PURPOSE: Septic shock is associated with high mortality and poor outcomes among sepsis patients with coagulopathy. Although traditional statistical methods or machine learning (ML) algorithms have been proposed to predict septic shock, these potential approaches have never been systematically compared. The present work aimed to develop and compare models to predict septic shock among patients with sepsis. METHODS: It is a retrospective cohort study based on 484 patients with sepsis who were admitted to our intensive care units between May 2018 and November 2022. Patients from the 908th Hospital of Chinese PLA Logistical Support Force and Nanchang Hongdu Hospital of Traditional Chinese Medicine were respectively allocated to training (n=311) and validation (n=173) sets. All clinical and laboratory data of sepsis patients characterized by comprehensive coagulation indexes were collected. We developed 5 models based on ML algorithms and 1 model based on a traditional statistical method to predict septic shock in the training cohort. The performance of all models was assessed using the area under the receiver operating characteristic curve and calibration plots. Decision curve analysis was used to evaluate the net benefit of the models. The validation set was applied to verify the predictive accuracy of the models. This study also used Shapley additive explanations method to assess variable importance and explain the prediction made by a ML algorithm. RESULTS: Among all patients, 37.2% experienced septic shock. The characteristic curves of the 6 models ranged from 0.833 to 0.962 and 0.630 to 0.744 in the training and validation sets, respectively. The model with the best prediction performance was based on the support vector machine (SVM) algorithm, which was constructed by age, tissue plasminogen activator-inhibitor complex, prothrombin time, international normalized ratio, white blood cells, and platelet counts. The SVM model showed good calibration and discrimination and a greater net benefit in decision curve analysis. CONCLUSION The SVM algorithm may be superior to other ML and traditional statistical algorithms for predicting septic shock. Physicians can better understand the reliability of the predictive model by Shapley additive explanations value analysis.
Humans ; Shock, Septic/blood* ; Machine Learning ; Male ; Female ; Retrospective Studies ; Middle Aged ; Aged ; Sepsis/complications* ; ROC Curve ; Cohort Studies ; Adult ; Intensive Care Units ; Algorithms ; Blood Coagulation ; Critical Illness

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Objective To explore the correlation between EBV DNA load and peripheral immune cells (including lymphocyte supsets and natural killer cells) before treatment in patients with NPC, and analyze the influence of circulating immune cell supsets related to EBV on the prognosis of NPC patients. Methods We retrospectively analyzed the general data of 203 NPC patients without distant metastasis at the first treatment, as well as the data of peripheral blood EBV DNA and circulating immune cell supset. The ROC curve analysis was used to determine the cutoff value of each circulating immune cell supset. Kaplan-Meier method was used for survival analysis, and Cox regression model was used for multi-factor prognostic correlation analysis. Results The 3-year OS, PFS, DMFS and LRFS of EBV DNA < 400 copies/ml group and EBV DNA≥400 copies/ml group were 99.2% vs. 90.1% (P=0.001), 96.7% vs. 90.1% (P=0.028), 98.4% vs. 90.1% (P=0.005) and 98.4% vs. 100% (P > 0.05), respectively. EBV DNA is negatively correlated with the ratio of CD19⁺ B cells before treatment (r=-0.138, P=0.040), and there was no significant correlation between EBV DNA and other circulating immune supgroups (P > 0.05). ROC analysis showed that the cut-off value of CD19⁺B cell ratio before treatment related to the 3-year OS was 8.33% (P=0.02). The 3-year OS, PFS, DMFS and LRFS of patients with CD19⁺B cells ratio ≤8.33% and CD19⁺B cells ratio > 8.33% were respectively 90.4% vs. 99.2% (P=0.003), 89.2% vs. 97.5% (P=0.008), 90.4% vs. 98.3% (P=0.008) and 98.8% vs. 99.2% (P > 0.05). However, ROC analysis showed that there was no significant correlation between OS and other peripheral immune cells (including the proportion of CD3⁺T, CD3⁺CD4⁺T, CD3⁺CD8⁺T and CD56⁺NK cells and CD4⁺/CD8⁺ ratio). Multivariate analysis showed that EBV DNA load was an independent prognostic factor of 3-year PFS of NPC patients, and the ratio of CD19⁺B cells was an independent prognostic factor of 3-year PFS, MFS and OS of NPC patients. Conclusion Before treatment, there is a negative correlation between plasma EBV DNA and the proportion of CD19⁺B cells in peripheral blood. Both can be used as the predictors of 3-year OS, PFS and DMFS of NPC patients.

This study was aimed to investigate the expression level of NOV and BNIP3 mRNA in mice myelomonocytic leukemia (AML-M(4)) and its significance. The mice were inoculated intravenously with myelomonocytic leukemia cells of WEHI-3, and divided randomly into chemotherapy group and control (untreated) group. Bone marrow samples were then collected from both groups at different times. The NOV and BNIP3 mRNA expression were detected by TaqMan quantitative real-time reverse transcription polymerase chain reaction (qRT-PCR), and the relationship between these expression levels and clinical significance in leukemia incidence and progression were analyzed with β-actin as the housekeeping gene. The results showed that the mean values of NOV and BNIP3 increased gradually from 2 weeks after inoculation and achieved highest level at death in control group. Expression level of NOV increased from 1.85E-05 before inoculation to 3.57E-02 at death (p < 0.05), and BNIP3 from 3.44E-03 to 3.48E-02. While 2 gene expression in the chemotherapy group decreased quickly to 2.51E-05 and 1.58E-03 (p < 0.05) respectively after chemotherapy, which were close to the level before inoculation (p > 0.05). The 2 gene expressions again rose at relapse, and difference of expression level between 2 group at death were no statistically significant (p > 0.05). It is concluded that the expression of NOV and BNIP3 in leukemia AML-M(4) is significantly higher than that in normal controls, of which high level expression is an important factor in the development of leukemia. Close relation between the therapeutic effect and expression level of these two genes suggests the great value in prognostic evaluation and MRD detection.
Animals ; Cell Line, Tumor ; Female ; Gene Expression ; Leukemia, Myeloid ; genetics ; Membrane Proteins ; genetics ; Mice ; Mitochondrial Proteins ; genetics ; Nephroblastoma Overexpressed Protein ; genetics