Tag: nevin manimala

Int J Nurs Educ Scholarsh. 2025 Mar 26;22(1). doi: 10.1515/ijnes-2024-0076. eCollection 2025 Jan 1.

ABSTRACT

OBJECTIVES: To explore post-pandemic mental wellbeing status and identify positive factors influencing mental wellbeing among nursing students.

METHODS: A cross-sectional survey of undergraduate nursing students from three public colleges in Thailand was conducted. A convenience sample of 983 participants completed a paper questionnaire.

RESULTS: The mental wellbeing mean score was 43.67 (SD=6.75, possible range of 10-60). Mental wellbeing was negatively associated with participant’s age and class level while positively associated with income, BMI, exercise hours/week, sleep hours/day, academic support, perceived social support, community involvement, and grit. Using hierarchical multiple regression, six significant predictors were identified: income, sleep hours/day, academic support, perceived social support, community involvement, and grit. These predictors combined explained 44 % of the variance, F(11, 722)=55.97, p<0.001, adjusted R²=0.44.

CONCLUSIONS: To promote mental wellbeing of nursing students, colleges should explore how to increase academic support, encourage healthy habits in students, and enhance their community involvement.

PMID:40132175 | DOI:10.1515/ijnes-2024-0076

Womens Health (Lond). 2025 Jan-Dec;21:17455057251326419. doi: 10.1177/17455057251326419. Epub 2025 Mar 25.

ABSTRACT

BACKGROUND: Gender differences in the associated health outcomes of different forms of intimate partner violence (IPV) are understudied. The long-term effects of IPV on specific physical health conditions are also under-researched in comparison to the effects on general health and mental health.

OBJECTIVES: To examine gender differences in the association between IPV and specific physical health conditions, accounting for differences in the types and number of types of IPV experienced.

DESIGN: We used data from the 2014 Adult Psychiatric Morbidity Survey, a cross-sectional survey using a stratified, multistage random sampling design to cover the household population of England aged 16 years and older.

METHODS: Descriptive and multivariable regression analyses of 4120 women and 2764 men who had ever had a partner. Lifetime IPV by types (physical, sexual, psychological, and economic), any lifetime and recent IPV, the number of IPV types experienced, and multiple chronic health conditions experienced over the past 12 months were included in the analyses.

RESULTS: Gender differences were observed in both the prevalence of IPV and associated health conditions. Women were more likely to experience any type and a higher number of IPV types than men. Women’s exposure to any lifetime and 12-month IPV were significantly associated with an increased likelihood of reporting 12 and 11 conditions, respectively, while men’s exposure to any lifetime and 12-month IPV were significantly associated with 4 and 1 conditions, respectively. Specific IPV types had varied health impacts, particularly among women. A cumulative association was evident for women but not for men.

CONCLUSION: Healthcare systems need to be mobilised to address IPV as a priority health issue for the female population. Our findings highlight the need for gender-informed approaches in IPV intervention strategies and healthcare provision, emphasising the development of IPV-responsive healthcare systems and comprehensive IPV curricula in medical and health training.

PMID:40132162 | DOI:10.1177/17455057251326419

Anesth Analg. 2025 Mar 25. doi: 10.1213/ANE.0000000000007504. Online ahead of print.

ABSTRACT

BACKGROUND: After fracture-related surgery, chronic posttraumatic and/or postsurgical pain (CPSP) has a high incidence rate of up to 43% a year after surgery. Yet the underlying mechanisms are poorly understood. Murine and clinical evidence suggest immunological modulation of postsurgical pain. However, the specific cytokine profiles of patients who develop CPSP after fracture-related surgery remain to be determined. Therefore, we analyzed in an exploratory manner cytokines, chemokines and adipocytokines in patients with and without CPSP up to 1 year after fracture-related surgery.

METHODS: A prospective longitudinal serum profiling of 30 patients with traumatic fractures that required osteosynthesis was conducted on the first day (D1), at 6 weeks (W6) and 1 year after surgery (Y1). Patients with CPSP at Y1 were compared to those who did not develop CPSP. A total of 22 pro- and anti-inflammatory serum cytokines, including adipocytokines, were quantified using Luminex technology. Statistical analyses included χ² test, t test, and Mann-Whitney U test, Spearman’s rank correlations, and repeated-measures mixed models with Bonferroni correction for cytokine differences between patients with and without CPSP. Receiver-operating characteristic (ROC) curves evaluated the discriminatory ability of specific cytokines regarding the development of CPSP.

