Nursing management of intracranial hypertension in adults with severe brain injury in a neurosurgery intensive care unit: a best practice implementation project.

Introduction: The nursing management of intracranial hypertension in adult patients with severe brain injury is crucial for maintaining the stability of intracranial pressure, which ultimately improves patient outcomes.

Objectives: This project aimed to implement evidence-based practices for the nursing management of intracranial hypertension in adult patients with severe brain injury.

Methods: This evidence implementation project was conducted in a neurosurgery intensive care unit in a large tertiary hospital in Guangzhou, China.

Development and Validation of a Sensitive UHPLC-MS/MS Method for the Measurement of Gardneramine in Rat Plasma and Tissues and its Application to Pharmacokinetics and Tissue Distribution Study.

As a novel monoterpenoid indole alkaloid, gardneramine has been confirmed to possess excellent nervous depressive effects. However, there have been no reports about the measurement of gardneramine in vitro and in vivo. The motivation of this study was to establish and validate a specific, sensitive, and robust analytical method based on UHPLC-MS/MS for quantification of gardneramine in rat plasma and various tissues after intravenous administration.

A validated UHPLC-MS/MS method for measurement of pharmacokinetics and tissue distribution of trolline in rat.

As a novel alkaloid, trolline is a potential methionine aminopeptidase Ⅱ inhibitor. However, up to now, no informations about the quantification of trolline were available in biosamples. In this study, a simple, specific and sensitive analytical method based on UHPLC-MS/MS method has been established and validated for determination of trolline in rat plasma and tissues after intravenous administration.

An efficient self-organizing RBF neural network for water quality prediction.

This paper presents a flexible structure Radial Basis Function (RBF) neural network (FS-RBFNN) and its application to water quality prediction. The FS-RBFNN can vary its structure dynamically in order to maintain the prediction accuracy. The hidden neurons in the RBF neural network can be added or removed online based on the neuron activity and mutual information (MI), to achieve the appropriate network complexity and maintain overall computational efficiency.