Objective To investigate the trends in the global burden due to cystic echinococcosis from 1990 to 2021, and to predict the global burden of cystic echinococcosis from 2022 to 2035, so as to provide insights into formulation of the cystic echinococcosis control strategy. Methods The global age-standardized prevalence, mortality, disability-adjusted life years (DALYs) rates and their 95% uncertainty intervals (UI) of cystic echinococcosis from 1990 to 2021 were captured from the Global Burden of Disease Study 2021 (GBD 2021) database, and the trends in the global burden of cystic echinococcosis from 1990 to 2021 were analyzed using the Joinpoint regression model. The associations between the global burden of cystic echinococcosis and socio-demographic index (SDI) were examined using a smoothing spline model and frontier analysis, and the global burden of cystic echinococcosis was projected from 2022 to 2035 using the Bayesian age-period-cohort (BAPC) model. Results The global agestandardized prevalence, mortality and DALYs rates of cystic echinococcosis were 7.69/10⁵ [95% UI: (6.27/10⁵, 9.51/10⁵)], 0.02/10⁵ [95% UI: (0.01/10⁵, 0.02/10⁵)], and 1.32/10⁵ [95% UI: (0.99/10⁵, 1.69/10⁵)] in 2021. The global age-standardized prevalence of cystic echinococcosis appeared a tendency towards a rise by 0.14% per year from 1990 to 2021, and the global age-standardized mortality and DALYs rates of cystic echinococcosis appeared a tendency towards a decline by 4.68% and 4.01% per year from 1990 to 2021, respectively. Joinpoint regression analysis showed that global age-standardized prevalence of cystic echinococcosis appeared a tendency towards a decline from 1990 to 2000 [annual percent change (APC) = −0.66%, 95% confidence interval (CI): (−0.70%, −0.61%)] and from 2005 to 2015 [APC = −0.88%, 95% CI: (−0.93%, −0.82%)], and towards a rise from 2000 to 2005 [APC = 3.68%, 95% CI: (3.49%, 3.87%)] and from 2015 to 2021 [APC=0.30%, 95%CI: (0.19%, 0.40%)].Theagestandardized prevalence (r = −0.17, P < 0.05), mortality (r = −0.67, P < 0.05) and DALYs rates of cystic echinococcosis (r = −0.60, P < 0.05) all correlated negatively with SDI across 21 geographical regions from 1990 to 2021, and the age-standardized mortality (r = −0.61, P < 0.05) and DALYs rates (r = −0.44, P < 0.05) both correlated negatively with SDI across 204 countries and territories in 2021. Frontier analysis revealed that the age-standardized DALYs rate of cystic echinococcosis was still not in line with the frontier in some high-SDI countries or territories. In addition, the global age-standardized prevalence was projected with the BAPC model to appear a tendency towards a rise among both men [estimated annual percent change (EAPC) = 0.18%, 95% CI: (0.13%, 0.23%)] and women [EAPC = 0.29%, 95% CI: (0.24%, 0.34%)] from 2022 to 2035, and the global age-standardized mortality [men: EAPC = −4.71%, 95% CI: (−4.71%, −4.37%); women: EAPC = −4.74%, 95% CI: (−4.74%, −4.74%)] and DALYs rates [men: EAPC = −3.35%, 95% CI: (−3.36%, −3.34%); women: EAPC = −3.17%, 95% CI: (−3.18%, −3.16%)] were projected to appear a tendency towards a decline among both men and women. Conclusions The global burden of cystic echinococcosis appeared an overall tendency towards a decline from 1990 to 2021; however, the global prevalence of cystic echinococcosis is projected to appear a tendency towards a rise from 2022 to 2035. Intensified cystic echinococcosis control programmes are recommended.

