Simulated microgravity attenuates skin wound healing by inhibiting dermal fibroblast migration via F-actin/YAP signaling pathway.

Skin and its cell components continuously subject to extrinsic and intrinsic mechanical forces and are mechanical sensitive. Disturbed mechanical homeostasis may lead to changes in skin functions. Gravity is the integral mechanical force on the earth, however, how gravity contributes to the maintenance of skin function and how microgravity in space affects the wound healing are poorly understood. Here, using microgravity analogs, we show that simulated microgravity (SMG) inhibits the healing of cutaneous wound and the accumulation of dermal fibroblasts in the wound bed. In vitro, SMG inhibits the migration of human foreskin fibroblast cells (HFF-1), and decreases the F-actin polymerization and YAP (yes-associated protein) activity. The SMG-inhibited migration can be recovered by activating YAP or F-actin polymerization using lysophosphatidic acid (LPA) or jasplakinolide (Jasp), suggesting the involvement of F-actin/YAP signaling pathway in this process. In SMG rats, LPA treatment improves the cutaneous healing with increased dermal fibroblasts in the wound bed. Together, our results demonstrate that SMG attenuates the cutaneous wound healing by inhibiting dermal fibroblast migration, and propose the crucial role of F-actin/YAP mechano-transduction in the maintenance of skin homeostasis under normal gravity, and YAP as a possible therapeutic target for the skin care of astronauts in space.

YAP Inhibition Alleviates Simulated Microgravity-Induced Mesenchymal Stem Cell Senescence via Targeting Mitochondrial Dysfunction.

Weightlessness in space leads to bone loss, muscle atrophy, and impaired immune defense in astronauts. Mesenchymal stem cells (MSCs) play crucial roles in maintaining the homeostasis and function of the tissue. However, how microgravity affects the characteristics MSCs and the related roles in the pathophysiological changes in astronauts remain barely known. Here we used a 2D-clinostat device to simulate microgravity. Senescence-associated-ß-galactosidase (SA-ß-gal) staining and the expression of senescent markers p16, p21, and p53 were used to evaluate the senescence of MSCs. Mitochondrial membrane potential (mΔΨm), reactive oxygen species (ROS) production, and ATP production were used to evaluate mitochondrial function. Western blot and immunofluorescence staining were used to investigate the expression and localization of Yes-associated protein (YAP). We found that simulated microgravity (SMG) induced MSC senescence and mitochondrial dysfunction. Mito-TEMPO (MT), a mitochondrial antioxidant, restored mitochondrial function and reversed MSC senescence induced by SMG, suggesting that mitochondrial dysfunction mediates SMG-induced MSC senescence. Further, it was found that SMG promoted YAP expression and its nuclear translocation in MSCs. Verteporfin (VP), an inhibitor of YAP, restored SMG-induced mitochondrial dysfunction and senescence in MSCs by inhibiting YAP expression and nuclear localization. These findings suggest that YAP inhibition alleviates SMG-induced MSC senescence via targeting mitochondrial dysfunction, and YAP may be a potential therapeutic target for the treatment of weightlessness-related cell senescence and aging.

Related factors influencing Chinese psychiatric nurses' turnover: A cross-sectional study.

