Nevin Manimala

J Surg Res. 2026 Feb 4;319:58-65. doi: 10.1016/j.jss.2026.01.004. Online ahead of print.

ABSTRACT

INTRODUCTION: Bariatric surgery is the most effective treatment modality for individuals with morbid obesity, providing significant and durable weight loss and comorbidity resolution. Glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic polypeptide receptor agonists have shown promise as weight loss drugs, in addition to their use in the treatment of metabolic disorders. While multimodal weight management is the standard of care for individuals with morbid obesity, the benefit of antecedent GLP-1 therapy prior to bariatric surgery has not been well-studied. The objective of this study is to conduct a clinical trial testing the hypothesis that preoperative treatment with a dual GLP-1/glucose-dependent insulinotropic polypeptide receptor agonist enhances preoperative weight loss and decreases tissue inflammation, resulting in improved postoperative outcomes.

MATERIALS AND METHODS: We designed a randomized controlled trial (RCT) comparing preoperative treatment with tirzepatide versus standard medical care prior to minimally invasive bariatric surgery with a target enrollment of 50 patients randomized 1:1. For 3 mo preoperatively, the control arm will receive standard care in the form of dietary and lifestyle modification recommendations, whereas the treatment arm will receive weekly tirzepatide, in addition to standard care. Blood will be collected at enrollment through 12-mo postoperatively and analyzed for inflammatory and metabolic markers. Tissues (adipose, stomach, and liver) will be collected intraoperatively for transcriptome profiling and histological assessment.

RESULTS: This is an ongoing trial with no reportable results.

CONCLUSION: Completion of this pilot RCT will provide data to support initiation of a multicenter RCT to determine therapeutic efficacy, and mechanisms of action, by which patients could benefit from preoperative treatment with tirzepatide.

PMID:41643256 | DOI:10.1016/j.jss.2026.01.004

J Surg Res. 2026 Feb 4;319:40-46. doi: 10.1016/j.jss.2025.11.074. Online ahead of print.

ABSTRACT

INTRODUCTION: Best Practice Advisories (BPAs) are electronic medical record (EMR) tools that help increase uptake of recommended health care behaviors, such as cancer screenings, by identifying eligible patients and alerting providers. However, incomplete/inaccurate documentation within the EMR can be a potential barrier to BPA utility. The purpose of this work was to investigate the effectiveness of a BPA tool to identify eligible patients for lung cancer screening (LCS) using available EMR smoking histories.

MATERIALS AND METHODS: Retrospective observational review was conducted of a BPA programmed to identify LCS-eligible patients at a single quaternary, LCS-accredited, academic medical center. Programming targeted patients aged 50-77 y classified as “current” or “former smokers,” excluding patients with recent lung computed tomography scans and/or lung cancer diagnoses. Data analyzed included frequency of BPA activation and the associated smoking history. Descriptive statistics were used to analyze outcomes.

RESULTS: Between January 2017 and December 2021, there were 25,172 BPA activations, of which 11,701 were removed because they occurred outside a clinical/telehealth visit. This left 14,101 BPAs linked to 3150 patients. EMR information was not sufficient to calculate pack-year history for 48.9% (1541/3150), and the LCS order rate was 2.5% (78/3150). Although pulmonary disease specialists accounted for 13.7% (236/1721) of total LCS orders, the BPA did not activate for them.

CONCLUSIONS: Incomplete EMR data entry may contribute to the complexities of identifying LCS-eligible patients. This highlights the value of improving the completeness of EMR smoking history data and conducting targeted BPA audits to understand optimal activation parameters to improve clinician orders for LCS.

PMID:41643255 | DOI:10.1016/j.jss.2025.11.074

J Anxiety Disord. 2026 Jan 30;118:103127. doi: 10.1016/j.janxdis.2026.103127. Online ahead of print.

