Development and Validation of an Interpretable Machine Learning Model for Early Prognosis Prediction in ICU Patients with Malignant Tumors and Hyperkalemia.

This study aims to develop and validate a machine learning (ML) predictive model for assessing mortality in patients with malignant tumors and hyperkalemia (MTH). We extracted data on patients with MTH from the Medical Information Mart for Intensive Care-IV, version 2.2 (MIMIC-IV v2.2) database. The dataset was split into a training set (75%) and a validation set (25%). We used the Least Absolute Shrinkage and Selection Operator (LASSO) regression to identify potential predictors, which included clinical laboratory indicators and vital signs. Pearson correlation analysis tested the correlation between predictors. In-hospital death was the prediction target. The Area Under the Curve (AUC) and accuracy of the training and validation sets of 7 ML algorithms were compared, and the optimal 1 was selected to develop the model. The calibration curve was used to evaluate the prediction accuracy of the model further. SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) enhanced model interpretability. 496 patients with MTH in the Intensive Care Unit (ICU) were included. After screening, 17 clinical features were included in the construction of the ML model, and the Pearson correlation coefficient was <0.8, indicating that the correlation between the clinical features was small. eXtreme Gradient Boosting (XGBoost) outperformed other algorithms, achieving perfect scores in the training set (accuracy: 1.000, AUC: 1.000) and high scores in the validation set (accuracy: 0.734, AUC: 0.733). The calibration curves indicated good predictive calibration of the model. SHAP analysis identified the top 8 predictive factors: urine output, mean heart rate, maximum urea nitrogen, minimum oxygen saturation, minimum mean blood pressure, maximum total bilirubin, mean respiratory rate, and minimum pH. In addition, SHAP and LIME performed in-depth individual case analyses. This study demonstrates the effectiveness of ML methods in predicting mortality risk in ICU patients with MTH. It highlights the importance of predictors like urine output and mean heart rate. SHAP and LIME significantly enhanced the model's interpretability.

Ferroptosis, pyroptosis and necroptosis in hepatocellular carcinoma immunotherapy: Mechanisms and immunologic landscape (Review).

Hepatocellular carcinoma (HCC), one of the leading causes of cancer­related mortality worldwide, is challenging to identify in its early stages and prone to metastasis, and the prognosis of patients with this disease is poor. Treatment options for HCC are limited, with even radical treatments being associated with a risk of recurrence or transformation in the short term. Furthermore, the multi­tyrosine kinase inhibitors approved for first­line therapy have marked drawbacks, including drug resistance and side effects. The rise and breakthrough of immune checkpoint inhibitors (ICIs) have provided a novel direction for HCC immunotherapy but these have the drawback of low response rates. Since avoiding apoptosis is a universal feature of cancer, the induction of non­apoptotic regulatory cell death (NARCD) is a novel strategy for HCC immunotherapy. At present, NARCD pathways, including ferroptosis, pyroptosis and necroptosis, are novel potential forms of immunogenic cell death, which have synergistic effects with antitumor immunity, transforming immune 'cold' tumors into immune 'hot' tumors and exerting antitumor effects. Therefore, these pathways may be targeted as a novel treatment strategy for HCC. In the present review, the roles of ferroptosis, pyroptosis and necroptosis in antitumor immunity in HCC are discussed, and the relevant targets and signaling pathways, and the current status of combined therapy with ICIs are summarized. The prospects of targeting ferroptosis, pyroptosis and necroptosis in HCC immunotherapy are also considered.

Apparent differences in prostate zones: susceptibility to prostate cancer, benign prostatic hyperplasia and prostatitis.

Men are inevitably plagued by prostate disease throughout their lives. However, the understanding of the pathogenesis of prostate diseases is still limited. In the 1960s, McNeal proposed the theory of prostate zones: the prostate was divided into three main zones: transition zone, central zone, and peripheral zone. Over the past 50 years, significant differences between different prostate zones have been gradually revealed. We summarized the most significant differences in different zones of the prostate. For the first time, we proposed the "apparent difference in prostate zones" concept. This new concept has been proposed to understand the different zones of the prostate better. It also provided new ideas for exploring the susceptibility of lesions in different prostate zones. Despite the reported differences between zones, the treatment of prostate-related diseases remains partition agnostic. Therefore, we also discussed the clinical significance of the "apparent difference in the prostate zone" and emphasized the necessity of prostate zones.