not-yet-known not-yet-known not-yet-known unknown Pancreatic cancer (PACA) remains the most aggressive tumor, with no observed improvement in prognosis over the last decade. The current TNM staging system, which is based on anatomic structure, is not effective in precisely identifying patients who would respond well to treatment. Therefore, there is an urgent need for suitable biomarkers in precision medicine. Regulated cell death (RCD) is a controlled mechanism directed by genes that eliminate infected, damaged, or sick cells. It is unclear, however, how exactly the majority of RCD patterns regulate the microenvironment of PACA. The study utilized a range of bioinformatics techniques to investigate the involvement of diverse types of RCD patterns in PACA. The aim was to gain fresh perspectives on the prognosis and treatment of PACA. The study conducted a screening of RCD-related genes using consensus cluster analysis, the weighted gene co-expression network analysis (WGCNA), and univariate Cox regression based on the expression files of 1576 PACA patients from 12 multicenter cohorts. Furthermore, the study developed the RCDS signature utilizing 101 machine-learning algorithms, which consisted of six genes (UNC13D, GAPDH, VEGFA, ANGPTL4, CREB3L1, and NT5E). The performance of RCDS signature in predicting the prognosis of PACA patients was superior to those of clinical features such as grade, stage, and age. Additionally, the RCDS signature has a guiding influence on immunotherapy based on the characteristics of the immunological score, immune cell infiltration level, and immunotherapy markers.

Introduction: Celiac disease (CeD) is an autoimmune condition characterized by a reversible inflammatory reaction in the mucous membrane of the small intestine. Nevertheless, there is a limited availability of efficient control approaches. Prior research has demonstrated that pharmacological targets supported by genetic evidence can greatly enhance the efficacy of drug development. Hence, the study aims to integrate transcriptomic and proteomic information to identify candidate targets for CeD. Methods: The study employed proteome-wide Mendelian randomization (MR) analysis of circulating plasma proteins to investigate their causal association with CeD. The candidate targets for CeD were further assessed employing colocalization analysis, transcriptome-wide summary-data-based Mendelian randomization (SMR) analysis, multimarker analysis of genomic annotation (MAGMA) gene-based analysis, and bulk RNAseq-based differential expression analysis. For the proteins that were identified, extended Phenome-wide association studies (PheWAS) were conducted to assess their side-effect profiles, while the DGIdb database provided information on the approved or investigated drugs for candidate targets. Results: Systematic MR analysis identified 22 candidate targets for CeD. Among the proteins analyzed, BTN2A1 passed all subsequent verification analyses. Additionally, three proteins, including CatH, IL-18R1, and PTPRC, passed the majority of the subsequent verification analyses. The other 18 proteins were also candidate targets (Trehalase, CD226, SH2B3, ICOSLG, ULK3, Park7, ALDH2, RABEP1, TNFRSF9, COL11A2, GNPDA1, IL-1RL1, B3galt6, TNFSF11, CCL21, BTN3A3, OLFM2 and Colipase). Conclusions: The study employed a combination of human transcriptomic and proteomic information, employing several analytical methods. As a result, 22 proteins, divided into four tiers, were identified as prospective therapeutic targets for CeD.

To explore peripheral blood indicators that may serve as early indicators for MDR infections in this demographic, with the goal of providing reference suggestions for the clinical prevention of MDR infections in elderly inpatients. (2) Methods: Clinical data of patients were divided into the MDR-infected group (n=488) and the MDR-uninfected group (n=233) according to the results of drug sensitivity experiments, Risk factors for MDR infection and peripheral blood indicators related to MDR infections were analyzed using univariate and multivariate logistic regression in conjunction with the construction of a CHAID decision tree model, considering statistical significance at P-value<0.05. (3) Results: Univariate and multivariate regression analyses revealed that prolonged hospitalization, use of antibiotics pre-admission, duration on antibiotics, invasive procedures or recent surgery, and coexisting lung disease were independent risk factors for contracting MDR. Subsequent analysis comparing the aforementioned influences with peripheral blood cells revealed associations between the number of antibiotic treatment days and increased PLR, NLR, neutrophils, decreased lymphocytes, and increased eosinophils; pre-admission antibiotic use correlated with increased PLR, NLR, neutrophils, and decreased lymphocytes; and invasive manipulation or surgery correlated with increased PLR and NLR. (4) Conclusions: Elevated NLR, PLR, neutrophils, lowered lymphocytes, and eosinophils may serve as early indicators of MDR infections in elderly hospitalized patients.