Tag: statistics

CNS Neurol Disord Drug Targets. 2026 Mar 26. doi: 10.2174/0118715273434957260202115619. Online ahead of print.

ABSTRACT

INTRODUCTION: Inflammatory and coagulation pathways are crucial in the pathogenesis and clinical progression of ischemic stroke. The objective of this study was to evaluate serum concentrations of interleukin-2 (IL-2) and interleukin-10 (IL-10), as well as the gene expression of tissue factor (TF) in peripheral blood mononuclear cells (PBMCs), and to examine their correlations with stroke severity and clinical outcomes.

MATERIALS AND METHODS: We enrolled 148 patients with ischemic stroke and 30 healthy controls matched for age and sex in a cross-sectional design. We used ELISA to measure the levels of IL-2 and IL-10 in serum and real-time PCR to look at TF gene expression in PBMCs. The NIH Stroke Scale (NIHSS) was used to measure the severity of strokes, and the results were compared to clinical variables.

RESULTS: Patients with severe stroke showed significantly lower levels of IL-2 and IL-10 (p < 0.001), and TF expression in PBMCs was significantly higher in both mild and severe stroke groups compared to controls (p < 0.001). There was no statistically significant difference between the mild and severe groups (p = 0.213). In severe cases, IL-2 and TF were negatively correlated (p = 0.036). Nonetheless, none of the biomarkers independently forecasted survival outcomes.

DISCUSSION: The results show that the immune-coagulation axis is not working properly in severe ischemic stroke. Lower levels of IL-2 and IL-10 may indicate that regulatory T-cells aren’t working properly and that anti-inflammatory control isn’t working, which can cause monocytes to become active and TF levels to rise. This interaction probably makes thromboinflammatory cascades worse, which leads to more damage to the nervous system. These changes, even though they don’t predict survival, give us a better understanding of how strokes work and open up new possibilities for targeted immunomodulatory therapy.

CONCLUSION: The changes in IL-2, IL-10, and TF expression indicate a coordinated disruption of immune and thrombotic pathways in individuals with severe ischemic stroke. Although not prognostic of mortality, these biomarkers may indicate disease severity and represent potential targets for future therapeutic interventions. Longitudinal studies are necessary to validate their prognostic significance and investigate their incorporation into clinical decision-making algorithms for stroke.

PMID:41930584 | DOI:10.2174/0118715273434957260202115619

Cancer Treat Res Commun. 2026 Mar 12;47:101171. doi: 10.1016/j.ctarc.2026.101171. Online ahead of print.

ABSTRACT

BACKGROUND: TFE3-RCC is rare, hard to distinguish from common RCC on CT, posing preoperative diagnostic challenges for clinicians. This two-center study aimed to develop interpretable machine learning models using radiomics to differentiate Xp11.2/TFE3 translocation renal cell carcinoma (TFE3-RCC) from common renal cell carcinoma (RCC) subtypes using computed tomography (CT) images.

METHODS: Retrospective data from 1394 patients (39 TFE3-RCC, 1355 non-TFE3 RCC) were analyzed. A propensity score matching (PSM) was applied, resulting in 234 cases (TFE3: n = 39, non-TFE3: n = 195) included in the radiomics study. CT images were segmented using an AI-based model, and 102 radiomic features (shape, first-order statistics, texture) were extracted. Recursive feature elimination (RFE) with random forest and gradient boosting models were used for feature selection and model development. Performance was evaluated via area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity.

RESULTS: The patients with TFE3-RCC were significantly younger (36.51 ± 12.68 vs. 57.30 ± 12.00 years, P < 0.05), and had more frequent calcification (30.8% vs. 6.4%, P < 0.05) and were larger (5.50 ± 3.17 cm vs. 4.11 ± 2.06 cm, P = 0.005) than those with non-TFE3 RCC, and preferred to implicate females (female: 46.2% vs. 29.3%, P = 0.023). The model identified six optimal features, with skewness (relative weight: 44.57%) and first-order statistics as key predictors. The training set and test set achieved stable performances with AUC (0.951 (95% CI: 0.920-0.983) and 0.864 (95% CI: 0.749-0.979)) and accuracy (0.878 and 0.852).

CONCLUSION: Interpretable radiomics-based machine learning models effectively differentiate TFE3-RCC from common RCC subtypes, with skewness and intensity features as critical biomarkers. This approach may improve preoperative diagnosis, though larger multi-center studies and integration of multi-omics data are needed for clinical translation.

PMID:41930555 | DOI:10.1016/j.ctarc.2026.101171

Int Emerg Nurs. 2026 Apr 1;86:101815. doi: 10.1016/j.ienj.2026.101815. Online ahead of print.

