Schwann cells and macrophages are the main immune cells involved in peripheral nerve injury. After injury, Schwann cells produce an inflammatory response and secrete various chemokines, inflammatory factors, and some other cytokines to promote the recruitment and M2 polarization of blood-derived macrophages, enhancing their phagocytotic ability, and thus play an important role in promoting nerve regeneration. Macrophages have also been found to promote vascular regeneration after injury, promote the migration and proliferation of Schwann cells along blood vessels, and facilitate myelination and axon regeneration. Therefore, there is a close interaction between Schwann cells and macrophages during peripheral nerve regeneration, but this has not been systematically summarized. In this review, the mechanisms of action of Schwann cells and macrophages in each other's migration and phenotypic transformation are reviewed from the perspective of each other, to provide directions for research on accelerating nerve injury repair.
Schwann Cells/metabolism* ; Peripheral Nerve Injuries/physiopathology* ; Animals ; Macrophages/immunology* ; Nerve Regeneration/physiology* ; Humans ; Cell Movement/physiology*

In clinical dentistry,addressing unique conditions such as tilted,elongated,and torsion teeth during prepa-ration can be effectively managed through digital tooth morphology design.The production of a multi-stage 3D-printed guide offered a more efficient and accurate solution.This article presented a case of significant inclination,elongation,and torsion in the maxillary and mandibular canines that were successfully treated using crown restoration modification.A crown preparation guide was fabricated based on the final form design of the restoration using the target restorative space analysis technique to ensure precise tooth preparation.A tooth preparation guide was also designed and utilized fur-ther to enhance accuracy and efficiency during complex tooth preparation.The combined application of these multi-stage guides demonstrated promising clinical prospects.

A 11-year old female patient with severe thalassemia, receipt a lentivirus-based cell and gene therapy (CGT) therapy in Shenzhen Children′s Hosptial on July 27th, 2021. At the two follow-up visits after discharge, patient were continuously tested positive for HIV screening through HIV Ag/Ab Combo assay (chemiluminescence Immunoassay), and the viral load results of HIV-1 nucleic acid testing (NAT) were both>5 000 copies/ml. The patient can be diagnosed with HIV infection according to the National Guideline for Detection of HIV/AIDS(2020 Revised Edition). The thorough investigation findings and supplementary experiment results indicated that the false-positive HIV-1 NAT results was caused by cross-reactivity between the target sites detected by conventional HIV-1 NAT reagents and the lentiviral vectors fragments integrated into the genome of patient′s hematopoietic stem/progenitor cells. In conclusion, it is important for laboratories to select appropriate HIV-1 NAT testing platforms which won′t cause cross-reactivity for the testing of samples from patients who have been treated with HIV-derived vectors. It is also recommended to design and develop NAT testing platforms with multiple target regions labeled by different fluorescents for HIV NAT supplementation experiment to reduce the risk of false-positive diagnoses of HIV infection.

Objective:To explore the mediating effect of experiential avoidance and emotional eating between intolerable uncertainty and life satisfaction of college students.Methods:From October to December 2021, a total of 890 college students in the Beijing-Tianjin-Hebei region were tested by the intolerable uncertainty scale (simplified version), the acceptance action scale second edition, the Dutch eating behavior questionnaire and the life satisfaction scale. All data processing and analysis were preformed by SPSS 22.0 software, and the mediating effect was tested by Pearson correlation analysis and Bootstrap analysis.Results:The intolerable uncertainty score was (26.43±7.64), the experiential avoidance score was (22.63±9.67), the emotional eating score was (26.43 ±11.83), and the life satisfaction score was (16.74±6.03). Intolerable uncertainty was significantly and negatively correlated with life satisfaction( r=-0.76, P<0.01), and was significantly and positively correlated with experience avoidance and emotional eating( r=0.66, 0.78, both P<0.01). Experience avoidance was significantly and positively correlated with emotional eating( r=0.70, P<0.01), and was significantly and negatively correlated with life satisfaction( r=-0.62, P<0.01). Emotional eating was significantly and negatively correlated with life satisfaction( r=-0.68, P<0.01). Intolerable uncertainty affects life satisfaction through four paths.The direct effect value of intolerable uncertainty on life satisfaction was -0.53, accounting for 70.23% of the total effect. The separate mediating effect value of experience avoidance was -0.11, accounting for 50.00% of the total indirect effect.The separate mediating effect value of emotional eating was -0.08, accounting for 36.36% of the total indirect effect, and the chain mediating effect value of experience avoidance and emotional eating was -0.03, accounting for 13.64% of the total indirect effect. Conclusion:Intolerable uncertainty can directly affect life satisfaction of college students and indirectly through experiencing avoidance and emotional eating.

With the development of information technology and the changes in people's lifestyle, E-health has become a new way for people to receive health services and has been widely applied in the prevention and control of HIV infection, including behavioral intervention, biomedical intervention, mental health intervention, improving access to health services and so on. This paper summarizes the application of E-health intervention in HIV prevention and control both at home and abroad to provide reference for the further optimization of planning and strategies of E-health intervention in HIV infection prevention.