WHAT IS KNOWN ON THE SUBJECT: Because of increasingly stressful, dangerous and unpredictable psychiatric nursing work, psychiatric nurses have experienced higher job stress than general ward nurses. Little is known about the factors that affect the turnover intention of Chinese psychiatric nurses. Understanding the influencing factors of nurses' turnover intention will help to formulate targeted measures to stabilize psychiatric nursing teams. WHAT DOES THIS PAPER ADD TO EXISTING KNOWLEDGE: The results showed that 70.2% of psychiatric nurses had higher turnover intention. The strong turnover intention of Chinese psychiatric nurses is a problem that needs to be considered by managers. The results showed that having more children, between 31 and 39 years old, and having a part-time job were strongly associated with turnover intention. In addition, "job stress" was also an important factor, psychiatric nurses' turnover intention decreased as their job stress level decreased. WHAT ARE THE IMPLICATIONS FOR PRACTICE: Nursing managers should pay attention to nurses who have more children, between 31 and 39 years old, and take on part-time jobs. Additionally, nursing managers should reduce job stress and implement targeted programmes to prevent psychiatric nurses' turnover. Experience-sharing meetings and mindfulness-based stress reduction training are also useful to improve the mental health status of psychiatric nurses with great job stress. Nursing managers should arrange human resources and shifts appropriately to give nurses with more children more time with their families. Provide more development opportunities for psychiatric nurses between 31 and 39 years old. Managers explore the reasons why nurses take on part-time jobs and take targeted interventions (such as increasing income) to reduce the behaviour that happens. ABSTRACT: Introduction Nurses' turnover is the main cause of nursing shortages, greatly affected by nurses' intention to leave. Nurses' turnover rate is particularly high in psychiatric wards. Several factors influencing the turnover intention of psychiatric nurses have not been well identified in China, and the association between job stress and turnover intention is still limited. Aims To examine the relationship between job stress and turnover intention and identify the influencing factors of psychiatric nurses' turnover intention. Methods Data were collected from 2355 psychiatric Chinese nurses using a cross-sectional design with an online questionnaire investigation. Results Psychiatric nurses had higher turnover intention. Significant factors influencing their turnover intention were job stress, having more children, age between 31 and 39 years old, part-time jobs, education, income and patient-to-nurse ratio. Discussion Demographics and job-related factors should be considered when developing strategies to reduce the turnover intention of psychiatric nurses. Implications for practice Nursing managers should pay attention to nurses with higher job stress levels and different demographic characteristics. Effective measures should be taken to reduce psychiatric nurses' job stress and turnover intention, such as arranging reasonable shifts, implementing targeted family-friendly policies, increasing their occupational possibilities and promoting mental health.

Active Domain Adaptation With Application to Intelligent Logging Lithology Identification.

Lithology identification plays an essential role in formation characterization and reservoir exploration. As an emerging technology, intelligent logging lithology identification has received great attention recently, which aims to infer the lithology type through the well-logging curves using machine-learning methods. However, the model trained on the interpreted logging data is not effective in predicting new exploration well due to the data distribution discrepancy. In this article, we aim to train a lithology identification model for the target well using a large amount of source-labeled logging data and a small amount of target-labeled data. The challenges of this task lie in three aspects: 1) the distribution misalignment; 2) the data divergence; and 3) the cost limitation. To solve these challenges, we propose a novel active adaptation for logging lithology identification (AALLI) framework that combines active learning (AL) and domain adaptation (DA). The contributions of this article are three-fold: 1) the domain-discrepancy problem in intelligent logging lithology identification is first investigated in this article, and a novel framework that incorporates AL and DA into lithology identification is proposed to handle the problem; 2) we design a discrepancy-based AL and pseudolabeling (PL) module and an instance importance weighting module to query the most uncertain target information and retain the most confident source information, which solves the challenges of cost limitation and distribution misalignment; and 3) we develop a reliability detecting module to improve the reliability of target pseudolabels, which, together with the discrepancy-based AL and PL module, solves the challenge of data divergence. Extensive experiments on three real-world well-logging datasets demonstrate the effectiveness of the proposed method compared to the baselines.

Simulated Microgravity Inhibits Rodent Dermal Fibroblastic Differentiation of Mesenchymal Stem Cells by Suppressing ERK/ß-Catenin Signaling Pathway.

Studies have shown that bone marrow-derived mesenchymal stem cells (BMSCs) can differentiate into dermal fibroblasts to participate in skin-repairing. However, at present, little is known about how microgravity affects dermal fibroblastic differentiation of BMSCs in space. The aim of this study was to investigate the effect of simulated microgravity (SMG) on the differentiation of BMSCs into dermal fibroblasts and the related molecular mechanism. Here, using a 2D-clinostat device to simulate microgravity, we found that SMG inhibited the differentiation and suppressed the Wnt/ß-catenin signaling and phosphorylation of extracellular regulated protein kinases 1/2 (ERK1/2). After upregulating the Wnt/ß-catenin signaling with lithium chloride (LiCl) treatment, we found that the effect of the differentiation was restored. Moreover, the Wnt/ß-catenin signaling was upregulated when phosphorylation of ERK1/2 was activated with tert-Butylhydroquinone (tBHQ) treatment. Taken together, our findings suggest that SMG inhibits dermal fibroblastic differentiation of BMSCs by suppressing ERK/ß-catenin signaling pathway, inferring that ERK/ß-catenin signaling pathway may act as a potential intervention target for repairing skin injury under microgravity conditions.

