Tag: pubmed

Disabil Rehabil. 2025 Jun 8:1-12. doi: 10.1080/09638288.2025.2514260. Online ahead of print.

ABSTRACT

PURPOSE: To explore the impact of self-efficacy on self-management with the parallel mediating effects of impulsivity and good control, as well as the moderating effect of emotion regulation.

METHODS: Convenience sampling method was used in this study. From March to July 2024, stroke patients were recruited from a tertiary hospital in Henan Province, China. Questionnaires were administered to collect sociodemographic data, self-control, self-efficacy, emotion regulation, and self-management. Descriptive statistics and Process Macro Models 4 and 14 in the SPSS program (SPSS Inc., Chicago, IL) were used for data analysis.

RESULTS: A total of 519 stroke survivors reported self-management with a moderate standardization score. Impulsivity and good control co-mediating the association of self-efficacy and self-management. After adding the cognitive reappraisal as the moderator, the moderated mediation model of impulsivity was confirmed with adequate fit indices. However, cognitive reappraisal did not play a moderating role in good control and self-management.

CONCLUSIONS: Self-efficacy can promote self-management in stroke survivors by reducing impulsivity and enhancing self-control. Notably, cognitive reappraisal may suppress impulsive thoughts in stroke survivors and promote self-management.

PMID:40483588 | DOI:10.1080/09638288.2025.2514260

J Child Adolesc Ment Health. 2025 Jun 8:1-17. doi: 10.2989/17280583.2025.2490650. Online ahead of print.

ABSTRACT

Background: Adolescence is a critical period marked by increased vulnerability to anxiety-related conditions. With the widespread use of the internet, persistent concerns about health may manifest as cyberchondria in this age group.Objective: The study aimed to evaluate the effect of students’ characteristics (e.g., sex, student’s grade level) and their cyberchondria levels on their health anxiety levels.Methods: A descriptive cross-sectional study was completed with 328 students (mean age = 15.63 years, SD = 2.07 years). The study was conducted among 14 to 17-year-old students studying in the 1^st, 2^nd, 3^rd, and 4^th grades of a public high school between February 2021 and April 2023. Study data were collected using sociodemographic information, health anxiety, and cyberchondria severity measures. Backward elimination linear regression analyses were performed to reveal the factors that predicted health anxiety.Results: Almost half of the sample (n = 132, 40.2%) were male and 196 (59.8%) were female. There were statistically significant differences in terms of total and subscale scores of the Health Anxiety Inventory by sex. Specifically, the mean scores of female students (mean = 16.41, SD = 7.19) were significantly higher than those of male students (mean = 13.49, SD = 6.49) on the total scale (p < 0.001). Accordingly, the mean hypersensitivity to physical symptoms and anxiety subscale scores were significantly higher for female students (mean = 12.83, SD = 5.84) than for males (mean = 10.41, SD = 5.14) (p < 0.05). Similarly, the mean negative consequences of illness subscale scores were significantly higher for females (mean = 3.58, SD = 2.41), than for males (mean = 3.08, SD = 2.49) (p < 0.05). Regression analysis results indicated that sex (β = -0.222), doing research on the internet very frequently (β = 0.175), and Cyberchondria Severity Scale total scores (β = 0.428) significantly predicted health anxiety scores, respectively (F(3.324) = 47.732, p < 0.001). Together, all three significant variables explain 31% of the variance.Conclusions: There was a positive and weak to moderate correlation between students’ health anxiety levels and cyberchondria levels, and their health anxiety levels increased with an increase in their cyberchondria levels.

PMID:40483571 | DOI:10.2989/17280583.2025.2490650

Br J Clin Pharmacol. 2025 Jun 8. doi: 10.1002/bcp.70119. Online ahead of print.