Objective:To explore the mediating roles of rumination and avoidance coping between information anxiety and suicidal ideation.Methods:In March 2022, a total of 896 college students were surveyed by the information anxiety scale(IAS), ruminative responses scale(RRS), coping style questionnaire(CSQ)and self-rating idea of suicidal ideation scale(SIOSS). The ANOVA analysis, Pearson correlation analysis and mediating effect analysis were performed by the SPSS 22.0 software.Results:The detection rate of suicidal ideation among college students was 9.15%(82/896). The information anxiety score was(73.84±17.29), the rumination score was(47.73±12.16), the avoidance coping score was(3.76±2.52), and the suicidal ideation score was(5.41±4.09). Information anxiety was significantly positively correlated with rumination, avoidance coping and suicidal ideation( r=0.49, 0.36, 0.37, all P<0.05). Rumination was significantly positively correlated with avoidance coping and suicidal ideation( r=0.42, 0.59, both P<0.05). There was a significantly positive correlation between avoidance coping and suicidal ideation( r=0.45, P<0.05). Information anxiety affected suicidal ideation of college students through four paths.The direct effect value of information anxiety on suicidal ideation was 0.06, accounting for 16.67% of the total effect. The effect values of the separate mediating effect of rumination and avoid coping were 0.22 and 0.04, and accounting for 61.11% and 11.11% of the total effect respectively. The chain mediating effect value of rumination and avoid coping was 0.04, accounting for 11.11% of the total effect. Conclusion:Information anxiety can directly affect suicidal ideation of college students and indirectly affect suicidal ideation through rumination and avoidance coping.

In the diagnosis of cardiovascular diseases, the analysis of electrocardiogram (ECG) signals has always played a crucial role. At present, how to effectively identify abnormal heart beats by algorithms is still a difficult task in the field of ECG signal analysis. Based on this, a classification model that automatically identifies abnormal heartbeats based on deep residual network (ResNet) and self-attention mechanism was proposed. Firstly, this paper designed an 18-layer convolutional neural network (CNN) based on the residual structure, which helped model fully extract the local features. Then, the bi-directional gated recurrent unit (BiGRU) was used to explore the temporal correlation for further obtaining the temporal features. Finally, the self-attention mechanism was built to weight important information and enhance model's ability to extract important features, which helped model achieve higher classification accuracy. In addition, in order to mitigate the interference on classification performance due to data imbalance, the study utilized multiple approaches for data augmentation. The experimental data in this study came from the arrhythmia database constructed by MIT and Beth Israel Hospital (MIT-BIH), and the final results showed that the proposed model achieved an overall accuracy of 98.33% on the original dataset and 99.12% on the optimized dataset, which demonstrated that the proposed model can achieve good performance in ECG signal classification, and possessed potential value for application to portable ECG detection devices.
Humans ; Electrocardiography ; Algorithms ; Cardiovascular Diseases ; Databases, Factual ; Neural Networks, Computer

Objective:To establish a differential diagnosis model for IgA nephropathy and non-IgA nephropathy based on machine learning algorithms.Methods:Retrospective study adopted,from 2019 to 2020,260 patients were referred to the Department of Nephrology at the First Affiliated Hospital of Kunming Medical University, the First People′s Hospital in Yunnan province, and Yan′an Hospital of Kunming city. All patients were diagnosed by renal pathology, 130 cases of primary IgA nephropathy, the 130 cases of non-IgA nephropathy. Collection of materials, including gender and age, 28 clinical data, and routine laboratory test results,the sex ratio of IgA nephropathy group and non-IgA nephropathy group were 59∶71 and 64∶66 respectively, the ages were 37.20 (21.89, 53.78) and 43.30 (27.77, 59.18) years, respectively. 260 patients were divided into a training set (70%, 182 cases) and a test set (30%, 78 cases). Using the decision tree, random forests, support vector machine, extreme gradient boosting to establish a differential diagnosis model for IgA nephropathy and non-IgA nephropathy. Based on the true positive rate, true negative rate, false-positive rate, false-negative rate, accuracy, subjects features work area under the curve(AUC), the precision ratio, recall ratio, and F1 score, comprehensively evaluate the performance of each model, finally, the best performance of the model was chosen. Using SPSS 25.0 to analyze the data, P<0.05 was considered to be statistically significant. Results:The accuracy of the decision tree, support vector machine, random forests and extreme gradient boosting establish differential diagnosis model was 67.95%, 70.51%, 80.77% and 83.33%, respectively; AUC values was 0.74, 0.76, 0.80 and 0.83; Judgment for primary IgA nephropathy F1 score was 0.73, 0.72, 0.80 and 0.83, respectively. The efficiency of the extreme gradient boosting model based on the above evaluation indicators is the highest, its diagnosis of IgA nephropathy of the sensitivity and specificity respectively 89% and 79%. The variable importance from high to low was blood albumin, IgA/C3, serum creatinine, age, urine protein, urine albumin, high-density lipoprotein cholesterol, urea.Conclusion:The differential diagnosis model for IgA nephropathy was established successfully and non-IgA nephropathy and the efficiency performance of the extreme gradient boosting algorithm was the best.

Metabolic regulation has been proven to play a critical role in T cell antitumor immunity. However, cholesterol metabolism as a key component of this regulation remains largely unexplored. Herein, we found that the low-density lipoprotein receptor (LDLR), which has been previously identified as a transporter for cholesterol, plays a pivotal role in regulating CD8