Protocol for a randomised controlled trial: optimisation of perioperative analgesia protocol for uniportal video-assisted thoracoscopic surgery.

INTRODUCTION: Postoperative pain after thoracic surgery impairs patients' quality of life and increases the incidence of respiratory complications. Optimised analgesia strategies include minimally invasive incisions, regional analgesia and early chest tube removal. However, little is known about the optimal analgesic regimen for uniportal video-assisted thoracoscopic surgery (uVATS). METHODS AND ANALYSIS: We will conduct a single-centre, prospective, single-blind, randomised trial. The effects of postoperative analgesia will be tested using thoracic paravertebral block (PVB) in combination with patient-controlled intravenous analgesia (PVB+PCIA), erector spinae plane block (ESPB) in combination with patient-controlled intravenous analgesia (ESPB+PCIA) or PCIA alone; 102 patients undergoing uVATS will be enrolled in this study. Patients will be randomly assigned to the PVB group (30 mL of 0.33% ropivacaine with dexamethasone), ESPB group (40 mL of 0.25% ropivacaine with dexamethasone) or control groups. PCIA with sufentanil will be administered to all patients after surgery. The primary outcome will be total opioid consumption after surgery. Secondary outcomes include postoperative pain score; postoperative chronic pain at rest and during coughing; sensations of touch and pain in the chest wall, non-opioid analgesic consumption; length of stay; ambulation time, the total cost of hospitalisation and long-term postoperative analgesia. Adverse reactions to analgesics and adverse events related to the regional blocks will also be recorded. The statisticians will be blinded to the group allocation. Comparison of the continuous data among the three groups will be performed using a one-way analysis of variance to assess differences among the means. ETHICS AND DISSEMINATION: The results will be published in patient education courses, academic conferences and peer-reviewed journals. TRIAL REGISTRATION NUMBER: NCT06016777.

Genetic Learning Particle Swarm Optimization.

Social learning in particle swarm optimization (PSO) helps collective efficiency, whereas individual reproduction in genetic algorithm (GA) facilitates global effectiveness. This observation recently leads to hybridizing PSO with GA for performance enhancement. However, existing work uses a mechanistic parallel superposition and research has shown that construction of superior exemplars in PSO is more effective. Hence, this paper first develops a new framework so as to organically hybridize PSO with another optimization technique for "learning." This leads to a generalized "learning PSO" paradigm, the *L-PSO. The paradigm is composed of two cascading layers, the first for exemplar generation and the second for particle updates as per a normal PSO algorithm. Using genetic evolution to breed promising exemplars for PSO, a specific novel *L-PSO algorithm is proposed in the paper, termed genetic learning PSO (GL-PSO). In particular, genetic operators are used to generate exemplars from which particles learn and, in turn, historical search information of particles provides guidance to the evolution of the exemplars. By performing crossover, mutation, and selection on the historical information of particles, the constructed exemplars are not only well diversified, but also high qualified. Under such guidance, the global search ability and search efficiency of PSO are both enhanced. The proposed GL-PSO is tested on 42 benchmark functions widely adopted in the literature. Experimental results verify the effectiveness, efficiency, robustness, and scalability of the GL-PSO.

Occupational exposure to blood and compliance with standard precautions among health care workers in Beijing, China.

This cross-sectional survey assessed both risk and prevention of health care workers to bloodborne virus transmission in 2 hospitals in Beijing. The identified discrepancy between the high level of occupational blood exposure and suboptimal compliance with standard precautions underscores the urgent need for interventions to enhance occupational safety of health care workers in China.

Multiple Populations for Multiple Objectives: A Coevolutionary Technique for Solving Multiobjective Optimization Problems.

Traditional multiobjective evolutionary algorithms (MOEAs) consider multiple objectives as a whole when solving multiobjective optimization problems (MOPs). However, this consideration may cause difficulty to assign fitness to individuals because different objectives often conflict with each other. In order to avoid this difficulty, this paper proposes a novel coevolutionary technique named multiple populations for multiple objectives (MPMO) when developing MOEAs. The novelty of MPMO is that it provides a simple and straightforward way to solve MOPs by letting each population correspond with only one objective. This way, the fitness assignment problem can be addressed because the individuals' fitness in each population can be assigned by the corresponding objective. MPMO is a general technique that each population can use existing optimization algorithms. In this paper, particle swarm optimization (PSO) is adopted for each population, and coevolutionary multiswarm PSO (CMPSO) is developed based on the MPMO technique. Furthermore, CMPSO is novel and effective by using an external shared archive for different populations to exchange search information and by using two novel designs to enhance the performance. One design is to modify the velocity update equation to use the search information found by different populations to approximate the whole Pareto front (PF) fast. The other design is to use an elitist learning strategy for the archive update to bring in diversity to avoid local PFs. CMPSO is comprehensively tested on different sets of benchmark problems with different characteristics and is compared with some state-of-the-art algorithms. The results show that CMPSO has superior performance in solving these different sets of MOPs.

[Effect of health education on farmers' intention of joining in the new rural cooperative medical system].

OBJECTIVE: To evaluate the impact of health education intervention on promoting rural residents to join new rural cooperative medical system (NCMS) and their intention of joining NCMS based on Health Belief Model in project areas in Henan and Jilin Provinces. METHODS: Quasi-experiment study was used to evaluate intervention impact. Following the evidence-based approach, according to needs assessment, a half-year health education intervention was implemented among farmers in the experimental counties in Henan and Jilin Provinces respectively. A questionnaire survey was conducted among farmers in intervention and control counties before and after intervention, and intervention impact was evaluated by comparing the indicators' changes in intervention and control counties. RESULTS: After health education intervention, the knowledge level of farmers in two intervention counties increased by 29.0% and 37.8% respectively, their scores of perceived threatens of health risk and perceived barriers of joining NCMS among the respondents were decreased. Meanwhile, their score of perceived benefit of joining NCMS were increased, and the rate of willingness to join NCMS increased remarkably in both intervention counties. CONCLUSION: Health education was effective and helpful in increasing farmer's knowledge, understanding and cognitive level of NCMS, and it should play an important role for the sustainable development of NCMS.