ABSTRACT

BACKGROUND: Workplace violence (WPV) is a major occupational hazard for emergency nurses, with verbal, psychological, and physical threats undermining safety. Multiple safety strategies have been proposed, yet little research has examined their prevalence or link to perceived safety.

METHODS: A cross-sectional design was employed using an anonymous electronic survey of emergency nurses across 17 U.S. states. The survey, validated by nursing experts, assessed demographics, workplace characteristics, exposure to WPV, and 14 safety precautions. Perceived safety was rated on a 10-point scale. Descriptive statistics, bivariate tests, and multivariate regression with bootstrapping were conducted.

RESULTS: Among 134 participants (M age = 42.8 years, 84.3% female), 48.1% reported experiencing WPV in the past month. Mean safety rating was 6.84 (SD = 2). De-escalation and security presence were most prevalent (90.3%), followed by controlled access (80.6%) and security cameras (77.6%). Regression analysis showed urban nurses reported lower safety than suburban nurses (b = – 1.62, 95% CI [-2.438, -0.913], p < 0.001). Security presence, controlled access, and lighting were associated with higher safety perceptions (b = 1.31, 95% CI [0.487, 2.322], p = 0.018; b = 1.12, 95% CI [0.396, 1.881], p = 0.008; b = 1.09, 95% CI [0.478, 1.784], p = 0.004, respectively).

CONCLUSION: Findings highlight the prevalence of WPV in EDs and identify key safety interventions linked to nurses’ perceptions of safety. Results can inform policy and guide workplace improvements.

PMID:41930553 | DOI:10.1016/j.ienj.2026.101815

Neural Netw. 2026 Mar 22;201:108889. doi: 10.1016/j.neunet.2026.108889. Online ahead of print.

ABSTRACT

Federated learning, a vital paradigm in modern machine learning, enables private and decentralised training of models that is crucial for learning from sensitive data. Noisy label learning, another vital paradigm in modern machine learning, addresses the training of models from the data with potentially incorrect labels. Their integration, namely federated learning with noisy labels (FLNL), is an emerging but challenging topic arising from the practice of machine learning, which, however, still lacks a review of its research progress. The aim of this paper is to fill in this gap. We first summarise four core challenges to FLNL: localised label noise, across-client heterogeneity of label noise, localised overfitting to label noise, and inadequate benchmarking. We then propose a taxonomy to categorise current FLNL studies into four types that address the four challenges correspondingly: sample-wise methods, client-wise methods, model-wise methods, and benchmark-wise studies. This work offers the first comprehensive and concise review dedicated to FLNL; moreover, we also provide future research directions for this rapidly evolving and practically significant field.

PMID:41930546 | DOI:10.1016/j.neunet.2026.108889

J Pediatr Nurs. 2026 Apr 1;88:635-644. doi: 10.1016/j.pedn.2026.03.041. Online ahead of print.

ABSTRACT

BACKGROUND: Children with disabilities increasingly encounter digital environments with opportunities and risks, while relationships among digital parenting self-efficacy, parental self-efficacy, and family support remain understudied.

METHODS: This cross-sectional, descriptive, and correlational study was conducted with 195 parents of children aged 0-18 years with disabilities registered in a disability services unit in Türkiye. Data were collected using a sociodemographic form, the Digital Parenting Self-Efficacy Scale, the Parental Self-Efficacy Scale, and the Family Support Scale. Data were analyzed using descriptive statistics, Pearson correlation analysis, and multiple linear regression. Missing data were handled using multiple imputation with the fully conditional specification method, and pooled estimates were calculated according to Rubin’s rules.

RESULTS: Most children used the internet daily and for extended periods, and some exhibited behaviors suggestive of problematic use. Parents demonstrated moderate-to-high levels of digital literacy, digital security awareness, and digital communication skills. Parental self-efficacy showed positive associations with digital competencies and perceived family support. In the adjusted regression model controlling for maternal age, maternal education, family income, and social security status, digital security and perceived family support emerged as significant predictors of parental self-efficacy, with digital security representing the strongest predictor.

CONCLUSIONS: Digital security and family support play important roles in strengthening parental self-efficacy among families raising children with disabilities.

IMPLICATIONS FOR PEDIATRIC NURSING: Pediatric nurses can play a key role in assessing digital use in families of children with disabilities, strengthening parents’ digital literacy and self-efficacy, and designing family-centred interventions to reduce digital risks and enhance family support.

PMID:41930534 | DOI:10.1016/j.pedn.2026.03.041

Heart Lung. 2026 Apr 1;78:102775. doi: 10.1016/j.hrtlng.2026.102775. Online ahead of print.

