Category: Nevin Manimala

Medicine (Baltimore). 2025 May 2;104(18):e42334. doi: 10.1097/MD.0000000000042334.

ABSTRACT

Previous studies have suggested a potential connection between schizophrenia (SCZ) and sleep and eating disorders. However, these studies have not sufficiently accounted for potential confounding factors, leaving the true causal relationship unclear. Understanding this causal link is essential for developing effective prevention and treatment strategies. To address this gap, we aim to investigate the causal effect of SCZ on sleep and eating disorders using Mendelian randomization (MR) analysis. This method offers a more robust approach by leveraging genetic variants as instrumental variables to rigorously examine the relationship between SCZ and its comorbidities. We conducted bidirectional MR analyses using genome-wide association study summary statistics of SCZ, sleep disorders, and eating disorders. These analyses were conducted after confirming adherence to the 3 core MR assumptions, removing instrumental variables with confounding effects, and directionally harmonizing all data. Then we used Cochran Q test, MR-Egger intercept analysis, and leave-one-out method to conduct the sensitivity analysis of this study to test its heterogeneity and pleiotropy. The results of the inverse-variance weighted (IVW) approach suggest that SCZ increases the risk of sleep disorders (IVW: odds ratio [OR] = 1.041, 95% confidence interval [CI]: 1.012-1.070, P < .01), whereas studies in the opposite direction have not found an effect of sleep disorders on SCZ. The results of IVW suggest that SCZ increases the risk of eating disorders (IVW: OR = 1.228, 95% CI: 1.090-1.384, P < .001), and the weighted median (WM) method similarly provided evidence that SCZ increases the risk of eating disorders (WM: OR = 1.200, 95% CI: 1.019-1.407, P < .05). This study concluded that SCZ is causally associated with sleep and eating disorders. In clinical practice, psychiatrists should pay attention to the daily sleep and eating status of patients with SCZ, and take appropriate measures and countermeasures as early as possible if there is any abnormality.

PMID:40324258 | DOI:10.1097/MD.0000000000042334

Medicine (Baltimore). 2025 May 2;104(18):e42271. doi: 10.1097/MD.0000000000042271.

ABSTRACT

Transcription factor EB (TFEB) is an endogenous protective factor. Here, we sought to discern the possibility of serum TFEB as a prognostic biomarker of moderate-severe traumatic brain injury (msTBI). Serum TFEB levels of 141 patients with msTBI and 70 controls were quantified in this prospective cohort study. Rotterdam computed tomography (CT) classification and Glasgow coma scale (GCS) were considered as the severity metrics. Glasgow outcome scale (GOS) scores of 1 to 3 at 6 months after trauma meant a poor prognosis. The results were analyzed using multivariate analysis. Patients versus controls had a notable reduction of serum TFEB levels. Serum TFEB levels of independent correlation with Rotterdam CT scores and GCS scores were independently relevant to continuous GOS scores and ordinal GOS scores. Serum TFEB levels of linear relation to risk of poor prognosis under restricted cubic spline were independently predictive of poor prognosis. Using receiver operating characteristic curve analysis, serum TFEB levels displayed analogous prognostic predictive ability to Rotterdam CT scores and GCS scores. The constructed model by merging the 3 prognostic independent predictors, that is serum TFEB, Rotterdam CT scores and GCS scores, was pictorially exhibited via the nomogram, and was demonstrated to perform well by adopting several statistical approaches. An obvious decline of serum TFEB levels subsequent to msTBI are firmly related to trauma severity and poor neurological outcomes of patients, reinforcing the clinical meaningfulness of serum TFEB as a prognostic biochemical indicator of msTBI.

PMID:40324256 | DOI:10.1097/MD.0000000000042271

Medicine (Baltimore). 2025 May 2;104(18):e42295. doi: 10.1097/MD.0000000000042295.