ABSTRACT

Developing high-quality pharmacology multiple-choice questions (MCQs) is challenging in large part due to continually evolving therapeutic guidelines and the complex integration of basic science and clinical medicine in this subject area. Large language models (LLMs) like ChatGPT-4 have repeatedly demonstrated proficiency in answering medical licensing exam questions, prompting interest in their use for generating high stakes exam-style questions. This study evaluates the performance of ChatGPT-4o in generating USMLE-style pharmacology questions based on American Society for Pharmacology and Experimental Therapeutics/Association of Medical School Pharmacology Chairs (ASPET/AMSPC) knowledge objectives and assesses the impact of retrieval-augmented generation (RAG) on question accuracy and quality. Using standardized prompts, 50 questions (25 RAG and 25 non-RAG) were generated and subsequently evaluated by expert reviewers. Results showed higher accuracy for non-RAG questions (88.0% vs. 69.2%), though the difference was not statistically significant. No significant differences were observed in other quality dimensions. These findings suggest that sophisticated LLMs can generate high-quality pharmacology questions efficiently without RAG, though human oversight remains crucial.

PMID:40483567 | DOI:10.1002/bcp.70119

Neurol Res. 2025 Jun 7:1-10. doi: 10.1080/01616412.2025.2515194. Online ahead of print.

ABSTRACT

BACKGROUND: Artificial intelligence (AI) tools for large vessel occlusion (LVO) detection are increasingly used in acute stroke triage to expedite diagnosis and intervention. However, variability in access and workflow integration limits their potential impact. This study assessed current usage patterns, access disparities, and integration levels across U.S. stroke programs.

METHODS: Cross-sectional, web-based survey of 97 multidisciplinary stroke care providers from diverse institutions. Descriptive statistics summarized demographics, AI tool usage, access, and integration. Two-proportion Z-tests assessed differences across institutional types.

RESULTS: Most respondents (97.9%) reported AI tool use, primarily Viz AI and Rapid AI, but only 62.1% consistently used them for triage prior to radiologist interpretation. Just 37.5% reported formal protocol integration, and 43.6% had designated personnel for AI alert response. Access varied significantly across departments, and in only 61.7% of programs did all relevant team members have access. Formal implementation of the AI detection tools did not differ based on the certification (z = -0.2; p = 0.4) or whether the program was academic or community-based (z =-0.3; p = 0.3).

CONCLUSIONS: AI-enabled LVO detection tools have the potential to improve stroke care and patient outcomes by expediting workflows and reducing treatment delays. This survey effectively evaluated current utilization of these tools and revealed widespread adoption alongside significant variability in access, integration, and workflow standardization. Larger, more diverse samples are needed to validate these findings across different hospital types, and further prospective research is essential to determine how formal integration of AI tools can enhance stroke care delivery, reduce disparities, and improve clinical outcomes.

PMID:40483553 | DOI:10.1080/01616412.2025.2515194

Brief Bioinform. 2025 May 1;26(3):bbaf266. doi: 10.1093/bib/bbaf266.

ABSTRACT

Depicting a global landscape of essential gene-targeting drugs would provide more opportunities for cancer therapy. However, a systematic investigation on drugs targeting essential genes still has not been reported. We suppose that drugs targeting cancer-type-specific essential genes would generally have less toxicity than those targeting pan-cancer essential genes. A scoring function-based strategy was developed to identify cancer-type-specific targets and drugs. The EssentialitySpecificityScore ranked the essential genes in 19 cancer types, and 1151 top genes were identified as cancer-type-specific targets. Combining target-drug interaction databases with research/marketing status, 370 cancer-type-specific drugs were identified, bound to 100 out of all identified targets. Profiles of applied cancer types of identified targets and drugs illustrate the scoring strategy’s effectiveness: most drugs apply to cancer types <10. Seven drugs with no previous anticancer evidence were validated in 11 lung adenocarcinoma cell lines, and lower inhibition rates (from 9.4% to 44.0%) were observed in 10 normal cell lines. This difference is statistically significant (Student’s t-test, P ≤ .0001), confirming the rationality of our supposition. Our built EGKG (Essential Gene Knowledge Graph) forms a computational basis to uncover essential gene targets and drugs for specific cancer types. It is available at http://gepa.org.cn/egkg/. Also, our experimental result suggests that combining drugs with orthogonal essentiality may be an alternative way to improve anticancer effects while maintaining biocompatibility. The code and data are available at https://github.com/KKINGA1/EGKG_data_process.

PMID:40483547 | DOI:10.1093/bib/bbaf266

Diabet Med. 2025 Jun 7:e70082. doi: 10.1111/dme.70082. Online ahead of print.

