Objective To investigate the effects of cucurbitacin B (CucB) on alleviating skin lesions and inflammation in psoriasis mice via the cGAS-STING signaling pathway. Methods The expression of genes associated with the cGAS-STING signaling pathway in psoriatic lesions and non-lesional skin was analyzed, and hallmark gene set enrichment analysis was performed. The cytotoxicity of CucB on BMDMs was evaluated using the CCK-8 assay. The expression levels of genes and proteins related to the cGAS-STING signaling pathway, along with the secretion of inflammatory cytokines, were measured at different concentrations of CucB using quantitative PCR, Western blotting, and ELISA. Imiquimod-induced psoriasis BALB/c mice were divided into four groups: normal group, model group, low-dose CucB group [0.1 mg/ (kg.d)], and high-dose CucB group [0.4 mg/ (kg.d)], with five mice per group. PASI scoring was performed to assess the severity of psoriasis after 6 days of treatment, and HE staining was conducted to observe pathological damage. Meanwhile, the mRNA levels of inflammatory cytokines and their secretion were detected by qPCR and ELISA. Results Most cGAS-STING signaling-related genes were upregulated in lesional skin of psoriasis patients, and the hallmark gene set enrichment analysis revealed that the most significantly upregulated genes were primarily associated with immune response signaling pathways. CucB inhibited dsDNA-induced phosphorylation of interferon regulatory factor 3 (IRF3) and STING proteins in both bone-marrow derived macrophages(BMDMs) and THP-1 cells. CucB also suppressed dsDNA-induced mRNA expression of IFNB1, TNF, IFIT1, CXCL10, ISG15, and reduced the secretion of cytokines such as IFN-β, IL-1β, and TNF-α in THP-1 cells. In the imiquimod-induced psoriasis mouse model, CucB treatment reduced psoriatic symptoms, alleviated skin lesions, and attenuated inflammation. ELISA and qPCR results showed that CucB significantly reduced serum secretion levels of IL-6, TNF-α, and IL-1β, as well as the mRNA levels of IL23A, IL1B, IL6, TNF, and IFNB1. Conclusion CucB inhibits cytoplasmic DNA-induced activationc of the GAS-STING pathway. CucB significantly attenuates skin lesions and inflammation in IMQ-induced psoriatic mice, and the potential molecular mechanism may be related to the down-regulation of the cGAS-STING pathway.
Animals ; Psoriasis/pathology* ; Signal Transduction/drug effects* ; Membrane Proteins/genetics* ; Mice ; Nucleotidyltransferases/genetics* ; Disease Models, Animal ; Mice, Inbred BALB C ; Skin/metabolism* ; Triterpenes/therapeutic use* ; Humans ; Cytokines/metabolism* ; Inflammation/drug therapy* ; Male

ObjectiveA multiplex amplification system was constructed based on the capillary electrophoresis platform for simultaneous detection of saliva, semen, and vaginal secretions using tissue-specific RNA markers. The aim of this study is to identify the tissue origin of suspicious body fluid stains found at crime scenes and determine whether the body fluid stains at the crime scene are one or several types among saliva, semen, and vaginal secretions. MethodsThirty saliva samples, forty semen samples, and forty vaginal secretion samples (half from 2015 and half from 2024) were collected from healthy adult volunteers. Through primer designing, system formulation, and PCR condition optimization, a multiplex fluorescent amplification system was constructed. The specificity, sensitivity, and detection ability for mixed samples of this system were investigated, and it was tested using real crime scene materials. In the primer design stage, to reduce the requirements for RNA template quality, the amplification products were set within 80-300 bp. In the system formulation stage, dominant and subordinate primers were mainly considered. By reducing the concentration of dominant primers and increasing that of subordinate primers, a capillary electrophoresis spectrum with an appropriate peak height ratio was finally obtained. Additionally, gradient experiments were designed to adjust the concentrations of PCR reagents and PCR amplification conditions, and multiple versions of DNA amplification enzymes were optimized to achieve the best experimental results. ResultsThrough statistical analysis, there was no significant difference in the capillary electrophoresis of the 3 types of body fluid samples from the two years (2015 and 2024), demonstrating that the sample preservation method in this study can preserve samples for a relatively long time. The composite amplification system constructed in this study exhibited high specificity for all 3 types of body fluid, with no cross-reactions between the markers of each type of body fluid. The minimum detection thresholds for the 3 types of body fluid reached 0.002 9, 0.001 5, and 0.42 mg/L, respectively. This system also had a high degree of discrimination for mixed samples, especially for semen-saliva mixtures, where each body fluid marker could still be successfully detected when the concentration ratio of semen to saliva was 100:1. Meanwhile, in the two actual cases presented in this article, the application of this composite amplification system performed outstandingly. ConclusionThe composite amplification detection system constructed in this study can achieve the correct screening of saliva, semen, and vaginal secretions, overcoming the problems such as low specificity and sensitivity of marker tests and unbalanced RFU values of each marker in previous studies. The specificity and sensitivity meet the practical work requirements, and the operation is simple. It provides an analytical and identification method for body fluid stains in actual case and is applicable to the identification of the tissue origin of biological evidence at crime scenes involving sexual assault, indecent assault, and other criminal acts. In the future, more types of body fluid markers will be screened to expand the types of body fluids detected by the system, and body fluid-specific cSNP and cInDel genetic markers will be introduced to infer the sources (individuals and types) of mixed and complex stains more accurately.