ABSTRACT

To examine the correlation between negative cognitive processing bias and the mental health, sleep status of peacekeepers across various time periods to establish a benchmark for peacekeeper mental health interventions. Symptom checklist 90 (SCL-90), Pittsburgh sleep quality index (PSQI), and negative cognitive processing bias questionnaire (NCPBQ) were adopted to investigate 172 Chinese peacekeepers in the Democratic Republic of the Congo at the early and middle stages of the mission. There was no statistically significant difference in the overall score of PSQI between the early and middle stages of the mission (P = .699). However, there was a statistically significant difference in the overall score of SCL-90 and NCPBQ (P < .05). Furthermore, there was a positive correlation between the negative cognitive processing bias and the scores of SCL-90 and PSQI (R = 0.114-0.528, P < .05). A negative cognitive processing bias was also found to be a strong predictor of the overall score on the SCL-90 and PSQI assessments, with explanation rates of 27.3% and 17.5%, respectively. Peacekeepers are prone to experiencing psychological issues during the initial phase of their mission, necessitating careful attention. The presence of negative cognitive processing bias significantly impacts both mental health and sleep quality. Alleviating negative cognitive processing bias can potentially enhance the mental well-being and sleep quality of peacekeepers.

PMID:40324253 | DOI:10.1097/MD.0000000000042295

Medicine (Baltimore). 2025 May 2;104(18):e42277. doi: 10.1097/MD.0000000000042277.

ABSTRACT

Lung ultrasound (LUS) is a noninvasive, easily repeatable, and radiation-free technique that can be applied at the bedside and is used increasingly often in patient management. This study aims to examine the relationship between the mortality of patients with COVID-19 pneumonia followed in the intensive care unit and their LUS scores obtained by evaluating different areas of the thorax and to reveal the prognostic value of this method. Fifty patients diagnosed with COVID-19 by PCR tests and followed in our intensive care unit were included in this study. The LUS scores were obtained within 24 hours of the patients’ hospitalization. The patients’ demographic data, APACHE II and SOFA scores, laboratory data, and survival status were retrospectively reviewed. Comparisons were made according to 28-day mortality. The mortality rate was found to be 31 (62%) among all patients included in the study. The LUS scores of the patients who died were statistically significantly higher than those of patients who survived (30.77 ± 4.31 vs 24.21 ± 5.4, P = .001). The APACHE II (P = .001) and SOFA scores (P < .001) and lactate (P = .020) and ferritin (P = .005) levels of the patients who died were also significantly higher. In correlation analysis, LUS scores were positively correlated with APACHE II (R = 0.379, P = .007) and SOFA (R = 0.457, P = .001) scores, while they were negatively correlated with PaO2 (r = -0.483, P = .001) and PaO2/FiO2 (r = -0.297, P = .036). ROC curve analysis revealed that LUS scores of ≥ 30 predicted mortality with 80.65% sensitivity and 84.21% specificity (AUC: 0.836, P = .001). The likelihood of observing a LUS score of ≥ 30 in the patients who died was 22.222 times higher compared to surviving patients (odds ratio for LUS score: 22.222, 95% confidence interval: 4.854-101.741). We determined in this study that LUS scores successfully predicted the prognosis of COVID-19 patients in the intensive care unit. Therefore, the LUS score can provide significant information to clinicians for patient management and the determination of the degree of lung involvement.

PMID:40324231 | DOI:10.1097/MD.0000000000042277

Medicine (Baltimore). 2025 May 2;104(18):e42214. doi: 10.1097/MD.0000000000042214.

ABSTRACT

The aim was to study the morphological and positional characteristics of temporomandibular joints (TMJ) in adult females with different vertical bony facial types of bony class II. A total of 60 adult females with bony class II were divided into high-angle group, low-angle group, and an average-angle group in the Frankfort horizontal plane-gonion-gnathion angle (MP-FH). In the control group, there were 20 patients with bony class I homogeneous angle, and the ages of each group were between 18 and 35 years old. Dolphin software was used to generate lateral cranial views and perform fixed-point tracing. Invivo 5.3 software was used to reconstruct the 3-dimensional image of the TMJ, and the following items were measured under the view of the TMJ: (1) bony structures such as condyle and fossa and (2) condyle position. The statistical software SPSS27.0 was used to collate and analyze the data. There was no significant difference in bilateral TMJ measurements between the groups (P > .05). Compared with the other groups, the long axis of the condyle, short axis, and supra-articular space (SS) were larger, the fossa depth was deeper, articular eminence inclination was steeper, the long-term axis and the short axis of the high angle were smaller, the fossa depth, and articular eminence inclination were smaller, the anterior articular space was larger (P < .05), and the supra-articular and posterior spaces were smaller. The position of the condyle was mainly in the median and anterior positions in the control group, the anterior and posterior positions in the average angle group, the median and posterior positions in the low-angle group, and the posterior position in the high-angle group. The morphology and position of bilateral TMJ were basically symmetrical between the skeletal class II adult females and the control group, and the position and morphology of the TMJ in the vertical skeletal type of adult females with bony class II were different.

