Objective: We aim to determine the optimal timing and approaches for the first tangential excision (TE) of severely burned patients with ≥70% total body surface area (TBSA). Background: Early TE is the gold-standard surgical therapy for full-thickness burns. However, there are debates about its optimal timing and approaches for severely burned patients with ≥70% TBSA. Methods: This study included 185 patients in the Kunshan factory explosion. We focused on surgical timing, surgical area, and main wound covering method for the first TE. We determined the optimal cutoff values for surgical timing and area using R language “surv_cutpoint” and “surv_categorize” functions and utilized the Kaplan-Meier survival analysis to investigate their influences on patients’ overall survival (OS). Then, we utilized multivariate Cox regression analysis to identify independent factors and performed subgroup analyses to find confounding factors. Finally, we employed a 10-year single-center cohort of 144 patients with burn injury ≥70% TBSA for external validation. Results: Performing first TE in >3 days (hazard ratio = 2.37, 95% CI = 1.05–5.40, P = 0.039) and surgical area >48% TBSA (hazard ratio = 2.41, 95% CI = 1.46–4.00, P < 0.001) were independent risk factors in the Kunshan cohort for OS of patients with ≥70% TBSA, and were associated with a higher rate of complications. Subgroup analysis revealed that the timing and TBSA of the first TE were influenced by several treatment and complication factors (hemodynamic status and types of hospitals). Main wound-covering methods were a significant confounding factor. Patients with more autograft showed better OS than those with more extensive use of temporary skin substitutes in the Kaplan-Meier survival analysis ( P < 0.001). However, main wound-covering methods are influenced by the severity of injury, availability of autologous skin source, as well as medical resources. In addition, external validation demonstrated consistent results and acceptable calibration. Conclusions: This study identified the optimal timing and approaches for first TE, validating them as independent prognostic factors for patients with ≥70% TBSA. However, considering the various confounding factors, treatment for each patient should be tailored.

Background: Burn injuries with ≥70% total body surface area (TBSA) are especially acute and life-threatening, leading to severe complications and terrible prognosis, while a powerful model for the prediction of overall survival (OS) is lacking. The objective of this study is to identify prognostic factors for the OS of patients with burn injury ≥70% TBSA and construct and validate a feasible predictive model. Materials and methods: Patients diagnosed with burns ≥70% TBSA admitted and treated between 2010 and 2020 in our hospital were included. A cohort of the patients from the Kunshan explosion were assigned as the validation set. The χ ² test and K-M survival analysis were conducted to identify potential predictors for OS. Then, multivariate Cox regression analysis was performed to identify the independent factors. Afterward, we constructed a nomogram to predict OS probability. Finally, the Kunshan cohort was applied as an external validation set. Results: Sex, the percentage of third-degree and fourth-degree burns as well as organ dysfunction were identified as significant independent factors. A nomogram only based on the factors of the individuals was built and evidenced to have promising predictive accuracy, accordance, and discrimination by both internal and external validation. Conclusions: This study recognized significant influencing factors for the OS of patients with burns ≥70% TBSA. Furthermore, our nomogram proved to be an effective tool for doctors to quickly evaluate patients’ outcomes and make appropriate clinical decisions at an early stage of treatment.

Background: Dipeptidyl peptidase 4 (DPP4) plays diverse physiological roles, but its pan-cancer significance and immunomodulatory functions remain poorly characterized. Methods: We performed an integrative pan-cancer analysis of DPP4, incorporating transcriptomic, genomic, and immunogenomic approaches. Differential expression, ceRNA networks, protein interactions, immune infiltration, drug sensitivity, molecular docking, and molecular dynamics were systematically evaluated. Single-cell sequencing analysis, virtual knockout analysis and TIDE analysis were conducted to validate the role of DPP4 in prostate cancer. Further clinical validation was conducted in a prostate cancer cohort (n=97) using immunohistochemistry, Kaplan-Meier survival analysis, and clinicopathological correlation studies were also conducted. DPP4 expression was assessed by qPCR in 22Rv1 and C4–2 cells treated with dasatinib or midostaurin at IC ₅₀ concentrations. Results: DPP4 exhibited tumor-specific dysregulation across multiple cancer types. Its expression correlated significantly with patient prognosis, tumor stage, genomic alterations, immune cell composition, and therapeutic response. In prostate cancer, DPP4 was markedly downregulated ( p < 0.001) and higher expression predicted better overall survival ( p < 0.001) and progression-free survival ( p < 0.001). Significant associations were observed with Gleason score ( p = 0.03) and WHO/ISUP grade ( p = 0.03). After dasatinib treatment, DPP4 expression in C4–2 was significantly elevated ( p < 0.001). On the contrary, DPP4 expression in both 22Rv1 and C4–2 was reduced after treatment with midostarin ( p < 0.05). Conclusion: Our study establishes DPP4 as a multifaceted pan-cancer biomarker with prognostic significance and immunotherapeutic implications, particularly in prostate cancer.

