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With the widespread adoption of smartphones， mobile phone addiction among adolescents has emerged as a challenging public health concern. This paper aims to undertake a comprehensive literature review on mobile phone addiction among high school students， both domestically and internationally. It primarily focuses on exploring the conceptual framework， measurement tools， epidemic status， influencing factors and intervention strategies associated with mobile phone addiction in this specific population， so as to provide references for interventions targeting mobile phone addiction among high school students， with the ultimate goal of reducing the incidence rate within this population. ［Funded by Sichuan Provincial Primary Health Development Research Center in 2022， North Sichuan Medical College （number， SWFZ22-C-89）， Mianyang City Social Science Research Key Base-Sichuan Mianyang Minor Psychological Growth Guidance and Research Center 2022 Annual Funding Project （number， SCWCN 2022YB07）］

ObjectiveTo investigate the mediating role of loneliness and social anxiety in the relationship between basic psychological needs satisfaction and smartphone addiction among high school students， and to provide references for the intervention to smartphone addiction for high school students.MethodsIn April 2022， a total of 14 666 high school students from 5 middle schools in a county of Sichuan Province were included by random sampling method. Basic Psychological Needs Scales （BPNS）， the UCLA Loneliness Scale-3 edition （UCLA-3）， Interaction Anxiousness Scale （IAS） and the Mobile Phone Addiction Index （MPAI） were used for cross-sectional investigation. Bootstrap method was used to analyze the mediating role of loneliness and social anxiety in the relationship between the basic psychological needs satisfaction and smartphone addiction.Results① In this study， 14 036 valid questionnaires were returned， and 1 752 （12.48%， 95% CI： 0.114~0.136） high school students were found to be addicted to smartphone phone. The BPNS score was negatively correlated with UCLA-3， IAS and MPAI scores （r=-0.771， -0.486， -0.417， P

ObjectiveTo clarify the relationship between childhood maltreatment and insomnia in middle school students， and to explore the mediating role of post-traumatic stress disorder （PTSD） symptoms and the moderating role of mental resilience involvement.MethodsFrom April to May 2021， a total of 3 412 students in 3 middle schools in a city in western China were selected using convenient sampling method， and all students were assessed by Childhood Trauma Questionnaire （CTQ）， Post Traumatic Stress Disorder Scale （PCL）， Insomnia Severity Index （ISI） and mental resilience scale. The moderated mediating effect analysis was conducted using Process v3.4.ResultsAmong the middle school students， CTQ scores were positively correlated with PCL and ISI scores （r=0.540， 0.320， P

In the process of workpieces production, surface defects are prone to occur, and these defects come in a wide variety and are often intermixed, making defect detection and classification exceptionally challenging. With the development of artificial intelligence and deep learning, to tackle this problem, this paper introduces an enhanced single shot multibox detector algorithm based on MobileNetV2 for the detection of surface defects. The method utilizes MobileNetv2 as the backbone of the feature extraction network to obtain six feature layers with different detection scales from the baseline network, that is, the original 1 × 1 feature prediction layer is deleted and a 75 × 75 feature prediction layer is added, which is closer to the specific features of the defects on the surface of the workpiece. In the additional feature layer, two parallel dilated convolution structures are connected, introducing depth‐separable convolution and dilated convolution, combining skip connections and pixel‐wise addition operations. A special feature fusion structure is proposed to perform feature fusion for small, medium and large target detection layers, which effectively solves the problem of missed and false detection. Moreover, it refines candidate bounding box aspect ratios within the training set through the utilization of the K‐means clustering algorithm, ensuring a better match with real boxes. The experimental results demonstrate the effectiveness of the enhanced model, and the mean average precision value reaches 88.72%. Compared to other state‐of‐the‐art detection methods, it exhibits superior capabilities. [ABSTRACT FROM AUTHOR]

Fulminant type 1 diabetes (FT1D) is a novel type of type 1 diabetes that is caused by extremely rapid destruction of the pancreatic β cells. Early diagnosis or prediction of FT1D is critical for the prevention or timely treatment of diabetes ketoacidosis, which can be life‐threatening. Understanding its triggers or promoting factors plays an important role in the prevention and treatment of FT1D. In this review, we summarised the various triggering factors of FT1D, including susceptibility genes, immunological factors (cellular and humoural immunity), immune checkpoint inhibitor therapies, drug reactions with eosinophilia and systemic symptoms or drug‐induced hypersensitivity syndrome, pregnancy, viral infections, and vaccine inoculation. This review provides the basis for future research into the pathogenetic mechanisms that regulate FT1D development and progression to further improve the prognosis and clinical management of patients with FT1D. [ABSTRACT FROM AUTHOR]

