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As a result of the obesity epidemic, more people are concerned about losing weight; however, weight regain is common, leading to repeated weight loss and weight cycling. The health benefits of early weight loss are nullified by weight regain after weight cycling, which has much more severe metabolic consequences. Weight cycling alters body composition, resulting in faster fat recovery and slower muscle reconstruction. This evident fat accumulation, muscle loss, and ectopic fat deposition destroy the intestinal barrier, increase the permeability of the small intestinal epithelium, and cause the lipotoxicity of lipid metabolites and toxins to leak into extraintestinal tissues and circulation. It causes oxidative stress and hypoxia in local tissues and immune cell infiltration in various tissues, all contributing to the adaptation to this metabolic change. Immune cells transmit inflammatory responses in adipose and skeletal muscle tissue by secreting cytokines and adipokines, which mediate immune cell pathways and cause metaflammation and inefficient metabolic degradation. In this review, we focus on the regulatory function of the immunological microenvironment in the final metabolic outcome, with a particular emphasis on the cellular and molecular processes of local and systemic metaflammation induced by weight cycling-induced changes in body composition. Metaflammation in adipose and muscle tissues that is difficult to relieve may cause weight cycling. As this chronic low-grade inflammation spreads throughout the body, metabolic complications associated with weight cycling are triggered. Inhibiting the onset and progression of metabolic inflammation and enhancing the immune microenvironment of adipose and muscle tissues may be the first step in addressing weight cycling.

Background Obesity is a global public health concern. The goal of this study was to see if eating habits could mediate the relationship between psychological distress and weight maintenance in a population with a history of weight cycling. Methods A 3-month outpatient intervention consisting of a diet and exercise program was provided to 153 participants. Psychological distress, appetite, and behavior were assessed at the beginning and end of the study. Anthropometric measurements were taken at baseline and six months. Results After the structural equation model was developed, it was discovered that the psychological status of people with obesity and weight cycling histories correlated with the weight loss outcome effect (three and six months). This effect was mediated by factors related to eating behavior. Associative psychological factors had a direct effect on eating behavior (three months: β = 0.181, 95% CI: 0.055–0.310; six months: β = 0.182, 95% CI: 0.039–0.332) and appetite had a direct effect on eating behavior (three months: β = 0.600, 95% CI: 0.514–0.717; six months: β = 0.581, 95% CI: 0.457–0.713), both of which were significant ( p  < 0.01). At three months, psychological distress has a more substantial positive impact on weight change, with eating behavior acting as a partial mediator. At six months, there was no support for appetite’s moderating role in eating behavior. Conclusions The findings suggest that psychological interventions should be strengthened to improve weight loss effectiveness, particularly in participants with a history of weight cycling, making weight loss more complicated and prone to rebound. Clinical trial registration The study has been registered in Clinical Trials (NCT05311462).

In this paper, the laser shot peening (LSP) technology of H13 steel is studied to improve the friction and wear performance of shield machine hob. And to utilize the LSP experiment and simulation analysis, the influence of LSP parameters on the friction and wear performance of H13 steel after strengthening is studied. The numerical simulation study reveals the variation law of laser parameters on residual stress and stress layer depth. Study on friction and wear characteristics of H13 steel after LSP. The results show that the maximum residual stress of H13 steel is 911 MPa, and the hardness is 650.7 HV, when there are three times of black paint absorption and LSP. Compared with the raw material, the residual stress is increased by 125%, and the hardness is increased by 18%. And its friction coefficients and wear volume were relatively lower than other schemes. The average friction coefficient and wear volume were reduced by 10.8% and 57.2% respectively.

This article reviews the current status of automotive brake disc research and the prospects for future research. At present, the research of brake disc performance mainly includes thermal conductivity, thermal fatigue resistance, wear resistance, and brake noise. It is found that a new alloy composite, heat treatment process, ceramic composite, new structure, and new materials are emerging. At the same time, it was found that ceramic and resin were used as the matrix, fiber materials were used as reinforcements to prepare brake discs, the addition of new fillers and the study of special reinforcement materials have become new hotspots in the study of brake discs. In the future development, carbon-fiber ceramic brake discs may become the main research focus of brake discs.

