EBioMedicine. 2025 Dec 16;123:106075. doi: 10.1016/j.ebiom.2025.106075. Online ahead of print.
NO ABSTRACT
PMID:41406507 | DOI:10.1016/j.ebiom.2025.106075
Tag: pubmed
EBioMedicine. 2025 Dec 16;123:106081. doi: 10.1016/j.ebiom.2025.106081. Online ahead of print.
ABSTRACT
BACKGROUND: Treatment decisions in patients with unresectable colorectal liver metastases (CRLM) are largely guided by radiological response to induction systemic therapy. However, radiological assessment alone provides an imprecise estimate of underlying tumour biology or treatment response. Circulating tumour DNA (ctDNA) is an emerging biomarker that can support clinical decision-making. This study evaluated the independent prognostic value of radiological tumour burden and DELFI-TF, a tumour tissue- and mutation-independent cell-free DNA (cfDNA) fragmentome-based ctDNA assay.
METHODS: We analysed 202 plasma samples and CT scans collected at baseline and following induction systemic therapy from 101 patients with unresectable, liver-limited CRC enrolled in the phase-III CAIRO5 trial (NCT02162563), treated with FOLFOX/FOLFIRI plus bevacizumab. Total tumour volume (TTV) was centrally quantified via semi-automated segmentation of liver metastases. ctDNA was measured using the DELFI-TF score. Associations with overall survival (OS) and early recurrence were evaluated using multivariable Cox regression models.
FINDINGS: At baseline, TTV (median = 139 mL, IQR = 23-497 mL) strongly correlated with DELFI-TF (median = 0.29, IQR = 0.13-0.41; Spearman’s ρ = 0.70). DELFI-TF showed a more pronounced reduction than TTV on-treatment (-97.6% vs -49.9%). Baseline levels and on-treatment changes of DELFI-TF (P = 0.001; P = 0.012) and TTV (P = 0.002; P = 0.002) were independently associated with OS in the multivariable model; their combination improved prognostic performance (Uno’s C-statistic 0.78 vs 0.73; P = 0.036). Baseline (P = 0.016) and on-treatment DELFI-TF (P = 0.001) also predicted early recurrence after local therapy.
INTERPRETATION: Following further validation, integrating cfDNA fragmentome-based testing with radiological tumour volume may provide complementary and clinically meaningful insights for prognostication and treatment response in patients with unresectable CRLM. This exploratory study supports a multimodal biomarker approach to guide personalised treatment strategies.
FUNDING: German Research Foundation (DFG, 513004649), Heidelberg Medical Faculty, Dutch Cancer Society/KWF Kankerbestrijding (10438), PPP Allowance via Health ∼ Holland (LSHM22027), Dr. Miriam and Sheldon G. Adelson Medical Research Foundation, Stand Up To Cancer (SU2C)in-Time Lung Cancer Interception Dream Team Grant, SU2C-Dutch Cancer Society International Translational Cancer Research Dream Team Grant (SU2C-AACR-DT1415), Gray Foundation, Commonwealth Foundation, Cole Foundation, Delfi Diagnostics (research grant), US National Institutes of Health (CA121113, CA233259, CA271896).
PMID:41406506 | DOI:10.1016/j.ebiom.2025.106081
Physiol Res. 2025 Dec 15;74(6):981-987.
ABSTRACT
This study aimed to evaluate the predictive value of serum 25 hydroxyvitamin D (25(OH) D) levels in relation to the onset and glycemic control of gestational diabetes mellitus (GDM). This retrospective study analyzed clinical data of pregnant women who received routine prenatal care and were hospitalized at the Second People’s Hospital of Hefei between January 2023 and January 2025. The study included 200 pregnant women diagnosed with GDM (study group) and 200 gestational age-matched pregnant women with normoglycemia (control group), selected through random sampling. Within the study group, 146 participants exhibited standard glycemic control (Y1 group), while 54 participants exhibited non-standard glycemic control (Y2 group) during hospitalization in the third trimester. Significant differences in serum 25(OH)D levels were observed between the control and study groups across all trimesters (53.82 ± 9.43), (56.73 ± 11.28), (49.65 ± 10.65) nmol/L, and (45.87 ± 8.45), (44.42 ± 10.04), (46.63 ± 9.87) nmol/L (p < 0.05). In the second trimester, serum 25(OH)D levels were negatively correlated with the oral glucose tolerance test (OGTT) values in the study group (p < 0.05). Comparison of the 25(OH)D levels in the third trimester between the Y1 group (48.95 ± 9.46) and the Y2 group (42.75 ± 10.23) nmol/L indicated that there was no significant statistical difference between the study group and the control group (49.65 ± 10.65 nmol/L) (p > 0.05). A receiver operating characteristic curve for first trimester 25(OH)D levels of pregnant women in the study group yielded an area under the curve of 0.84. Lower serum 25(OH)D levels were associated with an elevated risk of developing GDM and with poorer glycemic control in affected women. These findings indicate that first trimester serum 25(OH)D levels may serve as a valuable biomarker for the early prediction and management of GDM. Keywords Blood glucose ” Correlation ” Gestational diabetes mellitus ” Pregnant women ” 25-hydroxyvitamin D.
