Objective:To evaluate the efficacy of gadolinium ethoxybenzyl- diethy-lenetriamine pentaacetic acid (Gd-EOB-DTPA) enhanced MRI features in the preoperative prediction of tertiary lymphoid structures (TLS) within hepatocellular carcinoma (HCC) lesions.Methods:This retrospective cross-sectional study included clinical and pathological data from 297 HCC patients treated at the Southwest Hospital, Army Medical University between June 2021 and November 2022. Based on postoperative pathology, patients were categorized into TLS-negative ( n=93) and TLS-positive ( n=204) groups. MRI features of HCC lesions using Gd-EOB-DTPA enhancement and relevant clinical data were analyzed. Intergroup comparisons of imaging features and laboratory findings were performed using independent sample t-test, Mann-Whitney U test, χ2 test, or Fisher exact test, as appropriate. The logistic regression analysis was conducted to identify independent predictors of TLS positivity. A predictive model was constructed and visualized using a nomogram. The model′s predictive performance and clinical utility were assessed using the receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). The area under the ROC curve (AUC) was compared using the DeLong test. Results:Significant differences were observed between the TLS-negative and TLS-positive groups in alpha-fetoprotein (AFP) levels, intratumoral hemorrhage, and peritumoral satellite nodules in the hepatobiliary phase ( P<0.05). Multivariate logistic regression identified intratumoral hemorrhage ( OR=0.123, 95% CI 0.070-0.216, P<0.001) and peritumoral satellite nodules in the hepatobiliary phase ( OR=0.236, 95% CI 0.093-0.596, P=0.002) as independent predictive factors for TLS-positivity. The imaging model based on these two features yielded an AUC of 0.764 (95% CI 0.709-0.809) for predicting TLS-positivity. When combined with AFP levels, the resulting clinical-imaging model achieved a superior AUC of 0.784 (95% CI 0.732-0.829), which was significantly higher than that of the imaging model alone ( Z=2.20, P=0.028). A nomogram was constructed based on the clinical-imaging model. The calibration curve demonstrated good predictive performance of the nomogram, and the DCA showed that the curve remained above the default line across a range of reasonable threshold probabilities, indicating that patients could derive clinical benefit. Conclusion:A nomogram model based on Gd-EOB-DTPA enhanced MRI features combined with AFP levels can effectively predict the presence of TLS in HCC.

Objective:To investigation the situation of water improvement projects in villages affected by drinking water-type endemic fluorosis in Qinghai Province and the prevalence of dental fluorosis among children, in order to provide a basis for consolidating the achievements in prevention and control of drinking water-type endemic fluorosis and adjusting prevention and control measures.Methods:The monitoring data on drinking water-type endemic fluorosis were collected from the disease prevention and control centers in various counties of Qinghai Province from 2021 to 2023, the situation of water improvement projects, the fluorine content of domestic drinking water and the prevalence of dental fluorosis in children aged 8 to 12 years old were retrospectively analyzed.Results:From 2021 to 2023, the numbers of villages affected by drinking water-type endemic fluorosis in Qinghai Province were 338, 335, and 328, respectively. The numbers of water improvement projects were 125, 127 and 124, respectively. The normal operation rates were 100%, 100% and 99.19% (123/124), respectively. The qualified rates of water fluoride level were 100%, 99.21% (126/127) and 99.19% (123/124), respectively. The detection rates of dental fluorosis among children aged 8 to 12 were 4.34% (515/11 877), 5.70% (646/11 331) and 4.48% (490/10 943), respectively. There was a statistically significant difference in the detection rate of dental fluorosis among children in different years (χ 2 = 22.79, P < 0.001). Conclusions:The overall operation status of water improvement project in villages affected by drinking water-type endemic fluorosis in Qinghai Province is generally good, but there has been some relaxation in management and maintenance in the later stage, and there is a phenomenon of project intermittency. The detection rate of dental fluorosis among children aged 8 to 12 remains low, and endemic fluorosis caused by drinking water is under continuous control.

