Mindfulness Meditation Reduces Stress and Hospital Stay in Gastrointestinal Tumor Patients During Perioperative Period.

BACKGROUND The study aims to explored the impact of short-term mindfulness meditation training on physiological and psychological stress levels in patients with gastrointestinal tumors during the perioperative period. MATERIAL AND METHODS We randomly allocated 78 participants to either the experimental group (n=39) or the control group (n=39). The control group received perioperative nursing, while the experimental group engaged in a daily mindfulness meditation practice from the second day of admission until the fifth day after surgery, excluding the day of surgery.

Nurse anesthetists' perceptions and experiences of managing emergence delirium: A qualitative study.

Background: This study employs a descriptive phenomenological approach to investigate the challenges anesthesia nurses face in managing emergence delirium (ED), a common and complex postoperative complication in the post-anesthesia care unit. The role of nurses in managing ED is critical, yet research on their understanding and management strategies for ED is lacking.

Aim: To investigate anesthetic nurses' cognition and management experiences of ED in hopes of developing a standardized management protocol.

Comparative physicochemical, hormonal, transcriptomic and proteomic analyses provide new insights into the formation mechanism of two chemotypes of Pogostemon cablin.

Patchouli (Pogostemon cablin) is an aromatic plant, and its oil has diverse applications in medicine, food, and cosmetics. Patchouli alcohol is the principal bioactive constituent of its volatile oil. In China, patchouli is typically categorized into two types: patchoulol-type (PA-type) and pogostone-type (PO-type).

Combining Computational Screening and Machine Learning to Predict Metal-Organic Framework Adsorbents and Membranes for Removing CH or H from Air.

Separating and capturing small amounts of CH or H from a mixture of gases, such as coal mine spent air, at a large scale remains a great challenge. We used large-scale computational screening and machine learning (ML) to simulate and explore the adsorption, diffusion, and permeation properties of 6013 computation-ready experimental metal-organic framework (MOF) adsorbents and MOF membranes (MOFMs) for capturing clean energy gases (CH and H) in air. First, we modeled the relationships between the adsorption and the MOF membrane performance indicators and their characteristic descriptors.