A Machine Learning Model Based on CT Imaging Metrics and Clinical Features to Predict the Risk of Hospital-Acquired Pneumonia After Traumatic Brain Injury.

Objective: To develop a validated machine learning (ML) algorithm for predicting the risk of hospital-acquired pneumonia (HAP) in patients with traumatic brain injury (TBI).

Materials And Methods: We employed the Least Absolute Shrinkage and Selection Operator (LASSO) to identify critical features related to pneumonia. Five ML models-Logistic Regression (LR), Extreme Gradient Boosting (XGB), Random Forest (RF), Naive Bayes Classifier (NB), and Support Vector Machine (SVC)-were developed and assessed using the training and validation datasets.

Elevated Glucose-Potassium Ratio Predicts Preoperative Rebleeding in Patients With Aneurysmal Subarachnoid Hemorrhage.

Recent reports revealed that higher serum glucose-potassium ratio (GPR) levels at admission were significantly associated with poor outcomes at 3 months following aneurysmal subarachnoid hemorrhage (aSAH). This study aimed to investigate the association between GPR and the risk of rebleeding following aSAH. This single-center retrospective study of patients with aSAH was conducted in our hospital between January 2008 and December 2020.

Novel KTiO Anode via Na/Al Co-Intercalation Mechanism for Rechargeable Aqueous Al-Ion Battery with Superior Rate Capability.

A promising aqueous aluminum ion battery (AIB) was assembled using a novel layered KTiO anode against an activated carbon coated on a Ti mesh cathode in an AlCl-based aqueous electrolyte. The intercalation/deintercalation mechanism endowed the layered KTiO as a promising anode for rechargeable aqueous AIBs. NaAc was introduced into the AlCl aqueous electrolyte to enhance the cycling stability of the assembled aqueous AIB.