Insulin-like growth factor 2 (IGF2) is a critical endocrine mediator implicated in male reproductive physiology. To investigate the correlation between IGF2 protein levels and various aspects of male infertility, specifically focusing on sperm quality, inflammation, and DNA damage, a cohort of 320 male participants was recruited from the Women's Hospital, Zhejiang University School of Medicine (Hangzhou, China) between 1 st January 2024 and 1 st March 2024. The relationship between IGF2 protein concentrations and sperm parameters was assessed, and Spearman correlation and linear regression analysis were employed to evaluate the independent associations between IGF2 protein levels and risk factors for infertility. Enzyme-linked immunosorbent assay (ELISA) was used to measure IGF2 protein levels in seminal plasma, alongside markers of inflammation (tumor necrosis factor-alpha [TNF-α] and interleukin-1β [IL-1β]). The relationship between seminal plasma IGF2 protein levels and DNA damage marker phosphorylated histone H2AX (γ-H2AX) was also explored. Our findings reveal that IGF2 protein expression decreased notably in patients with asthenospermia and teratospermia. Correlation analysis revealed nuanced associations between IGF2 protein levels and specific sperm parameters, and low IGF2 protein concentrations correlated with increased inflammation and DNA damage in sperm. The observed correlations between IGF2 protein levels and specific sperm parameters, along with its connection to inflammation and DNA damage, underscore the importance of IGF2 in the broader context of male reproductive health. These findings lay the groundwork for future research and potential therapeutic interventions targeting IGF2-related pathways to enhance male fertility.
Humans ; Male ; Insulin-Like Growth Factor II/metabolism* ; Infertility, Male/genetics* ; DNA Damage ; Adult ; Inflammation/metabolism* ; Spermatozoa/metabolism* ; Semen Analysis ; Semen/metabolism* ; Tumor Necrosis Factor-alpha/metabolism* ; Histones/metabolism* ; Interleukin-1beta/metabolism*

AIM To investigate the effects of Yiqi Juanbi Formula on chondrocyte pyroptosis in rat models of collagen-induced arthritis(CIA).METHODS Fifty rats were subcutaneously injected at the tail base with an emulsion containing equal volumes of bovine type Ⅱ collagen and incomplete Freund's adjuvant(IFA)to establish the CIA models.These rats were then randomly assigned to the model group,the methotrexate group(0.35 mg/kg),and the low-dose,medium-dose,and high-dose Yiqi Juanbi Formula groups(9.4,18.7,37.4 g/kg),in contrast to the ten intact rats serving in the normal control group.Following four weeks of intragastric administration,the rats had their general conditions observed;their joint swelling and arthritis indices measured;their ankle joint pathology assessed by HE staining;their serum levels of IL-1β,IL-18 and TNF-ɑ detected by ELISA;their mRNA expressions of NLRP3,Caspase-1,GSDMD,IL-1β,IL-18 and TNF-ɑ in ankle cartilage quantified by RT-qPCR;their protein expressions of NF-κB,NLRP3 and Caspase-1 in ankle cartilage analyzed by Western blot;and their NLRP3 and GSDMD positive expressions in ankle cartilage examined by immunohistochemistry.RESULTS Compared to the control group,the model group showed significantly increased joint swelling and arthritis indices(P＜0.01);elevated serum levels of IL-1 β,IL-18 and TNF-ɑ(P＜0.01);pathological changes including cartilage surface defects,reduced cell count,altered cellular morphology,irregular cell arrangement,and significant inflammatory cell infiltration in synovial tissue;upregulated mRNA expressions of NF-κB,NLRP3,Caspase-1,GSDMD,IL-1β,IL-18 and TNF-ɑ(P＜0.01)and increased protein expressions of NF-κB,NLRP3 and Caspase-1(P＜0.01)in ankle cartilage;enhanced positive expressions of NLRP3 and GSDMD in ankle cartilage(P＜0.01).Compared to the model group,the groups intervened with methotrexate or medium-or high-dose Yiqi Juanbi Formula exhibited reduced joint swelling and arthritis indices(P＜0.01);alleviated pathological damage in ankle joints;decreased serum levels of IL-1β,IL-18 and TNF-ɑ(P＜0.01);downregulated mRNA expressions of NF-κB,NLRP3,Caspase-1,GSDMD,IL-1β,IL-18 and TNF-ɑ(P＜0.05,P＜0.01),and reduced protein expressions of NF-κB,NLRP3 and Caspase-1(P＜0.05,P＜0.01)in ankle cartilage;and diminished positive expressions of NLRP3 and GSDMD in ankle cartilage(P＜0.01).CONCLUSION Yiqi Juanbi Formula alleviates inflammation in CIA rats,potentially by inhibiting the activation of the NF-κB/NLRP3/Caspase-1 signaling pathway,thereby suppressing chondrocyte pyroptosis.