ABSTRACT

AIMS: To conduct a multi-study, cross-country examination of diabetes stigma among adults with type 1 and type 2 diabetes (T1D, T2D).

METHODS: Pre-existing, cross-sectional studies of adults (aged ≥18) completing the T1D or T2D Diabetes Stigma Assessment Scales (DSAS-1/DSAS-2) were collated. Descriptive statistics were calculated for (sub)scale and item scores. Variance-components linear random-effect multi-level modelling (nested random intercepts for country and study) estimated overall mean (sub)scale scores, 95% confidence intervals, intraclass correlation coefficients (ICC) and 95% prediction intervals. Likelihood ratio (LR) tests provided inference for country- and study-specific heterogeneity.

RESULTS: Eleven studies were included from six countries (Australia k = 2, Canada k = 1, Japan k = 2, New Zealand k = 1, UAE k = 1, USA k = 4) in four languages (Arabic k = 1, English k = 7, Japanese k = 2, Spanish k = 1). Six studies included n = 3114 adults with T1D (insulin pump: 42%; 75% aged <60 years). Ten studies included n = 6586 adults with T2D (insulin-treated: 37%; 44% aged <60 years). Most reported ≥1 experience of diabetes stigma (T1D = 91%; study range: 84%-96%; T2D = 77%; 69%-89%). In 10 studies, the ‘blame and judgment’ subscale was most endorsed (T1D = 83%; 62%-89%, T2D = 70%; 53%-79%). Most adults with T1D reported ‘identity concerns’ (73%; 62%-80%), and 47% of adults with T2D reported ‘self-stigma’ (30-60%). Being ‘treated differently’ was least common (T1D = 46%; 40%-54%, T2D = 37%; 28%-47%). Low levels of heterogeneity were observed in mean [SE] total scores (DSAS-1: 54 [0.94] ICC = 0.02, p < 0.001; DSAS-2: 44 [1.1], ICC ≤0.4, p < 0.001).

CONCLUSIONS: Findings suggest a high and relatively consistent prevalence of diabetes stigma across studies and within and across countries, supporting calls for local and global action.

PMID:40483539 | DOI:10.1111/dme.70082

BMC Public Health. 2025 Jun 7;25(1):2135. doi: 10.1186/s12889-025-23266-2.

NO ABSTRACT

PMID:40483506 | DOI:10.1186/s12889-025-23266-2

BMC Sports Sci Med Rehabil. 2025 Jun 7;17(1):146. doi: 10.1186/s13102-025-01186-3.

ABSTRACT

BACKGROUND: The purpose of this study was to examine the impact of Life Kinetik (LK) exercises on balance, agility, jumping performance, proprioception, and cognitive functions in recreationally active preadolescents participating in fencing.

METHODS: The study lasted 8 weeks and included 22 preadolescents recreationally engaged in fencing, aged 11.6 ± 1.2 years, with an average weight of 49.5 ± 10.5 kg and an average height of 157.5 ± 10.0 cm. Preadolescents were randomly assigned to either the LK group or the control group. The experimental group participated in LK exercises twice per week, with each session lasting one hour.

RESULTS: The results indicate that the changes in Stroop, agility, vertical jump, and proprioception test performance between the pre-test and post-test for the control group are not statistically significant. However, the LK group showed statistically significant improvements in Stroop, agility, and vertical jump performance between the pre-test and post-test (p < 0.001). Conversely, the change in proprioception test performance between the pre-test and post-test for the LK training group was not statistically significant. The changes in star balance test performance between the pre-test and post-test for the control group was not statistically significant. In contrast, the LK group showed a statistically significant improvement in star balance test performance between the pre-test and post-test (p < 0.001).

CONCLUSIONS: These findings indicate that while LK exercises are effective in boosting overall athletic performance, they may not be sufficient for developing specific skills such as proprioception.

TRIAL REGISTRATION: The randomized controlled trial was registered on 04/01/2025 at ClinicalTrials.gov, under the registration number NCT06781268.

PMID:40483500 | DOI:10.1186/s13102-025-01186-3

Stem Cell Res Ther. 2025 Jun 7;16(1):294. doi: 10.1186/s13287-025-04419-x.

