Tag: nevin manimala

Dermatol Pract Concept. 2025 Jan 30;15(1). doi: 10.5826/dpc.1501a4237.

ABSTRACT

INTRODUCTION: An emerging hypothesis suggests a potential link between enhanced intestinal permeability and the advancement of chronic spontaneous urticaria (CSU).

OBJECTIVE: This study aimed to investigate the role of intestinal permeability in the etiopathogenesis of CSU by measuring serum zonulin levels, a marker of intestinal permeability, in both CSU patients and control subjects. Additionally, the study sought to explore the correlation between the severity of the illness and zonulin levels.

METHODS: The study involved 61 patients with CSU and 59 healthy control individuals. For the CSU patients, comprehensive data were collected encompassing various aspects: age at onset of the condition, duration of the most recent attack, presence of any comorbid conditions, dosage of antihistamines being used, and urticaria activity score as well as detailed personal and family medical histories. Additionally, demographic information for these patients was also meticulously documented.

RESULT: The study revealed a statistically significant difference in zonulin levels between the CSU patient group and the control group, with a p-value of 0.000, indicating a highly significant disparity. Furthermore, among the CSU patients, those who presented with angioedema exhibited considerably higher zonulin levels compared to those without angioedema. This variation in zonulin levels based on the presence of angioedema was also statistically significant, with a p-value of 0.023.

CONCLUSION: The observed results suggest that increased intestinal permeability, as indicated by elevated zonulin levels, may play a crucial role in the pathophysiology of both CSU and angioedema. This association highlights the potential significance of intestinal permeability in the development and manifestation of these conditions.

PMID:40117602 | DOI:10.5826/dpc.1501a4237

J Med Internet Res. 2025 Mar 21;27:e67967. doi: 10.2196/67967.

ABSTRACT

BACKGROUND: Clinical reasoning (CR) is an essential skill; yet, physicians often receive limited feedback. Artificial intelligence holds promise to fill this gap.

OBJECTIVE: We report the development of named entity recognition (NER), logic-based and large language model (LLM)-based assessments of CR documentation in the electronic health record across 2 institutions (New York University Grossman School of Medicine [NYU] and University of Cincinnati College of Medicine [UC]).

METHODS: The note corpus consisted of internal medicine resident admission notes (retrospective set: July 2020-December 2021, n=700 NYU and 450 UC notes and prospective validation set: July 2023-December 2023, n=155 NYU and 92 UC notes). Clinicians rated CR documentation quality in each note using a previously validated tool (Revised-IDEA), on 3-point scales across 2 domains: differential diagnosis (D0, D1, and D2) and explanation of reasoning, (EA0, EA1, and EA2). At NYU, the retrospective set was annotated for NER for 5 entities (diagnosis, diagnostic category, prioritization of diagnosis language, data, and linkage terms). Models were developed using different artificial intelligence approaches, including NER, logic-based model: a large word vector model (scispaCy en_core_sci_lg) with model weights adjusted with backpropagation from annotations, developed at NYU with external validation at UC, NYUTron LLM: an NYU internal 110 million parameter LLM pretrained on 7.25 million clinical notes, only validated at NYU, and GatorTron LLM: an open source 345 million parameter LLM pretrained on 82 billion words of clinical text, fined tuned on NYU retrospective sets, then externally validated and further fine-tuned at UC. Model performance was assessed in the prospective sets with F₁-scores for the NER, logic-based model and area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC) for the LLMs.

RESULTS: At NYU, the NYUTron LLM performed best: the D0 and D2 models had AUROC/AUPRC 0.87/0.79 and 0.89/0.86, respectively. The D1, EA0, and EA1 models had insufficient performance for implementation (AUROC range 0.57-0.80, AUPRC range 0.33-0.63). For the D1 classification, the approach pivoted to a stepwise approach taking advantage of the more performant D0 and D2 models. For the EA model, the approach pivoted to a binary EA2 model (ie, EA2 vs not EA2) with excellent performance, AUROC/AUPRC 0.85/ 0.80. At UC, the NER, D-logic-based model was the best performing D model (F₁-scores 0.80, 0.74, and 0.80 for D0, D1, D2, respectively. The GatorTron LLM performed best for EA2 scores AUROC/AUPRC 0.75/ 0.69.

CONCLUSIONS: This is the first multi-institutional study to apply LLMs for assessing CR documentation in the electronic health record. Such tools can enhance feedback on CR. Lessons learned by implementing these models at distinct institutions support the generalizability of this approach.

