by a 1-month washout period. The AI system (FxMammo) provided heatmaps and malignancy risk scores to support decision-making. Area under the curve of the receiver operating characteristic (AUROC) was used to evaluate diagnostic performance.

RESULTS: Among the 500 cases, 250 were malignant and 250 were non-malignant. Of the malignant cases, 16%(80/500) were ductal carcinoma in situ and 84%(420/500) were invasive cancers. Among non-malignant cases, 69.2%(346/500) were normal, 17.6%(88) benign, and 3.6%(18/500) possibly benign but stable on follow-up. Masses (54.4%, 272) and calcifications (10.8%, 54/500) were the most common findings in cancer cases. A majority of both malignant (66.8%, 334/500) and non-malignant (68%, 340/500) cases had heterogeneously or extremely dense breasts (BIRADS [Breast Imaging Reporting and Data System] categories C and D). The AI model achieved an AUROC of 0.93 (95% CI 0.91-0.95), slightly higher than consultant radiologists (AUROC 0.90, 95% CI 0.89-0.92; P=.21). With AI assistance, AUROC improved among junior residents (from 0.84 to 0.86; P=.38) and senior residents (from 0.85 to 0.88; P=.13), with senior residents approaching consultant-level performance (AUROC difference 0.02; P=.051). Diagnostic gains with AI were greatest in women with dense breasts and among less experienced radiologists. AI also improved inter-reader agreement and time efficiency, particularly in benign or normal cases.

CONCLUSIONS: This is the first study in Asia to evaluate AI assistance in mammography interpretation by radiologists of varying experience. AI significantly improved diagnostic performance and efficiency among residents, helping to narrow the experience-performance gap without compromising specificity. These findings suggest a role for AI in enhancing diagnostic consistency, improving workflow, and supporting training. Integration into clinical and educational settings may offer scalable benefits, though careful attention to threshold calibration, feedback loops, and real-world validation remains essential. Further studies in routine screening settings are needed to confirm generalizability and cost-effectiveness.

PMID:41284978 | DOI:10.2196/66931

JMIR Mhealth Uhealth. 2025 Nov 24;13:e73903. doi: 10.2196/73903.

ABSTRACT

BACKGROUND: Mobile health (mHealth) apps are believed to be an effective method to support family caregivers to better care for patients with stroke. This study’s purpose was to explore the status and the influencing factors of mHealth app use among family caregivers of patients with stroke via machine learning (ML) models.

OBJECTIVE: This study aimed to understand the status quo of mHealth app use among community family caregivers of patients with stroke and the factors influencing their use behavior. Six ML models were used to construct the classifier, and the Shapley Additive Explanations (SHAP) algorithm was introduced to interpret the best ML model.

METHODS: In this cross-sectional study, family carers of patients with stroke were recruited. Data on their basic profile and mHealth app use were obtained through face-to-face questionnaires. Hedonic motivation, usage habits, and other relevant information were additionally measured among app users. A total of 12 models were constructed using six ML algorithms. The top-performing logistic regression and random forest models were further analyzed with SHAP to interpret key influencing factors.

RESULTS: A total of 360 family caregivers of patients with stroke were included in this study from March 2023 to November 2023, of which 206 (57.2%) reported having used mHealth apps. Of the 6 ML models, the logistic regression model performed the best in terms of whether caregivers used the mHealth app, with an area under the receiver operating characteristic curve of 0.753 (95% CI 0.698-0.802), accuracy of 0.694 (95% CI 0.647-0.742), sensitivity of 0.748 (95% CI 0.688-0.806), and specificity of 0.623 (95% CI 0.547-0.698). SHAP analysis showed that the top 5 most influencing factors were educational level, age, the patient’s self-care ability, the relationship with the cared-for individual, and the duration of illness. The random forest model performed best in terms of use behavior with an area under the receiver operating characteristic curve of 0.773 (95% CI 0.725-0.818), accuracy of 0.602 (95% CI 0.534-0.665), sensitivity of 0.476 (95% CI 0.420-0.533), and specificity of 0.769 (95% CI 0.738-0.797). The SHAP analysis revealed that hedonic motivation, habits, occupation, convenience conditions, and effort expectations were the 5 most significant influencing factors.

