Influencing factors of inter-nursing lateral violence: A qualitative systematic review.

BACKGROUND: Lateral violence is a global social problem that has attracted considerable attention in the field of public health. This has seriously affected the quality of care, the safety of patients' lives and the career development of nurses. OBJECTIVE: To systematically evaluate the factors influencing of nursing lateral violence and provide evidence for preventing and reducing inter-nursing lateral violence. METHODS: A systematic review of qualitative study was performed in accordance with the Enhancing Transparency in Reporting the Synthesis of Qualitative Research (ENTREQ) guidelines. We collected qualitative studies on the factors influencing of inter-nursing lateral violence by searching PubMed, EMbase, The Cochrane Library, Web of Science, CINAHL, Science Direct, WanFang Data, China National Knowledge Infrastructure (CNKI), Chinese Scientific Journal Database (VIP) and Chinese Biomedical Literature Database (CBM). Data from inception to September 2023. Literature screening and data extraction were independently conducted by two reviewers. The Critical Appraisal Skills Program (CASP) scale was employed to assess the quality of the studies, including objectives, methodologies, designs, results and contributions. RESULTS: A total of 25 studies involving 882 participants were included. The results of the thematic analysis indicated that inter-nursing lateral violence was influenced by hospital management, perpetrators, victims and sociodemographic factors. CONCLUSION: Inter-nursing lateral violence was influenced by multidimensional factors. To reduce the occurrence of horizontal violence among nurses, hospitals need to explore the establishment and improvement of a horizontal violence resolution mechanism, and schools should pay attention to the joint support and education of nursing students, create a good working environment and harmonious nursing culture, and promote mutual respect among nurses. RELEVANCE TO CLINICAL PRACTICE: This review emphasises the importance of the influencing factors of horizontal violence among nurses, analyses the importance of influencing factors from different perspectives, and proposes corresponding measures to reduce inter-nursing lateral violence. NO PATIENT OR PUBLIC CONTRIBUTION: This study was mostly a literature review; neither patients nor pertinent staff were involved in either the design or conduct of the investigation.

BAG3 promotes autophagy and suppresses NLRP3 inflammasome activation in Parkinson's disease.

Background: Neuroinflammation mediated by microglia plays a key role in the pathogenesis of Parkinson's disease (PD), and our previous studies showed this was significantly inhibited by enhanced autophagy. In the autophagy pathway, Bcl2-associated athanogene (BAG)3 is a prominent co-chaperone, and we have shown BAG3 can regulate autophagy to clear the PD pathogenic protein α-synuclein. However, the connection between BAG3 and microglia mediated neuroinflammation is not clear. Methods: In this study, we explored whether BAG3 regulated related neuroinflammation and its original mechanism in PD. An inflammatory model of PD was established by injecting adeno-associated virus (AAV)-BAG3 into the bilateral striatum of C57BL/6 male mice to induce overexpression of BAG3, followed by injection of lipopolysaccharide (LPS). The striatum was extracted at 3 days after injection of LPS for Western blotting and reverse transcription quantitative polymerase chain reaction (RT-qPCR), and immunohistochemical staining was performed at 21 days after injection. At the same time, LPS was used to induce activation of BV2 cells to verify the effect of BAG3 in vitro. Results: Overexpression of BAG3 reduced LPS-induced pyroptosis by reducing activation of caspase-1, the NOD-like receptor family, and the pyrin domain-containing 3 (NLRP3) inflammasome, and by release of interleukin (IL)-1ß and tumor necrosis factor (TNF)-α. The LPS-induced inflammatory environment inhibits autophagy, and overexpression of BAG3 can restore autophagy, which may be the mechanism by which BAG3 reduces neuronal inflammation in PD. Conclusions: Our results demonstrate BAG3 promotes autophagy and suppresses NLRP3 inflammasome formation in PD.

Analysis of Diagnostic Value of Electrogastrography for Parkinson's Disease and its Predictive Value for the Disease Progression.

The study aimed to evaluate the diagnostic and prognostic value of indexes detected by electrogastrography in Parkinson's disease patients. One hundred twenty early Parkinson's disease patients and 120 healthy controls were recruited, and underwent electrogastrography to detect dominant frequency (DF), instability coefficient of DF (ICDF), low frequency range (LFR), high frequency range (HFR), and normal frequency range (NFR). The receiver operating characteristic (ROC) curve was drawn for the diagnostic value analysis. The motor function was scored with the Unified Parkinson's Disease Rating Scale (UPDRS). Sniffin' Sticks test was used for the olfactory evaluation, and the TDI score consisting of odor threshold (T), odor discrimination (D) and odor identification (I) tests was calculated. The preprandial ICDF of Parkinson's disease patients was significantly higher than that of the control group, and decreased slowly during the late postprandial phase. The levels of LFR%, HFR% and NFR% in Parkinson's disease patients were higher than the control group during both the preprandial and late postprandial phase, and the changes of each index before and after meals were not obvious. Preprandial ICDF value and TDI score had the ability to distinguish Parkinson's disease patients with the AUC of 0.874 and 0.859 respectively. The ICDF detected by electrogastrography has high clinical value in the early diagnosis of Parkinson's disease, and the combination of ICDF and TDI can improve the diagnostic sensitivity and specificity of a single indicator. High ICDF levels during the preprandial phase are related to the poor prognosis of Parkinson's disease patients after treatment.