Primary blast lung injury is an acute lung injury caused by the direct impact of explosive shock wave acting on the lung;the serious case can develop acute respiratory distress syndrome,which is one of the leading causes of mortality in blast injury personnel.The pathophysiological characteristics of primary lung blast injury are relatively clear:rupture of the alveolar capillaries and subsequent intrapulmonary haemorrhage and edema,accompanied by oxidative stress,apoptosis,inflammatory reaction and other physiological processes.However,the molecular mechanism needs to be further studied.This paper focuses on the research progress of phosphatidylinositol 3-kinase/protein kinase,nuclear factor κB,mitogen-activated protein kinase and other signaling pathways in the research of primary blast lung injury.

The inference of tissue origin of body fluid stains is crucial for case investigation and court proceedings.However,traditional methods for identification of body fluid stains,such as morpho-logical,chemical,and immunoassay identifications have certain limitations,and there is an urgent need for more efficient methods for confirmatory experiments.In recent years,the rapid development of tran-scriptomics technology has provided new means for the identification of tissue origin of body fluid stains.Different types of RNA in the transcriptome have their own advantages.This paper elaborates in detail on the application of different types of RNA,such as mRNA,miRNA,circRNA,lncRNA,piRNA and microbial transcriptomics in body fluid identification,and summarizes their respective ad-vantages and limitations,in order to provide a reference for related research.

Objective To classify the foot types of young Chinese males,extract characteristic indicators of foot types,and construct a standard foot type database.Methods Foot type data from 1 483 healthy young male individuals were collected.Spectral clustering algorithm was utilized to categorize foot types,and a deep neural network(DNN)was employed for training the classification model.Layer-wise relevance propagation(LRP)and the correlation coefficient method were combined to extract foot type features,and the differences in various foot type characteristics were compared.Results Spectral clustering yielded 4 distinct foot type categories.Foot type 1 was characterized by a high-arched foot with a prominent big toe,inwardly rotated 5th toe,and a high heel with a wide foot;foot type 2 was characterized by a narrow foot with hallux valgus;foot type 3 was characterized by a low-arched foot with hallux valgus;foot type 4 was characterized by a high-arched foot with a prominent big toe.By integrating interpretable neural networks and the correlation coefficient method,nine indicators were extracted from 27 foot type indicators,including the heel to the sole length,hallux height,navicular bone height,lateral malleolus height,hallux valgus angle,5th toe angle,dorsal foot circumference,heel angle,and longitudinal arch angle.The classification model constructed with these extracted indicators achieved an overall discrimination accuracy rate of 93.67％.Conclusions Chinese young males can be classified into four typical foot types.By examining the rearfoot,midfoot,and forefoot regions,nine key foot morphology parameters,including length,height,circumference,and angle,can be extracted.These parameters provide both theoretical and empirical support for establishing normative data on foot morphology for Chinese young males and for advancing biomechanical researches on the foot and ankle.