Factors influencing depression in primary caregivers of patients with dementia in China: A cross-sectional study.

This study aims to estimate the prevalence and factors of depression in primary caregivers of people with dementia in China, based on a biopsychosocial medical model. A sample of 285 caregiver-patient dyads was recruited from a tertiary psychiatric hospital in Nanjing, between December 2018 and November 2019. The prevalence of depression among primary caregivers of people with dementia was 42.8%. Binary logistic regression analyses revealed that caregivers' gender (OR=4.692), social support (OR=0.131), health condition (OR=12.994), extraversion (OR=0.102) and neuroticism (OR=2.978) were predictive of depression in those caregivers. Of the above, health condition was the major factor associated with caregiver's depression. The Box-Tidwell method was used to show a linear relationship between continuous independent variables and dependent variable logit conversion values (p = 0.0045). Suggestions are provided to develop support service programs and interventions tailored to caregivers, to help meet their basic substance and mental health needs. (147 words).

Anxiety, depression and cognitive emotion regulation strategies in Chinese nurses during the COVID-19 outbreak.

AIMS: To explore the association between cognitive emotion regulation strategies and anxiety and depression among nurses during the COVID-19 outbreak. BACKGROUND: Nurses play a vital role in responding to the COVID-19 outbreak, but many of them suffer from psychological problems due to the excessive workload and stress. Understanding the correlation between cognitive emotion regulation strategies and anxiety and depression will promote targeted psychosocial interventions for these affected nurses. METHODS: This cross-sectional study of 586 nurses was conducted in Eastern China. Participants completed online questionnaires that investigated anxiety, depression and cognitive emotion regulation strategies. RESULTS: The prevalence of nurses' anxiety and depression was 27.6% and 32.8%, respectively. Lower self-blame, rumination and catastrophizing, as well as greater acceptance and positive refocusing, were related to fewer symptoms of anxiety or depression. CONCLUSION: The cognitive emotion regulation strategies of acceptance and positive refocusing contribute to reducing anxiety or depression. These strategies should be considered when implementing psychotherapeutic interventions to improve nurses' adverse emotional symptoms. IMPLICATIONS FOR NURSING MANAGEMENT: This study highlights the need to assess cognitive emotion regulation strategies use in screening for anxiety and depression. Nurse managers should develop psychosocial interventions including appropriate strategies to help nurses with adverse emotions during a pandemic.

Frequency-Temporal Disagreement Adaptation for Robotic Terrain Classification via Vibration in a Dynamic Environment.

The accurate terrain classification in real time is of great importance to an autonomous robot working in field, because the robot could avoid non-geometric hazards, adjust control scheme, or improve localization accuracy, with the aid of terrain classification. In this paper, we investigate the vibration-based terrain classification (VTC) in a dynamic environment, and propose a novel learning framework, named DyVTC, which tackles online-collected unlabeled data with concept drift. In the DyVTC framework, the exterior disagreement (ex-disagreement) and interior disagreement (in-disagreement) are proposed novely based on the feature diversity and intrinsic temporal correlation, respectively. Such a disagreement mechanism is utilized to design a pseudo-labeling algorithm, which shows its compelling advantages in extracting key samples and labeling; and consequently, the classification accuracy could be retrieved by incremental learning in a changing environment. Since two sets of features are extracted from frequency and time domain to generate disagreements, we also name the proposed method feature-temporal disagreement adaptation (FTDA). The real-world experiment shows that the proposed DyVTC could reach an accuracy of 89.5%, but the traditional time- and frequency-domain terrain classification methods could only reach 48.8% and 71.5%, respectively, in a dynamic environment.

Well Logging Based Lithology Identification Model Establishment Under Data Drift: A Transfer Learning Method.

