Fuyang Second People's Hospital

Patients with peripheral nerve injury (PNI) often experience neuropathic pain (NP), which is difficult to treat effectively due to ongoing inflammation and oxidative stress that impede nerve repair. Traditional anti-inflammatory antioxidants are limited by short half-lives and significant side effects. This study introduces a supramolecular hydrogel formed by combining anti-inflammatory betamethasone phosphate (Betp) with calcium chloride to create betamethasone phosphate hydrogel (Betp@Gel). The antioxidant curcumin (Cur) was incorporated into this gel to form Cur/Betp@Gel, which can be injected directly at the injury site for sustained Cur release. Cur/Betp@Gel demonstrates superior sustained-release capabilities and therapeutic effectiveness compared to Betp@Gel or Cur alone. It enhances pain relief, supports functional recovery after nerve damage, and promotes nerve repair and regeneration. This is achieved by Betp@Gel's anti-inflammatory effects, which inhibit TNF-α, IL-1β, and IL-6, combined with Cur's continuous slow release, which scavenges reactive oxygen species (ROS). Additionally, Cur/Betp@Gel mitigates PNI-induced spinal inflammation by reducing GFAP and Iba-1 expression in the spinal cord. Overall, in-situ injection of Cur/Betp@Gel is a promising strategy for aiding nerve repair and alleviating neuropathic pain.

The red cell distribution width-to-albumin ratio (RAR) is a composite biomarker integrating inflammatory, nutritional, and stress status; however, its association with short-term prognosis in patients with intracerebral hemorrhage (ICH) remains unclear. This study was a retrospective dual-cohort study. A total of 2,327 ICH patients from the MIMIC-IV database were included as the derivation cohort, and 428 patients from a tertiary hospital were collected as the external validation cohort. The association between RAR and outcomes was analyzed using multivariable Cox regression, with restricted cubic splines employed to examine non-linear relationships. Multiple machine learning algorithms were utilized to screen key prognostic variables, and a logistic regression-based risk prediction model was constructed. Its discriminative ability and stability were validated in both internal and external cohorts. After adjusting for multiple confounders, including demographic characteristics, comorbidities, disease severity, and treatment measures, a higher RAR level remained an independent risk factor for 28-day ICU mortality (adjusted HR = 1.17, 95% CI: 1.08-1.27) and 28-day in-hospital all-cause mortality (adjusted HR = 1.14, 95% CI: 1.05-1.23) in ICH patients. Restricted cubic spline analysis further indicated a significant non-linear dose-response relationship between RAR and these outcomes (P for non-linearity < 0.05). In addition, incorporating RAR significantly improved the predictive performance of six traditional critical illness scoring systems, including APACHE II, SOFA, and SAPS II (AUC improvement ranging from 0.016 to 0.188; all DeLong tests P < 0.01). Using five machine learning algorithms, we identified seven key variables-age, RAR, INR, total bilirubin, blood urea nitrogen, aspartate aminotransferase, and systolic blood pressure-to construct a short-term mortality risk prediction model for ICH. This model demonstrated robust discriminative ability in the internal training, internal validation, and external validation sets (AUC values of 0.761, 0.723, and 0.723, respectively), outperforming conventional scoring systems. RAR is an independent predictor of short-term mortality risk in patients with ICH. The prediction model incorporating RAR exhibits good discriminative ability and cross-cohort stability, offering a practical tool for early identification of high-risk patients and optimization of management strategies. However, this study has certain limitations, including its retrospective design, limited sample size and single-center source for external validation, and lack of neuroimaging data (e.g., hematoma location/volume). Future prospective multi-center studies are needed to further validate its clinical value.

A core challenge in the diagnosis and treatment of esophageal cancer (EC) lies in accurately identifying patients who will benefit from neoadjuvant therapy (NAT). Yang et al reported a predictive model for NAT response in EC, constructed using radiomics from T2-weighted magnetic resonance imaging (MRI) and machine learning. The model achieved an area under the curve of 0.932 in the training cohort and 0.900 in the validation cohort. While encouragingly, we urge caution with limitation. First, the study’s single-center, retrospective design with an insufficient sample size limits the model’s generalizability and significantly increases the risk of overfitting. Second, the study only extracted features from the T2-weighted MRI sequence, failing to integrate data from other functional MRI sequences such as diffusion-weighted imaging and dynamic contrast-enhanced MRI. Third, the model suffers from a "black box" issue regarding its extracted features—its low interpretability hinders clinicians’ trust in and acceptance of the model. This editorial reviews the study by Yang et al, identifies its limitations, and puts forward in-depth suggestions to further optimize the model.

