The study aimed to elucidate the modulatory role of MMP14 on mCD100 shedding and sCD100 production,and its subsequent effects on CD8+T cell dysfunction in lung cancer patients.Total of 56 non-small cell lung cancer(NSCLC)patients were from January 2020 to January 2023 and compared them with 88 healthy controls.Bronchoalveolar lavage fluid(BALF)was obtained from both tumor and non-tumor sites of the patient group.Peripheral blood mononuclear cells(PBMC)were isolated from both groups,and the expression of CD72 and mCD100 in PBMC were assessed via flow cytometry.CD8+T cells from tumor sites were stimulated with recombinant human MMP14 and CD100.Post-cultivation,supernatant levels of TNF-α and IFN-γ were determined by ELISA,while granulysin B and perforin levels were analyzed through an ELISPOT assay.The rate of target cell death was also observed.Data showed no significant difference in the proportion of CD3+mCD100+,CD3+CD72+ cells,and the average fluorescence intensity of CD72 in CD100 and CD3+ monocytes in CD3+CD8+T cells between the patient and control groups.However,as compared with non-tumor sites,these indexes of tumor sites were significantly elevated.Stimulation with CD100 led to increase in IFN-γ,TNF,perforin,and granulozyme B secretion levels in CD8+T cells.After MMP14 stimulation,the proportions of CD3+mCD10 0+ and target cell death,along with sCD100,TNF-α,IFN-γ,and granulozyme B levels in CD8+T cells from NSCLC tumor sites,were notably increased.Interestingly,the addition of anti-CD100 to MMP14-stimulated CD8+T cells resulted in a significant drop in the levels of sCD100,TNF-α,IL-1β,and granulozyme B,as well as in the proportion of target cell death.Taken together,in NSCLC patients,the inhibition of CD100 shedding in CD8+T cells at tumor sites and the blockade of sCD100 production result in impaired CD8+T cell killing function.MMP14 appears to enhance mCD100 shedding and sCD100 production,thereby potentially restoring the cytotoxic function of CD8+T cells against primary NSCLC cells.

Objective To investigate the risk factors associated with necrosis in interstitial oedematous pancreatitis(IOP)and to develop a nomogram model for the early prediction of necrosis in IOP.Methods A retrospective analysis was conducted on 306 patients diagnosed with IOP.Patients were stratified into necrosis and edema groups based on the presence or absence of pancreatic necrosis through follow-up CT-enhanced examinations.Logistic regression analysis was employed to identify independent predictive factors for necrosis in IOP.Subsequently,a nomogram model was developed,and its discriminative ability,accuracy,and practicality were assessed through receiver operating characteristic(ROC)curve,calibration curve,and decision curve analysis(DCA).Results Balthazar computed tomography severity index(CTSI),gender,lactate dehydrogenase(LDH),and triglyceride(TG)were finally identified as four independent predictors for constructing the nomogram model.The area under the curve(AUC)of the nomogram model was 0.800[95％confidence interval(CI)0.731-0.869].The calibration curve indicated good consistency between the predicted probabil-ity and the actual probability of necrosis in IOP(P=0.737).DCA suggested high practicality of the nomogram model within the threshold probability range of 3％to 66％and 75％to 96％.Conclusion The nomogram model based on Balthazar CTSI,gender,LDH,and TG demonstrates good efficacy in early prediction of necrosis in IOP.

As an important protein acylation modification, lysine succinylation (Ksucc) is involved in diverse biological processes, and participates in human tumorigenesis. Here, we collected 26,243 non-redundant known Ksucc sites from 13 species as the benchmark data set, combined 10 types of informative features, and implemented a hybrid-learning architecture by integrating deep-learning and conventional machine-learning algorithms into a single framework. We constructed a new tool named HybridSucc, which achieved area under curve (AUC) values of 0.885 and 0.952 for general and human-specific prediction of Ksucc sites, respectively. In comparison, the accuracy of Hybrid-Succ was 17.84％–50.62％better than that of other existing tools. Using HybridSucc, we conducted a proteome-wide prediction and prioritized 370 cancer mutations that change Ksucc states of 218 important proteins, including PKM2, SHMT2, and IDH2. We not only developed a high-profile tool for predicting Ksucc sites, but also generated useful candidates for further experimental con-sideration. The online service of HybridSucc can be freely accessed for academic research at http://hybridsucc.biocuckoo.org/.