PMID:40117575 | DOI:10.2196/67967

J Med Internet Res. 2025 Mar 21;27:e60148. doi: 10.2196/60148.

ABSTRACT

BACKGROUND: Despite the rapid growth of research in artificial intelligence/machine learning (AI/ML), little is known about how often study results are disclosed years after study completion.

OBJECTIVE: We aimed to estimate the proportion of AI/ML research that reported results through ClinicalTrials.gov or peer-reviewed publications indexed in PubMed or Scopus.

METHODS: Using data from the Clinical Trials Transformation Initiative Aggregate Analysis of ClinicalTrials.gov, we identified studies initiated and completed between January 2010 and December 2023 that contained AI/ML-specific terms in the official title, brief summary, interventions, conditions, detailed descriptions, primary outcomes, or keywords. For 842 completed studies, we searched PubMed and Scopus for publications containing study identifiers and AI/ML-specific terms in relevant fields, such as the title, abstract, and keywords. We calculated disclosure rates within 3 years of study completion and median times to disclosure-from the “primary completion date” to the “results first posted date” on ClinicalTrials.gov or the earliest date of journal publication.

RESULTS: Of 842 completed studies (n=357 interventional; n=485 observational), 5.5% (46/842) disclosed results on ClinicalTrials.gov, 13.9% (117/842) in journal publications, and 17.7% (149/842) through either route within 3 years of completion. Higher disclosure rates were observed for trials: 10.4% (37/357) on ClinicalTrials.gov, 19.3% (69/357) in journal publications, and 26.1% (93/357) through either route. Randomized controlled trials had even higher disclosure rates: 11.3% (23/203) on ClinicalTrials.gov, 24.6% (50/203) in journal publications, and 32% (65/203) through either route. Nevertheless, most study findings (82.3%; 693/842) remained undisclosed 3 years after study completion. Trials using randomization (vs nonrandomized) or masking (vs open label) had higher disclosure rates and shorter times to disclosure. Most trials (85%; 305/357) had sample sizes of ≤1000, yet larger trials (n>1000) had higher publication rates (30.8%; 16/52) than smaller trials (n≤1000) (17.4%; 53/305). Hospitals (12.4%; 42/340), academia (15.1%; 39/259), and industry (13.7%; 20/146) published the most. High-income countries accounted for 82.4% (89/108) of all published studies. Of studies with disclosed results, the median times to report through ClinicalTrials.gov and in journal publications were 505 days (IQR 399-676) and 407 days (IQR 257-674), respectively. Open-label trials were common (60%; 214/357). Single-center designs were prevalent in both trials (83.3%; 290/348) and observational studies (82.3%; 377/458).

CONCLUSIONS: For over 80% of AI/ML studies completed during 2010-2023, study findings remained undisclosed even 3 years after study completion, raising questions about the representativeness of publicly available evidence. While methodological rigor was generally associated with higher publication rates, the predominance of single-center designs and high-income countries may limit the generalizability of the results currently accessible.

PMID:40117574 | DOI:10.2196/60148

J Clin Psychiatry. 2025 Mar 12;86(2):24m15424. doi: 10.4088/JCP.24m15424.

ABSTRACT

Objective: To evaluate factors associated with suicide mortality among Veterans Health Administration (VHA) patients with bipolar disorder.

Methods: VHA patients diagnosed with bipolar disorder in calendar year (CY) 2014 who utilized VHA health care services in CY2013 were included in the study cohort. Suicide mortality in the 5 years following the first documented bipolar disorder diagnosis during CY2014 was examined using Cox proportional hazards regression.

Results: 725 of 126,655 VHA patients who had a bipolar disorder diagnosis in CY2014 (0.6%) died by suicide in the following 5 CYs (2014-2019). Suicide was associated with suicide high-risk flags (hazard ratio [HR] = 2.21), prior year emergency department visit (HR = 1.25), having a new bipolar disorder diagnosis (HR= 1.23), and receiving a benzodiazepine prescription of ≥30 days of supply (HR = 1.58). Prescriptions of benzodiazepines of <30 days of supply, other anxiolytics (ie, buspirone), and sedatives were not significantly associated with suicide mortality in the multivariable model.