Aims and Objectives: Fulminant type 1 diabetes (FT1D) could present diabetes ketoacidosis (DKA) at early onset. It is crucial to identify FT1D from DKA manifestations in time at clinical practice. This study was aimed at investigating whether the fulminant index (FI), encompassing plasma glucose (PG) to glycated haemoglobin (HbA1c) ratio (PG/HbA1c), serum potassium ion (K+) to HbA1c ratio (K+/HbA1c) and serum sodium ion (Na+) multiplied by HbA1c (Na+*HbA1c), is a feasible indicator for early FT1D diagnosis. Materials and Methods: A total of 78 subjects were enroled, including 40 FT1D patients and 38 non‐FT1D patients with DKA. We utilised receiver operating characteristic (ROC) curve analysis to determine the FI cut‐off values between FT1D and non‐FT1D groups and examined efficacies of FI based on statistics. Results: ROC curve analyses showed that the maximum Youden's index for PG/HbA1c bonding to a cut‐off value of 4.389, with the sensitivity of 75.0% and specificity of 81.6% in identifying FT1D from DKA. And optimal K+/HbA1c cut‐off value was 0.728 with a sensitivity of 90.0% and specificity of 84.2%. For Na+*HbA1c, the best cut‐off value was 923.65, and its sensitivity and specificity were 85% and 73.7%, respectively. Conclusions: These results suggested FI could work as a valid and convenient indicator for differentiating FT1D from initial DKA patients. FI (K+/HbA1c) presented the best efficacy as an independent index. [ABSTRACT FROM AUTHOR]

Background: Type 1 diabetes (T1D) is an autoimmune disease with a complex aetiology. B cells play an important role in the pathogenesis of T1D. Regulatory B cells (Bregs) are a subset of B cells that produce and secrete the inhibitory factor interleukin-10 (IL-10), thereby exerting an anti-inflammatory effect. It was recently discovered that T-cell immunoglobulin mucin domain 1 (Tim-1) is essential for maintaining Bregs function related to immune tolerance. However, the detailed understanding of Tim-1+ Bregs and IL-10+ Bregs in T1D patients is lacking. This study aimed to characterize the profile of B cell subsets in T1D patients compared with that in controls and determine whether Tim-1+ Bregs and IL-10+ Bregs play roles in T1D. Materials and Methods: A total of 47 patients with T1D, 30 patients with type 2 diabetes (T2D) and 24 healthy controls were recruited in this study. Flow cytometry was used to measure the levels of different B cell subsets (including B cells, plasmablasts, and Bregs) in the peripheral blood. Radiobinding assays were performed to detect the antibody titres of T1D patients. In addition, the correlations between different B cell subsets and patient parameters were investigated. Results: Compared with healthy controls, differences in frequency of Tim-1+ Bregs were significantly decreased in patients with T1D (36.53 ± 6.51 vs. 42.25 ± 6.83, P =0.02*), and frequency of IL-10+ Bregs were lower than healthy controls (17.64 ± 7.21 vs. 24.52 ± 11.69, P =0.009**), the frequency of total Bregs in PBMC was also decreased in patients with T1D (1.42 ± 0.53 vs. 1.99 ± 0.93, P =0.002.**). We analyzed whether these alterations in B cells subsets were associated with clinical features. The frequencies of Tim-1+ Bregs and IL-10+ Bregs were negatively related to fasting blood glucose (FBG) (r =-0.25 and -0.22; P =0.01* and 0.03*, respectively). The frequencies of Tim-1+ Bregs and IL-10+ Bregs are positively correlated with fast C-peptide (FCP) (r =0.23 and 0.37; P =0.02* and 0.0001***, respectively). In addition, the frequency of IL-10+ Breg was also negatively related to glycosylated haemoglobin (HbA1c) (r =-0.20, P =0.04*). The frequencies of Tim-1+ Bregs, IL-10+ Bregs and Bregs in T2D patients were reduced, but no statistically significant difference was found between other groups. Interestingly, there was positive correlation between the frequencies of Tim-1+ Bregs and IL-10+ Bregs in T1D (r =0.37, P =0.01*). Of note, it is worth noting that our study did not observe any correlations between B cell subsets and autoantibody titres. Conclusions: Our study showed altered Tim-1 and IL-10 expression in regulatory B cell in T1D patients. Tim-1, as suggested by the present study, is associated with islet function and blood glucose levels. These findings indicate that Tim-1+ Bregs and IL-10+ Bregs were involved in the pathogenesis of T1D. [ABSTRACT FROM AUTHOR]

