Category: Nevin Manimala

BMC Health Serv Res. 2025 Aug 6;25(1):1031. doi: 10.1186/s12913-025-13207-5.

ABSTRACT

BACKGROUND: Artificial Intelligence (AI) can empower clinicians to make data-driven decisions, treatments and streamline administrative tasks. However, it is vital to understand their perception towards AI for seamless implementation in practice. Therefore, the study aimed to assess the attitude, receptivity and readiness of medical and dental practitioners towards the use of AI in clinical practice.

METHODS: A cross-sectional study employing non-probability convenience sampling was conducted from April to August 2024. A questionnaire was distributed among practitioners working in public and private sector hospitals. The questionnaire included a validated tool, the General Attitude towards Artificial Intelligence Scale (GAAIS), comprising of total 20 items with two subscales; positive and negative attitudes. They were rated on a 5-point Likert scale, ranging from strongly disagree (1) to strongly agree (5). The items of negative attitudes were reverse coded. Self-formulated questions to assess the readiness of medical and dental practitioners to incorporate AI in practice were also included. Data was analyzed using IBM SPSS v.25. Results were reported as frequencies and percentages. Statistical analysis was performed using Mann-Whitney U, and Chi-squared test for continuous and categorical variables. The association between two continuous variables was assessed through Spearman’s correlation.

RESULTS: A total of 451 responses were analyzed. The mean score for positive attitudes toward AI was 3.6 ± 0.54, whereas for negative attitudes it was estimated to be 2.8 ± 0.71. Approximately 187 (41.5%) respondents believed AI was superior to humans in routine jobs. About 190 (42.1%) respondents agreed that AI can make errors. Most respondents 334 (74.1%) were aware of AI applications in healthcare, and 329 (72.9%) reported familiarity with AI technologies. However, only 153 (33.9%) felt confident in operating AI systems. While 282 (62.5%) expressed eagerness to incorporate AI into diagnosis and treatment planning, a significant difference was observed between groups, with dental practitioners showing greater willingness (p = 0.004). Additionally, dentists exhibited higher confidence in using AI compared to medical practitioners (p = 0.047).

CONCLUSIONS: The findings suggest that most practitioners had a positive attitude towards AI and were receptive towards incorporating the technology as a beneficial tool in their practice. Ethical guidelines and extensive training can mitigate negative perceptions associated with the use of AI technology. It is also imperative that resources should be provided, specially to public-sector healthcare systems as they serve underprivileged communities. This will help practitioners gain familiarity with AI technology and will enable them to develop proficiency in AI use.

PMID:40764570 | DOI:10.1186/s12913-025-13207-5

BMC Psychiatry. 2025 Aug 5;25(1):761. doi: 10.1186/s12888-025-07237-w.

ABSTRACT

BACKGROUND: This study addresses the challenge of accurately classifying the severity of schizophrenia in patients through a clever approach. By leveraging electroencephalography (EEG) signals, we aim to establish a method for evaluating patient conditions, thereby contributing to the psychiatric diagnosis and treatment field.

METHODS: Our research methodology encompasses a comprehensive system framework designed to analyze EEG signals with the Positive and Negative Syndrome Scale (PANSS) for correlation analysis. The process involves: (1) administering the PANSS test to create a database of schizophrenia patients; (2) developing a visual concentration test system that measures EEG signals in real-time; (3) processing these signals to construct an EEG feature database; (4) employing support vector machine and decision tree methods for illness severity classification; (5) conducting statistical analysis to correlate PANSS scores with EEG features, assessing the effectiveness of these correlations in clinical applications.

RESULTS: The study successfully demonstrated the potential of a concentration detection system, integrating EEG signal analysis with PANSS scores, to classify schizophrenia severity accurately. Applying SVM and decision tree methods established significant correlations between EEG features and clinical scales, indicating the system’s efficacy in supporting psychiatric diagnosis.

CONCLUSIONS: Our findings suggest that the proposed analytical methods, focusing on EEG signals and employing a novel system framework, can effectively assist in classifying the severity of schizophrenia. This approach offers promising implications for enhancing diagnostic accuracy and tailoring treatment strategies for patients with schizophrenia.

PMID:40764561 | DOI:10.1186/s12888-025-07237-w

BMC Public Health. 2025 Aug 5;25(1):2658. doi: 10.1186/s12889-025-23654-8.