CONCLUSIONS: The research results indicate that the software developers and policymakers of mHealth apps should take the abovementioned influencing factors into consideration when developing and promoting the software. We should focus on the older adults with lower educational levels, lower the threshold for software use, and provide more convenient conditions. By grasping the hedonistic tendencies and habitual usage characteristics of users, they can provide them with more concise and accurate health information, which will enhance the popularity and effectiveness of mHealth apps.

PMID:41284965 | DOI:10.2196/73903

Hepatol Commun. 2025 Nov 24;9(12):e0856. doi: 10.1097/HC9.0000000000000856. eCollection 2025 Dec 1.

ABSTRACT

BACKGROUND: Dietary interventions are key for managing metabolic dysfunction-associated steatotic liver disease (MASLD), yet optimal diets and the role of PNPLA3 in modulating response to diet remain unclear. We evaluated the efficacy of a Mediterranean diet (MD) versus a low-fat diet (LFD) on hepatic fat and fibrosis, assessing interactions with PNPLA3 genotype.

METHODS: Two hundred fifty adults with MASLD with BMI ≥25 kg/m2 were randomized to a 12-week moderately hypocaloric MD or LFD intervention. Individuals with excess alcohol intake and other etiologies of steatosis were excluded. Subjects were genotyped for PNLPA3 single-nucleotide polymorphism. Anthropometric measures, blood tests, and liver assessments [controlled attenuation parameter (CAP) and liver stiffness measurement (LSM)] were conducted at baseline and follow-up. Essential food items were provided, and adherence was tracked using validated questionnaires. The primary outcome was CAP, analyzed using linear mixed models adjusted for age and metabolic syndrome.

RESULTS: Both diets significantly reduced CAP, LSM, and body weight at follow-up, with no significant differences between groups. The mean difference between MD and LFD was -0.13 dB/m for CAP (p=0.976, 95% CI: -8.54, 8.28), -0.19 kPa for LSM (p=0.355, 95% CI: -0.58, 0.21), and 3.01 kg for weight (p=0.159, 95% CI: -7.21, 1.19). PNPLA3 genotype did not significantly interact with diet for CAP, LSM, or weight (p=0.286, p=0.464, p=0.622, respectively).

CONCLUSIONS: Weight reduction achieved by MD and LFD is similarly efficient in steatosis and fibrosis reduction, while PNPLA3 genotype does not affect the response to diet. Further studies investigating the impact of diet and nutrigenetics on liver-related outcomes are warranted.

PMID:41284948 | DOI:10.1097/HC9.0000000000000856

Integr Environ Assess Manag. 2025 Nov 24:vjaf175. doi: 10.1093/inteam/vjaf175. Online ahead of print.

ABSTRACT

This study investigates the environmental and economic factors driving pollution-related mortality in 37 Sub-Saharan African countries from 1990 to 2022. The analysis combines two complementary approaches. The first is a Panel Panel Quantile Autoregressive Distributed Lag (QARDL) model, which captures both short- and long-run relationships across different levels of the mortality distribution. The second is a Light Gradient Boosting Machine (LightGBM) model, a machine-learning method that detects nonlinear patterns and reveals interactions that may be missed by traditional statistical models. Together, these methods integrate structured econometric inference with flexible pattern recognition, offering a clearer and more reliable picture of how environmental and economic forces jointly shape mortality outcomes. The LightGBM partial dependence plots further confirm the Panel QARDL results, showing consistent directional effects across all variables. Fine particulate matter and consumer price index display the strongest nonlinear responses, while methane, health expenditures and Gross Domestic Product exhibit moderate but coherent patterns that reinforce the robustness of the findings. The results show that higher levels of fine particulate matter are consistently linked to increased mortality across all quantiles. Economic growth reduces mortality at higher quantiles of the distribution, where health burdens are most severe, indicating that stronger economies are better able to mitigate pollution-related deaths. Inflation exhibits a positive relationship with mortality, particularly at higher quantiles, indicating that rising prices can limit access to essential health services and increase vulnerability. Health spending, in contrast, reduces mortality, though its impact varies by both time horizon and income level. Overall, the findings highlight the importance of cleaner air, stable prices and stronger healthcare systems for reducing pollution-related mortality in Sub-Saharan Africa. The study provides policy-relevant insights for promoting health resilience under economic and environmental stress.