The widespread implementation of low-dose computed tomography screening has led to a substantial rise in the detection of pulmonary nodules,with multiple pulmonary nodules(MPNs)now representing a common clinical presentation.Conventional diagnostic and therapeutic paradigms,characterized by sequential,multi-site processes,are often limited by inefficiency,safety concerns,and suboptimal patient outcomes.In contemporary thoracic surgery,a transformative"one-stop"precision management strategy has emerged within hybrid operating theaters.This integrated approach synergizes intraoperative imaging,advanced navigation,and surgical intervention to address these limitations.Cone-beam CT plays a pivotal role in this model by leveraging its maneuverability,low radiation,and high spatial resolution to greatly facilitate the development of precise diagnosis and treatment for MPNs.

Objective:To investigate the clinical value of enhanced magnetic resonance imaging (MRI)-based deep learning model in preoperative prediction of proliferative hepatocellular carcinoma (HCC).Methods:The retrospective cohort study was conducted. The clinical data of 906 HCC patients who were admitted to The First Affiliated Hospital of Army Medical University and The Second Affiliated Hospital of Chongqing Medical University from May 2017 to October 2022 were collected. There were 769 males and 137 females, aged (53.2±10.9)years. Of the 906 patients, 815 cases who were admitted to The First Affiliated Hospital of Army Medical University were divided into the training set of 634 patients and the internal validation set of 181 patients using a random number table method with a ratio of 8:2, and 91 patients who were admitted to The Second Affiliated Hospital of Chongqing Medical University were divided into the external validation set. The training set was used to construct the prediction model, while the validation set was used to validate the prediction model. Observation indicators: (1) analysis of factors influencing the pathological classification of HCC patients; (2) deep learning imaging features of HCC patients; (3) evaluation of the efficacy of prediction model for proliferative HCC; (4) validation of the prediction model for proliferative HCC; (5) prognosis of HCC patients. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test. Comparison of count data between groups was conducted using the chi-square test. Multivariate analysis was conducted using the binary Logistic regression model. The model perfor-mance was evaluated through five-fold cross-validation, and receiver operating characteristic (ROC) curve was plotted to assess the diagnostic value of the model based on the area under curve (AUC), sensitivity, and specificity. The Delong test was used to compare the diagnostic performance of models. The Hosmer-Lemeshow test was employed to evaluate the calibration of models. The optimal cutoff value of the prediction model was determined by the maximum Youden index, with the value >0.175 indicating high-risk patients and value ≤0.175 indicating low-risk patients.The Kaplan-Meier method was used to calculate the survival rate and the Log-rank test was used for survival analysis. Results:(1) Analysis of factors influencing the pathological classification of HCC patients. Of 634 patients in the training set, there were 190 cases of proliferative HCC and 444 cases of non-proliferative HCC. Results of multivariate analysis showed that alpha fetoprotein (AFP) ≥400 μg/L and tumor diameter >5 cm were independent risk factors for pathological type of HCC as proli-ferative [ odds ratio=1.73, 1.88, 95% confidence interval ( CI) as 1.19-2.50, 1.30-2.71, P<0.05]. (2) Deep learning imaging features of HCC patients. In the training set of 634 patients, the probability predicted by MRI-based deep learning model was 84.8%(30.5%,95.4%) for proliferative HCC and 5.8%(3.2%,12.5%) for non-proliferative HCC, showing a significant difference