ABSTRACT

BACKGROUND: Diabetes mellitus (DM) is a significant public health concern, particularly among older adults who face an elevated risk of complications and increased mortality. This study assessed overtime changes in the prevalence, awareness, treatment coverage, and glycemic control of DM in Iranian adults aged 60 and older.

METHODS: This is a nationwide repeated cross-sectional study based on data from the 2016 and 2021 STEPwise Approach to Non-communicable Disease Risk Factor Surveillance (STEPS), the most recent surveys conducted across all provinces of Iran. We reported prevalence estimates as weighted percentages with 95% confidence intervals (CIs). Logistic regression was used to evaluate the association between selected demographic and health characteristics and diabetes mellitus (DM). All analyses were performed using R statistical software version 4.0.5.

RESULTS: From 2016 to 2021, the prevalence of prediabetes increased from 23.86 to 28.96% in males and from 22.86 to 30.86% in females, while DM prevalence rose from 19.37 to 25.43% in males and from 26.22 to 30.26% in females. In 2016, DM awareness was 74.47% in males and 82.19% in females, and in 2021, it was 71.20% in males and 78.73% in females. Treatment coverage was 60.18% in males and 67.63% in females in 2016, and 63.71% in males and 71.14% in females in 2021. Glycemic control was 59.77% in males and 57.98% in females in 2016, and 55.96% in males and 53.66% in females in 2021. Obesity significantly increased the odds of DM (odds ratio (OR) 1.97, p < 0.001), while being underweight (OR 0.21, p < 0.001) and having sufficient physical activity (OR 0.77, p = 0.02) were protective factors.

CONCLUSION: The findings from the 2016 and 2021 STEPS surveys indicate a rising burden of prediabetes and DM among older adults in Iran, particularly among females. Although awareness and treatment coverage were high, optimal glycemic control remains a challenge, emphasizing the need to improve access to healthcare resources and develop tailored community-based interventions.

PMID:40764559 | DOI:10.1186/s12889-025-23654-8

Virol J. 2025 Aug 5;22(1):267. doi: 10.1186/s12985-025-02900-w.

ABSTRACT

Antiretroviral therapy (ART) has transformed HIV from a rapidly progressive and fatal disease to a chronic disease with limited impact on life expectancy. However, people living with HIV(PLWHs) faced high critical illness risk due to the increased prevalence of various comorbidities and are admitted to the Intensive Care Unit(ICU). This study aimed to use machine learning to predict ICU admission risk in PLWHs. 1530 HIV patients (199 admitted to ICU) from Beijing Ditan Hospital, Capital Medical University were enrolled in the study. Classification models were built based on logistic regression(LOG), random forest (RF), k-nearest neighbor (KNN), support vector machine (SVM), artificial neural network(ANN), and extreme gradient boosting(XGB). The risk of ICU admission was predicted using the Brier score, area under the receiver operating characteristic curve (ROC-AUC), and area under the precision-recall curve(PR-ROC) for internal validation and ranked by Shapley plot. The ANN model performed best in internal validation (Brier score = 0.034, ROC-AUC = 0.961, PR-AUC = 0.895) to predict the risk of ICU admission for PLWHs. 11 important features were identified to predict predict ICU admission risk by the Shapley plot: respiratory failure, multiple opportunistic infections in the respiratory system, AIDS defining cancers, baseline viral load, PCP, baseline CD4 cell count, and unexplained infections. An intelligent healthcare prediction system could be developed based on the medical records of PLWHs, and the ANN model performed best in effectively predicting the risk of ICU admission, which helped physicians make timely clinical interventions, alleviate patients suffering, and reduce healthcare cost.

PMID:40764558 | DOI:10.1186/s12985-025-02900-w

BMC Health Serv Res. 2025 Aug 5;25(1):1027. doi: 10.1186/s12913-025-13048-2.

ABSTRACT

BACKGROUND: Monitoring a patient’s functional status in the rehabilitation process is essential for the ongoing improvement of the quality of the rehabilitation services offered. This possibility is provided by the use of the International Classification of Functioning, Disability and Health (ICF) and the ICF Core Sets developed on its basis. The purpose of this study was to validate and evaluate the use of the ICF Rehabilitation Set in rehabilitation patients in clinical practice in southeastern Poland.

