Background: Metals and their ions have been used to reduce bacterial infection risks. Among them, minerals containing rubidium (MCR), natural minerals containing metal ions, show potential as novel and tunable materials. Objective: This study aimed to investigate the antibacterial activity and mechanism of MCR. Methods: The inhibitory effect of MCR on bacteria was clarified using the growth curve method, turbidimetric method, and minimum inhibitory concentration method. Physiological and biochemical indices were employed to investigate the inhibitory mechanism of MCR. Results: The results revealed that MCR inhibited Staphylococcus aureus, Listeria monocytogenes, and Escherichia coli with minimum inhibitory concentrations of 11.95, 2.60, and 2.60 mg/mL, respectively. The inhibitory activity of MCR was insignificant against Bacillus subtilis, Salmonella typhimurium, and Helicobacter pylori at 3.25 mg/mL. Mechanistic assessments showed that MCR affected bacterial conductivity, protein and nucleic acid levels, reducing sugar content, respiratory chain dehydrogenase activity, bacterial lipid peroxidation, intracellular adenosine triphosphate, and extracellular alkaline phosphatase. Conclusion: MCR has bacteriostatic activity and the mechanism primarily involves adhesion to bacteria, disrupting the integrity of their cell walls and membranes, and altering their permeability. This disruption leads to the release of intracellular molecules of various sizes, inhibiting cellular respiration and metabolism, and causing oxidative damage. These combined effects impair cellular functions, affecting cell growth and metabolism, or leading to cell death. These findings provide a theoretical reference for the development of MCR as a bacteriostatic agent.

Objective To investigate the relationships of nutritional status and length of stay (LOS) with phase angle (PA) in patients with severe craniocerebral injury. Methods A total of 100 patients [Glasgow Coma Scale (GCS) score ≤8 points] admitted outside the department of neurology were selected as study objects. Body composition analysis and blood samples were used to determine the intracellular and extracellular water content, skeletal muscle, PA, prognostic nutritional index (PNI) and LOS. Patients were divided into low PA group (n=42) and normal PA group (n=58) based on PA values. Body composition related indexes, PNI and LOS were compared between the two groups. The correlation between PA and each index was analyzed. The influencing factors of malnutrition were analyzed. The influencing factors of malnutrition were analyzed. Results Intracellular water, total body water, body cell content and PNI in the low PA group were significantly lower, and LOS was significantly longer than that in the normal PA group (P < 0.05). PA was positively correlated with intracellular water, extracellular water, total body water, skeletal muscle, body cell content, bone mineral content, basal metabolic rate and PNI (P < 0.05), while PA was negatively correlated with LOS (P < 0.05). PA (OR=5.441, P=0.001, 95%CI, 2.011 to 14.719) and LOS (OR=8.373, P < 0.001, 95%CI, 3.079 to 22.765) were the influential factors in the occurrence of malnutrition. Conclusion PA is significantly correlated with nutritional status and LOS in patients with severe craniocerebral injury.

How to detect the P300 component in EEG accurately and instantly is a hot problem in the research field of Brain-Computer Interface. In this paper, an algorithm based on F-score feature selection and support vector machines was introduced for P300 detection. Using F-score feature selection method, we reduced input features to overcome the shortcoming of support vector machines in terms of low detection speed, and then implemented the detection of P300 component with support vector machines, which have good classification performance. The algorithm was tested with a P300 dataset from the BCI competition 2003. The results showed that the algorithm achieved an accuracy of 100% in P300 detection within five repetitions, and the detection speed of this algorithm was 2 times higher than that of the traditional support vector machines algorithm without F-score feature selection.
Algorithms ; Electroencephalography ; methods ; Event-Related Potentials, P300 ; physiology ; Humans ; Pattern Recognition, Automated ; methods ; Principal Component Analysis ; Signal Processing, Computer-Assisted