Objective:To explore the value of the vesical imaging-reporting and data system (VI-RADS) score based on multiparametric MRI (mpMRI) combined with quantitative tumor MRI parameters in assessing the muscle invasion of bladder cancer.Methods:The study was a case-control study. The data of 87 bladder cancer patients confirmed by pathology who underwent mpMRI of the bladder were retrospectively collected from the First Medical Center of Chinese PLA General Hospital between January 2019 and April 2023 The pathological findings were used as the gold standard to categorize them into the muscle invasive bladder cancer (MIBC) group (29 cases) and non-muscle invasive bladder cancer (NMIBC) group (58 cases). Quantitative parameters were measured based on preoperative mpMRI images, including the length of tumor bladder wall contact, the perpendicular distance between the bladder tumor and the tangent of the bladder wall, the maximal diameter of the bladder tumor, and the volume of the bladder tumor. Bladder cancer was classified according to the VI-RADS scoring criteria. The Mann-Whitney U test was used for intergroup comparisons. Multivariate logistic regression analysis was performed to obtain the independent risk factors related to muscle invasion of bladder cancer and to establish the model. The receiver operating characteristic curves were analyzed for MRI quantitative parameters and logistic regression models, and area under the curve (AUC) comparisons were performed using the DeLong test. Results:The differences in tumor bladder wall contact length, perpendicular distance from the tumor to the tangent line of the bladder wall, maximum diameter, bladder tumor volume, and the VI-RADS scores were statistically significant between the MIBC group and the NMIBC group ( P<0.05). Multifactorial logistic regression analysis showed that tumor bladder wall contact length ( OR=21.07, 95% CI 3.56-124.89, P=0.001) and VI-RADS score ( OR=11.90, 95% CI 3.53-40.12, P<0.001) were the independent risk factors for evaluating the muscle invasion of bladder cancer. The difference between the VI-RADS score and the tumor bladder wall contact length for assessing muscular infiltration of bladder cancer had AUCs of 0.802 (95% CI 0.704-0.899) and 0.759 (95% CI 0.652-0.865). The combined model of VI-RADS score combined with tumor bladder wall contact length had an AUC of 0.891 (95% CI 0.812-0.970), which was higher than the diagnostic efficacy of applying tumor bladder wall contact length or VI-RADS score alone ( Z=3.05, 2.37, P=0.002, 0.018). Conclusion:Tumor contact length with the bladder wall is an independent risk factor for assessing muscle invasion of bladder cancer and the combination of VI-RADS score may enhances diagnostic accuracy.

Objective:To evaluate the efficacy of preoperative prediction of regional lymph node (RLN) metastasis in renal cell carcinoma (RCC) using a machine learning model based on habitat imaging radiomics from renal MRI.Methods:This cross-sectional study retrospectively analyzed 220 patients with RCC who underwent nephrectomy and RLN dissection at four medical centers of Chinese PLA General Hospital from January 2010 to August 2023. The cohort included 65 patients with RLN metastasis and 155 without. A stratified random sampling method was used to divide 175 patients from the first medical center into a training set ( n=140) and an internal test set ( n=35) in an 8∶2 ratio, while 45 patients from the third, fourth, and fifth medical centers constituted the external test set. The primary RCC lesions were categorized into 15 habitat subregions based on corticomedullary-phase enhancement and T 2WI signal intensity on MRI, and the volume fractions of different subregions were analyzed. In the training cohort, radiomics features derived from the habitat subregions were used to construct a radiomics model employing various machine learning algorithms, including extremely random trees (ET), gradient boosting decision trees (GBDT), random forest (RF), and support vector machine (SVM). The optimal model was selected and combined with RLN short-axis diameter to develop a combined model. The efficacy of each model in predicting RLN metastasis was evaluated using the receiver operating characteristic (ROC) curve. Results:The volume fraction of hyper-enhanced hyper-intense regions in the non-metastatic group was significantly higher than that in the metastatic group (0.05±0.09 vs. 0.02±0.03; t=3.00, P=0.003). Among the machine learning models constructed using 15 optimal habitat radiomics features, the SVM model demonstrated the best performance, with area under the ROC curve (AUC) values of 0.85 (95% CI 0.72-0.98) in the internal test set and 0.82 (95% CI 0.67-0.98) in the external test set, surpassing those of the ET, GBDT, and RF models. The combined model, integrating the SVM model with RLN short-axis diameter, achieved AUC values of 0.94 (95% CI 0.85-1.00) in the internal test set and 0.89 (95% CI 0.78-1.00) in the external test set, with RLN short-axis diameter contributing AUC values of 0.81 (95% CI 0.66-0.96) and 0.81 (95% CI 0.68-0.94), respectively. The diagnostic sensitivity of the combined model was 91.7% in the internal test set and 85.7% in the external test set, with specificities of 78.3% and 67.7%, respectively. Conclusion:The combined model based on MRI habitat imaging radiomics and RLN short-axis diameter demonstrates excellent preoperative assessment capability for RLN metastasis in RCC.

