Artificial intelligence enhances the management of esophageal squamous cell carcinoma in the precision oncology era.

Esophageal squamous cell carcinoma (ESCC) is the most common histological type of esophageal cancer with a poor prognosis. Early diagnosis and prognosis assessment are crucial for improving the survival rate of ESCC patients. With the advancement of artificial intelligence (AI) technology and the proliferation of medical digital information, AI has demonstrated promising sensitivity and accuracy in assisting precise detection, treatment decision-making, and prognosis assessment of ESCC. It has become a unique opportunity to enhance comprehensive clinical management of ESCC in the era of precision oncology. This review examines how AI is applied to the diagnosis, treatment, and prognosis assessment of ESCC in the era of precision oncology, and analyzes the challenges and potential opportunities that AI faces in clinical translation. Through insights into future prospects, it is hoped that this review will contribute to the real-world application of AI in future clinical settings, ultimately alleviating the disease burden caused by ESCC.

To Establish an Early Prediction Model for Acute Respiratory Distress Syndrome in Severe Acute Pancreatitis Using Machine Learning Algorithm.

OBJECTIVE: To develop binary and quaternary classification prediction models in patients with severe acute pancreatitis (SAP) using machine learning methods, so that doctors can evaluate the risk of patients with acute respiratory distress syndrome (ARDS) and severe ARDS at an early stage. METHODS: A retrospective study was conducted on SAP patients hospitalized in our hospital from August 2017 to August 2022. Logical Regression (LR), Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT), and eXtreme Gradient Boosting (XGB) were used to build the binary classification prediction model of ARDS. Shapley Additive explanations (SHAP) values were used to interpret the machine learning model, and the model was optimized according to the interpretability results of SHAP values. Combined with the optimized characteristic variables, four-class classification models, including RF, SVM, DT, XGB, and Artificial Neural Network (ANN), were constructed to predict mild, moderate, and severe ARDS, and the prediction effects of each model were compared. RESULTS: The XGB model showed the best effect (AUC = 0.84) in the prediction of binary classification (ARDS or non-ARDS). According to SHAP values, the prediction model of ARDS severity was constructed with four characteristic variables (PaO2/FiO2, APACHE II, SOFA, AMY). Among them, the overall prediction accuracy of ANN is 86%, which is the best. CONCLUSIONS: Machine learning has a good effect in predicting the occurrence and severity of ARDS in SAP patients. It can also provide a valuable tool for doctors to make clinical decisions.

The CBF1-dependent low temperature signalling pathway, regulon and increase in freeze tolerance are conserved in Populus spp.

The meristematic tissues of temperate woody perennials must acclimate to freezing temperatures to survive the winter and resume growth the following year. To determine whether the C-repeat binding factor (CBF) family of transcription factors contributing to this process in annual herbaceous species also functions in woody perennials, we investigated the changes in phenotype and transcript profile of transgenic Populus constitutively expressing CBF1 from Arabidopsis (AtCBF1). Ectopic expression of AtCBF1 was sufficient to significantly increase the freezing tolerance of non-acclimated leaves and stems relative to wild-type plants. cDNA microarray experiments identified genes up-regulated by ectopic AtCBF1 expression in Populus, demonstrated a strong conservation of the CBF regulon between Populus and Arabidopsis and identified differences between leaf and stem regulons. We studied the induction kinetics and tissue specificity of four CBF paralogues identified from the Populus balsamifera subsp. trichocarpa genome sequence (PtCBFs). All four PtCBFs are cold-inducible in leaves, but only PtCBF1 and PtCBF3 show significant induction in stems. Our results suggest that the central role played by the CBF family of transcriptional activators in cold acclimation of Arabidopsis has been maintained in Populus. However, the differential expression of the PtCBFs and differing clusters of CBF-responsive genes in annual (leaf) and perennial (stem) tissues suggest that the perennial-driven evolution of winter dormancy may have given rise to specific roles for these 'master-switches' in the different annual and perennial tissues of woody species.