T cell Ig and mucin domain (Tim) protein family members were identified to be important regulators of the immune response. As their name indicates, Tim proteins were originally considered a T cell-specific markers, and they mainly regulate the responses of T helper cells. However, accumulating evidence indicates that Tims are also expressed on antigen-presenting cells (APCs), such as monocytes, macrophages, dendritic cells (DCs) and B cells, and even plays various roles in natural killer cells (NKs) and mast cells. In recent years, the expression and function of Tims on different cells and the identification of new ligands for the Tim family have suggested that the Tim family plays a crucial role in immune regulation. In addition, the relationship between Tim family gene polymorphisms and susceptibility to several autoimmune diseases has expanded our knowledge of the role of Tim proteins in immune regulation. In this review, we discuss how the Tim family affects immunomodulatory function and the potential role of the Tim family in typical autoimmune diseases, including multiple sclerosis (MS), rheumatoid arthritis (RA), systemic lupus erythematosus (SLE) and type 1 diabetes (T1D). A deeper understanding of the immunoregulatory mechanism of the Tim family might provide new insights into the clinical diagnosis and treatment of autoimmune diseases. [ABSTRACT FROM AUTHOR]

Industrial Internet of Things (IIoT) provide significant support for observing and controlling industrial machinery. In this article, a novel hierarchical feature enhancement network (HFEN) is proposed by combining signal processing and representation learning. The signal processing block extracts features with definite physical significance. Then, the representability of the physical features is improved by connecting stacked denoising autoencoders and squeeze-and-excitation networks. A novel two-stream architecture is designed for HFEN to fuse two types of features. Consequently, HFEN can extract features that can be analyzed for physical significance and that are also representative in terms of recognizable patterns. The experimental results prove that the performance of HFEN is satisfactory in terms of accuracy and efficiency when compared to other methods. Finally, this article also aims to demonstrate the potential of a new pairing that fuses the model- and data-driven strategies for IIoT. [ABSTRACT FROM AUTHOR]

Biclustering analysis of gene expression data can reveal a large number of biologically significant local gene expression patterns. Therefore, a large number of biclustering algorithms apply meta-heuristic algorithms such as genetic algorithm (GA) and cuckoo search (CS) to analyze the biclusters. However, different meta-heuristic algorithms have different applicability and characteristics. For example, the CS algorithm can obtain high-quality bicluster and strong global search ability, but its local search ability is relatively poor. In contrast to the CS algorithm, the GA has strong local search ability, but its global search ability is poor. In order to not only improve the global search ability of a bicluster and its coverage, but also improve the local search ability of the bicluster and its quality, this paper proposed a meta-heuristic algorithm based on GA and CS algorithm (GA-CS Biclustering, Georgia Association of Community Service Boards (GACSB)) to solve the problem of gene expression data clustering. The algorithm uses the CS algorithm as the main framework, and uses the tournament strategy and the elite retention strategy based on the GA to generate the next generation of the population. Compared with the experimental results of common biclustering analysis algorithms such as correlated correspondence (CC), fast, local clustering (FLOC), interior search algorithm (ISA), Securities Exchange Board of India (SEBI), sum of squares between (SSB) and coordinated scheduling/beamforming (CSB), the GACSB algorithm can not only obtain biclusters of high quality, but also obtain biclusters of high-biologic significance. In addition, we also use different bicluster evaluation indicators, such as Average Correlation Value (ACV), Mean-Squared Residue (MSR) and Virtual Error (VE), and verify that the GACSB algorithm has a strong scalability. [ABSTRACT FROM AUTHOR]

