Positive spiritual climate supports transformational leadership as means to reduce nursing burnout and intent to leave.

AIM: To explore the relationship between spiritual climate and transformational leadership, and examine their impact on nurses perceived emotional exhaustion and intentions to quit. BACKGROUND: Transformational leadership is known to have a significant positive effect on work environment and job satisfaction. Additionally, promoting spiritual climate amongst staff can benefit workers by increasing self-worth. The relationship between the two is unknown. METHODS: Nurse clinicians from 2 sites in the Jiangsu Province of China completed self-report questionnaires based on spiritual climate, emotional exhaustion, clinical leadership and Turnover Intention Scales. Mediation analysis was applied to evaluate impact of spiritual climate. RESULTS: Perceived positive spirituality amongst nurse clinicians reinforces transformational leadership to reduce emotional exhaustion (indirect effect of -0.089, p < .01). Burnout and intention to leave showed significantly positive correlation with lower levels of perceived spirituality (r = .545, p < .01). CONCLUSION: Transformational leadership in the workplace can reduce nurses' burnout, and a positive spiritual climate increases meaningfulness in their work. This may help in nurse retention. IMPLICATIONS FOR NURSING MANAGEMENT: Health care leaders must look beyond transformational leadership to maintain a positive and supportive clinical climate, and this may involve acknowledgement of nurses' spiritual needs.

Relationship between burnout and intention to leave amongst clinical nurses: The role of spiritual climate.

AIM: This study aims to identify the role that spiritual climate has in reducing burnout and intentions to leave amongst clinical nurses. BACKGROUND: Both shortages and the high turnover of nurses are challenging problems worldwide. Enhancing the spiritual climate amongst nurses can enhance teamwork, organisational commitment and job satisfaction and can play a role in reducing burnout and turnover intention. METHODS: A total of 207 clinical nurses working at a tertiary university hospital were included in this cross-sectional, single-site study. Independent-samples t test and ANOVA, Pearson correlation analysis and hierarchical regression analysis were used to explore the relationships amongst related factors. RESULTS: Most clinical departments showed a moderate spiritual climate (60.24 ± 0.82) with high job burnout (33.62 ± 0.28) and turnover intention (2.37 ± 0.57). A good spiritual climate was correlated with high job satisfaction (r = 0.412, p < 0.01), low burnout and turnover intention (r = -0.423, p < 0.01 and r = -0.292, p < 0.01, respectively). Spiritual climate could also indirectly influence nurses' job burnout and turnover intention (R2  = 10.31%). CONCLUSIONS: Different departments have different spiritual climates. The findings from this study indicate that spiritual climate may impact nursing burnout and turnover. IMPLICATIONS FOR NURSING MANAGEMENT: Using a spiritual climate scale provides health care decision-makers with clear information about staff spirituality well-being. Interventions to improve spiritual climate can benefit teamwork in clinical departments.

Polydopamine Nanoparticles-Based Three-Line Lateral Flow Immunoassay for COVID-19 Detection.

Currently, the global trend of several hundred thousand new confirmed COVID-19 patients per day has not abated significantly. Serological antibody detection has become an important tool for the self-screening of people. While the most commonly used colorimetric lateral flow immunoassay (LFIA) methods for the detection of COVID-19 antibodies are limited by low sensitivity and a lack of quantification ability. This leads to poor accuracy in the screening of early COVID-19 patients. Therefore, it is necessary to develop an accurate and sensitive autonomous antibody detection technique that will effectively reduce the COVID-19 infection rate. Here, we developed a three-line LFIA immunoassay based on polydopamine (PDA) nanoparticles for COVID-19 IgG and IgM antibodies detection to determine the degree of infection. The PDA-based three-line LFIA has a detection limit of 1.51 and 2.34 ng/mL for IgM and IgG, respectively. This assay reveals a good linearity for both IgM and IgG antibodies detection and is also able to achieve quantitative detection by measuring the optical density of test lines. In comparison, the commercial AuNP-based LFIA showed worse quantification results than the developed PDA-based LFIA for low-concentration COVID-19 antibody samples, making it difficult to distinguish between negative and positive samples. Therefore, the developed PDA-based three-line LFIA platform has the accurate quantitative capability and high sensitivity, which could be a powerful tool for the large-scale self-screening of people.

