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ObjectivesTo distinguish squamous cell carcinoma (SCC) from lung adenocarcinoma (ADC) based on a radiomic signatureMethodsThis study involved 129 patients with non-small cell lung cancer (NSCLC) (81 in the training cohort and 48 in the independent validation cohort). Approximately 485 features were extracted from a manually outlined tumor region. The LASSO logistic regression model selected the key features of a radiomic signature. Receiver operating characteristic curve and area under the curve (AUC) were used to evaluate the performance of the radiomic signature in the training and validation cohorts.ResultsFive features were selected to construct the radiomic signature for histologic subtype classification. The performance of the radiomic signature to distinguish between lung ADC and SCC in both training and validation cohorts was good, with an AUC of 0.905 (95% confidence interval [CI]: 0.838 to 0.971), sensitivity of 0.830, and specificity of 0.929. In the validation cohort, the radiomic signature showed an AUC of 0.893 (95% CI: 0.789 to 0.996), sensitivity of 0.828, and specificity of 0.900.ConclusionsA unique radiomic signature was constructed for use as a diagnostic factor for discriminating lung ADC from SCC. Patients with NSCLC will benefit from the proposed radiomic signature.Key points• Machine learning can be used for auxiliary distinguish in lung cancer.• Radiomic signature can discriminate lung ADC from SCC.• Radiomics can help to achieve precision medical treatment.

•Machine learning models could effectively predict postoperative delirium (POD).•AdaBoost algorithms had better performance compared to other algorithms.•Ensemble models have better performance than single models in predicting POD. This meta-analysis aimed to evaluate the performance of machine learning (ML) models in predicting postoperative delirium (POD) and to provide guidance for clinical application. PubMed, Embase, Cochrane Library, and Web of Science databases were searched from inception to April 29, 2024. Studies reported ML models for predicting POD in adult patients were included. Data extraction and risk of bias assessment were performed using the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis - AI (TRIPOD-AI) and Prediction model Risk Of Bias ASsessment Tool (PROBAST) tools. Meta-analysis with the area under the curve (AUC) was performed using MedCalc software. A total of 23 studies were included after screening. Age (n = 20, 86.95%) and Random Forest (RF) (n = 24, 17.27%) were the most frequently used feature and ML algorithm, respectively. The meta-analysis showed an overall AUC of 0.792. The ensemble models (AUC = 0.805) showed better predictive performance than single models (AUC = 0.782). Additionally, considerable variations in AUC were found among different ML algorithms, with AdaBoost (AB) demonstrating good performance with AUC of 0.870. Notably, the generalizability of these models was uncertain due to limitations in external validation and bias assessment. The performance of ensemble models were higher than single models, and the AB algorithms demonstrated better performance, compared with other algorithms. However, further research was needed to enhance the generalizability and transparency of ML models. [Display omitted]

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has spread to many countries around the world. In addition to lung disease, severe cases also displayed varying degrees of liver injury. This article will describe the latest developments regarding coronavirus and the pathogenesis of liver injury, the prone population and clinical characteristics of these patients, as well as providing some suggestions for clinical treatment.

Further evidence is required regarding the influence of metal mixture exposure on mortality. Therefore, we employed diverse statistical models to evaluate the associations between eight urinary metals and the risks of all-cause and cardiovascular mortality. We measured the levels of 8 metals in the urine of adults who participated in the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2018. Based on follow-up data, we determined whether they died and the reasons for their deaths. We estimated the association between urine metal exposure and all-cause mortality using Cox regression, weighted quantile sum (WQS) regression, and Bayesian kernel machine regression (BKMR) models. Additionally, we used a competing risk model to estimate the relationship between metal exposure and cardiovascular mortality. Among the 14,305 individuals included in our final analysis, there were 2,066 deaths, with 1,429 being cardiovascular-related. Cox regression analysis showed that cobalt (Co) (HR: 1.21; 95% CI: 1.13, 1.30) and antimony (Sb) (HR: 1.26; 95% CI: 1.12, 1.40) were positively associated with all-cause mortality (all P for trend <0.001). In the competing risk model, Co (HR: 1.29; 95% CI: 1.12, 1.48), lead (Pb) (HR: 1.18; 95% CI: 1.03, 1.37), and Sb (HR: 1.44; 95% CI: 1.18, 1.75) were significantly associated with an increased risk of cardiovascular mortality (all P for trend <0.001). Sb, Pb, cadmium (Cd), and molybdenum (Mo) had the highest weight rankings in the final WQS model. All metals showed a complex non-linear relationship with all-cause mortality, with high posterior inclusion probabilities (PIPs) in the final BKMR models. Combining all models, it is possible that Sb may have a more stable impact on all-cause and cardiovascular mortality. Meaningful metal effects in individual statistical models still require careful attention.