PMID:41406483
JMIR Mhealth Uhealth. 2025 Dec 17;13:e69795. doi: 10.2196/69795.
ABSTRACT
BACKGROUND: Effective follow-up and management after organ transplantation are crucial for transplant recipients. Mobile health (mHealth) interventions have emerged as a significant approach for facilitating follow-up and management. However, there is a lack of systematic reviews and meta-analyses of their effectiveness.
OBJECTIVE: This study aimed to systematically review and synthesize evidence regarding the effectiveness of mHealth interventions in enhancing follow-up and management for transplant recipients.
METHODS: This study included both randomized controlled trials (RCTs) and nonrandomized studies of interventions (NRSIs) that compared the effects of mHealth interventions with usual care in transplant recipients by searching PubMed, Web of Science, Scopus, Embase, CINAHL, and CENTRAL from database inception to June 2025. The primary outcomes included self-care ability, medical regimen adherence, self-monitoring, communication and counseling, medication adherence, physical activity, nutrition, all-cause mortality, complications, rehospitalization, and emergency and outpatient department visits. The risk of bias for each study was assessed using version 2 of the Cochrane risk-of-bias tool for RCTs and the Risk of Bias in Nonrandomized Studies of Interventions tool for NRSIs. Data extraction and quality assessment were conducted by 2 reviewers independently. Data synthesis was conducted using Review Manager. Both a meta-analysis and a narrative synthesis were carried out.
RESULTS: A total of 23 studies (n=15, 65% RCTs and n=8, 35% NRSIs) with 2022 transplant recipients were included. Compared to the control group, mHealth interventions significantly improved self-care ability (mean difference 14.49, 95% CI 9.61-19.36; P<.001) and reduced rehospitalization (odds ratio [OR] 0.49, 95% CI 0.34-0.71; P<.001). The meta-analysis demonstrated no statistically significant difference in mortality rates (OR 0.73, 95% CI 0.39-1.35; P=.31), rejection (OR 0.55, 95% CI 0.25-1.19; P=.13), or infection (OR 0.33, 95% CI 0.06-1.82; P=.20) between the mHealth intervention and control groups. The narrative synthesis indicated that mHealth interventions could effectively promote adherence to medical regimens and medications, facilitate self-monitoring, and improve communication and consultation.
CONCLUSIONS: mHealth interventions significantly improved self-care ability and reduced rehospitalization rates among organ transplant recipients. However, these interventions did not demonstrate a significant effect on all-cause mortality or complications. mHealth interventions showed potential benefits for various self-management behaviors in organ transplant recipients, but these findings need to be further verified. Future research should prioritize high-quality studies that investigate the impact of mHealth on physical activity, nutrition, and other patient-centered outcomes.
TRIAL REGISTRATION: International Platform of Registered Systematic Review and Meta-Analysis Protocols INPLASY202480101; https://inplasy.com/inplasy-2024-8-0101/.
INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.37766/inplasy2024.8.0101.
PMID:41406471 | DOI:10.2196/69795
JMIR Form Res. 2025 Dec 17;9:e84251. doi: 10.2196/84251.
ABSTRACT
BACKGROUND: Assessment of medical information provided by artificial intelligence (AI) chatbots like ChatGPT and Google’s Gemini and comparison with international guidelines is a burgeoning area of research. These AI models are increasingly being considered for their potential to support clinical decision-making and patient education. However, their accuracy and reliability in delivering medical information that aligns with established guidelines remain under scrutiny.
OBJECTIVE: This study aims to assess the accuracy of medical information generated by ChatGPT and Gemini and its alignment with international guidelines for sepsis management.
METHODS: ChatGPT and Gemini were asked 18 questions about the Surviving Sepsis Campaign guidelines, and the responses were evaluated by 7 independent intensive care physicians. The responses generated were scored as follows: 3=correct, complete, and accurate; 2=correct but incomplete or inaccurate; and 1=incorrect. This scoring system was chosen to provide a clear and straightforward assessment of the accuracy and completeness of the responses. The Fleiss κ test was used to assess the agreement between evaluators, and the Mann-Whitney U test was used to test for the significance of differences between the correct responses generated by ChatGPT and Gemini.