Objective:The influencing factors of intraoperative hypoxemia in patients with benign central airway stenosis were investigated by machine learning algorithm, and the prediction model of hypoxemia was constructed and verified.Methods:A case-control study was used in this study. The clinical data of 650 patients with benign central airway stenosis who who received surgical treatment in the First Affiliated Hospital of PLA Naval Medical University from June 2022 to April 2024 were retrospectively analyzed. And they were divided into a training set ( n=455) and a test set ( n=195) according to 7:3. The training set was used for establishing Logistic regression model and conducting internal verification, and the test set was used for external verification. The least absolute shrinkage and selection operator (LASSO) regression and Boruta algorithm were used to select the factors affecting intraoperative hypoxemia in patients with benign central airway stenosis. A Logistic regression prediction model was constructed, and the model was evaluated using area under the receiver operating characteristic curve (AUC), decision curve analysis (DCA) and calibration curve. Shapley additive interpretation (SHAP) were used to analyze the importance of influencing factors. Results:Among 650 patients, 279 were males and 371 were females, aged (37.86 ± 8.82) years. Nine feature variables were screened by LASSO regression, while 7 feature variables were screened by Boruta algorithm, the intersection of the two was operation time, complications, degree of airway stenosis, thermal ablation therapy, balloon dilation, and airway stent, respectively, based on this, a logistic regression prediction model was constructed.The AUC values of the training set, validation set and test set of the model were 0.928 (95% CI 0.903-0.954), 0.922 (95% CI 0.843-0.995) and 0.919 (95% CI 0.872-0.965), respectively. The calibration curve showed that the predicted results of the model were in good agreement with the actual results, and the DCA curve showed that the model had clinical application value. SHAP analysis showed that the importance of variables affecting intraoperative hypoxemia in benign central airway stenosis patients was ranked as operation time, thermal ablation therapy, degree of airway stenosis, comorbidification, balloon dilation, and airway stent. Conclusions:The Logistic regression prediction model of intraoperative hypoxemia built based on machine learning algorithm has good prediction efficiency, which is helpful to early identification of risk groups and prevention of hypoxemia.

Objective To classify the foot types of young Chinese males,extract characteristic indicators of foot types,and construct a standard foot type database.Methods Foot type data from 1 483 healthy young male individuals were collected.Spectral clustering algorithm was utilized to categorize foot types,and a deep neural network(DNN)was employed for training the classification model.Layer-wise relevance propagation(LRP)and the correlation coefficient method were combined to extract foot type features,and the differences in various foot type characteristics were compared.Results Spectral clustering yielded 4 distinct foot type categories.Foot type 1 was characterized by a high-arched foot with a prominent big toe,inwardly rotated 5th toe,and a high heel with a wide foot;foot type 2 was characterized by a narrow foot with hallux valgus;foot type 3 was characterized by a low-arched foot with hallux valgus;foot type 4 was characterized by a high-arched foot with a prominent big toe.By integrating interpretable neural networks and the correlation coefficient method,nine indicators were extracted from 27 foot type indicators,including the heel to the sole length,hallux height,navicular bone height,lateral malleolus height,hallux valgus angle,5th toe angle,dorsal foot circumference,heel angle,and longitudinal arch angle.The classification model constructed with these extracted indicators achieved an overall discrimination accuracy rate of 93.67％.Conclusions Chinese young males can be classified into four typical foot types.By examining the rearfoot,midfoot,and forefoot regions,nine key foot morphology parameters,including length,height,circumference,and angle,can be extracted.These parameters provide both theoretical and empirical support for establishing normative data on foot morphology for Chinese young males and for advancing biomechanical researches on the foot and ankle.