PMID:41284938 | DOI:10.1093/inteam/vjaf175

Genomics Proteomics Bioinformatics. 2025 Nov 24:qzaf111. doi: 10.1093/gpbjnl/qzaf111. Online ahead of print.

ABSTRACT

Depicting gene expression in a spatial context through spatial transcriptomics is beneficial for inferring cellular mechanisms. Identifying spatially variable genes is a crucial step in leveraging spatial transcriptome data to understand intricate spatial dynamics. In this study, we developed Spanve, a nonparametric statistical method for detecting spatially variable genes in large-scale spatial transcriptomics datasets by quantifying expression differences between each spot or cell and its local neighbors. This method offers a nonparametric approach for identifying spatial dependencies in gene expression without distributional assumptions. Compared with existing methods, Spanve yields fewer false positives, leading to more accurate identification of spatially variable genes. Furthermore, Spanve improves the performance of downstream spatial transcriptomics analyses including spatial domain detection and cell type deconvolution. These results show the broad application potential of Spanve in advancing our understanding of spatial gene expression patterns within complex tissue microenvironments. Spanve is publicly available at https://github.com/zjupgx/Spanve and https://ngdc.cncb.ac.cn/biocode/tool/BT7724.

PMID:41284930 | DOI:10.1093/gpbjnl/qzaf111

Int J Occup Saf Ergon. 2025 Nov 24:1-5. doi: 10.1080/10803548.2025.2586887. Online ahead of print.

ABSTRACT

Objectives. This study aimed to evaluate occupational safety perception and its influencing factors among students in the Department of Occupational Health and Safety at Sinop University, with a particular focus on the impact of socio-demographic variables and participation in occupational health and safety (OHS)-related training. Methods. A cross-sectional study was conducted with 154 OHS students in the 2022-2023 autumn term. Data from 128 students (83.1%) were collected using an informed consent form, a descriptive questionnaire and the occupational safety scale (OSS). The OSS is a 32-item Likert scale with a reliability of 0.75. Data analysis used SPSS version 25, including descriptive statistics, t tests, analysis of variance and Pearson correlation. Results. Participants had a mean age of 21.72 ± 1.33 years, 50.8% were male and 43% were second-year students. Most (73.4%) reported middle-income levels, and 66.4% had not received personal protective equipment (PPE) training. No significant correlation was found between socio-demographic factors and OHS perception scores (p > 0.05). However, students who received PPE training or participated in OHS activities had significantly higher perception scores (p = 0.018 and p = 0.002). Conclusion. OHS-related training, particularly in PPE and OHS activities, significantly improves safety perception. Expanding such training in educational settings can enhance future professionals’ safety awareness.

PMID:41284928 | DOI:10.1080/10803548.2025.2586887

Proc Natl Acad Sci U S A. 2025 Dec 2;122(48):e2500535122. doi: 10.1073/pnas.2500535122. Epub 2025 Nov 24.