ABSTRACT

BACKGROUND: Inspiratory muscle weakness is common among patients with chronic heart failure undergoing cardiac resynchronization therapy (CRT). Home-based inspiratory muscle training (IMT) could be a vital therapeutic strategy, particularly for those with limited access to cardiac rehabilitation programs.

OBJECTIVES: This study aimed to investigate the effects of a 12-week home-based IMT program on inspiratory muscle strength, cardiac function, NT-proBNP levels, exercise capacity, quality of life, and daily activities. It examined the relationships between changes in NT-proBNP levels, walking distance, maximal inspiratory pressure (MIP), and left ventricular ejection fraction (LVEF) in patients with heart failure at least one year after CRT implantation.

METHODS: In this randomized controlled trial, 32 patients with CRT devices were assigned to either the IMT group (n = 19) or the control group (n = 13). Outcome measures included MIP, LVEF, NT-proBNP levels, the 6-minute walk test, performance of activities of daily living (PMADL-8), and quality of life (Nottingham Health Profile-NHP).

RESULTS: Compared to the control group, the IMT group showed statistically significant improvements in MIP (p < 0.001), LVEF (p = 0.025), NT-proBNP levels (p = 0.003), walk distance (p < 0.001), PMADL-8 score (p < 0.001), and NHP total score. Significant correlations were observed among the changes in NT-proBNP, MIP, LVEF, and walk distance.

CONCLUSION: In this small-sample study home-based IMT was associated with improvements in respiratory strength, cardiac function, functional capacity, patient-reported functionality, and quality of life. This confirms the role of home-based IMT as a supportive preventive strategy for CRT patients who lack access to rehabilitation. Larger-scale, long-term studies are needed to confirm its effects on remodeling and clinical outcomes.

PMID:41930533 | DOI:10.1016/j.hrtlng.2026.102775

Comput Biol Chem. 2026 Mar 27;123:109039. doi: 10.1016/j.compbiolchem.2026.109039. Online ahead of print.

ABSTRACT

Disruption of transcription termination (DoTT) occurs when RNA polymerase II reads past a gene’s normal 3′ end, generating downstream “readthrough” RNA. DoTT has been reported under stresses such as viral infection and metabolic perturbation. But, many existing detection tools analyze samples one at a time or rely on rigid downstream windows, limiting direct condition-to-condition testing. We present DoTT-ML, a condition-aware pipeline for detecting transcription termination disruption from conventional short-read RNA-seq. This pipeline extends gene annotations downstream by a tunable window, applies an optional gap to reduce termination-proximal noise, and applies differential analysis between conditions using a robust statistical workflow. An optional machine learning approach provides a post-hoc prioritization when curated reference annotations are available. We benchmarked DoTT-ML against ARTDeco and DoGFinder across three public datasets: influenza A virus total RNA-seq, HSV-1 nascent 4sU-RNA, and HSV-1 Z-RNA RIP-seq. DoTT-ML showed comparably to, or better than, existing tools (high ROC AUC across datasets). Finally, in an in-house mouse, high-carbohydrate diet (HCD) liver model, DoTT-ML identified diet-associated readthroughs at metabolic genes. Experimental validation confirmed a stable readthrough transcript at the Scd1 locus under dietary stress, serving as a proof of principle for the pipeline’s biological relevance. Together, DoTT-ML provides a practical framework for condition-aware, readthrough detection and comparison across diverse RNA-seq assays.

PMID:41930502 | DOI:10.1016/j.compbiolchem.2026.109039

Mol Med Rep. 2026 May;33(5):156. doi: 10.3892/mmr.2026.13866. Epub 2026 Apr 3.

ABSTRACT

Lung cancer is an aggressive malignancy associated with a rapid progression and poor prognosis, for which immunotherapy only exhibits modest efficacy in most patients. In lung cancer, high lactate is associated with a low immunotherapy response and shortened survival; however, causal lactylation‑related genes remain to be elucidated. In the present study, candidate genes were screened using Mendelian randomization (MR) analysis, with expression quantitative trait loci data and genome‑wide association study summary statistics used as analytical resources. A total of 46 lactylation‑related genes were included in the MR analysis, and multiple testing correction was performed using the false discovery rate (FDR) and Bonferroni methods to control the false‑positive risk. MR identified three core genes [platelet‑type phosphofructokinase; SWI/SNF related, matrix associated, actin dependent regulator of chromatin, subfamily a, member 4; and stathmin 1 (STMN1)]. Among these genes, only STMN1 was significantly associated with increased lung cancer risk (inverse variance weighting original P=0.005, FDR‑corrected P=0.014995, Bonferroni‑corrected P=0.014995, odds ratio=1.741, 95% confidence interval: 1.182‑2.564), with robust results confirmed by heterogeneity/pleiotropy/sensitivity analyses. Subsequently, transcriptomic analysis was conducted to assess STMN1 expression in lung cancer tissues and its association with patient survival. In vitro (cell proliferation, migration, invasion and apoptosis assays) and in vivo experiments (murine tumor models) were also conducted to explore the function of STMN1. STMN1 exhibited upregulation in lung cancer tissues, and was associated with a shorter survival, reduced antitumor immune cell infiltration and an immunosuppressive tumor microenvironment (TME) phenotype. STMN1 knockdown inhibited lung cancer malignancy both in vitro and in vivo, and modulated key markers, whereas its overexpression exhibited the opposite effects. Additionally, STMN1 promoted global histone lactylation and histone H3 lysine 18 lactylation in lung cancer cells, establishing a direct functional link between STMN1 and the lactylation pathway. In conclusion, STMN1 is a lactylation‑related causal oncogene in lung cancer, driving progression via malignant phenotypes, and its high expression is associated with an immunosuppressive TME that may synergistically facilitate tumor progression. Therefore, STMN1 may be considered a novel target for lung cancer therapy.