METHODS: This study included patients who required comprehensive rehabilitation in a tertiary care rehabilitation centre. The study used the Polish version of a rating reference guide for the ICF Rehabilitation Set and the Barthel Index. The assessment was carried out by an interdisciplinary team, which established the ICF qualifier values by consensus. Interrater reliability was assessed using intraclass correlation coefficients (ICCs). Convergent validity was assessed using Spearman correlation coefficients between the Disability Index (DI) and the Barthel Index (BI). To assess the effectiveness of rehabilitation in the study group, a paired Wilcoxon test was used to compare the measurements of the two ICF codes.

RESULTS: This study demonstrated the consistency and reliability of the reference guide for the ICF Rehabilitation Set. The ICC values were very high (for B620; D450; D640, the ICC was 1.000) and high (for E155, the ICC was 0.793). The individual entries from the ICF Rehabilitation Set were found to have very high concordance for assessing individual qualifiers between researchers (the ICC for the DI was 0.995). The ICF Rehabilitation Set scores and the calculated DI were correlated with the BI scores. The ICF Rehabilitation Set is sensitive to changes in the functional status of patients undergoing rehabilitation and is consistent with the results obtained by the BI measure. There was also a statistically significant improvement in patients’ functional status after rehabilitation.

CONCLUSIONS: The ICF Rehabilitation Set and the developed rating reference guide are reproducible, consistent and relevant and can be used in clinical settings to collect health information from people undergoing rehabilitation. Further research is needed to strengthen the evidence to validate the ICF in Poland.

TRIAL REGISTRATION: The study was registered in the clinical trial registry at ClinicalTrials.gov (NCT06718036, date of registration 2024-11-30, retrospectively registered).

PMID:40764554 | DOI:10.1186/s12913-025-13048-2

BMC Public Health. 2025 Aug 5;25(1):2655. doi: 10.1186/s12889-025-23853-3.

ABSTRACT

BACKGROUND: Self-directed violence encompasses both suicide and self-injury. United States correctional settings face high self-directed violence rates. Training correctional behavioral health clinicians (BHCs) in evidence-based self-directed violence prevention practices represents one solution. The Core Competency Model for Corrections (CCM-C) is a self-directed violence prevention training program addressing clinician self-management (e.g., managing personal reactions to self-directed violence) and clinical care (e.g., eliciting evidence-based risk and protective factors) skills. The present study held aims to: (1) assess CCM-C feasibility, appropriateness, acceptability, and usability; (2) evaluate short-term impacts on BHC self-directed violence knowledge, attitudes, and skill usage; and (3) explore short-term impacts on BHC compassion fatigue.

METHODS: The present study was a statewide hybrid feasibility-effectiveness trial evaluating the CCM-C taking place between January and December 2024. Pre-training feedback was gathered from a corrections advisory panel (N = 7). For the trial implementation, we conducted a waitlist control sequential cross-over design. BHCs (N = 60) were randomly assigned to two training groups: Baseline training versus waitlist control. BHCs provided quantitative and qualitative survey input on CCM-C feasibility outcomes (aim 1), and completed self-report inventories of self-directed violence-related outcomes (aims 2 and 3). Descriptive statistics and thematic analysis assessed feasibility outcomes. Repeated-measures analysis of variance (ANOVA) tests examined CCM-C outcomes.

RESULTS: CCM-C was highly acceptable, appropriate, feasible, and usable. Recommended improvements included removing non-corrections content, enhancing opportunities for BHC participation and interaction, and creating participant handout packages. CCM-C increased BHC self-directed violence prevention knowledge, perceived skill mastery, intent/actual use of training content, and lowered compassion fatigue levels. Attitudes toward intervening with a suicidal person only improved for the waitlist control group. Attitudes towards incarcerated individuals who self-harm remained unchanged.

DISCUSSION: Early results show CCM-C to be a feasible, effective self-directed violence prevention training program for correctional BHCs. Results support broader CCM training literature and a social-cognitive training model. Statewide partners will generate the CCM-C Toolkit, a package comprising training materials, implementation guidance, and train-the-trainer materials. The Toolkit will provide accessible resources for further CCM-C implementation, adaptation, and evaluation.

TRIAL REGISTRATION: This study was registered at clinicaltrials.gov (NCT06359574).

PMID:40764547 | DOI:10.1186/s12889-025-23853-3

BMC Infect Dis. 2025 Aug 5;25(1):986. doi: 10.1186/s12879-025-11382-8.