Objective:The influencing factors of intraoperative hypoxemia in patients with benign central airway stenosis were investigated by machine learning algorithm, and the prediction model of hypoxemia was constructed and verified.Methods:A case-control study was used in this study. The clinical data of 650 patients with benign central airway stenosis who who received surgical treatment in the First Affiliated Hospital of PLA Naval Medical University from June 2022 to April 2024 were retrospectively analyzed. And they were divided into a training set ( n=455) and a test set ( n=195) according to 7:3. The training set was used for establishing Logistic regression model and conducting internal verification, and the test set was used for external verification. The least absolute shrinkage and selection operator (LASSO) regression and Boruta algorithm were used to select the factors affecting intraoperative hypoxemia in patients with benign central airway stenosis. A Logistic regression prediction model was constructed, and the model was evaluated using area under the receiver operating characteristic curve (AUC), decision curve analysis (DCA) and calibration curve. Shapley additive interpretation (SHAP) were used to analyze the importance of influencing factors. Results:Among 650 patients, 279 were males and 371 were females, aged (37.86 ± 8.82) years. Nine feature variables were screened by LASSO regression, while 7 feature variables were screened by Boruta algorithm, the intersection of the two was operation time, complications, degree of airway stenosis, thermal ablation therapy, balloon dilation, and airway stent, respectively, based on this, a logistic regression prediction model was constructed.The AUC values of the training set, validation set and test set of the model were 0.928 (95% CI 0.903-0.954), 0.922 (95% CI 0.843-0.995) and 0.919 (95% CI 0.872-0.965), respectively. The calibration curve showed that the predicted results of the model were in good agreement with the actual results, and the DCA curve showed that the model had clinical application value. SHAP analysis showed that the importance of variables affecting intraoperative hypoxemia in benign central airway stenosis patients was ranked as operation time, thermal ablation therapy, degree of airway stenosis, comorbidification, balloon dilation, and airway stent. Conclusions:The Logistic regression prediction model of intraoperative hypoxemia built based on machine learning algorithm has good prediction efficiency, which is helpful to early identification of risk groups and prevention of hypoxemia.

Objective:To investigate the clinical effect of extraperitoneal robot-assisted laparoscopic modified Y-V plasty(LMYVP)in the treatment of refractory bladder neck contracture(BNC).Methods:We retrospectively analyzed the clinical data on 10 cases of refractory BNC after transurethral resection of the prostate between September 2020 and January 2023,all with a history of recurrent urethral dilatation and at least two failures in transurethral surgical treatment of scarring.After definite diagnosis and re-moval of some of the scar tissues to flatten the elevated bladder neck under the cystoscope,we performed robot-assisted LMYVP using the da Vinci Si robotic system and a four-port extraperitoneal approach.The surgical procedure involved an inverted T-shaped incision in the bladder neck and urethral stricture ring,an inverted V-shaped excision of the scar area at the 3-9 o'clock position on the ventral side of the prostatic urethra,continuous full-layer suturing of the bladder neck and inverted V-shaped urethra with 3-0 barbed thread,and indwelling of an F20 silicone catheter for 2 weeks.At 3 months after surgery,we performed cystoscopic examination,measured the maximum urinary flow rate(Qmax),and obtained the IPSS and quality of life(QOL)scores of the patients.Results:Operations were successfully completed in all the cases.At 3 months after surgery,the patients showed significantly increased Qmax([3.65±1.27]vs[20.3±1.77]ml/s,P＜0.05),IPSS(5.9±2.02 vs 30±1.15,P＜0.05)and QOL score(1.3±0.95 vs 5.2±0.79,P＜0.05)compared with the baseline.Cystoscopy revealed a wide and flat bladder neck with good survival and hemody-namics of the bladder flap.All the patients met the criteria for clinical cure at a median follow-up of 13.2 months.Conclusion:Extraper-itoneal robot-assisted LMYVP provides a new strategy for urinary tract reconstruction in the management of refractory BNC,with the ad-vantages of minimal invasiveness,high efficiency and high success rate.

Glycine receptors (GlyRs) belong to the ligand-gated ion channel receptor superfamily and are widely distributed throughout the central nervous system. GlyRs are essential for maintaining visual, auditory, sensory and motor functions, and abnormalities in its structure and function can lead to various neurological disorders. This review aims to provide an extensive analysis of the structure, function and regulatory mechanisms of GlyRs, and evaluate its role in various central nervous system diseases. Ultimately, this review will provide theoretical support for the development of novel drugs specifically targeting GlyRs.
Receptors, Glycine/physiology* ; Humans ; Animals ; Central Nervous System Diseases/metabolism*