Wearable Hydrogel-Based Epidermal Sensor with Thermal Compatibility and Long Term Stability for Smart Colorimetric Multi-Signals Monitoring.

Hydrogel-based wearable epidermal sensors (HWESs) have attracted widespread attention in health monitoring, especially considering their colorimetric readout capability. However, it remains challenging for HWESs to work at extreme temperatures with long term stability due to the existence of water. Herein, a wearable transparent epidermal sensor with thermal compatibility and long term stability for smart colorimetric multi-signals monitoring is developed, based on an anti-freezing and anti-drying hydrogel with high transparency (over 90% transmittance), high stretchability (up to 1500%) and desirable adhesiveness to various kinds of substrates. The hydrogel consists of polyacrylic acid, polyacrylamide, and tannic acid-coated cellulose nanocrystals in glycerin/water binary solvents. When glycerin readily forms strong hydrogen bonds with water, the hydrogel exhibits outstanding thermal compatibility. Furthermore, the hydrogel maintains excellent adhesion, stretchability, and transparency after long term storage (45 days) or at subzero temperatures (-20 °C). For smart colorimetric multi-signals monitoring, the freestanding smart colorimetric HWESs are utilized for simultaneously monitoring the pH, T and light, where colorimetric signals can be read and stored by artificial intelligence strategies in a real time manner. In summary, the developed wearable transparent epidermal sensor holds great potential for monitoring multi-signals with visible readouts in long term health monitoring.

Similar color analysis based on deep learning (SCAD) for multiplex digital PCR via a single fluorescent channel.

Digital PCR (dPCR) has recently attracted great interest due to its high sensitivity and accuracy. However, the existing dPCR depends on multicolor fluorescent dyes and multiple fluorescent channels to achieve multiplex detection, resulting in increased detection cost and limited detection throughput. Here, we developed a deep learning-based similar color analysis method, namely SCAD, to achieve multiplex dPCR in a single fluorescent channel. As a demonstration, we designed a microwell chip-based diplex dPCR system for detecting two genes (blaNDM and blaVIM) with two kinds of green fluorescent probes, whose emission colors are difficult to discriminate by traditional fluorescence intensity-based methods. To verify the possibility of deep learning algorithms to distinguish the similar colors, we first applied t-distributed stochastic neighbor embedding (tSNE) to make a clustering map for the microwells with similar fluorescence. Then, we trained a Vision Transformer (ViT) model on 10 000 microwells with two similar colors and tested it with 262 202 microwells. Lastly, the trained model was proven to have highly accurate classification ability (>98% for both the training set and the test set) and precise quantification ability on both blaNDM and blaVIM (ratio difference <0.10). We envision that the developed SCAD method would significantly expand the detection throughput of dPCR without the need for other auxiliary equipment.

Artificial intelligence-assisted colorimetric lateral flow immunoassay for sensitive and quantitative detection of COVID-19 neutralizing antibody.

Currently, vaccination is the most effective medical measure to improve group immunity and prevent the rapid spread of COVID-19. Since the individual difference of vaccine effectiveness is inevitable, it is necessary to evaluate the vaccine effectiveness of every vaccinated person to ensure the appearance of herd immunity. Here, we developed an artificial intelligent (AI)-assisted colorimetric polydopamine nanoparticle (PDA)-based lateral flow immunoassay (LFIA) platform for the sensitive and accurate quantification of neutralizing antibodies produced from vaccinations. The platform integrates PDA-based LFIA and a smartphone-based reader to test the neutralizing antibodies in serum, where an AI algorithm is also developed to accurately and quantitatively analyze the results. The developed platform achieved a quantitative detection with 160 ng/mL of detection limit and 625-10000 ng/mL of detection range. Moreover, it also successfully detected totally 50 clinical serum samples, revealing a great consistency with the commercial ELISA kit. Comparing with commercial gold nanoparticle-based LFIA, our PDA-based LFIA platform showed more accurate quantification ability for the clinical serum. Therefore, we envision that the AI-assisted PDA-based LFIA platform with sensitive and accurate quantification ability is of great significance for large-scale evaluation of vaccine effectiveness and other point-of-care immunoassays.