Recent years have witnessed the development of the applications of machine learning technologies to well logging-based lithology identification. Most of the existing work assumes that the well loggings gathered from different wells share the same probability distribution; however, the variations in sedimentary environment and well-logging technique might cause the data drift problem; i.e., data of different wells have different probability distributions. Therefore, the model trained on old wells does not perform well in predicting the lithologies in newly-coming wells, which motivates us to propose a transfer learning method named the data drift joint adaptation extreme learning machine (DDJA-ELM) to increase the accuracy of the old model applying to new wells. In such a method, three key points, i.e., the project mean maximum mean discrepancy, joint distribution domain adaptation, and manifold regularization, are incorporated into extreme learning machine. As found experimentally in multiple wells in Jiyang Depression, Bohai Bay Basin, DDJA-ELM could significantly increase the accuracy of an old model when identifying the lithologies in new wells.

Antitumor Activity and Mechanism Study of Riluzole and Its Derivatives.

To explore novel antitumor agents with high efficiency and low toxicity, riluzole alkyl derivatives (4a-4i) were synthesized. Their anti-proliferative activities against HeLa, HepG2, SP2/0, and MCF-7 cancer cell lines were assessed by the CCK-8 assay and compared with human normal liver (LO2) cells. Most of them showed potent cytotoxic effects against four human tumor cell lines and low toxic to LO2 cells. In particular, 2-(N-ethylamine)-6-trifluoromethoxy- benzothiazole (4a) showed a IC50 value of 7.76 µmol/L in HeLa cells and was found to be nontoxic to LO2 cells up to 65 µmol/L. Furthermore, flow cytometry indicated that 4a could induce remarkable early apoptosis and G2/M cell cycle arrest in HeLa cells. It also impaired the migration ability of HeLa cells in wound healing assays. Western blot results demonstrated that 4a suppressed Bcl-2 protein expression but increased the level of Bax in HeLa cells, and elevated the Bax/Bcl-2 expression ratio. These new findings suggest that 4a exhibited beneficially anti-cervical cancer effect on HeLa cells by inducing HeLa cell apoptosis.

Job burnout and quality of working life among Chinese nurses: A cross-sectional study.

AIM: To explore the relationship between job burnout and quality of working life, and identify influencing factors of nurses' quality of working life. BACKGROUND: Understanding the influencing factors of quality of working life is important to improve nursing retention strategies. Job burnout can negatively influence work efficiency and quality of work. However, studies examining the association between the two remain limited. METHODS: The cross-sectional survey of 2,504 nurses was performed in Eastern China. Nurses were invited to complete self-report questionnaires online. The collected data were analysed using Pearson's correlation and multiple regression. RESULTS: About 64.0% of nurses experienced job burnout, and their quality of working life was at a moderate level. Job burnout, hospital level, age, income, night shift and patient-to-nurse ratio were significant factors of quality of working life. CONCLUSION: Job burnout has a negative effect on nurses' quality of working life. Some demographic and work-related factors should be considered when developing interventions to improve nurses' quality of working life. IMPLICATIONS FOR NURSING MANAGEMENT: Hospital and nurses managers should develop strategies to reduce nurses' job burnout and enhance their quality of working life, such as optimum nurse staffing, offer reasonable pecuniary compensation and establish an appropriate shift work schedule.

Comparative Study of Different Methods in Vibration-Based Terrain Classification for Wheeled Robots with Shock Absorbers.

Autonomous robots that operate in the field can enhance their security and efficiency by accurate terrain classification, which can be realized by means of robot-terrain interaction-generated vibration signals. In this paper, we explore the vibration-based terrain classification (VTC), in particular for a wheeled robot with shock absorbers. Because the vibration sensors are usually mounted on the main body of the robot, the vibration signals are dampened significantly, which results in the vibration signals collected on different terrains being more difficult to discriminate. Hence, the existing VTC methods applied to a robot with shock absorbers may degrade. The contributions are two-fold: (1) Several experiments are conducted to exhibit the performance of the existing feature-engineering and feature-learning classification methods; and (2) According to the long short-term memory (LSTM) network, we propose a one-dimensional convolutional LSTM (1DCL)-based VTC method to learn both spatial and temporal characteristics of the dampened vibration signals. The experiment results demonstrate that: (1) The feature-engineering methods, which are efficient in VTC of the robot without shock absorbers, are not so accurate in our project; meanwhile, the feature-learning methods are better choices; and (2) The 1DCL-based VTC method outperforms the conventional methods with an accuracy of 80.18%, which exceeds the second method (LSTM) by 8.23%.