BackgroundEpidemiological studies have established that autoimmune diseases (AD) increase cardiovascular disease (CVD) risk. We aimed to elucidate their shared genetic architecture and clinical implications.MethodsWe conducted a cross-trait multivariate genome-wide association study (GWAS) for three autoimmune diseases (type 1 diabetes, systemic lupus erythematosus, and rheumatoid arthritis) and four cardiovascular diseases (coronary artery disease, heart failure, stroke, and peripheral artery disease). We performed both genome-wide and locus-based analysis including fine-mapping, functional annotation, enrichment analyses, transcriptome-wide association studies (TWAS), and proteome-wide and drug-repurposing Mendelian randomization (MR). We constructed a polygenic risk score (PRS) and explored its pleiotropic effects beyond AD and CVD in the UK Biobank population.ResultsIn the multivariate GWAS, we identified 259 genome-wide significant association signals that were enriched primarily in arterial tissues and lipid metabolism pathways. Through convergent evidence from TWAS and MR analyses, we prioritized 15 therapeutic targets including TGFB1 and IL6R, and identified histone deacetylase inhibitors as candidate drugs. The polygenic risk score showed discriminative ability in cardiovascular risk stratification among autoimmune patients, with individuals in the highest PRS decile exhibiting significantly elevated CVD risk compared to those in the 2nd-9th deciles (hazard ratios: 1.60 [95% CI: 1.19–2.15] for type 1 diabetes, 2.28 [95% CI: 1.14–4.56] for systemic lupus erythematosus, and 2.33 [95% CI: 1.94–2.80] for rheumatoid arthritis). Additionally, the PRS revealed pleiotropic associations with risk of various health conditions, including polyneuropathy, chronic renal failure, and depressive episodes.ConclusionOur study unveils the shared genetic architecture between autoimmune and cardiovascular diseases, providing insights for therapeutic development and risk stratification.Graphical abstractIn this multivariate genome-wide association study, we derived a shared genetic factor (Faid_cvd) using seven autoimmune and cardiovascular diseases: rheumatoid arthritis, systemic lupus erythematosus, type 1 diabetes, coronary artery disease, heart failure, stroke, and peripheral artery disease to describe the shared genetic liability of autoimmune and cardiovascular diseases. We identified the genomic loci, cells, tissues and pathways associated with Faid-cvd, and 15 potential therapeutic target genes or drug repurposing opportunities. We examined the clinical risk stratification performance of the Faid-cvd PRS in individuals of European ancestry in UK biobank. Additionally, we examined the association between Faid-cvd PRS and various health outcomes. PRS, polygenic risk score. MR, Mendelian randomization. transcriptome-wide association studies, TWAS.Graphical abstractSupplementary InformationThe online version contains supplementary material available at 10.1186/s12933-025-03041-8.

AIM: This study aimed to identify key ultrasound (US) characteristics that differentiate complicated acute appendicitis (CAA) from non-complicated acute appendicitis (NCAA) and to develop and validate a US-based predictive model for preoperative diagnosis. METHODS: A retrospective analysis was conducted on 178 patients with surgically confirmed acute appendicitis between June 2022 and May 2025. All patients underwent a standardized preoperative US examination. Clinical and sonographic variables were compared between the CAA (n = 63) and NCAA (n = 115) groups. Least Absolute Shrinkage and Selection Operator (LASSO) regression was used for variable selection, followed by multivariable logistic regression to construct a predictive model. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration plots, and decision curve analysis (DCA). RESULTS: Compared to the NCAA group, the CAA group had significantly older age, longer symptom duration, higher white blood cell (WBC), and higher neutrophil percentage (NE%) (p &lt; 0.05). Significant US-based predictors included greater appendiceal outer diameter (AOD), increased periappendiceal inflammatory fat thickness (PIFT), and higher incidences of periappendiceal fluid collection (PAFC), marginal indistinctness (MI), and altered bowel peristalsis (ABP). The final model identified eight independent predictors: age, symptom duration, WBC, NE%, PIFT, PAFC, MI, and ABP. The nomogram showed excellent discrimination (AUC = 0.890), good calibration (Hosmer–Lemeshow test, p = 0.108), and sustained performance during internal validation (AUC = 0.902). DCA confirmed high clinical utility. CONCLUSIONS: The proposed US-based nomogram provides an accurate, non-invasive tool for preoperative differentiation of CAA from NCAA, potentially aiding in risk stratification and treatment decision-making.