The modern industry data is characterized by large volume, large variety, low density value and high processing velocity. Hence, it is difficult to use industry big data for effectively analyzing the trend and the production state by traditional methods. Aiming to solve a problem, a technology platform and data processing framework are established, and the LS-ANN (least square-artificial neural network) method is applied to process the industry big data by analyzing the corresponding technological process and the working principle. By efficiently processing the time series data, this method gives the industry production process with the ability of self-adaptation and fault tolerance. The effectiveness of the proposed method is demonstrated by experimental simulations. [ABSTRACT FROM PUBLISHER]

Farmers are the heart of rural tourism destinations, and their land-responsibility behaviours affect sustainable development. In this study, four rural tourist sites in the suburbs of Chengdu were selected, and the structural equation model was used to analyse the influence of farmers' self-identity on their land-responsibility behaviours intention under the condition of agricultural multifunction perception as a mediation variable. The results show that, in rural tourism destinations of suburban districts of China, farmers' self-identity is an important variable that affects their land-responsibility behaviour intention. Agricultural economic function perception mediates the relationship between farmers' self-identity and land-responsibility behaviour intention. Agricultural non-economic function perception positively affects their agricultural economic function perception. [ABSTRACT FROM AUTHOR]

Data on the effective operation of new pumping station is scarce, and the unit structure is complex, as the temperature changes of different parts of the unit are coupled with multiple factors. The multivariable grey system prediction model can effectively predict the multiple parameter change of a nonlinear system model by using a small amount of data, but the value of its q parameters greatly influences the prediction accuracy of the model. Therefore, the particle swarm optimization algorithm is used to optimize the q parameters and the multi-sensor temperature data of a pumping station unit is processed. Then, the change trends of the temperature data are analyzed and predicted. Comparing the results with the unoptimized multi-variable grey model and the BP neural network prediction method trained under insufficient data conditions, it is proved that the relative error of the multi-variable grey model after optimizing the q parameters is smaller. [ABSTRACT FROM AUTHOR]

Objective: Circular RNAs (circRNAs) are associated with diabetes, but their role in fulminant type 1 diabetes (FT1D) is unclear. Thus, we characterized the role of circRNAs in FT1D., Research Design and Methods: CircRNA expression profiles were detected in peripheral blood mononuclear cells (PBMCs) of five FT1D patients and five controls using a circRNA microarray. An independent cohort comprised of 40 FT1D cases, 75 type 1 diabetes (T1D) cases, and 115 controls was used to verify the circRNAs using quantitative real-time polymerase chain reaction (qRT-PCR). Spearman's correlation analysis and receiver operating characteristic (ROC) curve analysis were performed to determine the clinical diagnostic capability of circRNAs. Bioinformatics was used to identify potential biological functions and circRNA-miRNA-mRNA interactions., Results: There were 13 upregulated and 13 downregulated circRNAs in PBMCs of patients with FT1D. Five circRNAs were further verified in a second cohort. Hsa&#95;circRNA&#95;100632 was significantly upregulated in the FT1D and T1D groups. Hsa&#95;circRNA&#95;100632 was differentiated between patients with FT1D and controls [area under the curve (AUC) 0.846; 95% CI 0.776-0.916; P <0.0001] as well as between patients with FT1D and patients with T1D (AUC 0.726; 95% CI 0.633-0.820; P <0.0001). Bioinformatics analysis showed that hsa&#95;circRNA&#95;100632 may be involved in 47 circRNA-miRNA-mRNA signaling pathways associated with diabetes., Conclusions: CircRNAs were aberrantly expressed in PBMCs of patients with FT1D, and hsa&#95;circRNA&#95;100632 may be a diagnostic marker of FT1D., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Yin, Luo, Qiu, Xiao, Zhang, Xie, Li and Zhou.)