PMID:40324230 | DOI:10.1097/MD.0000000000042214

Medicine (Baltimore). 2025 May 2;104(18):e42345. doi: 10.1097/MD.0000000000042345.

ABSTRACT

The COVID-19 pandemic has raised critical concerns about its long-term effects on cardiovascular health, particularly concerning autonomic nervous system (ANS) function. Little is known about the differences in ANS function between physically active and inactive individuals exposed and nonexposed to COVID-19. This study aimed to compare the impact of self-reported physical activity on ANS function using heart rate variability (HRV) metrics in individuals exposed and nonexposed to COVID-19. In total 142 participants from the Riyadh region, Saudi Arabia, were divided into exposed (n = 70) and nonexposed (n = 71) groups based on their COVID-19 exposure. HRV was assessed using photoplethysmography and analyzed using time and frequency domains. Physical activity was assessed using simple yes or no question, and duration was categorized into less than 30 minutes, 30 minutes, and more than 30 minutes. Physically active participants generally exhibited higher HRV metrics, suggesting better autonomic function, although this effect was more pronounced in the nonexposed group. Interestingly, the low-frequency to high-frequency ratio was the only HRV metric that showed a statistically significant difference between active and inactive participants in the nonexposed group (P = .04). There were no significant differences in HRV metrics based on the duration of reported physical activity in either the COVID-19-exposed or nonexposed groups. The study underscored the importance of monitoring cardiovascular health in post-COVID-19 populations and suggested that while physical activity is beneficial, the virus may blunted its benefits. Further research is needed to explore the long-term implications of COVID-19 on autonomic function and the potential for physical activity to mitigate these effects.

PMID:40324227 | DOI:10.1097/MD.0000000000042345

Cogn Neuropsychol. 2025 May 5:1-22. doi: 10.1080/02643294.2025.2498154. Online ahead of print.

ABSTRACT

We investigated visual working memory (VWM) for faces and two novel non-face pattern types (Blobs and Mondrians) in individuals with developmental prosopagnosia (DP) and age-matched controls. Participants completed both simultaneous and sequential encoding tasks, judging whether a probe item matched one shown at encoding. DPs showed a consistent face disadvantage across both encoding types, while controls showed a face advantage, but only during simultaneous encoding. Compared to controls, DPs had impaired face VWM in both tasks but performed equivalently for abstract shapes and patterns. Face VWM impairments in DP were not exacerbated by increased memory load or updating demands, suggesting these deficits stem from face perception difficulties that affect encoding rather than general VWM mechanisms. Our group-based analyses were supplemented by individual case statistics. Overall, our findings indicate that DPs do not exhibit general VWM deficits, but rather specific difficulties with face processing across formats.

PMID:40324216 | DOI:10.1080/02643294.2025.2498154

J Med Internet Res. 2025 May 5;27:e69284. doi: 10.2196/69284.

ABSTRACT

BACKGROUND: Mental health is emerging as an increasingly prevalent public issue globally. There is an urgent need in mental health for efficient detection methods, effective treatments, affordable privacy-focused health care solutions, and increased access to specialized psychiatrists. The emergence and rapid development of large language models (LLMs) have shown the potential to address these mental health demands. However, a comprehensive review summarizing the application areas, processes, and performance comparisons of LLMs in mental health has been lacking until now.

OBJECTIVE: This review aimed to summarize the applications of LLMs in mental health, including trends, application areas, performance comparisons, challenges, and prospective future directions.