ABSTRACT

BACKGROUND: Mesenchymal stem cells (MSC) are pivotal bioengineering tools, offering significant promise for applications in bone regeneration. However, their therapeutic potential is limited by inter-donor variability and experimental issues. This study aimed to identify robust metabolic markers of osteodifferentiation applicable across multiple donors, while providing insight into the metabolic pathways actively involved in the process.

METHODS: Untargeted nuclear magnetic resonance (NMR) metabolomics was applied to characterize the intra- and extracellular metabolic adaptations of human adipose-derived MSC (hAMSC) undergoing osteogenic differentiation, compared to proliferation alone. Multivariate and univariate statistical analysis was carried out on data from three independent donors, and cross-validation was employed to evaluate the predictive capacity of the proposed markers.

RESULTS: Variations in the levels of selected (nine) intracellular and (seventeen) extracellular metabolites detect osteodifferentiation by day 7 (out of 21), with nearly 100% accuracy. These signatures suggest a metabolic shift from glycolysis/OxPhos to lactic fermentation, fatty acid β-oxidation and phosphocreatine hydrolysis. Intracellular glucose, lactate, citrate and specific amino acids are redirected towards protein synthesis and glycosylation, with some of the secreted metabolites (e.g., citrate) seemingly involved in biomineralization and other extracellular roles. Membrane metabolism, antioxidant mechanisms and adenosine metabolism are also impacted by osteodifferentiation.

CONCLUSIONS: These findings reveal effective donor-independent markers of hAMSC osteodifferentiation, with a robust extracellular signature standing out for potential rapid and non-invasive detection of osteocommitted cells.

PMID:40483499 | DOI:10.1186/s13287-025-04419-x

Biotechnol Biofuels Bioprod. 2025 Jun 7;18(1):60. doi: 10.1186/s13068-025-02662-1.

ABSTRACT

BACKGROUND: 2,3-butanediol (2,3-BDO) is an economically important platform chemical that can be produced by the fermentation of sugars using an engineered strain of Zymomonas mobilis. These fermentations require continuous monitoring and modification of fermentation conditions to maximize 2,3-BDO yields and minimize the production of the undesired coproducts glycerol and acetoin. Because of the time required for sampling and off-line chromatographic measurement of fermentation samples, the ability of fermentation scientists to modify fermentation conditions in a timely manner is limited. The goal of this study was to test if near-infrared spectroscopy (NIRS) along with multivariate statistics could reduce the time needed for this analysis and enable real-time monitoring and control of the fermentation.

RESULTS: In this work we developed partial least squares (PLS) calibration models to predict the concentrations of glucose, xylose, 2,3-BDO, acetoin, and glycerol in fermentations via NIRS using two different spectrometers and two different spectroscopy modalities. We first evaluated the feasibility of rapid NIRS monitoring through experiments where we measured the signals from each analyte of interest and built NIRS-based PLS models using spectra from synthetic samples containing uncorrelated concentrations of these analytes. All analytes showed unique spectral signatures, and this initial modeling showed that all analytes could be detected simultaneously. We then began work with samples from laboratory fermentation experiments and tested the feasibility of regression model development across two spectral collection modalities (at-line and on-line) and two instruments: a laboratory-grade instrument and a low-cost instrument with a more limited spectral range. All modalities showed promise in the ability to monitor Z. mobilis fermentations of glucose and xylose to 2,3-BDO. The low-cost instrument displayed a lower signal-to-noise ratio than the laboratory-grade instrument, which led to comparatively lower performance overall, but still provided sufficient accuracy to monitor fermentation trends. While the ease of use of on-line monitoring systems was favored as compared to at-line systems due to the lack of sampling required and potential for automated process control, we observed some decrease in performance due to the additional complexity of the sample matrix.

CONCLUSION: We have demonstrated that NIRS combined with multivariate analysis can be used for at-line and on-line monitoring of the concentrations of glucose, xylose, 2,3-BDO, acetoin, and glycerol during Z. mobilis fermentations. The decrease in signal-to-noise ratio when using a low-cost spectrometer led to greater prediction error than the laboratory-grade spectrometer for at-line monitoring. The on-line monitoring modality showed great promise for real time process control via NIRS.

PMID:40483497 | DOI:10.1186/s13068-025-02662-1