Effect of Breathing Meditation Training on Nursing Work Quality, Occurrence Risk of Adverse Events, and Attention Level of Operating Room Nurses.

Objective: To explore the effect of breathing meditation training on nursing work quality, occurrence risk of adverse events, and attention level of operating room nurses. Methods: Taking the starting time of breathing meditation training of operating room nurses in our hospital in July 2020 as the dividing line, operating room nurses who implemented routine management from April 2020 to June 2020 were selected as the control group (n=30), and operating room nurses who carried out breathing meditation training from July 2020 to September 2020 were included in the intervention group (n=30). The emotional state [Hamilton Anxiety Scale (HAMA) score, Hamilton Depression Scale (HAMD) score], Mindfulness Attention Awareness Scale (MAAS) score, electrocardiogram indicators (blood pressure, pulse, and respiration), electroencephalogram indicators (SMR wave, ß wave, and θ wave EEG frequency), attention level (attention quotient, visual attention, and auditory attention), nursing work quality (health education, theoretical knowledge, nursing operation, and operating room management) and the number of reported adverse events were compared between the two groups before and after training. Results: After breathing meditation training, the intervention group's Hamilton Anxiety Rating Scale (HAMA) and Hamilton Depression Rating Scale (HAMD) scores were significantly reduced (P < .05), while the Mindfulness Attention Awareness Scale (MAAS) score was significantly increased (P < .05). ). In addition, blood pressure and respiratory rate were reduced in the intervention group (P < .05), with significant differences compared with the control group (P < .05). The SMR waves and beta waves in the intervention group increased (P < .05), while theta waves decreased (P < .05). Attention quotient, visual attention and auditory attention scores were improved in the intervention group compared with the control group (P < .05). The scores of health education, theoretical knowledge, nursing operations and operating room management of the intervention group after training were higher than those of the control group (P < .05). The intervention group reported a lower number of adverse events than the control group (74.42% vs. 25.58%). The application of breathing meditation training in special training for operating room nurses can effectively relieve negative emotions, enhance mindfulness scores, reduce blood pressure and respiratory rate, regulate brain wave frequency, improve attention status and quality of nursing work, and reduce the risk of adverse events. These outcomes may have a positive impact on improving the quality of nursing practice and patient care in the operating room. For operating room nurses, the negative emotional stress caused by sustained high levels of mental concentration may affect work efficiency and the entire surgical process. Breathing meditation training can enhance nurses' emotional resilience, thereby improving the efficiency and safety of operating room care. Conclusion: The application of breathing meditation training in the special training of operating room nurses can effectively alleviate negative emotions, enhance the mindfulness score, reduce blood pressure and respiratory rate, regulate brain wave frequency, improve the attention state and nursing work quality, and reduce the occurrence risk of adverse events. Future research should conduct longitudinal studies to evaluate the long-term effects of breathing meditation training on the quality of nursing work and the prevention of adverse events. Additionally, research could explore advanced neuroimaging techniques to gain structural insights, integrate meditation into existing training programs, tailor interventions for different healthcare settings, assess patient outcomes, explore technology-assisted meditation, and investigate interprofessional collaboration. Through these pathways, a more complete understanding of the impact and best integration of breath meditation in healthcare settings can be achieved, providing valuable insights into improving the well-being of healthcare professionals and potentially overall patient care and satisfaction.

Advances in mitochondrial DNA base editing technology.

Mitochondria, the energy factories of higher eukaryotes, provide energy (ATP) for life activities through aerobic respiration. They possess their own genome, mitochondrial DNA (mtDNA), which encodes 37 genes. Mutations in mtDNA cause mitochondrial diseases, and more than 100 pathogenic mutations have been identified in human mtDNA, with a total incidence rate of about 1/5000. In recent years, advances in CRISPR-based base editing technology have enabled accurate editing of nuclear genes. However, it remains a challenge to achieve precise base editing on mtDNA due to the difficulty of guide RNA in the CRISPR system passing through the mitochondrial double-membrane. In 2020, David R. Liu's group at Harvard University reported a double-stranded DNA deaminase DddA from Burkholderia cenocepacia, which was fused with the programmable transcription activator-like effector (TALE) and uracil glycosylase inhibitor (UGI) to develop DddA-derived cytosine base editors (DdCBEs). Using DdCBEs, they were able to achieve specific and efficient C?G to T?A conversion on mtDNA for the first time. In this review, we summarize the recent progress of mitochondrial base editing technology based on DddA and prospect its future application prospects. The information presented may facilitate interested researchers to grasp the principles of mitochondrial base editing, to use relevant base editors in their own studies, or to optimize mitochondrial base editors in the future.

MKNK2 enhances chemoresistance of ovarian cancer by suppressing autophagy via miR-125b.