Background Kidney cancer is a worldwide prevalent urological malignancy and the leading cause of death. Sequencing and omics studies play a crucial role in unraveling its molecular mechanisms and diagnostic, prognostic, and therapeutic relevance. This study aims to offer a comprehensive review of the evolving trends and hotspots of sequencing and omics studies in kidney neoplasms. Methods We conducted the retrieval of scientific publications on sequencing and omics studies in kidney neoplasms from the Web of Science Core Collection (WoSCC) on July 3, 2023. The R-based bibliometrix package, VOSviewer, and CiteSpace were utilized to conduct the holistic bibliometric analysis to obtain objective and data-driven results. A comprehensive consultation of papers was then proceeded for an in-depth review. Results Our investigation yielded a dataset containing 1260 records from 509 sources, with 43,404 references, from 1960 to 2023. Publication and citation frequencies have been consistently growing. In country analysis, China and the USA led the research, displaying substantial collaboration. Notable contributors like TEH BT, SAUTER G, and FUTREAL PA shaped this research landscape. Key journals such as PLoS One, Cancer Research, and New England Journal of Medicine actively participated in and significantly influenced this field. Distinguished publications and references were also revealed, along with their historical citation and co-citation relationships. A panel of keywords, including RCC, biomarker, and multi-omics data were identified and clustered. Conclusion We obtained a profound understanding of the developing trends and hotspots of research investigating sequencing and omics studies in kidney neoplasms. Specifically, we have highlighted three hotspots: “explore molecular mechanisms of RCC pathogenesis, progression, and metastasis”, “identify molecular biomarkers of RCC for diagnosis, prognosis, and therapeutics”, “investigate tumor heterogeneity and tailor personalized therapeutic strategies for RCC”. Hopefully, our study will serve as a valuable reference for scientific researchers and clinical practitioners.

Engagement in learning is widely recognized as the time and effort students put into achieving their desired goals and can be measured by the Utrecht Work

Purpose: Stomach adenocarcinoma (STAD) is characterized by significant heterogeneity, within which myeloid cells play crucial yet incompletely understood roles. The relationship between the functional states of myeloid cells, patient prognosis, and therapeutic response requires further elucidation. Methods: We integrated single-cell RNA-seq profiles and 443 bulk RNA-seq profiles from the TCGA-STAD cohort. By integrating myeloid cell differentiation trajectories inferred from Monocle2 pseudotime analysis with survival analysis, we identified myeloid state-related prognostic genes (MSRPGs) and constructed a molecular classification (STAD-MSC). We also explored its prognostic significance and multi-omics features. Additionally, we utilized correlation analysis to establish regulatory networks and predict candidate inhibitors. The 5-gene risk model was evaluated in a public 355-patient validation cohort, and the STAD-MSC framework was further assessed at the protein level in a 70-patient retrospective cohort using immunohistochemistry for NNMT, AXL, and COL1A1. Results: We identified 32 MSRPGs across five distinct myeloid states. Consensus clustering stratified the patients into three subtypes, including low immune infiltration STAD (LI-STAD), moderate immune infiltration STAD (MI-STAD), and high immune infiltration STAD (HI-STAD). The HI-STAD subtype, characterized by high immune infiltration accompanied by an immunosuppressive and dysfunctional microenvironment, exhibited the poorest overall survival (global log-rank p = 0.018). The multi-omics analysis revealed subtype-specific genomic and immune landscapes. A 5-gene prognostic signature was constructed and evaluated as a risk-associated prognostic model. In silico analysis identified subtype-associated differences in predicted drug response. Exploratory pharmacogenomic analysis revealed nominal associations for dabrafenib (p = 0.0051) and ruxolitinib (p = 0.041), suggesting potential subtype−specific therapeutic vulnerabilities. Importantly, the three-protein classifier (NNMT/AXL/COL1A1) stratified a retrospective 70-patient cohort into three subgroups with significantly different OS and PFS. Conclusion: Using public-cohort and protein-level clinical validation, we established STAD-MSC, a myeloid state-centric molecular taxonomy that stratifies STAD patients into subgroups with distinct prognoses and immunosuppressive microenvironmental features, providing a framework for immune-informed patient stratification.