Objectives: Programmed death 1 (PD-1) associated fulminant type 1 diabetes (PFD) is a rare acute and critical in internal medicine, and its clinical characteristics are still unclear. This study aims to analyze the clinical characteristics of PFD patients to improve clinical diagnosis and treatment., Methods: We retrospectively analyzed the clinical data of 10 patients with PFD admitted to the Second Xiangya Hospital of Central South University, combined with the data of 66 patients reported in the relevant literature, analyzed and summarized their clinical and immunological characteristics, and compared the patients with PFD with different islet autoantibody status., Results: Combined with our hospital and literature data, a total of 76 patients with PFD were reported, with the age of (60.9±12.1) years old, 60.0% male and body mass index of (22.1±5.2) kg/m2. In 76 patients, the most common tumors were lung cancer (43.4%) and melanoma (22.4%). Among PD-1 inhibitors, the most common drugs are nivolumab (37.5%) and pembrolizumab (38.9%). 82.2% of PFD patients developed diabetes ketoacidosis. The median onset time from PD-1 related inhibitor treatment to hyperglycemia was 95 (36.0, 164.5) d, and the median treatment cycle before the onset of diabetes was 6 (2.3, 8.0) cycles. 26% (19/73) of PFD patients had positive islet autoantibodies, and the proportion of ketoacidosis in the positive group was significantly higher than that in the negative group (100.0% vs 75.0%, P<0.05). The onset time and infusion times of diabetes after PD-1 inhibitor treatment in the autoantibody positive group were significantly lower than those in the autoantibody negative group (28.5 d vs 120.0 d; 2 cycles vs 7 cycles, both P<0.001)., Conclusions: After initiation of tumor immunotherapy, it is necessary to be alert to the occurrence of adverse reactions of PFD, and the onset of PFD with islet autoantibody positive is faster and more serious than that of patients with autoantibodies negative. Detection of islet autoantibodies and blood glucose before and after treatment with PD-1 inhibitors is of great value for early warning and prediction of PFD.

Objective: Fulminant type 1 diabetes may uniquely occur as a fatal adverse event during immune checkpoint inhibitor (ICI) therapy. We investigated the clinical and immunological characteristics of ICI-associated fulminant type 1 diabetes (IFD)., Research Design and Methods: We enrolled 80 patients with IFD (77 cases from the literature), 56 patients with ICI-associated type 1 diabetes (IT1D) (55 cases from the literature), 45 patients with traditional fulminant type 1 diabetes (TFD), and 43 patients with acute-onset type 1 diabetes for comprehensive analysis including islet autoantibodies and subgroup analysis based on ethnic origin., Results: Patients with IFD accounted for 58.8% (80/136) of patients with ICI-related diabetes. IFD had a more rapid onset than IT1D after ICI therapy (90.5 days vs. 120 days, p <0.05). The onset time and number of infusions after ICI therapy initiation were lower in the antibody-positive IFD group than that in the antibody-negative IFD group (both p <0.001). IFD had a more rapid onset and more serious among Caucasians than that among Asians (p <0.01, p <0.05, respectively), and the prevalence of islet autoantibody positivity in the Caucasian IFD were prominently higher than those in the Asian IFD (p <0.05). Onset age and plasma glucose levels were significantly higher in the IFD group than those in the TFD and acute-onset type 1 diabetes groups. HbA1c levels were slightly higher in patients with IFD than those with TFD., Conclusions: IFD is relatively common in Caucasian population where TFD is very rare or almost absent. IFD occurrence is significantly related to islet autoantibody status and ethnic origin., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Qiu, Luo, Yin, Guo, Xiang, Li, Liu and Zhou.)

Latent autoimmune diabetes in adults (LADA) is a type of diabetes caused by slow progression of autoimmune damage to pancreatic beta cells. According to the etiological classification, LADA should belong to the autoimmune subtype of type 1 diabetes (T1D). Previous studies have found general immune genetic effects associated with LADA, but there are also some racial differences. Multicenter studies have been conducted in different countries worldwide, but it is still unclear how the Chinese and Caucasian populations differ. The epidemiology and phenotypic characteristics of LADA may vary between Caucasian and Chinese diabetic patients as lifestyle, food habits, and body mass index differ between these two populations. The prevalence of LADA in China has reached a high level compared to other countries. The prevalence of LADA in China has reached a high level compared to other countries, and the number of patients with LADA ranks first in the world. Previous studies have found general immune genetic effects associated with LADA, but some racial differences also exist. The prevalence of LADA among newly diagnosed type 2 diabetes patients over the age of 30 years in China is 5.9%, and LADA patients account for 65% of the newly diagnosed T1D patients in the country. As a country with a large population, China has many people with LADA. A summary and analysis of these studies will enhance further understanding of LADA in China. In addition, comparing the similarities and differences between the Chinese and the Caucasian population from the perspectives of epidemiology, clinical, immunology and genetics will help to improve the understanding of LADA, and then promote LADA studies in individual populations., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Qiu, Xiao, Zhang, Luo and Zhou.)