Conclusions: Among VHA patients diagnosed with bipolar disorder, receipt of a benzodiazepine prescription of ≥30 days was associated with increased suicide risk, even after controlling for clinical and demographic factors. Elucidating mechanisms through which benzodiazepine prescriptions increase suicide risk is an important avenue for future investigations. Additionally, VHA patients with newly diagnosed bipolar disorder may benefit from increased clinical attention, given the elevated suicide risk among this subgroup. Findings highlight targets for suicide prevention initiatives.

PMID:40117569 | DOI:10.4088/JCP.24m15424

J Clin Psychiatry. 2025 Mar 17;86(2):24m15654. doi: 10.4088/JCP.24m15654.

ABSTRACT

Objectives: Latiné youth in the US are at elevated risk for trauma exposure, but factors that contribute to their symptoms are not well studied. We examined the effects of interpersonal trauma (IPT) burden and negative affect about ethnic-racial identity (NERI-A) on internalizing symptoms following trauma exposure.

Method: Participants were 1,006 US-born youth of Latiné background (mean age 15.4 years, 60% female at birth, and 70% identified as White) from the Childhood Trauma Research Network, a research consortium examining long-term outcomes of childhood trauma in Texas. Participants were enrolled between October 2020 and February 2024. Analyses controlled for sex, age, race, non-interpersonal trauma, whether parents were of the immigrant generation, and mental health treatment received.

Results: Greater IPT burden and higher baseline NERI-A were associated with greater baseline anxiety (P < .001, P = .026) and depressive (P < .001, P = .040) symptoms. The effect of baseline IPT burden on direction and magnitude of longitudinal change in anxiety (0.038) and depression (0.002) differed for those with high NERI-A vs low NERI-A. In the context of low NERI-A, IPT burden showed steady or decreasing associations with symptoms over time. In contrast, for those reporting high NERI-A, IPT burden showed strengthening associations with both anxiety and depression over time.

Conclusion: Our study highlights the vulnerability of youth who experience IPT and report NERI-A. Further research is needed to determine how NERI-A develops, changes, and is moderated in the diverse groups of individuals of Latiné descent.

PMID:40117568 | DOI:10.4088/JCP.24m15654

Int Forum Allergy Rhinol. 2025 Mar 21:e3573. doi: 10.1002/alr.23573. Online ahead of print.

NO ABSTRACT

PMID:40116130 | DOI:10.1002/alr.23573

Hum Brain Mapp. 2025 Apr 1;46(5):e70161. doi: 10.1002/hbm.70161.

ABSTRACT

Missing data are a prevalent challenge in neuroimaging, with significant implications for downstream statistical analysis. Neglecting this issue can introduce bias and lead to erroneous inferential conclusions, making it crucial to employ appropriate statistical methods for handling missing data. Although the multiple imputation is a widely used technique, its application in neuroimaging is severely hindered by the high dimensionality of neuroimaging data, and the substantial computational demands. To tackle the critical computational challenges, we propose a novel approach, High dimensional Multiple Imputation (HIMA), based on Bayesian models specifically designed for large-scale neuroimaging datasets. HIMA introduces a new computational strategy to sample large covariance matrices based on a robustly estimated posterior mode, significantly improving both computational efficiency and numerical stability. To assess the effectiveness of HIMA, we conducted extensive simulation studies and real-data analysis from a Schizophrenia brain imaging dataset with around 1000 voxels. HIMA showcases a remarkable reduction of computational burden, for example, 1 hour by HIMA versus 800 hours by classic multiple imputation packages. HIMA also demonstrates improved precision and stability of imputed data.

PMID:40116075 | DOI:10.1002/hbm.70161

Toxicol Sci. 2025 Mar 20:kfaf036. doi: 10.1093/toxsci/kfaf036. Online ahead of print.