ABSTRACT

Using data on millions of internal US migrants, we document that historical homicide rates follow migrants around the United States. Individuals born in historically safe states remain safer wherever they go, while individuals born in historically dangerous states face a greater risk, including from police violence. This pattern holds across demographic characteristics such as age, gender, and marital status, across migrant groups with different average levels of education, income, and even when comparing migrants from different states who reside in the same county. To help understand why, we conducted a large national survey that oversampled internal White US migrants. The results suggest this persistence may reflect a sociocultural adaptation to dangerous settings. Residents and migrants from historically unsafe states-mainly former frontier states and the deep South-see the world as more dangerous, react more forcefully in aggressive scenarios, value toughness, distrust law enforcement, and say they rely on self and family in violent situations. These adaptations may have kept them safe in historically dangerous states, but may increase their vulnerability to harm in safer states.

PMID:41284885 | DOI:10.1073/pnas.2500535122

Proc Natl Acad Sci U S A. 2025 Dec 2;122(48):e2421768122. doi: 10.1073/pnas.2421768122. Epub 2025 Nov 24.

ABSTRACT

Dogs evolved through interactions between people and gray wolves during the Late Pleistocene and have been ubiquitous in human societies ever since. Instances of wolf-to-dog introgression are rare, but adaptive introgression has been shown in association with high-altitude survival. Any widespread gene flow, however, has fallen below thresholds of detection in genome-wide statistical assessments. To reexamine evidence of dog-wolf gene flow, we analyzed 2,693 published dog and wolf genomes and combined highly sensitive local ancestry inference and phylogenomic analyses of nuclear genes, mitochondrial genomes, and Y-chromosome sequences. Although dogs and wolves segregate decisively at the nuclear level, no individual nuclear gene tree supports dog monophyly. Uniparental markers show mixed and interleaved dog and wolf clades with strong support and incongruent phylogenetic topologies. Using local ancestry inference, we find that 64.1% of modern breed dogs carry wolf ancestry from admixture that occurred nearly a thousand generations ago on average and now covers ~0.14% of their individual nuclear genomes. Among modern free-living village dogs (n = 280), 100% of analyzed genomes carry wolf ancestry. We find that wolf ancestry in dog breeds correlates with functional traits including size, breed category, and personality characteristics. In village dogs, wolf ancestry is enriched at olfactory receptor genes, suggesting adaptive introgression for sensory acuity that may have helped these free-living dogs survive in more challenging environments. In total, dog-wolf admixture has likely been an important factor in shaping the evolution of modern dogs.

PMID:41284883 | DOI:10.1073/pnas.2421768122

J Med Internet Res. 2025 Nov 24;27:e80951. doi: 10.2196/80951.

ABSTRACT

BACKGROUND: Traditional obstetric nursing training faces limitations in inadequate interactivity and nonrepeatable demonstrations, limiting students’ development of clinical thinking. Virtual reality (VR) offers a solution for complex health care education, enhancing nursing students’ clinical thinking.

OBJECTIVE: This study applied the Nonimmersive Virtual Reality System for Clinical Thinking in Obstetric Nursing (NIVRSCTON), grounded in salutogenesis theory, to examine its effects on nursing students.

METHODS: The NIVRSCTON was applied under the auspices of the Nursing Virtual Teaching Hub in the Coastal Area (NVTHCA). In September 2023, a convenience sample of 88 undergraduate nursing students from 4 partner institutions participated in the study. A single-group pre-post design and an explanatory sequential mixed methods design were used to measure changes in clinical thinking ability following the training and to assess the system’s performance. The quantitative assessment tools included the general information questionnaire, the Clinical Thinking Ability Evaluation Scale (CTAES), and the Evaluation Instrument for Virtual Reality System (EIVRS). Each student was required to submit one reflective journal developed in accordance with the Bass model. Quantitative data were analyzed using IBM SPSS (version 22.0), and qualitative data were thematically coded using NVivo (version 12; QSR International Pty Ltd).