RESULTS: Patients with CPSP 1 year after surgery (n = 12/30, 40%) exhibited elevated resistin levels at Y1 (CPSP: 1.04 ± 1.04 vs no-CPSP: 0.41 ± 0.31 pg/mL; P < .001) as well as higher adiponectin levels at Y1 (CPSP: 9.37 ± 8.23 vs no-CPSP: 5.57 ± 2.75 μg/mL; P = .008). Patients with CPSP had higher Rantes/CCL5 (CC-chemokine ligand 5) levels immediately after surgery on D1 than patients without CPSP (mean difference [MD] = 5.5, confidence interval [CI], 1.7-9.3 ng/mL; P = .014). At W6 and Y1, adiponectin and CCL5 levels correlated with pain intensity in patients with CPSP (adiponectin: r = 0.50, P = .03; CCL5: r = -0.50, P = .03). Across the entire patient population, resistin levels were correlated with pain intensity (r = 0.34, P < .001; D1-Y1).

CONCLUSIONS: Our explorative cytokine analysis uncovered an imbalance in serum cytokines and chemokines during the chronification process in patients who developed CPSP 1 year after surgically treated fractures. In particular, adiponectin and resistin were noted to be novel biomarkers for CPSP development. These data provide preliminary insight into a potential unexplored crosstalk between chronic postoperative pain and adipocytokines in the chronification of CPSP, which remains to be further analyzed.

PMID:40132159 | DOI:10.1213/ANE.0000000000007504

Neurology. 2025 Apr 22;104(8):e213490. doi: 10.1212/WNL.0000000000213490. Epub 2025 Mar 25.

ABSTRACT

BACKGROUND AND OBJECTIVES: Among the participants of Alzheimer disease (AD) treatment trials, 40% do not show cognitive decline over 80 weeks of follow-up. Identifying and excluding these individuals can increase power to detect treatment effects. We aimed to develop machine learning-based predictive models to identify persons unlikely to show decline on placebo treatment over 80 weeks.

METHODS: We used the data from the placebo arm of EXPEDITION3 AD clinical trial and a subpopulation from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Participants in the EXPEDITION3 trial were patients with mild dementia and biomarker evidence of amyloid burden. For this study, participants were identified as those who demonstrated clinically meaningful cognitive decline (CMCD) or cognitively stable (CS) at final visit of the trial (week 80). Machine learning-based classifiers were trained to classify participants into CMCD vs CS groups using combinations of demographics, APOE genotype, neuropsychological tests, and biomarkers (volumetric MRI). The results were developed in 70% of the EXPEDITION3 placebo sample using 5-fold cross-validation. Trained models were then used to classify the participants in an internal validation sample and an external matched sample ADNI_AD.

RESULTS: Eight hundred ninety-four of the 1,072 participants in the placebo arm of the EXPEDITION3 trial had necessary follow-up data, who were on average aged 72.7 (±7.7) years and 59% female. 55.8% of those participants showed CMCD (∼2 years younger than those without) at the final visit. In the independent validation sample within the EXPEDITION3 data, all the models showed high sensitivity and modest specificity. Positive predictive values (PPVs) of models were at least 11% higher than base prevalence of CMCD observed at the end of the trial. The subset of matched ADNI participants (ADNI_AD, N = 105) were aged 74.5 (±6.4) years and 46% female. The models that were validated in ADNI_AD also showed high sensitivity, modest specificity, and PPVs of at least 15% higher than the base prevalence in ADNI_AD.

DISCUSSION: Our results indicate that predictive models have the potential to improve the design of AD trials through selective inclusion and exclusion criteria based on expected cognitive decline. Such predictive models need further validation across data from different AD clinical trials.

PMID:40132145 | DOI:10.1212/WNL.0000000000213490

Hepatology. 2025 Mar 25. doi: 10.1097/HEP.0000000000001318. Online ahead of print.

ABSTRACT

BACKGROUND AIMS: Prior work has shown that magnetic resonance imaging (MRI)-derived proton density fat fraction (PDFF) can diagnose metabolic dysfunction-associated steatotic liver disease (MASLD) noninvasively, but there is a paucity of data on the performance of PDFF to classify more advanced forms of the MASLD spectrum. The purpose of this study was to assess the diagnostic performance of PDFF for the diagnoses of MASLD, metabolic dysfunction-associated steatohepatitis (MASH), and fibrotic MASH in adults with obesity undergoing bariatric surgery, using contemporaneous intraoperative liver biopsy as reference.

APPROACH RESULTS: PDFF was evaluated alone and with other potential classifiers (imaging, serum and anthropometric), using Bayesian Information Criterion-based stepwise logistic regression models. Areas under the receiver operating characteristic (ROC) curves (AUC) were computed for all models and single classifiers. Cross-validated sensitivity and specificity were calculated at Youden-based PDFF classification thresholds. Data analysis from 140 patients demonstrated that PDFF was the most accurate single classifier, with high AUC for MASLD (0.95), MASH (0.85), and fibrotic MASH (0.82) (all p<0.001). Multivariable models including PDFF outperformed those without PDFF. The Youden-based threshold for PDFF was 4.4% for MASLD (sensitivity: 87%, specificity: 86%), 6.9% for MASH (sensitivity: 77%, specificity: 66%), and 13.5% for fibrotic MASH (sensitivity: 67%, specificity: 85%).