RESULTS: ChatGPT provided 5 (28%) perfect responses, 12 (67%) nearly perfect responses, and 1 (5%) low-quality response, with substantial agreement among the evaluators (Fleiss κ=0.656). Gemini, on the other hand, provided 3 (17%) perfect responses, 14 (78%) nearly perfect responses, and 1 (5%) low-quality response, with moderate agreement among the evaluators (Fleiss κ=0.582). The Mann-Whitney U test revealed no statistically significant difference between the two platforms (P=.48).
CONCLUSIONS: ChatGPT and Gemini both demonstrated potential for generating medical information. Despite their current limitations, both showed promise as complementary tools in patient education and clinical decision-making. The medical information generated by ChatGPT and Gemini still needs ongoing evaluation regarding its accuracy and alignment with international guidelines in different medical domains, particularly in the sepsis field.
PMID:41406470 | DOI:10.2196/84251
N Engl J Med. 2025 Dec 17. doi: 10.1056/NEJMp2513633. Online ahead of print.
NO ABSTRACT
PMID:41406466 | DOI:10.1056/NEJMp2513633
JMIR Public Health Surveill. 2025 Dec 17;11:e80381. doi: 10.2196/80381.
ABSTRACT
BACKGROUND: Overweight and obesity are major risk factors for numerous chronic diseases, requiring effective prevention and intervention strategies. Community-based BMI screening may enhance awareness of weight status, but its effectiveness remains uncertain.
OBJECTIVE: This study aimed to rigorously evaluate the long-term causal effects of community-based BMI screening with a light-touch intervention in Malaysia using a regression discontinuity design (RDD).
METHODS: Using data from 2 waves (2013 and 2018) of a Malaysian population-based cohort study (N=6561), we applied a sharp RDD to estimate the causal effects of community-based BMI screening on health outcomes for individuals near the BMI threshold. Participants were aged 35 years or older and completed both follow-ups. The exposure was BMI screening with a light-touch intervention, including height and weight measurement, feedback on results, and referral card distribution. Main outcomes were BMI, blood pressure, and random blood glucose 5 years post intervention, along with health behaviors, health care use, and mental health status.
RESULTS: BMI screening and intervention showed no significant impact on BMI after 5 years (0.4 kg/m², 95% CI -0.2 to 0.9, P=.16). Results remained robust after adjusting for covariates (eg, 0.4 kg/m², 95% CI -0.1 to 0.9 with age and sex; 0.5 kg/m², 95% CI -0.1 to 1.0 with demographic covariates) and modifying functional forms (0.4 kg/m², 95% CI -0.2 to 1.1 with quadratic specification). Robustness was also confirmed across different bandwidths, placebo tests, “donut” RDD, and when treating age as either a continuous or categorical variable. Interaction analysis revealed almost no substantial heterogeneity effects. Mechanism analysis and secondary outcomes indicated no significant effects on health behaviors (including smoking, physical activity, diet, and sedentary behavior), health care use (screening, diagnosis, and medication treatment of hypertension and diabetes), mental health outcomes (anxiety, depression, and stress levels), or cardiovascular risk factors (systolic blood pressure, diastolic blood pressure, random blood glucose; eg, systolic blood pressure showed a nonsignificant change of 0.2, 95% CI -3.5 to 4.0 mm Hg). These findings should be interpreted cautiously, as this study was sufficiently powered to detect larger, clinically meaningful changes but may have lacked power to identify more modest effects.
CONCLUSIONS: This study is the first to assess the causal effects of population-based BMI screening on long-term health outcomes in a Southeast Asian population. The findings suggest that merely informing individuals of their overweight or obese status and implementing light-touch interventions are insufficient to significantly reduce BMI or drive sustained behavior change. Nonetheless, the results do not exclude the possibility of short-term effects, and more frequent or sustained light-touch interventions may still be effective. Future studies should design more intensive interventions and include larger sample sizes.
PMID:41406464 | DOI:10.2196/80381
JMIR Med Educ. 2025 Dec 17;11:e65287. doi: 10.2196/65287.
ABSTRACT
BACKGROUND: Electronic medical records (EMRs) are a potentially rich source of information on an individual’s health care providers’ clinical activities. These data provide an opportunity to tailor web-based learning for health care providers to align closely with their practice. There is increasing interest in the use of EMR data to understand performance and support continuous and targeted education for health care providers.
OBJECTIVE: This study aims to understand the feasibility and acceptability of harnessing EMR data to adaptively deliver a web-based learning program to early-career physicians.
METHODS: The intervention consisted of a microlearning program where content was adaptively delivered using an algorithm input with EMR data. The microlearning program content consisted of a library of questions covering topics related to best practice management of common emergency department presentations. Study participants were early-career physicians undergoing training in emergency care. The study design involved 3 design cycles, which iteratively changed aspects of the adaptive algorithm based on an end-of-cycle evaluation to optimize the intervention. At the end of each cycle, an online survey and analysis of learning platform metrics were used to evaluate the feasibility and acceptability of the program. Within each cycle, participants were recruited and enrolled in the adaptive program for 6 weeks, with new cohorts of participants in each cycle.