between them ( Z=-16.01, P<0.05). (3) Evaluation of the efficacy of prediction model for proliferative HCC. In the training set, the AUC of clinical prediction model for proliferative HCC was 0.63(95% CI as 0.59-0.68, P<0.05), with sensitivity of 54.74% and specificity of 64.19%. The AUC of MRI-based deep learning prediction model was 0.90(95% CI as 0.87-0.93, P<0.05), with sensitivity of 80.53% and specificity of 86.94%. The AUC of combined MRI-based deep learning with clinical prediction model was 0.90 (95% CI as 0.87-0.93, P<0.05), with sensitivity of 83.16% and specificity of 86.04%. Results of Delong test showed that there was a significant difference between the combined MRI-based deep learning with clinical prediction model and the clinical prediction model ( P<0.05), and there was no signifi-cant difference between the combined MRI-based deep learning with clinical prediction model and the MRI-based deep learning prediction model ( P>0.05). Results of Hosmer-Lemeshow test showed good calibration for the clinical prediction model, the MRI-based deep learning prediction model and the combined MRI-based deep learning with clinical prediction model ( χ2=0.84, 6.38, 3.93, P>0.05), indicating that the predicted probabilities of these three prediction models matched the actual risk well. (4) Validation of the prediction model for proliferative HCC. Results of validation of the prediction model in internal validation set showed the AUC of MRI-based deep learning prediction model for proliferative HCC was 0.84(95% CI as 0.77-0.91, P<0.05), with sensitivity of 82.35% and specificity of 77.69%. Results of validation of the prediction model in external validation set showed the AUC of MRI-based deep learning prediction model for proliferative HCC was 0.81(95% CI as 0.71-0.92, P<0.05), with sensitivity of 70.00% and specificity of 81.69%. (5) Prognosis of HCC patients. Of the 906 patients, the 1-, 3-, and 5-year recurrence-free survival rates for 645 proliferative HCC patients were 56.9%, 31.4%, and 29.1%, respectively, and the 1-, 3-, and 5-year recurrence-free survival rates for 261 non-proliferative HCC patients were 88.8%, 68.6%, and 56.0%, respectively. There were significant differences in recurrence-free survival time between proliferative HCC and non-proliferative HCC patients of the training set, internal validation set and external validation set ( P<0.05). The 1-, 3-, 5-year recurrence-free survival rates for 331 high-risk HCC patients were 64.6%, 50.4%, 43.6%, versus 88.5%, 71.9%, 62.7% for 575 low-risk HCC patients. There were significant differences in recurrence-free survival time between high-risk HCC patients and low-risk HCC patients of the training set, internal validation set and external validation set ( P<0.05). Conclusion:The MRI-based deep learning model can effectively predict proliferative HCC and recurrence-free survival of patients before the surgery.

Peri-implant keratinized mucosa (PIKM) augmentation refers to surgical procedures aimed at increasing the width of PIKM. Consensus reports emphasize the necessity of maintaining a minimum width of PIKM to ensure long-term peri-implant health. Currently, several surgical techniques have been validated for their effectiveness in increasing PIKM. However, the selection and application of PIKM augmentation methods may present challenges for dental practitioners due to heterogeneity in surgical techniques, variations in clinical scenarios, and anatomical differences. Therefore, clear guidelines and considerations for PIKM augmentation are needed. This expert consensus focuses on the commonly employed surgical techniques for PIKM augmentation and the factors influencing their selection at second-stage surgery. It aims to establish a standardized framework for assessing, planning, and executing PIKM augmentation procedures, with the goal of offering evidence-based guidance to enhance the predictability and success of PIKM augmentation.
