Tag: pubmed

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

Local Dev Soc. 2025;6(1):30-42. doi: 10.1080/26883597.2023.2229962. Epub 2023 Jun 27.

ABSTRACT

Digital infrastructure and internet access facilitate the economic, educational, social, and recreational activities that are critical to supporting access to inclusive and equitable community participation. However, significant disparities, or digital divides, persist for many people and communities based on social, demographic, and environmental characteristics. This paper explores the associations between socio-demographic characteristics and internet access at the county level using geographic statistical analysis using spatial regression, specifically a spatial error model and a residual analysis. Our results highlight the spatial nature of persistent social, demo-graphic, and environmental digital divides, particularly as they pertain to race, rurality, and education. Results suggest that current efforts to expand access to digital opportunity continue to fall short in lower resource communities and communities with higher rates of marginalized individuals.

PMID:40132038 | PMC:PMC10961931 | DOI:10.1080/26883597.2023.2229962

PLoS One. 2025 Mar 25;20(3):e0320691. doi: 10.1371/journal.pone.0320691. eCollection 2025.

ABSTRACT

BACKGROUND: Unlike pulmonary tuberculosis, there is limited information on delays in diagnosis and treatment initiation in extrapulmonary tuberculosis (EPTB) and their consequences for disease outcomes and mortality. In low- and middle-income countries, most EPTB cases are presumed rather than microbiologically confirmed, which might lead to an underestimation of the mortality rates in EPTB.

OBJECTIVE: The study aimed to assess the delays in diagnosis and treatment in EPTB and their association with mortality in a setting with a high prevalence of both HIV and malnutrition.

METHOD: We included 106 EPTB patients from Mbeya Zonal Referral Hospital, who were followed up until the completion of their treatment. Patients were classified as having EPTB using a clinical case definition. In total, 37 of 106 (35%) EPTB cases resulted in death. The median (interquartile range) total diagnostic delay for survivors was 59 days (26-136), while for those who died, it was 78 days (32-165). The corresponding median (interquartile range) treatment delay was 66 days (33-140) for survivors and 78 days (27-189) for those who died. None of the differences reached statistical significance when analyzed with non-parametric tests. Surprisingly, 21 patients did not receive TB treatment, but this lack of therapy did not affect mortality or correlate with a longer diagnostic delay.

CONCLUSION: We were unable to demonstrate that diagnostic or treatment delays were higher in EPTB patients who died. Furthermore, EPTB patients who did not receive TB treatment did not exhibit higher mortality rates. Further prospective studies with larger sample sizes are needed to better understand the factors contributing to delays in diagnosis and treatment, as well as their potential impact on mortality in EPTB.

PMID:40132036 | DOI:10.1371/journal.pone.0320691

JMIR Form Res. 2025 Mar 25;9:e60476. doi: 10.2196/60476.

ABSTRACT

BACKGROUND: The global rise of video gaming and esports has raised significant concerns about hearing loss due to loud sound exposure. While these activities provide entertainment and have applications in health care, the auditory health risks and behavioral factors influencing listening habits among gamers remain underexplored. Research is needed to develop tailored interventions that address the unique barriers, attitudes, and beliefs of gamers and esports participants, promoting safer listening practices and minimizing auditory health risks.

OBJECTIVE: This study aimed to explore listening behaviors, attitudes, and awareness regarding hearing health risks among video gamers and esports participants. The findings are intended to guide the design and implementation of technological features that encourage safer listening practices, in alignment with the World Health Organization’s Safe Listening initiative.

METHODS: An open web-based survey was conducted from September 2022 to January 2023, targeting video gamers and esports enthusiasts. Participants were recruited via World Health Organization social media platforms and outreach to stakeholders. The survey assessed gaming behaviors, listening habits, awareness about hearing health, beliefs, readiness to change listening behaviors, and communication preferences. Data were analyzed using descriptive statistics and multinomial logistic regression.