Objective:To explore the risk factors of pneumoconiosis complicated with pulmonary tuberculosis, to construct a clinical prediction model for patients with pneumoconiosis complicated with pulmonary tuberculosis, and to provide a scientific basis for the prevention of pneumoconiosis complicated with pulmonary tuberculosis.Methods:In January 2024, a total of 232 patients with pneumoconiosis (including coal workers' pneumoconiosis and silicosis) who were treated in the Department of Respiratory and Critical Care Medicine of the Third People's Hospital of Xinjiang Uygur Autonomous Region (Xinjiang Uygur Autonomous Region Occupational Disease Hospital) from January 2022 to January 2023 were randomly selected as the study subjects. Collectted basic patient information and diagnostic data. Multivariate logistic regression analysis was used to screen the risk factors related to pneumoconiosis complicated with pulmonary tuberculosis. According to the results of multivariate logistic regression analysis, a nomogram was established, and the area under the receiver operating characteristic (ROC) curve (AUC), calibration curve and decision curve analysis (DCA) were used to evaluate the predictive ability.Results:Among the 232 patients with pneumoconiosis, 73 were complicated with pulmonary tuberculosis, accounting for 31.47% (73/232). Multivariate logistic regression analysis determined that dust exposure time, type of work, smoking history, and lung function level were all risk factors for pneumoconiosis complicated with tuberculosis ( OR=10.33, 95% CI=1.92~55.66, OR=5.43, 95% CI=1.91~15.44, OR=3.10, 95% CI=1.15~8.37, OR=4.00, 95% CI=1.62~9.87; P<0.05). The constructed nomogram model has good clinical applicability when the area under the receiver operating characteristic (ROC) curve is 0.77 [95% CI (0.69, 0.73) ], the calibration curve is close to the ideal diagonal, the absolute error between the simulation curve and the actual curve is 0.03, and the DCA decision curve shows that the probability threshold of the nomogram model is 1%-90%. Conclusion:The risk of pneumoconiosis complicated with tuberculosis is high, and the risk factors of dust exposure time, smoking history, type of work and lung function level are high. This nomogram model can be used to predict the risk of pulmonary tuberculosis in patients with pneumoconiosis, which is helpful for early intervention.

Objective:To investigate the anti-inflammatory activity of alcohol extract of Polyrhachis dives(PDAEs)against rheumatoid arthritis(RA)in vitro and in vivo.Methods:In vivo and in vitro anti-RA inflammatory response of PDAEs was investigated using a rat model of collagenous arthritis induced by bovine type Ⅱ collagen and an LPS-induced RAW264.7 cell inflammatory model.Results:PDAEs inhibited the polarization of M1 type macrophages in vivo and in vitro,reduced expressions of TNF-α,IL-1β,IL-6,iNOS,and promoted polarization of M2 type macrophages,enhanced expressions of anti-inflammatory cytokines,such as IL-10 and TGF-β,so as to achieve the anti-inflammatory effect.The experiments in vivo also showed that PDAEs had the immunomodulatory effect,the potential mechanism may be the regulation of Th17/Treg balance by regulating the expression of PD-1 and TGF-β,thus correcting the over-strong autoimmune response.Conclusion:PDAEs may reduce the inflammatory reaction of RA through anti-inflam-matory and immunomodulatory effects.

The National Essential Medicines System could protect public health and ensure access to essential medications.Although the current selection methods for China's National Essential Medicines Lists(NEMLs)are becoming more scientific and standardized,there are still problems such as much emphasis on expert experience and the lack of transparency of decision-making basis.To address these issues,it proposes an evidence-based decision-making pathway for NEMLs selection guided by clinical value.This approach ensures a strong integration of evidence and decision-making,offering valuable insights for improving the adjustment procedures and selection criteria of the NEMLs in China.

Objective:To investigate the anti-inflammatory activity of alcohol extract of Polyrhachis dives(PDAEs)against rheumatoid arthritis(RA)in vitro and in vivo.Methods:In vivo and in vitro anti-RA inflammatory response of PDAEs was investigated using a rat model of collagenous arthritis induced by bovine type Ⅱ collagen and an LPS-induced RAW264.7 cell inflammatory model.Results:PDAEs inhibited the polarization of M1 type macrophages in vivo and in vitro,reduced expressions of TNF-α,IL-1β,IL-6,iNOS,and promoted polarization of M2 type macrophages,enhanced expressions of anti-inflammatory cytokines,such as IL-10 and TGF-β,so as to achieve the anti-inflammatory effect.The experiments in vivo also showed that PDAEs had the immunomodulatory effect,the potential mechanism may be the regulation of Th17/Treg balance by regulating the expression of PD-1 and TGF-β,thus correcting the over-strong autoimmune response.Conclusion:PDAEs may reduce the inflammatory reaction of RA through anti-inflam-matory and immunomodulatory effects.