PMID:41930463 | DOI:10.3892/mmr.2026.13866

Pacing Clin Electrophysiol. 2026 Apr 3. doi: 10.1111/pace.70228. Online ahead of print.

ABSTRACT

BACKGROUND: Cardiac implantable electronic device (CIED) related complications occur frequently. Given the uncertainties, a comprehensive investigation of predictive factors is crucial. This study aimed to identify the determinants influencing the occurrence of CIED-related complications and to evaluate their predictive capability for the onset of CIED-related complications.

METHODS: This retrospective cohort study recruited 870 patients who underwent CIED implantation. The primary outcome was overall complications, and the secondary was pocket hematomas (PH). Logistic regression model was used to estimate the odds ratio (OR) with the 95% confidence interval (CI), and to establish the prediction models for all complications and PH.

RESULTS: 43 cases (4.95%) developed complications during follow-up, including 24 (2.8%) PH and 19 others. After adjusted for potential confounders, body mass index (BMI), having diabetes and chronic kidney disease (CKD), usage of anticoagulants and antiplatelets, device type, device replacement, and device electrode quantities were all associated with the risk of both the complications and PH. The prediction model with these variables displayed a good performance in predicting the complications occurrence, with AUC and C-statistic being 0.886 and 0.886 in training dataset, and 0.780 and 0.761 in the test dataset. Similar good performance in predicting PH onset were also observed.

CONCLUSION: The results indicate that BMI, diabetes, CKD, anticoagulants, antiplatelets, device types, device replacement, and device electrode quantities are critical risk factors, which can help predict the onset of the complications and PH.

PMID:41930459 | DOI:10.1111/pace.70228

Xi Bao Yu Fen Zi Mian Yi Xue Za Zhi. 2026 Mar;42(3):230-236.

ABSTRACT

Objective To investigate the concordance of programmed death ligand-1 (PD-L1) expression between primary non-small cell lung cancer (NSCLC) and paired brain metastasis, analyze its relationship with clinicopathological characteristics, and evaluate the feasibility of using primary tumor PD-L1 status to predict brain metastasis status. Methods Thirty-two paired primary NSCLC and brain metastasis samples, pathologically diagnosed between January 1, 2017 and July 1, 2022, were collected. PD-L1 expression was detected by immunohistochemistry and interpreted using the Tumor Proportion Score (TPS), with cut-off values set at 1% and 50%. The Chi-square test was used to analyze the relationship between PD-L1 expression and clinicopathological features. Paired Chi-square and Kappa consistency tests were employed to assess the concordance of PD-L1 expression between primary and metastatic sites. Results PD-L1 expression showed no significant correlation with patient gender, age, treatment history, or histologic type. At the 1% cut-off, the overall PD-L1 expression showed moderate concordance between primary and metastatic sites (Kappa=0.624). Subgroup analysis revealed high concordance in untreated patients (Kappa=0.761, P=0.001), whereas the treated group showed weak concordance without statistical significance (Kappa=0.324, P=0.205). At the 50% cut-off, both the treatment group and the untreated group showed weak concordance without statistical significance. Although the chemotherapy subgroup showed perfect agreement (Kappa=1.000) at the 50% cut-off, the high-expression concordance rate was only 20.00%, indicating limited clinical reference value. Conclusion The strong concordance of PD-L1 expression in the untreated group patients supports the use of primary tumor PD-L1 status to guide clinical decision-making when brain metastasis tissue is unavailable. Treatment status (mainly including chemotherapy) may be an important factor affecting the consistency of high PD-L1 expression between primary NSCLC and brain metastasis.

PMID:41930444