BackgroundAlthough studies have shown that cell pyroptosis is involved in the progression of asthma, a systematic analysis of the clinical significance of pyroptosis-related genes (PRGs) cooperating with immune cells in asthma patients is still lacking.MethodsTranscriptome sequencing datasets from patients with different disease courses were used to screen pyroptosis-related differentially expressed genes and perform biological function analysis. Clustering based on K-means unsupervised clustering method is performed to identify pyroptosis-related subtypes in asthma and explore biological functional characteristics of poorly controlled subtypes. Diagnostic markers between subtypes were screened and validated using an asthma mouse model. The infiltration of immune cells in airway epithelium was evaluated based on CIBERSORT, and the correlation between diagnostic markers and immune cells was analyzed. Finally, a risk prediction model was established and experimentally verified using differentially expressed genes between pyroptosis subtypes in combination with asthma control. The cMAP database and molecular docking were utilized to predict potential therapeutic drugs.ResultsNineteen differentially expressed PRGs and two subtypes were identified between patients with mild-to-moderate and severe asthma conditions. Significant differences were observed in asthma control and FEV1 reversibility between the two subtypes. Poor control subtypes were closely related to glucocorticoid resistance and airway remodeling. BNIP3 was identified as a diagnostic marker and associated with immune cell infiltration such as, M2 macrophages. The risk prediction model containing four genes has accurate classification efficiency and prediction value. Small molecules obtained from the cMAP database that may have therapeutic effects on asthma are mainly DPP4 inhibitors.ConclusionPyroptosis and its mediated immune phenotype are crucial in the occurrence, development, and prognosis of asthma. The predictive models and drugs developed on the basis of PRGs may provide new solutions for the management of asthma.

Autophagy has been proven to play an important role in the pathogenesis of asthma and the regulation of the airway epithelial immune microenvironment. However, a systematic analysis of the clinical importance of autophagy-related genes (ARGs) regulating the immune microenvironment in patients with asthma remains lacking. Clustering based on the k-means unsupervised clustering method was performed to identify autophagy-related subtypes in asthma. ARG-related diagnostic markers in low-autophagy subtypes were screened, the infiltration of immune cells in the airway epithelium was evaluated by the CIBERSORT, and the correlation between diagnostic markers and infiltrating immune cells was analyzed. On the basis of the expression of ARGs and combined with asthma control, a risk prediction model was established and verified by experiments. A total of 66 differentially expressed ARGs and 2 subtypes were identified between mild to moderate and severe asthma. Significant differences were observed in asthma control and FEV1 reversibility between the two subtypes, and the low-autophagy subtype was closely associated with severe asthma, energy metabolism, and hormone metabolism. The autophagy gene was identified as a diagnostic marker and was related to the infiltration of immune cells, such as activated mast cells and neutrophils. Combined with asthma control, a risk prediction model was constructed, the expression of five risk genes was supported by animal experiments, was established for ARGs related to the prediction model. Autophagy plays a crucial role in the diversity and complexity of the asthma immune microenvironment and has clinical value in treatment response and prognosis.

Iron deficiency has detrimental effects in patients with acute coronary syndrome (ACS), which is a common nutritional disorder and inflammation-related disease affects up to one-third people worldwide. However, the specific role of iron metabolism in ACS progression is opaque. In this study, we construct an iron metabolism-related genes (IMRGs) based molecular signature of ACS and to identify novel iron metabolism gene markers for early stage of ACS. The IMRGs were mainly collected from Molecular Signatures Database (mSigDB) and two relevant studies. Two blood transcriptome datasets GSE61144 and GSE60993 were used for constructing the prediction model of ACS. After differential analysis, 22 IMRGs were differentially expressed and defined as DEIGs in the training set. Then, the 22 DEIGs were trained by the Elastic Net to build the prediction model. Five genes, PADI4, HLA-DQA1, LCN2, CD7, and VNN1, were determined using multiple Elastic Net calculations and retained to obtain the optimal performance. Finally, the generated model iron metabolism-related gene signature (imSig) was assessed by the validation set GSE60993 using a series of evaluation measurements. Compared with other machine learning methods, the performance of imSig using Elastic Net was superior in the validation set. Elastic Net consistently scores the higher than Lasso and Logistic regression in the validation set in terms of ROC, PRC, Sensitivity, and Specificity. The prediction model based on iron metabolism-related genes may assist in ACS early diagnosis.