Publisher Correction: Study on the Visualization of Pigment in Haematococcus pluvialis by Raman Spectroscopy Technique.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Identification of heavy metal by testing microalgae using confocal Raman microspectroscopy technology.

Five copper concentrations (0, 0.5, 1, 2, and 4 mg/l) were used to stress C. pyrenoidosa continuously for five days. The biomass, chlorophyll, and carotenoids of microalgae were measured, and Raman mapping spectral data and Raman single-point spectral data of microalgae were acquired. Principal component-linear discriminant analysis, back propagation-artificial neural network (BP-ANN), and sensitive wavelengths-linear discriminant analysis were used to build models to identify different copper concentrations using the spectral data after pretreatment. The results showed that the BP-ANN model was optimal to identify copper concentrations with prediction accuracy of 92% on day 4.

Study on the Visualization of Pigment in Haematococcus pluvialis by Raman Spectroscopy Technique.

As an ideal raw material for the production of astaxanthin, H. pluvialis was drawing attention during the last few years, there are some research topics initiated to find out the synthetic pathway of astaxanthin in H. pluvialis. In this study, confocal microscopic Raman technology was utilized to analyze the point-by-point mapping for H. pluvialis, and the visualization of pigment such as carotenoid and astaxanthin content were achieved. By comparing the Raman spectra of H. pluvialis and standard product of astaxanthin, and using the C = C stretching vibration of the Raman intensity as the main indicator for carotenoids, the visual spatial distribution for the carotenoids content was obtained. The MCR-ALS was applied to analyze the Raman data of H. pluvialis, and the information of astaxanthin was extracted to achieve real-time spatial distribution. The visualization of astaxanthin content shows that MCR-ALS is very effective for extracting the information of astaxanthin content from H. pluvialis. By exploring the spatial distribution of carotenoids and astaxanthin contents, analyzing the spatial distribution rules during its growth, Raman spectroscopy technology can be utilized to investigate the internal components of the pigment (ataxanthin, etc.) in H. pluvialis, which make it as an effective methodology to monitor the accumulation and changing mechanism of pigment content in microalgae.

Grading of Chinese Cantonese Sausage Using Hyperspectral Imaging Combined with Chemometric Methods.

Fast and accurate grading of Chinese Cantonese sausage is an important concern for customers, organizations, and the industry. Hyperspectral imaging in the spectral range of 874-1734 nm, combined with chemometric methods, was applied to grade Chinese Cantonese sausage. Three grades of intact and sliced Cantonese sausages were studied, including the top, first, and second grades. Support vector machine (SVM) and random forests (RF) techniques were used to build two different models. Second derivative spectra and RF were applied to select optimal wavelengths. The optimal wavelengths were the same for intact and sliced sausages when selected from second derivative spectra, while the optimal wavelengths for intact and sliced sausages selected using RF were quite similar. The SVM and RF models, using full spectra and the optimal wavelengths, obtained acceptable results for intact and sliced sausages. Both models for intact sausages performed better than those for sliced sausages, with a classification accuracy of the calibration and prediction set of over 90%. The overall results indicated that hyperspectral imaging combined with chemometric methods could be used to grade Chinese Cantonese sausages, with intact sausages being better suited for grading. This study will help to develop fast and accurate online grading of Cantonese sausages, as well as other sausages.

A non-destructive method for quantification the irradiation doses of irradiated sucrose using Vis/NIR spectroscopy.

This article proposes a new method for fast discrimination of irradiation doses of sucrose based on visible-near infrared (Vis/NIR) spectroscopy technology. 250 sucrose samples were categorized into five groups to be irradiated at 0, 1.5, 3.0, 4.5, 6.0 kGy respectively and prepared for the discrimination analysis. The 50 samples of each group were randomly divided into a calibration set containing 40 samples, and a validation set containing the remaining 10 samples. Principal component clustering analysis (PCCA) was applied for the extraction of principal components (PCs) and for clustering analysis. The first five PCs were regarded as the inputs to develop the back propagation neural network (BPNN) model. The performance of the model was validated by the 50 unknown samples and the BPNN achieved an excellent precision and recognition ration of 100%. The results indicated that Vis/NIR spectroscopy could be utilized as a rapid and non-destructive method for the classification of different irradiation doses of irradiated sucrose.