Objective:To explore the value of the vesical imaging-reporting and data system (VI-RADS) score based on multiparametric MRI (mpMRI) combined with quantitative tumor MRI parameters in assessing the muscle invasion of bladder cancer.Methods:The study was a case-control study. The data of 87 bladder cancer patients confirmed by pathology who underwent mpMRI of the bladder were retrospectively collected from the First Medical Center of Chinese PLA General Hospital between January 2019 and April 2023 The pathological findings were used as the gold standard to categorize them into the muscle invasive bladder cancer (MIBC) group (29 cases) and non-muscle invasive bladder cancer (NMIBC) group (58 cases). Quantitative parameters were measured based on preoperative mpMRI images, including the length of tumor bladder wall contact, the perpendicular distance between the bladder tumor and the tangent of the bladder wall, the maximal diameter of the bladder tumor, and the volume of the bladder tumor. Bladder cancer was classified according to the VI-RADS scoring criteria. The Mann-Whitney U test was used for intergroup comparisons. Multivariate logistic regression analysis was performed to obtain the independent risk factors related to muscle invasion of bladder cancer and to establish the model. The receiver operating characteristic curves were analyzed for MRI quantitative parameters and logistic regression models, and area under the curve (AUC) comparisons were performed using the DeLong test. Results:The differences in tumor bladder wall contact length, perpendicular distance from the tumor to the tangent line of the bladder wall, maximum diameter, bladder tumor volume, and the VI-RADS scores were statistically significant between the MIBC group and the NMIBC group ( P<0.05). Multifactorial logistic regression analysis showed that tumor bladder wall contact length ( OR=21.07, 95% CI 3.56-124.89, P=0.001) and VI-RADS score ( OR=11.90, 95% CI 3.53-40.12, P<0.001) were the independent risk factors for evaluating the muscle invasion of bladder cancer. The difference between the VI-RADS score and the tumor bladder wall contact length for assessing muscular infiltration of bladder cancer had AUCs of 0.802 (95% CI 0.704-0.899) and 0.759 (95% CI 0.652-0.865). The combined model of VI-RADS score combined with tumor bladder wall contact length had an AUC of 0.891 (95% CI 0.812-0.970), which was higher than the diagnostic efficacy of applying tumor bladder wall contact length or VI-RADS score alone ( Z=3.05, 2.37, P=0.002, 0.018). Conclusion:Tumor contact length with the bladder wall is an independent risk factor for assessing muscle invasion of bladder cancer and the combination of VI-RADS score may enhances diagnostic accuracy.

The National Essential Medicines System could protect public health and ensure access to essential medications.Although the current selection methods for China's National Essential Medicines Lists(NEMLs)are becoming more scientific and standardized,there are still problems such as much emphasis on expert experience and the lack of transparency of decision-making basis.To address these issues,it proposes an evidence-based decision-making pathway for NEMLs selection guided by clinical value.This approach ensures a strong integration of evidence and decision-making,offering valuable insights for improving the adjustment procedures and selection criteria of the NEMLs in China.

Objective:To evaluate the efficacy of preoperative prediction of regional lymph node (RLN) metastasis in renal cell carcinoma (RCC) using a machine learning model based on habitat imaging radiomics from renal MRI.Methods:This cross-sectional study retrospectively analyzed 220 patients with RCC who underwent nephrectomy and RLN dissection at four medical centers of Chinese PLA General Hospital from January 2010 to August 2023. The cohort included 65 patients with RLN metastasis and 155 without. A stratified random sampling method was used to divide 175 patients from the first medical center into a training set ( n=140) and an internal test set ( n=35) in an 8∶2 ratio, while 45 patients from the third, fourth, and fifth medical centers constituted the external test set. The primary RCC lesions were categorized into 15 habitat subregions based on corticomedullary-phase enhancement and T 2WI signal intensity on MRI, and the volume fractions of different subregions were analyzed. In the training cohort, radiomics features derived from the habitat subregions were used to construct a radiomics model employing various machine learning algorithms, including extremely random trees (ET), gradient boosting decision trees (GBDT), random forest (RF), and support vector machine (SVM). The optimal model was selected and combined with RLN short-axis diameter to develop a combined model. The efficacy of each model in predicting RLN metastasis was evaluated using the receiver operating characteristic (ROC) curve. Results:The volume fraction of hyper-enhanced hyper-intense regions in the non-metastatic group was significantly higher than that in the metastatic group (0.05±0.09 vs. 0.02±0.03; t=3.00, P=0.003). Among the machine learning models constructed using 15 optimal habitat radiomics features, the SVM model demonstrated the best performance, with area under the ROC curve (AUC) values of 0.85 (95% CI 0.72-0.98) in the internal test set and 0.82 (95% CI 0.67-0.98) in the external test set, surpassing those of the ET, GBDT, and RF models. The combined model, integrating the SVM model with RLN short-axis diameter, achieved AUC values of 0.94 (95% CI 0.85-1.00) in the internal test set and 0.89 (95% CI 0.78-1.00) in the external test set, with RLN short-axis diameter contributing AUC values of 0.81 (95% CI 0.66-0.96) and 0.81 (95% CI 0.68-0.94), respectively. The diagnostic sensitivity of the combined model was 91.7% in the internal test set and 85.7% in the external test set, with specificities of 78.3% and 67.7%, respectively. Conclusion:The combined model based on MRI habitat imaging radiomics and RLN short-axis diameter demonstrates excellent preoperative assessment capability for RLN metastasis in RCC.