A better understanding of the level-grade inflammation for the development and worsening of heart failure (HF) in different gender groups is an unmet need. We performed an updated analysis on the impact of a series of systemic inflammation markers on HF. This compensatory cross-sectional study enrolled participants from the National Health and Nutrition Examination Survey (NHANES) 2015-2018. HF was based on the self-reported questions. Univariate and multivariate logistic regression were used to investigate the association between systemic immune-inflammation index (SII), high sensitivity C-reactive protein (hs-CRP), lymphocyte-to-monocyte ratio (LMR), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR) and HF. For patients of different genders, P for trend was used to analyze potential linear trend relationships and the restricted cubic splines (RCS) were used to describe non-linear relationships. The additive interaction was evaluated by the relative excess risk due to interaction (RERI), attributable proportion (AP), and the synergy index (SI). The multiplicative interaction was evaluated by odds ratio (OR) and 95% confidence interval (CI) of product-term. A total of 5,830 participants from the NHANES database were divided into two groups: the HF group (n = 210) and the non-HF group (n = 5620). After gender stratification, hs-CRP (OR: 1.01, 95% CI: 1.00-1.03), SII (OR: 1.00, 95% CI: 1.00-1.01), NLR (OR: 1.22, 95% CI: 1.11-1.35) and LMR (OR: 0.79, 95% CI: 0.65-0.93) were independent meaningful factors for HF in males, there was no non-linear relationship between the three factors (SII, NLR, hs-CRP, all P for non-linear > 0.05) and the prevalence of HF, but we detected a non-linear relationship between LMR and the prevalence of HF in males (P for non-linear < 0.05). An additive interaction of hs-CRP and NLR on the risk of HF in males (RERI (OR): 0.67, 95% CI: 0.12-1.34; AP (OR): 0.14, 95% CI: 0.02-0.24; SI (OR): 1.22, 95% CI: 1.03-1.44). In summary, hs-CRP, NLR, and LMR are superior meaningful markers for HF in males. SII may be a meaningful systemic inflammation warning marker for HF, which needs to be discriminated against with caution. Only detected a non-linear relationship between LMR and the prevalence of HF in males. NLR and hs-CRP may have an additive interaction in the prevalence of male HF patients. The outcome compensated for previous studies that still needed more studies for validation.

This study explored the non-linear relationship between pan-immune inflammation value (PIV) and probable depression using NHANES data (2005–2018, n = 27,049). Restricted cubic spline analysis identified an optimal PIV cutoff (5.74). Weighted logistic regression revealed that individuals with PIV > 5.74 had 14% higher risk of probable depression (OR=1.14, 95% CI:1.02–1.28). Gender-stratified analyses showed significant PIV and probable depression associations in females (OR=1.20, 95% CI:1.03–1.40) but not in males. In males, higher PIV interacted with age, education, and alcohol consumption to influence probable depression risk (interaction P < 0.05), whereas no such interactions occurred in females. The findings suggest PIV may serve as a sex-specific biomarker for probable depression risk, with more stable associations in females. Further research is needed to elucidate the mechanisms underlying these gender differences and validate these findings before any clinical application.

Background Postoperative delirium (POD) is a frequent neurologic dysfunction that often leads to more negative outcomes. Early identification of patients who are vulnerable to POD and early implementation of appropriate management strategies could decrease its occurrence and improve patient prognosis. Therefore, this meta-analysis comprehensively and quantitatively summarized the prevalence and related predictive factors of POD in head and neck cancer surgical patients. Methods PubMed, Embase, and Cochrane Library were searched for observational studies that reported the prevalence and risk factors for POD after head and neck cancer surgery and were published from their inception until December 31, 2022. Two reviewers independently selected qualified articles and extracted data. The qualities of related papers were assessed using the Newcastle-Ottawa scale (NOS). RevMan 5.3 and Stata 15.0 were applied to analysis the data and conduct the meta-analysis. Results Sixteen observational studies with 3289 inpatients who underwent head and neck cancer surgery were included in this review. The occurrence of POD ranged from 4.2 to 36.9%, with a pooled incidence of 20% (95% CI 15–24%, I 2  = 93.2%). The results of this pooled analysis demonstrated that the statistically significant risk factors for POD were increased age (OR: 1.05, 95% CI: 1.03–1.07, P  < 0.001), age > 75 years (OR: 6.52, 95% CI: 3.07–13.87, P  < 0.001), male sex (OR: 2.29, 95% CI: 1.06–4.97, P  = 0.04), higher American Society of Anesthesiologists grade (OR: 2.19, 95% CI: 1.44–3.33, P  < 0.001), diabetes mellitus (OR: 2.73, 95% CI: 1.24–6.01, P  = 0.01), and history of smoking (OR: 2.74, 95% CI: 1.13–6.65, P  = 0.03). Conclusions POD frequently occurs after head and neck cancer surgery. Several independent predictors for POD were identified, which might contribute to identifying patients at high risk for POD and play a prominent role in preventing POD in patients following head and neck cancer surgery.

