Background: Sepsis remains a significant challenge in patients with major trauma in the ICU. Early detection and treatment are crucial for improving outcomes and reducing mortality rates. Nonetheless, clinical tools for predicting sepsis among patients with major trauma are limited. This study aimed to develop and validate an artificial intelligence (AI) platform for predicting the risk of sepsis among patients with major trauma. Patients and methods: This study involved 961 patients, with a prospective analysis of data from 244 patients with major trauma at our hospital and a retrospective analysis of data from 717 patients extracted from a database in the United States. The patients from our hospital constituted the model development cohort, and the patients from the database constituted the external validation cohort. The patients in the model development cohort were randomly divided into a training cohort and an internal validation cohort at a ratio of 8:2. The machine-learning algorithms used to train models included logistic regression, decision tree, extreme gradient boosting machine (eXGBM), neural network (NN), random forest, and light gradient boosting machine (LightGBM). Results: The incidence of sepsis for the model development cohort was 43.44%. Twelve predictors, including gender, abdominal trauma, open trauma, red blood cell count, heart rate, respiratory rate, injury severity score, sequential organ failure assessment score, Glasgow coma scale, smoking, total protein concentrations, and hematocrit, were used as features in the final model. Internal validation showed that the NN model had the highest area under the curve (AUC) of 0.932 (95% CI: 0.917–0.948), followed by the LightGBM and eXGBM models with AUCs of 0.913 (95% CI: 0.883–0.930) and 0.912 (95% CI: 0.880–0.935), respectively. In the external validation cohort, the eXGBM model (AUC: 0.891, 95% CI: 0.866–0.914) had the highest AUC value, followed by the LightGBM model (AUC: 0.886, 95% CI: 0.860–0.906), and the AUC value of the NN model was only 0.787 (95% CI: 0.751–0.829). Considering the predictive performance for both the internal and external validation cohorts, the LightGBM model had the highest score of 82, followed by the eXGBM (81) and NN (76) models. Thus, the LightGBM has emerged as the optimal model, and it was deployed online as an AI application. Conclusions: This study develops and validates an AI application to effectively assess the susceptibility of patients with major trauma to sepsis. The AI application equips healthcare professionals with a valuable tool to promptly identify individuals at high risk of developing sepsis. This will facilitate clinical decision-making and enable early intervention.

Metabolism-related pathways are important targets for intervention in the treatment of hepatocellular carcinoma (HCC), but few studies have reported on the combination of inhibitors of folate metabolism-related enzymes and molecularly targeted drugs for HCC. The results of the present work are the first to reveal the effects of an inhibitor of dihydrofolate reductase (DHFR), pralatrexate, on the sensitivity of HCC cells to molecularly targeted agents examined using multiple assays. In HCC cells, knockdown of DHFR or treatment with pralatrexate enhanced the sensitivity of HCC cells to molecularly targeted agents, such as sorafenib, regorafenib, lenvatinib, cabozantinib, or anlotinib. Mechanically, pralatrexate decreased the methylation rates of miRNA-34a’s promoter region to enhance the expression of miRNA-34a. Treatment with pralatrexate inhibited the expression of Notch and its downstream factors by enhancing the expression of miRNA-34a in HCC cells. In clinical specimens, the expression of miRNA-34a was negatively correlated with DHFR expression, while DHFR expression was positively correlated with the Notch intracellular domain (NICD) and downstream factors of the Notch pathway. The expression of miRNA-34a was negatively correlated with DHFR expression, while the methylation rates of miRNA-34a’s promoter were positively related to DHFR. The effect of pralatrexate on the metabolic profile of HCC cells is very different from that of small molecule inhibitors related to glycolipid metabolism. Therefore, pralatrexate upregulates the sensitivity of HCC cells to molecularly targeted drugs. These results expand our understanding of folate metabolism and HCC and can help provide more options for HCC treatment.

The activation of the androgen receptor (AR) pathway is crucial in the progression of human prostate cancer. Results of the present study indicated that the target protein xenopus kinesin-like protein (TPX2) enhanced the transcription activation of AR and promoted the proliferation of LNCaP (ligand-dependent prostate carcinoma) cells. The protein-protein interaction between AR and TPX2 was investigated using coimmunoprecipitation assays. Results of the present study further demonstrated that TPX2 enhanced the transcription factor activation of AR and enhanced the expression levels of the downstream gene prostate-specific antigen (PSA). TPX2 did this by promoting the accumulation of AR in the nucleus and also promoting the recruitment of AR to the androgen response element, located in the promoter region of the PSA gene. Overexpression of TPX2 enhanced both the in vitro and in vivo proliferation of LNCaP cells. By revealing a novel role of TPX2 in the AR signaling pathway, the present study indicated that TPX2 may be an activator of AR and thus exhibits potential as a novel target for prostate carcinoma treatment.