Brucellosis, a zoonotic bacterial infection caused by species, exhibits a global distribution. The S2 vaccine strain is known to cause brucellosis. Current serological antibody assays cannot distinguish between infections caused by the S2 strain and those caused by wild-type . To develop a diagnostic method capable of specifically detecting S2 vaccine strain infections. Two probes were designed targeting single nucleotide polymorphism (SNP) loci upstream of the gene; quantitative polymerase chain reaction (qPCR) and droplet digital polymerase chain reaction (ddPCR) methods were established. The performances of these methods were evaluated. The transient stem-loop structure of the DNA template was predicted, and the impact of probe overlap with the transient stem-loop structure on detection sensitivity was analyzed. Clinical applicability was assessed using 50 blood samples from brucellosis patients. Both types of methods demonstrated high specificity. However, MGB-SNPdd showed greater sensitivity than other detection methods. Reduction of overlap between the probe sequence and the transient stem-loop structure enhanced detection sensitivity. In the clinical applicability analysis, ddPCR methods exhibited higher rates of S2 vaccine strain detection compared with qPCR methods. SNP-based ddPCR methods demonstrate higher sensitivity than qPCR methods and enable specific detection of brucellosis caused by the S2 vaccine strain. Reduction of probe overlap with the transient stem-loop structure improves detection sensitivity, providing valuable insights for enhanced PCR amplification efficiency.

Brucellosis is a zoonotic disease caused by Gram-negative bacteria. Most of the brucellosis vaccines in the application are whole-bacteria vaccines. Live-attenuated vaccines are widely used for brucellosis prevention in sheep, goats, pigs, and cattle. Thus, there is also a need for an adjuvanted vaccine for human brucellosis, because the attenuated Brucella vaccines now utilized in animals cause human illness. Here, we developed a live-attenuated Brucella suis strain 2 vaccine (S2) adjuvanted with Ag85a (Ag85a-S2). We found that Ag85a-S2 activated cGAS-STING pathways both in intestinal mucosal cells in vivo and in the BMDM and U937 cell line in vitro. We demonstrated that the cGAS knockout significantly downregulated the abundance of interferon and other cytokines induced by Ag85a-S2. Moreover, Ag85a-S2 triggered a stronger cellular immune response compared to S2 alone. In sum, Ag85a-S2-mediated enhancement of immune responses was at least partially dependent on the cGAS-STING pathway. Our results provide a new candidate for preventing Brucella pathogens from livestock, which might reduce the dosage and potential toxicity compared to S2.

Sanguisorba officinalis is well known for its incredible performance on stopping bleeding in herbal remedies in China. The chloroplast genome sequence of Sanguisorba officinalis was 155,479 bp length as the circular structure and typical quadripartite structure. It contained a large single-copy (LSC) region of 85,548 bp, a small single-copy (SSC) region of 18,769 bp and separated by two inverted repeat (IR) regions of 25,581 bp. The overall G + C content of the chloroplast genome sequence is 37.2%. This chloroplast genome contained 131genes that included 86 protein-coding (PC) genes, 37 transfer RNA (tRNA) genes and 8 ribosomal RNA (rRNA) genes. In IR region, 18 genes was found, contained 7 PCGs species, 7 tRNA genes species and 4 rRNA genes species. In phylogenetic analysis, the result shown that Sanguisorba officinalis was closed relationship with Sanguisorba tenuifolia in the phylogenetic relationship using the Maximum-Likelihood (ML) method.