While previous research has established an association between inflammatory bowel disease (IBD) and osteoporosis (OP), the nature of this association in different populations remains unclear. Our study used linkage disequilibrium scores(LDSC) regression analysis and Mendelian randomization(MR) to assess the genetic correlation and causal relationship between IBD and OP in European and East Asian populations. We performed separate genetic correlation and causal analyses for IBD and OP in European and East Asian populations, used the product of coefficients method to estimate the mediating effect of nutritional status on the causal relationship, and used multi-trait analysis to explore the biological mechanisms underlying the IBD-nutrition-OP causal pathway. Our analysis revealed a significant genetic correlation and causal relationship between IBD and OP in the European population. Conversely, no such correlation or causal relationship was observed in the East Asian population. Mediation analysis revealed a significant mediating effect of nutritional status on the causal pathway between IBD and OP in the European population. Multi-trait analysis of the IBD-nutrition-OP causal pathway identified MFAP2, ATP13A2, SERPINA1, FTO and VCAN as deleterious variants. Our findings establish a genetic correlation and causal relationship between IBD and OP in the European population, with nutritional status playing a crucial mediating role.

The application of large language models (LLMs) in health care holds significant potential for enhancing patient care and advancing medical research. However, the protection of patient privacy remains a critical issue, especially when handling patient health information (PHI). This scoping review aims to evaluate the adequacy of current approaches and identify areas in need of improvement to ensure robust patient privacy protection in the existing studies about PHI-LLMs within the health care domain. A search of the literature published from January 1, 2022, to July 20, 2025, was performed on July 20, 2025, using 2 databases (PubMed and Embase). This scoping review focused on the following three research questions: (1) What studies on the development and application of LLMs using PHI currently exist within the health care domain? (2) What patient privacy considerations are addressed in existing PHI-LLMs research, and are these measures sufficient? (3) How can future research on the development and application of LLMs using PHI better protect patient privacy? Studies were included if they focused on the development and application of LLMs within health care using PHI, encompassing activities such as model construction, fine-tuning, optimization, testing, and performance comparison. Eligible literature comprised original research articles written in English. Conversely, studies were excluded if they used publicly available datasets, under the assumption that such data have been adequately deidentified. Additionally, non-English publications, reviews, abstracts, incomplete reports, and preprints were excluded from the review due to the lack of rigorous peer review. This study systematically identified 9823 studies on PHI-LLM and included 464 studies published between 2022 and 2025. Among the 464 studies, (1) a small number of studies neglected ethical review (n=45, 9.7%) and patient informed consent (n=148, 31.9%) during the research process, (2) more than a third of the studies (n=178, 38.4%) failed to report whether to implement effective measures to protect PHI, and (3) there was a significant lack of transparency and comprehensive detail in anonymization and deidentification methods. We propose comprehensive recommendations across 3 phases-study design, implementation, and reporting-to strengthen patient privacy protection and transparency in PHI-LLM. This study emphasizes the urgent need for the development of stricter regulatory frameworks and the adoption of advanced privacy protection technologies to effectively safeguard PHI. It is anticipated that future applications of LLMs in the health care field will achieve a balance between innovation and robust patient privacy protection, thereby enhancing ethical standards and scientific credibility.

Qiliqiangxin (QL) can attenuate myocardial remodeling and improve cardiac function in some cardiac diseases, including heart failure and hypertension. This study was to explore the effects and mechanism of QL on atrial structural remodeling in atrial fibrillation (AF). Twenty-one rabbits were randomly divided into a sham-operation group, pacing group (pacing with 600 beats per minute for 4 weeks), and treatment group (2.5 g/kg/day). Before pacing, the rabbits received QL-administered p.o. for 1 week. We measured atrial electrophysiological parameters in all groups to evaluate AF inducibility and the atrial effective refractory period (AERP). Echocardiography evaluated cardiac function and structure. TUNEL detection, hematoxylin and eosin (HE) staining, and Masson’s trichrome staining were performed. Immunohistochemistry and western blotting (WB) were used to detect alterations in calcium channel L-type dihydropyridine receptor α2 subunit (DHPR) and fibrosis-related regulatory factors. AF inducibility was markedly decreased after QL treatment. Furthermore, we found that AERP and DHPR were reduced significantly in pacing rabbits compared with sham rabbits; treatment with QL increased DHPR and AERP compared to the pacing group. The QL group showed significantly decreased mast cell density and improved atrial ejection fraction values compared with the pacing group. Moreover, QL decreased interventricular septum thickness (IVSd) and left ventricular end-diastolic diameter (LVEDD). Compared with the sham group, the levels of TGFβ1 and P-smad2/3 were significantly upregulated in the pacing group. QL reduced TGF-β1 and P-smad2/3 levels and downstream fibrosis-related factors. Our study demonstrated that QL treatment attenuates atrial structural remodeling potentially by inhibiting TGF-β1/P-smad2/3 signaling pathway.