ABSTRACT

Climate anxiety has emerged as a significant global psychological and social response to climate change, potentially shaping public engagement and support for climate-related technologies and policies. Here we develop a framework for analyzing online climate anxiety using social media data from China based on 177,232 geo-referenced Weibo posts from 2010 to 2024. The analysis began with the investigation of climate anxiety themes using climate-anxious dictionaries and machine learning methods. Next, the emotional intensity of climate anxiety was assessed through the semantic similarity-based scoring approach. Finally, statistical models were applied to measure the factors influencing climate anxiety. Four major findings are arrived. First, extreme weather events (52.36 %) and livelihood and resource insecurity (22.87 %) were the most discussed and concerning themes, with a notable increase in discussions during summer and autumn. Second, the intensity of climate anxiety has risen significantly. The average intensity increased from 4.42 during the period of 2010-2017 to 7.08 during 2018-2024, with a further notable rise to 7.49 in the more recent period from 2020 to 2024. Third, regions such as Beijing (8.70), Guangdong (8.31), and Zhejiang (7.94) exhibited the highest levels of climate anxiety. Fourth, the intensity of climate anxiety is associated with key demographic and regional factors. Specifically, younger individuals and those residing in climate-vulnerable or informationally developed regions exhibited stronger emotional responses. The framework provides a scalable method for tracking the spatiotemporal dynamics of collective climate anxiety online. The findings demonstrate that digital expressions of climate anxiety constitute a measurable indicator of public concern and carry significant implications for anticipating societal responses and designing targeted communication within climate governance.

PMID:41643241 | DOI:10.1016/j.janxdis.2026.103127

Vaccine. 2026 Feb 4;75:128302. doi: 10.1016/j.vaccine.2026.128302. Online ahead of print.

NO ABSTRACT

PMID:41643234 | DOI:10.1016/j.vaccine.2026.128302

Vaccine. 2026 Feb 4;75:128225. doi: 10.1016/j.vaccine.2026.128225. Online ahead of print.

ABSTRACT

OBJECTIVE: To evaluate febrile seizure risk following monovalent COVID-19 mRNA vaccination among children aged 2-5 years.

METHODS: The primary analysis evaluated children who had a febrile seizure outcome in the 0-1 days following COVID-19 vaccination. A self-controlled case series analysis was performed in three commercial insurance databases to compare the risk of seizure in the risk interval (0-1 days) to a control interval (8-63 days). The exposure of interest was receipt of dose 1 and/or dose 2 of monovalent COVID-19 mRNA vaccinations. The primary outcome was febrile seizure (0-1 day risk interval). A conditional Poisson regression model was used to compare outcome rates in risk and control intervals and estimate incidence rate ratios (IRR) and 95% confidence intervals (CIs). Meta-analyses were used to pool results across databases.

RESULTS: The primary meta-analysis found a statistically significant increased incidence of febrile seizure, in the 0-1 days following mRNA-1273 vaccination compared to the control interval (IRR: 2.52, 95% CI: 1.35 to 4.69, risk difference (RD)/100,000 doses = 3.22 (95% CI -0.31 to 6.75)). For the BNT162b2 vaccination, the IRR was elevated but not statistically significant (IRR: 1.41, 95% CI: 0.48 to 4.11, RD/100,000 doses = -0.25 (95% CI -2.75 to 2.24).

CONCLUSIONS: Among children aged 2-5 years, the analysis showed a small elevated incidence rate ratio of febrile seizures in the 0-1 days following the mRNA-1273 vaccination.

PMID:41643233 | DOI:10.1016/j.vaccine.2026.128225

Sleep Med. 2026 Jan 30;141:108818. doi: 10.1016/j.sleep.2026.108818. Online ahead of print.

ABSTRACT

INTRODUCTION: Melatonin plays a key role in sleep regulation. Combining melatonin with its precursors, L-tryptophan (LT) and 5-Hydroxytryptophan (5HTP), may influence sleep-related outcomes, but evidence in children with neurodevelopmental disorders (NDDs) is limited. This exploratory study compares the effects of melatonin combined with 5HTP (M-5HTP) and melatonin combined with LT (M-LT) on sleep disorders in children with NDDs.

METHODS: This single-center, randomized pilot comparative trial involved children under five years of age with NDDs free of sleep-inducing drugs. Baseline evaluations and actigraphy monitoring were performed. Children were randomly assigned to either M-5HTP (1 mg Melatonin +10 mg 5HTP) or M-LT (1 mg Melatonin +20 mg LT) treatment at 8 p.m. for at least four weeks. Post-treatment actigraphy monitoring assessed sleep parameters.

RESULTS: Of 51 screened children, 26 were enrolled and 13 completed the study (M-5HTP: 9, M-LT: 4). No statistically significant between-group differences in change scores were observed. Within-group analyses showed a significant reduction in the Sleep Movement Index (SMI) from baseline to follow-up in the M-5HTP group (T0: 6.55; T1: 1.25; p=0.006), whereas no significant changes were observed in the M-LT group.