Chemoresistance is a major cause for high mortality and poor survival in patients with ovarian cancer. Changes of cellular autophagy is associated with tumor cell chemoresistance. MAP kinase interacting serine/threonine kinase 2 (MKNK2) belongs to the protein kinase superfamily mediating cell cycle, apoptosis and angiogenesis. However, its effects on chemoresistance during ovarian cancer development remain unclear. In this study, we found that MKNK2 expression levels were markedly up-regulated in chemoresistant ovarian cancer cells compared with the sensitive cells. In addition, significantly increased expression of MKNK2 was detected in clinical ovarian cancer tissues, particularly in tumor samples from patients with drug resistance, and high MKNK2 expression is closely associated with poor prognosis. Our in vitro experiments subsequently showed that MKNK2 knockdown markedly reduced the proliferation of chemoresistant ovarian cancer cells, which was confirmed in SKOV3/DDP xenograft mouse models. Importantly, MKNK2 knockdown considerably induced autophagy in ovarian cancer cells with drug resistance, which was involved in the suppression of cell proliferation. Of note, we showed that miR-125b directly targeted MKNK2, and a negative correlation was observed between the expression of them in clinical tumor tissues. MKNK2 silence also increased miR-125b expression levels in drug-resistant ovarian cancer cells. Intriguingly, MKNK2 knockdown-suppressed cell proliferation and -induced autophagy were almost abrogated by miR-125b inhibition in chemoresistant ovarian cancer cells. Together, these findings demonstrated that MNKN2 is responsible for chemoresistance in ovarian cancer through modulating autophagy by targeting miR-125b, which may be a promising therapeutic target to develop strategies against ovarian cancer with drug resistance.

Realizing asynchronous finite-time robust tracking control of switched flight vehicles by using nonfragile deep reinforcement learning.

In this study, a novel nonfragile deep reinforcement learning (DRL) method was proposed to realize the finite-time control of switched unmanned flight vehicles. Control accuracy, robustness, and intelligence were enhanced in the proposed control scheme by combining conventional robust control and DRL characteristics. In the proposed control strategy, the tracking controller consists of a dynamics-based controller and a learning-based controller. The conventional robust control approach for the nominal system was used for realizing a dynamics-based baseline tracking controller. The learning-based controller based on DRL was developed to compensate model uncertainties and enhance transient control accuracy. The multiple Lyapunov function approach and mode-dependent average dwell time approach were combined to analyze the finite-time stability of flight vehicles with asynchronous switching. The linear matrix inequalities technique was used to determine the solutions of dynamics-based controllers. Online optimization was formulated as a Markov decision process. The adaptive deep deterministic policy gradient algorithm was adopted to improve efficiency and convergence. In this algorithm, the actor-critic structure was used and adaptive hyperparameters were introduced. Unlike the conventional DRL algorithm, nonfragile control theory and adaptive reward function were used in the proposed algorithm to achieve excellent stability and training efficiency. We demonstrated the effectiveness of the presented algorithm through comparative simulations.

Intelligent L2-L∞ Consensus of Multiagent Systems under Switching Topologies via Fuzzy Deep Q Learning.

The problem of intelligent L 2-L ∞ consensus design for leader-followers multiagent systems (MASs) under switching topologies is investigated based on switched control theory and fuzzy deep Q learning. It is supposed that the communication topologies are time-varying, and the model of MASs under switching topologies is constructed based on switched systems. By employing linear transformation, the problem of consensus of MASs is converted into the issue of L 2-L ∞ control. The consensus protocol is composed of the dynamics-based protocol and learning-based protocol, where the robust control theory and deep Q learning are applied for the two parts to guarantee the prescribed performance and improve the transient performance. The multiple Lyapunov function (MLF) method and mode-dependent average dwell time (MDADT) method are combined to give the scheduling interval, which ensures stability and prescribed attenuation performance. The sufficient existing conditions of consensus protocol are given, and the solutions of the dynamics-based protocol are derived based on linear matrix inequalities (LMIs). Then, the online design of the learning-based protocol is formulated as a Markov decision process, where the fuzzy deep Q learning is utilized to compensate for the uncertainties and achieve optimal performance. The variation of the learning-based protocol is modeled as the external compensation on the dynamics-based protocol. Therefore, the convergence of the proposed protocol can be guaranteed by employing the nonfragile control theory. In the end, a numerical example is given to validate the effectiveness and superiority of the proposed method.

Fault Detection Filter Design and Optimization for Switched Systems with All Modes Unstable.

This problem of intelligent switched fault detection filter design is investigated in this article. Firstly, the mode-dependent average dwell time (MDADT) method is applied to generate the time-dependent switching signal for switched systems with all subsystems unstable. Afterwards, the switched fault detection filter is proposed for the generation of residual signal, which consists of dynamics-based filter and learning-based filter. The MDADT method and multiple Lyapunov function (MLF) method are employed to guarantee the stability and prescribed attenuation performance. The parameters of dynamics-based filter are given by solving a series of linear matrix inequalities. To improve the transient performance, the deep reinforcement learning is introduced to design learning-based filter in the framework of actor-critic. The output of learning-based filter can be viewed as uncertainties of dynamics-based filter. The deep deterministic policy gradient algorithm and nonfragile control are adopted to guarantee the stability of algorithm and compensate the external disturbance. Finally, simulation results are given to illustrate the effectiveness of the method in the paper.