Pancreatic adenocarcinoma remains highly lethal. How modern chemotherapy reshapes the full mortality profile, including competing non‑cancer deaths, is incompletely defined. Using Surveillance, Epidemiology, and End Results (2010-2021), we identified 9624 adults (20-89 years) with primary pancreatic adenocarcinoma who received systemic chemotherapy. Cause of death (International Classification of Diseases, Tenth Revision) was classified as pancreatic cancer, other cancers, or non‑cancer causes. Stage-stratified analyses characterized mortality heterogeneity. Standardized mortality ratios (SMRs) versus the general US population were calculated in SEER*Stat; multivariable Poisson regression assessed risk factors. Temporal patterns were examined across ≤1 year, 1 to 5 years, and >5 years from diagnosis. Over a median follow-up of 14.2 months, 8218 deaths occurred. Pancreatic cancer accounted for 90.6% (n = 7448); non‑cancer causes accounted for 6.1% (n = 498), and other cancers accounted for 3.3% (n = 272). Non-cancer mortality comprised 7.8% of stage I/II deaths versus 5.1% in stage III/IV, reflecting longer survival enabling competing risks. Overall, non‑cancer mortality was markedly elevated (SMR 15.38, 95% confidence interval: 14.06-16.79), peaking in year 1 (SMR 96.10) and declining thereafter (1-5 years, SMR 13.63; >5 years, SMR 3.19). Cardiovascular deaths carried the greatest non‑cancer burden (heart disease SMR 9.96; cerebrovascular disease SMR 12.7). Infectious causes showed the highest relative risks (septicemia SMR 20.3; pneumonia/influenza SMR 88.69), concentrated in the first year. Chronic obstructive pulmonary disease (SMR 17.57) and diabetes (SMR 19.3) were additional contributors. Older patients (70-89 years) experienced the steepest early mortality. Suicide risk was strikingly increased (SMR 113.68), underscoring substantial psychological distress. In chemotherapy‑treated pancreatic cancer, non‑cancer mortality is substantial, time‑dependent, and dominated by cardiovascular and infectious causes in the first year after diagnosis. These data support integrated cardio‑oncology pathways, aggressive infection prevention, metabolic and pulmonary co‑management, and early psychosocial interventions to reduce preventable deaths and improve outcomes.
Objective To analyze risk factors for progression from Mycoplasma pneumoniae pneumonia (MPP) to severe MPP (SMPP) in children and establish a predictive nomogram. Methods A retrospective cohort of 255 children diagnosed with MPP from April 2023 to June 2024 was randomly split into a training cohort (n = 174, 109 mild MPP, 65 SMPP) and a validation cohort (n = 81) at a 7:3 ratio. Univariate analysis and Lasso regression screened candidate predictors, followed by multivariate logistic regression to identify independent risk factors. A nomogram was built using R software. Receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were used to evaluate model performance. Results D-dimer, plasma interleukin (IL)-5, plasma IL-6, and bronchoalveolar lavage fluid (BALF) IL-8 were independent predictors of SMPP (all P < 0.05). The nomogram yielded an area under the ROC curve (AUC) of 0.935 (95% CI: 0.901–0.969) in the training set (specificity = 0.853, sensitivity = 0.862 at the maximum Youden index) and 0.928 (95% CI: 0.871–0.985) in the validation set (specificity = 0.765, sensitivity = 0.967). Calibration curves showed favorable agreement between predicted and observed SMPP risks, and DCA confirmed its clinical utility. Conclusion The nomogram based on D-dimer, plasma IL-5, plasma IL-6, and BALF IL-8 reliably predicts SMPP risk in children who undergo bronchoscopy, demonstrating strong predictive performance.
Colorectal cancer with liver metastases remains a clinical challenge. CXCL13 is widely recognized as a biomarker of immunotherapy response. However, the functional heterogeneity (protumor vs. antitumor) of CXCL13+CD4+ T-cell subsets has long been controversial. Through integrated analysis of single-cell RNA sequencing data from colorectal cancer clinical samples and pan-cancer datasets, combined with experimental validations, we first identified a prometastatic neuromedin B+ (NMB+)CXCL13+CD4+ T-cell subset and uncovered a mechanism by which this subset regulates tumor cell "senescence-malignant transition," the NMB-NPSR1 axis. These NMB+CXCL13+CD4+ T cells induced senescence in NPSR1+ malignant cells via NMB secretion, leading to enhanced invasiveness and migration despite reduced proliferation. Activation of NPSR1 triggered the Wnt signaling pathway and epithelial-mesenchymal transition, thereby enhancing cellular malignancy. This NPSR1+ senescent subpopulation also recruited endothelial cells and disrupted tight junction integrity, fostering a prometastatic microenvironment. As a proof-of-principle study, the combination of the NPSR1 inhibitor SHA68 and anti-PD-1 demonstrated remarkable antitumor effects using mouse models of colorectal cancer metastasis. Overall, this study uncovered the role of NMB+CXCL13+CD4+ T cells in promoting tumor cell senescence while influencing tumor metastasis, offering potential clinical implications for the diagnosis and treatment of metastatic colorectal cancer.
Background This study aimed to evaluate whether including cortical bone in vertebral segmentation improves the diagnostic performance of CT-based radiomics models for osteoporosis. Methods This retrospective study included patients who underwent lumbar spine CT between January 2020 and December 2022. Data from one center were used for training and validation, and data from the other center were used for external testing. The third lumbar vertebra was selected as the representative vertebral level for radiomic analysis in each patient. Two bone-compartment segmentation strategies were applied for radiomic feature extraction: trabecular bone segmentation and whole-bone segmentation that included both trabecular and cortical bone. After feature selection, logistic regression, support vector machine (SVM) and random forest models were developed for each segmentation strategy. Model performance was evaluated using receiver operating characteristic (ROC) analysis, and clinical utility was assessed with decision curve analysis (DCA). Results A total of 418 patients were included, of whom 173 had osteoporosis. In the external test set, the logistic regression model demonstrated the best performance among the trabecular bone models, achieving an AUC of 0.769 (95% CI 0.663–0.868). Among the whole-bone models, the random forest classifier performed best, with an AUC of 0.845 (95% CI 0.759–0.920). DCA showed greater clinical net benefit for the whole-bone models. Conclusions Incorporating cortical bone into vertebral segmentation enhances the clinical applicability of CT-based radiomics models for osteoporosis assessment.
Various acupuncture techniques are widely applied in clinics for pain control and dysfunction relief in patients with knee osteoarthritis (KOA). The purpose of this trial was to investigate whether the different acupuncture techniques were more effective in treating joint pain and dysfunction than were sham acupuncture or drug treatment in patients with KOA and to determine the differences in efficacy among them. In this multi-center, single-blind, randomized, controlled trial, participants were randomly assigned to the manual acupuncture (MA), electroacupuncture (EA), warm-needling acupuncture (WA), mild moxibustion (MM), sham acupuncture (SA), or drug treatment (Celebrex) groups. Each participant in the above groups received individual treatments for 4 consecutive weeks. The primary outcome measures included the visual analog scale score (VAS) and the physical function score of the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). Compared with the SA group, the acupuncture technique groups (MA, EA, WA, and MM) had markedly lower patient VAS scores (− 0.61; 95