METHODS: A scoping review was conducted to map the landscape of LLMs’ applications in mental health, including trends, application areas, comparative performance, and future trajectories. We searched 7 electronic databases, including Web of Science, PubMed, Cochrane Library, IEEE Xplore, Weipu, CNKI, and Wanfang, from January 1, 2019, to August 31, 2024. Studies eligible for inclusion were peer-reviewed articles focused on LLMs’ applications in mental health. Studies were excluded if they (1) were not peer-reviewed or did not focus on mental health or mental disorders or (2) did not use LLMs; studies that used only natural language processing or long short-term memory models were also excluded. Relevant information on application details and performance metrics was extracted during the data charting of eligible articles.

RESULTS: A total of 95 articles were drawn from 4859 studies using LLMs for mental health tasks. The applications were categorized into 3 key areas: screening or detection of mental disorders (67/95, 71%), supporting clinical treatments and interventions (31/95, 33%), and assisting in mental health counseling and education (11/95, 12%). Most studies used LLMs for depression detection and classification (33/95, 35%), clinical treatment support and intervention (14/95, 15%), and suicide risk prediction (12/95, 13%). Compared with nontransformer models and humans, LLMs demonstrate higher capabilities in information acquisition and analysis and efficiently generating natural language responses. Various series of LLMs also have different advantages and disadvantages in addressing mental health tasks.

CONCLUSIONS: This scoping review synthesizes the applications, processes, performance, and challenges of LLMs in the mental health field. These findings highlight the substantial potential of LLMs to augment mental health research, diagnostics, and intervention strategies, underscoring the imperative for ongoing development and ethical deliberation in clinical settings.

PMID:40324177 | DOI:10.2196/69284

JMIR Res Protoc. 2025 May 5;14:e72091. doi: 10.2196/72091.

ABSTRACT

BACKGROUND: Veterans are more likely to experience chronic pain than civilians, with significant negative impacts on long-term health outcomes. Evidence for the effectiveness of prescription opioids for chronic pain management is limited, and chronic use of opioids is associated with an increased risk of sleep-disordered breathing, cardiovascular complications, and bowel dysfunction, as well as opioid misuse and overdose. Veterans Affairs (VA) and Department of Defense guidelines are prioritizing low-risk, evidence-based interdisciplinary pain management strategies while optimizing pain-related outcomes (PRO) for veterans. Commission on Accreditation of Rehabilitation Facilities (CARF)-Accredited VA Interdisciplinary Pain Rehabilitation Programs (IPRPs) have shared characteristics, while maintaining their unique characteristics as individual pain management programs. Though little is known about the characteristics of VA’s IPRPs (eg, staffing, services, and patients served), implementation, and sustainability of these mandated programs.

OBJECTIVE: The goals of our operational partner-driven evaluation are to (1) characterize IPRPs across multiple program factors, including but not limited to, service delivery methods, team composition, program characteristics, services and modalities offered, and patients served; (2) triangulate data to inform data visualization that characterizes and illustrates the IPRPs individually and collectively as a system of care; and (3) identify patient-reported outcomes (PROs) and metrics to measure program effectiveness and determine overlap across IPRPs.

METHODS: This partnered-driven program evaluation will use a sequential mixed methods prospective design, including interviews and surveys. The Consolidated Framework for Implementation Research (CFIR), Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework, and Expert Recommendations for Implementing Change (ERIC) strategies will be used to contextualize qualitative data. Rapid content analysis will be used to iteratively analyze qualitative data, while descriptive statistics will be used to analyze quantitative data. Datasets will be triangulated to support data visualization for partners to inform clinical and operational decision support.

RESULTS: As of April 2025, All IPRP sites are engaged, and survey and interview data have been collected and prepared for analysis. The results and deliverables will inform VA CARF-accredited IPRP characterization, evaluation, and implementation as a learning health system.

CONCLUSIONS: The results of this evaluation will characterize CARF-accredited IPRPs and identify determinants affecting the implementation of this complex intervention, made up of multiple evidence-based practices. Partner-driven data will inform the state of implementation at each site, and quantitative measures will provide options for collecting standardized outcome measures for continued program evaluation. This operational partner-driven evaluation will inform future efforts for quality improvement to improve veterans’ pain management outcomes. This protocol informs the use of a mixed methods approach to evaluate a multimodal intervention (ie, IPRP), made up of multiple evidence-based practices to treat a complex comorbid condition. Future work may include data management infrastructure development and cost evaluations to inform clinical and operational decision-making.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/72091.

PMID:40324171 | DOI:10.2196/72091