Humans ; Consensus ; Dental Implants ; Mouth Mucosa/surgery* ; Keratins

Objective To analyze the correlation between lesion volume,lesion mass,and maximum lesion diameter in the assessment of advanced hepatocarcinoma with lung metastasis,and to evaluate the application value of total volume response and total mass response of lung metastatic lesions in efficacy assessment.Methods A retrospective analysis was conducted on the CT imaging data of 20 patients clinically confirmed with hepatocarcinoma and lung metastases,followed by subsequent follow-up to monitor their survival outcomes.Volume measurement software was used to measure the volume of lesions before and after treatment.We recored lesion diameter,volume measurements and CT values,calculated the mass of the lesions.The correlation between lesion volume,mass and diameter was analyzed,as well as the correlation between the change rates of volume,mass and lesion diameter.Additionally,the total volume and total mass of all lesions were calculated.The correlation between the change rates of total volume/total mass and the change rate of pulmonary lesion diameter under the RECIST 1.1 criteria,as well as the correlation with changes in patients'tumor markers,were analyzed.Furthermore,the overall volume response and overall mass response of lesions were evaluated based on changes in total volume and total mass,and their consistencies with the RECIST 1.1 criteria for efficacy evaluation were analyzed.Finally,univariate Cox regression analysis was performed to explore the association between these variables and patient survival outcomes.Results There was strong correlation between lesion volume,mass and tumor diameter(r=0.771,0.775),between the rate of change in mass and the rate of change in lesion diameter(r=0.846),and between the rates of change in total volume/total mass and the rate of change in pulmonary lesion diameter under the RECIST 1.1 criteria(r=0.800,0.896).The correlation between the rates of change in total volume/total mass and patients'tumor markers was not statistically significant.There was moderate correlation between the rate of change in volume and the rate of change in lesion diameter(r=0.692).The evaluation results of total volume response and total mass response for pulmonary lesions in advanced hepatocarcinoma with lung metastasis were generally consistent with the RECIST 1.1 criteria(Kappa=0.486,0.426).Univariate Cox regression analysis revealed that total lesion volume(P=0.047)and total lesion mass(P=0.049)were independent prognostic factors for survival outcomes.Conclusion Lesion volume,mass,and diameter,as well as their respective change rates,were found to be interrelated.Furthermore,total lesion volume and total lesion mass were identified as independent prognostic factors for survival outcomes.The total volume response and total mass response are promising evaluation methods in evaluating the efficacy of lung metastasis of hepatocarcinoma,which are different from the RECIST 1.1 evaluation criteria.

Objective:To explore the incidence status,influencing factors and short-term prognosis characteristics of early onset coronary heart disease in women in Wansheng District of Chongqing,and to provide scientific basis for formulating regional prevention and treatment strategies.Methods:This study was a single-center retrospective study,100 coronary heart disease in women from January 2022 to December 2023 at Chongqing Wansheng Economic and Technological Development Zone People's Hospital were prospective selected,and they were divided into early onset group of 40 cases(≤ 65 years old)and late onset group of 60 cases(＞65 years old)based on their age of onset.Another 60 healthy women who underwent physical examinations during the same period to exclude coronary heart disease were selected as the control group.Univariate factor and multiple factor logistic regression analysis were used to identify independent risk factors for early onset coronary heart disease in women.Draw receiver operating characteristic(ROC)curve for the subjects,the efficacy of risk factors in predicting early onset coronary heart disease based on the area under the curve(AUC)of ROC curve were evaluated.Patients were followed up for 1 year to observe the occurrence of major adverse cardiovascular events(MACE).Result:Among 100 fcoronary heart disease in women,the early onset group accounted for 40.00％(40/100).Univariate analysis showed that age,hyperlipidemia history,smoking history,hypertension history,family history,diabetes history,total cholesterol(TC),low-density lipoprotein cholesterol(LDL-C)were related to the early onset coronary heart disease.Multivariate analysis showed that,hyperlipidemia history(OR=4.124,95％CI:2.343-7.217),smoking history(OR=3.564),hypertension(OR=3.253),family history(OR=2.981),diabetes history(OR=2.873)were independent risk factors.ROC curve analysis results showed that joint evaluation had the best predictive value,with AUC of 0.829,which was higher than the AUC of individual evaluation for each factor.The incidence of MACE in the early onset group(45.00％)was significantly higher than that in the late onset group(P＜0.05).Conclusion:Early onset coronary heart disease in women in Wansheng District of Chongqing is related to the hyperlipidemia history,smoking,hypertension history,family history and diabetes history.The incidence of MACE in early-onset patients followed up for 1 year is higher than that in late-onset patients.