ABSTRACT

INTRODUCTION: Hepatitis E virus (HEV) infection poses a significant public health challenge, particularly in immunocompromised populations such as those living with HIV. Limited data exist on HEV seroprevalence and its correlates among HIV-positive individuals in urban low-resource settings, especially in the aftermath of disasters. This study investigates HEV seroprevalence and risk factors among HIV positive individuals in 2016, a post-earthquake period in Kathmandu, Nepal, with relevance to ongoing challenges in 2025.

METHODS: We conducted a cross-sectional serological study among 200 HIV-positive individuals from Sukraraj Tropical and Infectious Disease Hospital’s (STIDH) Antiretroviral Therapy (ART) center in Kathmandu. Serological testing determined anti-HEV IgG and IgM status using Wantai Hepatitis E (HEV-IgG/M) ELISA kits. Sociodemographic, behavioral, and clinical data were collected via structured interviews and medical records. Multivariable logistic regression identified independent predictors of anti-HEV IgG seropositivity. Statistical analyses used Chi-square, Mann-Whitney U, and Cochran’s Q tests, with p < 0.05 considered significant. A comparative general population sample (n = 100) from earthquake-affected districts was also analyzed.

RESULTS: Overall, 43.5% (87/200) of HIV-positive participants tested positive for anti-HEV IgG. Key demographic predictors included increasing age, which showed a significant non-linear association (adjusted OR = 3.95 for age, 0.60 for age²; p < 0.001), and a marginal association with male gender (aOR = 2.03; p = 0.059). In contrast, no significant associations were observed between HEV seropositivity and specific drinking water sources, water processing methods, eating habits, smoking status, CD4 count, HIV viral load suppression, common comorbidities, or routine liver and hematological markers. anti-HEV IgG seroprevalence was considerably higher than that of Hepatitis B virus (4.5%) and Hepatitis C virus (5.0%) in the cohort and notably absent in a comparative general population sample. The predictive model showed good performance (AUC = 0.80), supporting its exploratory utility.

CONCLUSION: Our findings reveal a high burden of past HEV exposure among HIV-positive individuals in post-earthquake Kathmandu, with age being a key correlate. The absence of HEV IgG positivity in the general population sample suggests that HEV exposure during this period may have been localized to vulnerable clinical groups. The lack of association with traditional behavioral or clinical markers highlights the complexity of transmission in this setting. These results underscore the need to integrate HEV awareness and targeted screening into HIV care in endemic regions. Future studies should prioritize longitudinal follow-up, genotype surveillance, and environmental sampling to inform public health preparedness and response.

PMID:40764531 | DOI:10.1186/s12879-025-11382-8

BMC Gastroenterol. 2025 Aug 5;25(1):554. doi: 10.1186/s12876-025-04150-0.

ABSTRACT

BACKGROUND: Delirium frequently occurs as a severe complication among patients with acute pancreatitis (AP), contributing to extended hospital stays, higher mortality rates, and lasting cognitive deficits. The pathogenesis of delirium in this setting is strongly influenced by metabolic abnormalities, including disturbances in electrolyte balance and widespread inflammation. Although the albumin-corrected anion gap (ACAG) is a recognized indicator of metabolic dysfunction, its relevance to delirium in AP patients has not been adequately investigated.

METHODS: This study utilized patient records from the MIMIC-IV database to investigate how ACAG relates to the onset of delirium in individuals with acute pancreatitis. Analytical approaches included the use of summary statistics, Kaplan-Meier survival analyses, receiver operating characteristic (ROC) curve evaluation, and both univariable and multivariable Cox proportional hazards models. To capture potential nonlinear effects, restricted cubic spline (RCS) modeling was implemented. Subgroup analyses were conducted to examine possible demographic and clinical effect modifiers. Additionally, several machine learning algorithms-such as the Random Forest-were employed to further evaluate the predictive power of ACAG.

RESULTS: Elevated levels of ACAG were independently linked to an increased likelihood of developing delirium during both the 28-day hospitalization period and throughout the ICU stay. Results from the multivariable Cox proportional hazards analysis indicated that each incremental rise in ACAG was associated with a greater risk of delirium (hazard ratio: 1.06, 95% confidence interval: 1.02-1.10, p < 0.001). The application of restricted cubic spline modeling verified the linear nature of this association. Among the machine learning models, the Random Forest achieved superior predictive accuracy (AUC = 0.81), and SHAP analysis highlighted ACAG as a primary determinant in model prediction.