ABSTRACT

Key Characteristics (KCs) are properties of chemicals that are associated with different types of human health hazard. KCs are used for systematic reviews in support of hazard identification. Transcriptomic data are a rich source of mechanistic data and are frequently interpreted through “enriched” pathways/gene sets. Such analyses may be challenging to interpret in regulatory science because of redundancy among pathways, complex data analyses, and unclear relevance to hazard identification. We hypothesized that by cross-mapping pathways/gene sets and KCs, the interpretability of transcriptomic data can be improved. We summarized 72 published KCs across 7 hazard traits into 34 umbrella KC terms. Gene sets from Reactome and Kyoto Encyclopedia of Genes and Genomes (KEGG) were mapped to these, resulting in “KC gene sets”. These sets exhibit minimal overlap and vary in the number of genes. Comparisons of the same KC gene sets mapped from Reactome and KEGG revealed low similarity, indicating complementarity. Performance of these KC gene sets was tested using publicly available transcriptomic datasets of chemicals with known organ-specific toxicity: Benzene and 2,3,7,8-tetrachlorodibenzo-p-dioxin tested in mouse liver, and drugs sunitinib and amoxicillin tested in human induced pluripotent stem cell-derived cardiomyocytes. We found that KC terms related to the mechanisms affected by tested compounds were highly enriched, while the negative control (amoxicillin) showed limited enrichment with marginal significance. This study’s impact is in presenting a computational approach based on KCs for the analysis of toxicogenomic data and facilitating transparent interpretation of these data in the process of chemical hazard identification.

PMID:40116072 | DOI:10.1093/toxsci/kfaf036

Environ Microbiol Rep. 2025 Apr;17(2):e70075. doi: 10.1111/1758-2229.70075.

ABSTRACT

The salt ponds of Trapani, Sicily, represent an extreme and under-explored ecosystem characterised by varying salinity gradients and environmental conditions. These ponds, integral to traditional salt extraction, include cold, driving, hot and crystallizer ponds, each hosting diverse microbial communities. This study aimed to explore the biological and physical-chemical diversity of 11 ponds during the salt production season in Trapani. We conducted comprehensive physical-chemical characterizations, including measurements of pH, conductivity, viscosity, density, organic carbon and ion concentration. Microbial DNA was extracted from salt pond waters and subjected to metabarcoding of 16S rRNA genes to determine the diversity of archaea and bacteria. High-throughput sequencing revealed significant variations in microbial communities across different pond types and seasons. Cold ponds showed a higher diversity of moderately halophilic organisms, while crystallizer and feeding ponds were dominated by extreme halophiles, particularly archaeal genus Halorubrum and Haloquadratum and bacterial genus Salinibacter. Statistical analyses indicated that environmental parameters, especially salinity and temperature, significantly influenced microbial community composition. Our findings enhance the understanding of microbial ecology in saline environments and highlight the potential of halophilic microorganisms. This study provides a foundation for future research into the functional roles of these microorganisms and their industrial applications.

PMID:40116066 | DOI:10.1111/1758-2229.70075

Helicobacter. 2025 Mar-Apr;30(2):e70026. doi: 10.1111/hel.70026.

ABSTRACT

PURPOSE: This meta-analysis aimed to assess the diagnostic performance of artificial intelligence (AI) based on endoscopy for detecting Helicobacter pylori (H. pylori) infection.

METHODS: A comprehensive literature search was conducted across PubMed, Embase, and Web of Science to identify relevant studies published up to January 10, 2025. The selected studies focused on the diagnostic accuracy of AI in detecting H. pylori. A bivariate random-effects model was employed to calculate pooled sensitivity and specificity, both presented with 95% confidence intervals (CIs). Study heterogeneity was assessed using the I² statistic.

RESULTS: Of 604 studies identified, 16 studies (25,002 images or patients) were included. For the internal validation set, the pooled sensitivity, specificity, and area under the curve (AUC) for detecting H. pylori were 0.91 (95% CI: 0.84-0.95), 0.91 (95% CI: 0.86-0.94), and 0.96 (95% CI: 0.94-0.97), respectively. For the external validation set, the pooled sensitivity, specificity, and AUC were 0.91 (95% CI: 0.86-0.95), 0.94 (95% CI: 0.90-0.97), and 0.98 (95% CI: 0.96-0.99). For junior clinicians, the pooled sensitivity, specificity, and AUC were 0.76 (95% CI: 0.66-0.83), 0.75 (95% CI: 0.70-0.80), and 0.81 (95% CI: 0.77-0.84). For senior clinicians, the pooled sensitivity, specificity, and AUC were 0.81 (95% CI: 0.74-0.86), 0.89 (95% CI: 0.86-0.91), and 0.92 (95% CI: 0.90-0.94).

CONCLUSIONS: Endoscopy-based AI demonstrates higher diagnostic performance compared to both junior and senior endoscopists. However, the high heterogeneity among studies limits the strength of these findings, and further research with external validation datasets is necessary to confirm the results.

PMID:40116054 | DOI:10.1111/hel.70026