DISCUSSION: In this exploratory pilot study, a within-group reduction in nocturnal motor activity was observed among M-5HTP completers. Given the small sample size, high attrition rate, and limited statistical power, these findings should be interpreted cautiously and considered hypothesis-generating.

PMID:41643230 | DOI:10.1016/j.sleep.2026.108818

Am J Clin Pathol. 2026 Jan 5;165(2):aqaf148. doi: 10.1093/ajcp/aqaf148.

ABSTRACT

OBJECTIVE: The prevalence of mistreatment in the laboratory workforce is concerning. This study aimed to develop an antibullying course for laboratories and to pilot-test its effectiveness in improving knowledge, readiness for organizational change, and coping self-efficacy. In addition, it used geographic information system mapping to explore the geospatial distribution of bullying and course participation and to describe the program for potential implementation by other institutions.

METHODS: An 8-module online course was developed for laboratory management and nonsupervisory staff, focusing on best practices to address incivility at the organizational and individual levels. Participants completed a pre-course assessment, including the Short Negative Acts Questionnaire and a pretest, before taking the course. The program’s effectiveness was evaluated by comparing pre-course and post-course scores using paired sample t tests and geospatial analysis.

RESULTS: Of 127 laboratory professionals who completed the pre-course survey, 92 (72.4%) completed the bullying prevention course and post-course evaluation. More than half (55.1%) were classified as victims of workplace bullying, with regional analysis showing the highest bullying intensity in the West and Midwest regions that also showed contrasting course completion rates (85%-87% in the West vs <70% in the Midwest). Post-course assessment revealed statistically significant improvements in participants’ knowledge (mean increase from 2.57 to 3.08, P < .001) and coping strategies (2.60-2.94, P < .001), supporting the course’s efficacy. Most participants (77.2%) rated the course positively.

CONCLUSIONS: The issue is persistent and will require deliberate interventions. The piloted bullying prevention educational platform presented is promising and can be a foundation for future targeted educational interventions for pathology laboratories.

PMID:41643208 | DOI:10.1093/ajcp/aqaf148

JMIR Mhealth Uhealth. 2026 Feb 5;14:e67514. doi: 10.2196/67514.

ABSTRACT

BACKGROUND: Integrating electronic patient-reported outcomes (ePROs) into electronic health records (EHRs) can enhance the quality of patient care. However, collecting longitudinal ePRO data throughout treatment and posttreatment surveillance remains challenging in patients with breast cancer. To address this, we implemented an automated system that enables ePRO acquisition and seamless integration into the EHR. The system delivers questionnaire weblinks via a mobile messaging app, allowing patients to complete ePROs before clinic visits, with responses automatically transferred to the EHR.

OBJECTIVE: This study aimed to assess patient response rates to the ePRO system and identify key factors influencing the response rate among patients with breast cancer who received radiotherapy and postradiotherapy follow-up.

METHODS: We conducted a retrospective analysis of prospectively collected ePRO data by using the BREAST-Q questionnaire, a validated patient-reported outcome measure for breast surgery, from patients who received adjuvant radiotherapy at our institution between May 2023 and April 2024. At a preradiotherapy or postradiotherapy visit, each patient was asked to complete the questionnaire via a weblink sent to their mobile messaging app, KakaoTalk. The questionnaire was dispatched from minutes to several days before each visit. The response rate was calculated as the percentage of patients whose responses were successfully recorded in the EHR among those who were requested to respond. A complete response (CR) was defined as completion of all required questionnaire items. CR rates were analyzed according to clinical factors using univariate and multivariate logistic regression.

RESULTS: Data from 1488 patients were analyzed, encompassing 2431 encounters (median 1, IQR 1-2 per patient). The median age of the patients was 51 (range 23-83) years, with 65.1% (n=968) patients aged 40 to 59 years. Comorbidities were present in 15% (223/1488) of the patients. The CR rate for the first, second, and third ePRO encounters was 89.9% (1338/1488), 98.3% (735/748), and 97.3% (180/185), respectively. Among first-time respondents, younger patients had a significantly higher CR rate (patients aged <60 years: 100/1104, 90.9%; patients aged ≥60 years: 334/384, 87%; P=.03). The timing of the questionnaire dispatch also affected the CR rate (P<.001). The CR rate was the highest when questionnaires were sent more than 1 hour before the visit (547/583, 93.3%) or in the afternoon of the previous day (505/545, 92.7%) and the lowest when sent 2 or more days before (100/130, 76.9%) or within 1 hour before the appointment (92/112, 81.7%). Both age (P=.006) and timing (P<.001) remained significant in the multivariate analysis.