RESULTS: A total of 488 responses were collected, with 67.2% (n=328) of participants identifying as male, and 56.4% (n=275) having a college degree or higher. Of the respondents, 90.8% (n=443) were actively engaged in video gaming, while 54.9% (n=268) viewed esports, and 13.9% (n=68) participated in esports events. Notably, 24.8% (n=110) of gamers, 18.3% (n=49) of esports viewers, and 37.1% (n=23) of esports players reported using high or very high volume settings. Despite around half of the participants experiencing symptoms indicative of hearing damage (eg, ringing in the ears), only 34.3% (n=152) of gamers, 35.8% (n=92) of esports players, and 39.7% (n=27) of esports viewers reported taking sound breaks every hour. The study identified a balanced distribution across readiness-to-change stages, with 30.3% (n=148) in the precontemplation stage, 35.3% (n=173) in the contemplation stage, and 34.2% (n=167) in the action stage. Factors such as perceived susceptibility to hearing loss, perceived benefits of preventive action, and self-efficacy significantly influenced readiness to change. Communication preferences indicated that 51% (n=249) of participants were interested in receiving more information on hearing health, with health care professionals and governmental agencies being the most trusted sources.

CONCLUSIONS: The findings highlight an urgent need for interventions to promote safe listening practices among gamers, emphasizing a gap between awareness and preventive action. The integration of safe listening features into video games and esports platforms, along with targeted communication strategies, can enhance auditory health awareness and protective behaviors. Future research should evaluate the effectiveness of these interventions to ensure comprehensive auditory health protection in the digital entertainment sector.

PMID:40131338 | DOI:10.2196/60476

JMIR Diabetes. 2025 Mar 25;10:e64505. doi: 10.2196/64505.

ABSTRACT

BACKGROUND: Technologies such as mobile apps, continuous glucose monitors (CGMs), and activity trackers are available to support adults with diabetes, but it is not clear how they are used together for diabetes self-management.

OBJECTIVE: This study aims to understand how adults with diabetes with differing clinical profiles and digital health literacy levels integrate data from multiple behavior tracking technologies for diabetes self-management.

METHODS: Adults with type 1 or 2 diabetes who used ≥1 diabetes medications responded to a web-based survey about health app and activity tracker use in 6 categories: blood glucose level, diet, exercise and activity, weight, sleep, and stress. Digital health literacy was assessed using the Digital Health Care Literacy Scale, and general health literacy was assessed using the Brief Health Literacy Screen. We analyzed descriptive statistics among respondents and compared health technology use using independent 2-tailed t tests for continuous variables, chi-square for categorical variables, and Fisher exact tests for digital health literacy levels. Semistructured interviews examined how these technologies were and could be used to support daily diabetes self-management. We summarized interview themes using content analysis.

RESULTS: Of the 61 survey respondents, 21 (34%) were Black, 23 (38%) were female, and 29 (48%) were aged ≥45 years; moreover, 44 (72%) had type 2 diabetes, 36 (59%) used insulin, and 34 (56%) currently or previously used a CGM. Respondents had high levels of digital and general health literacy: 87% (46/53) used at least 1 health app, 59% (36/61) had used an activity tracker, and 62% (33/53) used apps to track ≥1 health behaviors. CGM users and nonusers used non-CGM health apps at similar rates (16/28, 57% vs 12/20, 60%; P=.84). Activity tracker use was also similar between CGM users and nonusers (20/33, 61% vs 14/22, 64%; P=.82). Respondents reported sharing self-monitor data with health care providers at similar rates across age groups (17/32, 53% for those aged 18-44 y vs 16/29, 55% for those aged 45-70 y; P=.87). Combined activity tracker and health app use was higher among those with higher Digital Health Care Literacy Scale scores, but this difference was not statistically significant (P=.09). Interviewees (18/61, 30%) described using blood glucose level tracking apps to personalize dietary choices but less frequently used data from apps or activity trackers to meet other self-management goals. Interviewees desired data that were passively collected, easily integrated across data sources, visually presented, and tailorable to self-management priorities.

CONCLUSIONS: Adults with diabetes commonly used apps and activity trackers, often alongside CGMs, to track multiple behaviors that impact diabetes self-management but found it challenging to link tracked behaviors to glycemic and diabetes self-management goals. The findings indicate that there are untapped opportunities to integrate data from apps and activity trackers to support patient-centered diabetes self-management.

PMID:40131316 | DOI:10.2196/64505