CONCLUSIONS: The ACAG emerged as an independent predictor of delirium among individuals with acute pancreatitis, displaying a linear association with the risk of delirium onset. When compared to other commonly used biomarkers, ACAG exhibited enhanced predictive capacity for identifying patients at risk. These findings suggest that ACAG could serve as a practical clinical marker for the early detection and prompt management of delirium in this patient population.

PMID:40764528 | DOI:10.1186/s12876-025-04150-0

Sci Rep. 2025 Aug 5;15(1):28522. doi: 10.1038/s41598-025-10542-9.

ABSTRACT

To assess the 1-month outcomes of visual performance and positional stability of capsule-fixated intraocular lenses (IOLs) in patients with IOL-Bag complex dislocation. We enrolled 36 eyes (34 patients) with IOL-Bag complex dislocation due to pseudoexfoliation syndrome. Patients with intraoperative complications or prior posterior capsule Nd-YAG laser were excluded. Surgical intervention involved creating a superior service keratotomy and using introflective sutures for IOL fixation. Best Corrected Visual acuity (BCVA), endothelial cell counts, and tonometry were assessed at multiple postoperative time points. We also evaluated the mean spherical equivalent (SE), and the residual cylinder and sphere at each follow-up. This study has been successfully registered on ClinicalTrials.gov public (Identifier NCT06423079). The study included 36 eyes, with 22 having a one-piece IOL, 2 with a one-piece IOL plus capsular tension ring, and 12 with a three-piece IOL. Our technique demonstrates a statistically significant improvement in BCVA 1 year after surgery compared to BCVA at the time of IOL dislocation (preoperative BCVA 1.45 ± 0.81 vs 1 year 0.06 ± 0.09 logMAR; p < 0.001). There were no statistically significant differences when comparing the BCVA before the IOL dislocation with the 1-year post-operative BCVA (p > 0.13). No intraocular pressure fluctuations (preoperative IOP 15.0 ± 2.43 vs 1 year IOP 14.69 ± 2.27 p > 0.3), changes in endothelial cell counts (CC) (Preoperative CC: 1812 ± 461 cell/mm² vs 1 year 1760 ± 329 cell/mm²; p > 0.3), or significant complications were observed. This novel surgical technique may represent a viable, economic, and durable solution to restore dislocations of IOLs accessible from the anterior chamber that respect the cornea and restores visual function without damaging ocular structures.

PMID:40764517 | DOI:10.1038/s41598-025-10542-9

Sci Rep. 2025 Aug 5;15(1):28625. doi: 10.1038/s41598-025-09012-z.

ABSTRACT

The etiology of moyamoya disease (MMD) remains unknown. The main pathological finding is fibrocellular thickening of the intima, irregular undulation of the internal elastic lamina affecting the distal portions of the internal carotid artery and A1 and M1 segments. Our aim is to describe the histological and electron microscope ultrastructural characteristics of cortical MMD vessels (middle cerebral artery) in hemorrhagic and ischemic presentation along different Suzuki stages. From January 2022 to December 2022, we collected clinical and radiological data of 310 patients with MMD, among them we identified 52 patients that underwent superficial temporal artery-middle cerebral artery (STA-MCA) bypass. We collected arterial walls (excisional arteriotomy) of the recipient arteries specifically, M3 or M4 segments of the MCA. Observations and micrographs were captured utilizing an HT7700 transmission electron microscope. MMD patients exhibit severe internal elastic lamina (IEL) changes as compared to patients with intracranial atherosclerosis. Hemorrhagic MMD presentation showed a higher score of IEL ruptured when comparing to ischemic presentation. Endothelial cells in hemorrhagic MMD showed more significant contraction compared to those in ischemic moyamoya disease. Hemorrhagic and ischemic MMD patients showed no statistically significant differences when correlated to Suzuki stages and cerebral perfusion. MMD patients exhibit IEL changes and endothelial cells contraction extending into the distal segments of the middle cerebral artery. Hemorrhagic MMD presentation has higher IEL rupture score making these patients probably more susceptible for hemorrhage. This study provides an inside of the extension of MMD into the brain surface.

PMID:40764498 | DOI:10.1038/s41598-025-09012-z