RESULTS: After the NIVRSCTON training was completed, the students’ overall clinical thinking score increased from 49.08 (SD 11.30) to 80.50 (SD 11.01), indicating a significant improvement (t₈₇=-18.76; Cohen d=-2.82, 95% CI -34.74 to -28.08). All clinical thinking dimension scores improved, and the improvements were all statistically significant (P<.001). Critical thinking scores increased from 13.98 (SD 3.76) to 24.77 (SD 3.11; t₈₇=-20.37; Cohen d=-3.13, 95% CI -11.85 to -9.74), system thinking scores increased from 26.82 (SD 6.40) to 44.51 (SD 6.24; t₈₇=-19.18; Cohen d=-2.80, 95% CI -19.53 to -15.86), and evidence-based thinking scores improved from 18.10 (SD 4.40) to 27.31 (SD 4.61; t₈₇=-13.42; Cohen d=-2.04, 95% CI -10.57 to -7.84). The variable df is all 87. In terms of application effectiveness, the students provided the following ratings: 0.82 (SD 0.15; rated as good) for interface design, 0.82 (SD 0.15; rated as good) for technical performance, 0.83 (SD 0.14; rated as good) for learning content, and 0.85 (SD 0.15; rated as excellent) for learning function. The overall evaluation was 0.82 (SD 0.15; rated as good). Qualitative data revealed that the training not only improved the clinical thinking and decision-making skills of the nursing students but also fostered their professional attitudes, values, and emotions.

CONCLUSIONS: NIVRSCTON training enhances students’ clinical thinking and professionalism. It was well received, confirming its effectiveness. As an obstetric nursing teaching tool, it enhances clinical thinking and professional competence. It may also promote equity and access in nursing education, offering an innovative model for digital nursing education.

PMID:41284338 | DOI:10.2196/80951

Clin Spine Surg. 2025 Oct 24. doi: 10.1097/BSD.0000000000001958. Online ahead of print.

ABSTRACT

STUDY DESIGN: Prospective nonrandomized case-control study (level III).

OBJECTIVE: This study aims to evaluate the efficacy of ultrasound-guided ESPB versus LWI in postoperative analgesia and functional recovery following lumbar fusion surgeries.

SUMMARY OF BACKGROUND DATA: Major lumbar spine surgery is associated with severe postoperative pain. The ultrasound-guided Erector Spinae Plane Block (ESPB) and Local Wound Infiltration (LWI) are commonly used techniques for pain management, but their comparative effectiveness remains underexplored.

METHODS: A prospective nonrandomized case-control study was conducted at a tertiary care hospital from July 2023 to July 2024. A total of 35 patients receiving ESPB were compared with a control group receiving LWI. Postoperative pain was assessed using the numerical rating scale (NRS) at 30 minutes, 6, 12, and 24 hours. Additional parameters included opioid consumption, time to first opioid use, mobilization time, and inflammatory markers. Statistical analysis was performed, with P < 0.05 considered statistically significant.

RESULTS: ESPB provided superior postoperative pain control, with significantly lower NRS scores at 6 hours (2.93 ± 0.74 vs. 3.41 ± 0.89; P = 0.016), 12 hours (4.96 ± 1.28 vs. 5.73 ± 1.56; P = 0.027), and 24 hours with reduced opioid consumption (0.26 ± 0.086 g vs. 0.32 ± 0.14 g; P = 0.028) and delayed time to first opioid use (6.22 ± 2.68 h vs. 4.71 ± 2.88 h; P = 0.026), while inflammatory markers at 24 hours were significantly lower in the ESPB group. Patients receiving ESPB had earlier sitting (18.2 ± 7.61 h vs. 22.6 ± 9.53 h; P = 0.036) and mobilization (28.8 ± 8.46 h vs. 32.41 ± 9.36 h; P = 0.095).

CONCLUSION: Ultrasound-guided ESPB provides superior pain control, reduces opioid consumption, and effectively suppresses the inflammatory response than LWI following lumbar fusion surgeries while facilitating early mobilization, proving to be effective in multimodal pain management.

PMID:41284329 | DOI:10.1097/BSD.0000000000001958