RESULTS: Across each cycle, all 75 participants triggered at least 1 question from their EMR data, with the majority triggering 1 question per week. The majority of participants in the study indicated that the online program was engaging and the content felt aligned with clinical practice.
CONCLUSIONS: The use of EMR data to deliver an adaptive online learning program for emergency trainees is both feasible and acceptable. However, further research is required on the optimal design of such adaptive solutions to ensure training is closely aligned with clinical practice.
PMID:41406416 | DOI:10.2196/65287
JMIR Hum Factors. 2025 Dec 17;12:e76804. doi: 10.2196/76804.
ABSTRACT
BACKGROUND: Health messages are integral to smoking cessation interventions. Common approaches to health message development include expert-crafted messages and audience-generated messages, which produce messages that can be monotonic, didactic, and limited in number. We introduce an alternative approach to health message development that relies on user-generated content available on open-content platforms as a source of health messages.
OBJECTIVE: We examined the acceptability of user-generated content curated from Twitter (subsequently rebranded X) as a source of health support messages in a newly developed smoking cessation mobile intervention called Quit Journey and the optimal timing and frequency with which health messages can be deployed to support app users in real time.
METHODS: A total of 12 semistructured focus groups were held with 38 young adults with low socioeconomic status who smoked cigarettes, wanted to quit, and were aged 18 to 29 years. Focus groups were held virtually on GoTo Meeting, audio recorded, and transcribed verbatim. Deductive thematic analysis was used, with themes based on 5 constructs from the second unified theory of acceptance and use of technology (ie, effort expectancy, facilitating conditions, hedonic motivation, performance expectancy, and social influence) and negative, neutral, and positive sentiment.
RESULTS: Participants perceived user-generated content positively (56/108, 51.9% of the quotes) and focused on their perceived usefulness (37/108, 34.3% of the quotes). User-generated content was perceived as authentic, nonrepetitive support from people with similar real-life experiences. Negative or sarcastic user-generated content elicited negative reactions from participants. Participants preferred receiving 3 or fewer daily messages, ideally before cravings. Suggestions focused on the need to screen user-generated content before its inclusion in the app library and allow app users to customize message frequency and timing.
CONCLUSIONS: User-generated content was deemed an acceptable source of health messages. This content can improve the efficacy and effectiveness of smoking cessation interventions by increasing their pool of unique messages that may be better received and more persuasive than expert-curated content. User-generated content can be used to curate health messages for all medical conditions and behaviors with relevant publicly available online content for integration in behavioral interventions given its high volume, brevity, and narrative-like nature. Future research is needed to investigate the effects of user-generated content on health behaviors and identify the theoretical mechanisms for these effects.
PMID:41406415 | DOI:10.2196/76804
JMIR Med Inform. 2025 Dec 17;13:e79748. doi: 10.2196/79748.
ABSTRACT
BACKGROUND: Knee cartilage injury (KCI) poses significant challenges in the early clinical diagnosis process, primarily due to its high incidence, the complexity of healing, and the limited sensitivity of initial imaging modalities.
OBJECTIVE: This study aims to employ magnetic resonance imaging and machine learning methods to enhance the classification accuracy of the classifier for KCI, improve the existing network structure, and demonstrate important clinical application value.
METHODS: The proposed methodology is a multidimensional feature cross-level fusion classification network driven by the large separable kernel attention, which enables high-precision hierarchical diagnosis of KCI through deep learning. The network first fuses shallow high-resolution features with deep semantic features via the cross-level fusion module. Then, the large separable kernel attention module is embedded in the YOLOv8 network. This network utilizes the combined optimization of depth-separable and point-by-point convolutions to enhance features at multiple scales, thereby dramatically improving the hierarchical characterization of cartilage damage. Finally, five classifications of knee cartilage injuries are performed by classifiers.
RESULTS: To overcome the limitations of network models trained with single-plane images, this study presents the first hospital-based multidimensional magnetic resonance imaging real dataset for KCI, on which the classification accuracy is 99.7%, the Kappa statistic is 99.6%, the F-measure is 99.7%, the sensitivity is 99.7%, and the specificity is 99.9%. The experimental results validate the feasibility of the proposed method.
CONCLUSIONS: The experimental outcomes confirm that the proposed methodology not only achieves exceptional performance in classifying knee cartilage injuries but also offers substantial improvements over existing techniques. This underscores its potential for clinical deployment in enhancing diagnostic precision and efficiency.
PMID:41406414 | DOI:10.2196/79748