CONCLUSIONS: PDFF was the most accurate single classifier for diagnosing MASLD, MASH, and fibrotic MASH. The most accurate multivariable classification models for MASLD, MASH, and fibrotic MASH included PDFF, demonstrating the central importance of PDFF for noninvasive assessment of the MASLD spectrum.

PMID:40132140 | DOI:10.1097/HEP.0000000000001318

J Cataract Refract Surg. 2025 Mar 24. doi: 10.1097/j.jcrs.0000000000001658. Online ahead of print.

ABSTRACT

PURPOSE: The purpose of this study was to evaluate the influence of range between the predictions of 5 different calculation formulas in IOL power calculation accuracy.

SETTINGS: General University Hospital of Elche, Spain.

DESIGN: Retrospective Sequential Cohort.

METHODS: In this retrospective sequential cohort, the LenStar LS900 (Haag-Streit, Koeniz, Switzerland) was used for the preoperative biometry. The predicted spherical equivalent refraction of the implanted IOL were calculated for 5 different formulas: Barrett Universal II, Emmetropia Verifying Optical (EVO) 2.0, Hill RBF-3.0, Kane, PEARL-DGS. Multiformula Prediction Range was defined as the range of the refractive error predicted by the 5 formulas. According to the median of the Multiformula Prediction Range the sample was divided into a low and high spread group (LS and HS respectively).

RESULTS: 278 eyes were included. The standard deviation of the prediction error was significantly lower in the LS group for all included formulas. For the Barrett Universal II, EVO 2.0, RBF-3.0, Kane and PEARL-DGS formulae, the median absolute error (MdAE) was significantly lower in the LS group compared to the HS group (p-values of 0.001, 0.027, 0.004, 0.028 and 0.035, respectively). The percentage of eyes within the ±0.50D PE range was significantly higher in the LS group for all five analyzed formulas.

CONCLUSIONS: Multiformula Prediction Range can be a novel univariate predictor of IOL power calculation accuracy and a potential determinant for identifying patient suitable for immediate sequential cataract surgery. Accuracy was consistently higher for all five included formulas in the Low Spread group.

PMID:40132121 | DOI:10.1097/j.jcrs.0000000000001658

Cornea. 2025 Mar 25. doi: 10.1097/ICO.0000000000003861. Online ahead of print.

ABSTRACT

PURPOSE: To evaluate the long-term tomographic, refractive, and visual characteristics of eyes with extreme corneal flattening after corneal cross-linking (CXL) for progressive keratoconus.

METHODS: A retrospective observational study included eyes that underwent corneal CXL with epithelial removal between June 2006 and March 2017 and had extreme keratometric flattening [greater than 5 diopters (D)] and a minimum follow-up of 5 years. Visual, tomographic, pachymetric, and refractive characteristics were evaluated.

RESULTS: Mean follow-up time was 7.6 ± 2.6 years (range 5-13 years). Fifteen eyes were included in the study. Mean maximum keratometric (Kmax) flattening was -7.58 ± 2.63 D [range 5.0-12.2 D, (P <0.001)]. Approximately 56.25% (9/15) of the eyes experienced progressive flattening over the years. And 40% (6/15) presented an improvement of one or more lines of corrected distance visual acuity (CDVA), and 26.6% (5/16) of the eyes showed a worsening of CDVA. Logistic regression analysis revealed that postoperative Kmax flattening greater than 2 D at the first year postop (odds ratio 17.7, 95% confidence interval, 4.4-71.2) and preoperative Kmax greater than 55 D (odds ratio 8.8, 95% confidence interval, 2.7-28.3) were significant risk factors for extreme postop keratometric flattening.

CONCLUSIONS: Progressive extreme corneal flattening when accompanied with a decrease of CDVA was a late complication of CXL that may have required corneal transplantation for visual rehabilitation. Preoperative steeper corneas and keratometric flattening greater than 2 D at the first year postoperative period were risk factors associated with long-term extreme postoperative corneal flattening.

PMID:40132089 | DOI:10.1097/ICO.0000000000003861

Nucleosides Nucleotides Nucleic Acids. 2025 Mar 25:1-11. doi: 10.1080/15257770.2025.2484317. Online ahead of print.