CONCLUSIONS: This study demonstrates the feasibility of integrating ePRO into EHR through a mobile messaging app-based system, with high patient adherence. Response rates were significantly influenced by patient age and the timing of questionnaire dispatch. These findings provide practical insight for optimizing ePRO implementation in routine oncology care.

PMID:41643191 | DOI:10.2196/67514

J Clin Exp Neuropsychol. 2026 Feb 5:1-8. doi: 10.1080/13803395.2026.2627996. Online ahead of print.

ABSTRACT

INTRODUCTION: PTSD is among the most commonly diagnosed mental health conditions in the military and veteran populations. Shura et al. (2023) and Schroeder and Bieu (2024) developed three symptom validity indices for the PTSD Checklist for the DSM-5 (PCL-5) examining overreported PTSD symptomatology in veteran populations. The current study aimed to cross-validate these SVTs in an active-duty United States military sample.

METHODS: The sample consisted of multiple criterion groups comprised active-duty service members (N = 165). Criterion for valid or invalid responses were based on Personality Assessment Inventory symptom validity scales. The three PCL-5 symptom validity indices included PCL-5 Symptom Severity (PSS), PCL-5 Extreme Symptom (PES), and PCL-5 Rare Items (PRI).

RESULTS: Areas under the curve ranged from .73 to .76 for valid full sample group versus invalid group, which met classification accuracy goals. Optimal cutoff scores were identified for each scale (PES ≥ 16, PSS ≥ 59, PRI ≥ 3) with corresponding sensitivities of PSS = 0.50, PES = 0.40, and PRI = 0.20 with specificity ≥0.90.

CONCLUSION: These findings provide support for the cross-validation of all three embedded symptom validity indices at the identified cutoff scores as PCL-5 over-report measures within an active-duty sample. The PSS and PES demonstrated superior classification statistics, as the PRI was hampered by lower sensitivity.

PMID:41643189 | DOI:10.1080/13803395.2026.2627996

JMIR Med Educ. 2026 Feb 5;12:e75534. doi: 10.2196/75534.

ABSTRACT

BACKGROUND: Artificial intelligence (AI) shows promise in clinical diagnosis, treatment support, and health care efficiency. However, its adoption in real-world practice remains limited due to insufficient clinical validation and an unclear impact on practitioners’ competence. Addressing these gaps is essential for effective, confident, and ethical integration of AI into modern health care settings.

OBJECTIVE: This study aimed to evaluate the effectiveness of informed AI use, following a tailored AI training course, on the performance of general practitioners (GPs) and internists in test-based clinical competence assessments and their attitudes toward clinical AI applications.

METHODS: A pre-post intervention study was conducted with 326 physicians from 39 countries. Participants completed a baseline test of clinical decision-making skills, covering diagnosis, treatment planning, and patient counseling; attended a 1.5-hour online training on effective AI use; and then took a similar postcourse test with AI assistance permitted (GPT-4.0). Test performance and time per question were compared before and after the training. Participants also rated AI accuracy, efficiency, perceived need for structured AI training, and their willingness to use AI in clinical practice before and after the course.

RESULTS: The average test scores improved from 56.9% (SD 15.7%) to 77.6% (SD 12.7%; P<.001), and the pass rate increased from 6.4% (21/326) to 58.6% (191/326), with larger gains observed among GPs and younger physicians. All skill domains (diagnosis, treatment planning, and patient counseling) improved significantly (all P<.001), while time taken to complete the test increased slightly from before to after the course (mean 40.25, SD 16.14 min vs 42.29, SD 14.02 min; P=.03). By the end of the intervention, physicians viewed AI more favorably, reporting increased confidence in its accuracy and time efficiency, greater appreciation for the need for structured AI training, and increased confidence and willingness to integrate AI into patient care.

CONCLUSIONS: Informed use of AI, based on tailored training, was associated with higher performance in test-based clinical decision-making assessments and greater confidence in using AI among GPs and internists. Building on previous research that often lacked structured training, focused primarily on model performance, or was limited in clinical scope, this study provides empirical evidence of both competence and perceptual improvement following informed AI use in a large, multinational cohort, enhancing the generalizability. These findings support the integration of structured AI training into medical education and continuing professional development to improve clinical performance and promote competent use of AI in clinical practice.

PMID:41643188 | DOI:10.2196/75534