ABSTRACT

Ulcerative colitis (UC) is a chronic condition characterized by inflammation in the colon. Free radicals and oxidative stress play a significant role in the pathophysiology of UC. Excessive production of reactive oxygen species can damage the mitochondrial genome, leading to mutations such as the7436-bp deletion. The aim of this study was to identify the presence of the 7436-bp mtDNA deletion in patients with UC and its association with susceptibility to colon inflammation. This case-control study, included 195 patients with UC and 250 healthy individuals from the Iranian population. The Multiplex PCR method was used to detect the 7436-bp mtDNA deletion. Statistical analysis was performed using SPSS software. The frequency of 7436-bp mtDNA deletion in patients was 41.5% and 6.8% in healthy individuals. Statistical analysis showed a significant association between the frequency of the 7436-bp mtDNA deletion and UC (p = 0.016). Furthermore, a significant difference was found between the presence of this deletion and an increased risk of severe (p = 0.003) and extensive (p = 0.002) forms of UC. There was no statistically significant difference in the frequency of this deletion between younger patients and the control group. This study suggests that the presence of the 7436-bp mtDNA deletion is a risk factor for UC and plays a significant role in the pathogenesis of the disease. Further research involving larger and more diverse populations is necessary to validate or challenge these findings. Identifying these mutations can enhance our understanding of genetic factors influencing UC.

PMID:40132088 | DOI:10.1080/15257770.2025.2484317

PLoS One. 2025 Mar 25;20(3):e0271270. doi: 10.1371/journal.pone.0271270. eCollection 2025.

ABSTRACT

Electrocardiogram (ECG) datasets tend to be highly imbalanced due to the scarcity of abnormal cases. Additionally, the use of real patients’ ECGs is highly regulated due to privacy issues. Therefore, there is always a need for more ECG data, especially for the training of automatic diagnosis machine learning models, which perform better when trained on a balanced dataset. We studied the synthetic ECG generation capability of 5 different models from the generative adversarial network (GAN) family and compared their performances, the focus being only on Normal cardiac cycles. Dynamic Time Warping (DTW), Fréchet, and Euclidean distance functions were employed to quantitatively measure performance. Five different methods for evaluating generated beats were proposed and applied. We also proposed 3 new concepts (threshold, accepted beat and productivity rate) and employed them along with the aforementioned methods as a systematic way for comparison between models. The results show that all the tested models can, to an extent, successfully mass-generate acceptable heartbeats with high similarity in morphological features, and potentially all of them can be used to augment imbalanced datasets. However, visual inspections of generated beats favors BiLSTM-DC GAN and WGAN, as they produce statistically more acceptable beats. Also, with regards to productivity rate, the Classic GAN is superior with a 72% productivity rate. We also designed a simple experiment with the state-of-the-art classifier (ECGResNet34) to show empirically that the augmentation of the imbalanced dataset by synthetic ECG signals could improve the performance of classification significantly.

PMID:40132047 | DOI:10.1371/journal.pone.0271270

PLoS One. 2025 Mar 25;20(3):e0319577. doi: 10.1371/journal.pone.0319577. eCollection 2025.

ABSTRACT

INTRODUCTION: Ptosis surgery outcomes are measured by one-dimensional metrics like Marginal Reflex Distance (MRD) and Palpebral Fissure Height (PFH) using ImageJ. However, these methods are insufficient to capture the full range of changes post-surgery. Eyeball Exposure Rate (EER) offers a more comprehensive two-dimensional perspective as metric. This study compares AI-based EER measurements with conventional ImageJ methods for assessing outcome of ptosis surgery. Methods: Images from 50 patients (total 100 eyes) taken before and after surgery were analyzed using manual ImageJ and the AI-tool “Anigma-View”. Statistical tests assessed the accuracy and consistency of both methods, using intraclass correlation coefficients (ICCs) and Bland-Altman plots for comparison.

RESULTS: EER measured by the AI-tool at pre- and post-operation were 58.85% and 75.36%, respectively. Similarly, manual measurements using ImageJ showed an increase from 58.22% to 75.27%. The Intraclass Correlation Coefficients (ICCs) between the AI-tool and manual measurements ranged from 0.984 to 0.994, indicating excellent agreement, with the repeated AI-tool demonstrating high reproducibility (ICC = 1). Bland-Altman plots showed excellent agreement between the two methods and reproducibility of AI-based measurements. Additionally, EER improvement was more prominent in the moderate to severe ptosis group with a 45.94% increase, compared to the mild group with 14.39% increase.

DISCUSSION: The findings revealed no significant differences between AI-tool and manual methods, suggesting that AI-tool is just as reliable. AI-tool to automate measurements offers efficiency and objectivity, making it a valuable method in clinical fields.

CONCLUSION: AI-based EER analysis is accurate and efficient, providing comparable results to manual methods. Its ability to simplify surgical outcome assessments makes it a promising addition to clinical practice. Further exploration of AI in evaluating three-dimensional changes in ptosis surgery could enhance future surgical assessments and outcomes.

PMID:40132041 | DOI:10.1371/journal.pone.0319577