Objective To comprehensively investigate the levels of exposure and distribution characteristics of trihalomethanes (THMs) in drinking water in Guangzhou City, and evaluate the health risks of different groups of children, adolescents and adults, and to provide data and evidence for protecting human health and promoting risk control of drinking water. Methods According to the technical requirements of the ^"Standards for Drinking Water Quality Testing Methods^" (GB/T 5750-2023), the concentration of THMs, including trichloromethane (TCM), bromodichloromethane (BDCM), dibromochloromethane (DBCM), and tribromomethane (TBM) in drinking water in Guangzhou City from 2023-2024 were detected. The health risk model recommended by USEPA was used for risk assessment．Results TCM, BDCM and DBCM were detected in the factory water and terminal water, with TCM contributing the most. There was a statistically significant difference (P<0.05) between the wet and dry seasons, and the concentration of TCM in the wet season was higher than that in the dry season. Among the multiple exposure factors, the amount of exposure through drinking water intake was much greater than that through skin absorption. The carcinogenic risk index of THMs for children, adolescents, and adults was 22.0×10^-6, 12.2×10^-6, and 11.4×10^-6, respectively, while the non-carcinogenic risk was less than 1. Conclusion The exposure risks of THMs in children, adolescents, and adults is within an acceptable range, but monitoring needs to be strengthened, with a particular focus on children.

Objective: This study investigated the value of multiparametric MRI (mpMRI) in predicting Gleason score (GS) upgrading and downgrading in radical prostatectomy (RP) compared with presurgical biopsy. Materials and Methods: Clinical and mpMRI data were retrospectively collected from 219 patients with prostate disease between January 2015 and December 2021. All patients underwent systematic prostate biopsy followed by RP. MpMRI included conventional diffusion-weighted and dynamic contrast-enhanced imaging. Multivariable logistic regression analysis was performed to analyze the factors associated with GS upgrading and downgrading after RP. Receiver operating characteristic curve analysis was used to estimate the area under the curve (AUC) to indicate the performance of the multivariable logistic regression models in predicting GS upgrade and downgrade after RP. Results: The GS after RP was upgraded, downgraded, and unchanged in 92, 43, and 84 patients, respectively. The AUCs of the clinical (percentage of positive biopsy cores [PBCs], time from biopsy to RP) and mpMRI models (prostate cancer [PCa] location, Prostate Imaging Reporting and Data System [PI-RADS] v2.1 score) for predicting GS upgrading after RP were 0.714 and 0.749, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, tPSA, PCa location, and PIRADS v2.1 score) was 0.816, which was larger than that of the clinical factors alone (P < 0.001). The AUCs of the clinical (age, percentage of PBCs, ratio of free/total PSA [F/T]) and mpMRI models (PCa diameter, PCa location, and PI-RADS v2.1 score) for predicting GS downgrading after RP were 0.749 and 0.835, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, F/T, PCa diameter, PCa location, and PI-RADS v2.1 score) was 0.883, which was larger than that of the clinical factors alone (P < 0.001). Conclusion Combining clinical factors and mpMRI findings can predict GS upgrade and downgrade after RP more accurately than using clinical factors alone.

Objective: This study investigated the value of multiparametric MRI (mpMRI) in predicting Gleason score (GS) upgrading and downgrading in radical prostatectomy (RP) compared with presurgical biopsy. Materials and Methods: Clinical and mpMRI data were retrospectively collected from 219 patients with prostate disease between January 2015 and December 2021. All patients underwent systematic prostate biopsy followed by RP. MpMRI included conventional diffusion-weighted and dynamic contrast-enhanced imaging. Multivariable logistic regression analysis was performed to analyze the factors associated with GS upgrading and downgrading after RP. Receiver operating characteristic curve analysis was used to estimate the area under the curve (AUC) to indicate the performance of the multivariable logistic regression models in predicting GS upgrade and downgrade after RP. Results: The GS after RP was upgraded, downgraded, and unchanged in 92, 43, and 84 patients, respectively. The AUCs of the clinical (percentage of positive biopsy cores [PBCs], time from biopsy to RP) and mpMRI models (prostate cancer [PCa] location, Prostate Imaging Reporting and Data System [PI-RADS] v2.1 score) for predicting GS upgrading after RP were 0.714 and 0.749, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, tPSA, PCa location, and PIRADS v2.1 score) was 0.816, which was larger than that of the clinical factors alone (P < 0.001). The AUCs of the clinical (age, percentage of PBCs, ratio of free/total PSA [F/T]) and mpMRI models (PCa diameter, PCa location, and PI-RADS v2.1 score) for predicting GS downgrading after RP were 0.749 and 0.835, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, F/T, PCa diameter, PCa location, and PI-RADS v2.1 score) was 0.883, which was larger than that of the clinical factors alone (P < 0.001). Conclusion Combining clinical factors and mpMRI findings can predict GS upgrade and downgrade after RP more accurately than using clinical factors alone.

Objective: This study investigated the value of multiparametric MRI (mpMRI) in predicting Gleason score (GS) upgrading and downgrading in radical prostatectomy (RP) compared with presurgical biopsy. Materials and Methods: Clinical and mpMRI data were retrospectively collected from 219 patients with prostate disease between January 2015 and December 2021. All patients underwent systematic prostate biopsy followed by RP. MpMRI included conventional diffusion-weighted and dynamic contrast-enhanced imaging. Multivariable logistic regression analysis was performed to analyze the factors associated with GS upgrading and downgrading after RP. Receiver operating characteristic curve analysis was used to estimate the area under the curve (AUC) to indicate the performance of the multivariable logistic regression models in predicting GS upgrade and downgrade after RP. Results: The GS after RP was upgraded, downgraded, and unchanged in 92, 43, and 84 patients, respectively. The AUCs of the clinical (percentage of positive biopsy cores [PBCs], time from biopsy to RP) and mpMRI models (prostate cancer [PCa] location, Prostate Imaging Reporting and Data System [PI-RADS] v2.1 score) for predicting GS upgrading after RP were 0.714 and 0.749, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, tPSA, PCa location, and PIRADS v2.1 score) was 0.816, which was larger than that of the clinical factors alone (P < 0.001). The AUCs of the clinical (age, percentage of PBCs, ratio of free/total PSA [F/T]) and mpMRI models (PCa diameter, PCa location, and PI-RADS v2.1 score) for predicting GS downgrading after RP were 0.749 and 0.835, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, F/T, PCa diameter, PCa location, and PI-RADS v2.1 score) was 0.883, which was larger than that of the clinical factors alone (P < 0.001). Conclusion Combining clinical factors and mpMRI findings can predict GS upgrade and downgrade after RP more accurately than using clinical factors alone.

Objective: This study investigated the value of multiparametric MRI (mpMRI) in predicting Gleason score (GS) upgrading and downgrading in radical prostatectomy (RP) compared with presurgical biopsy. Materials and Methods: Clinical and mpMRI data were retrospectively collected from 219 patients with prostate disease between January 2015 and December 2021. All patients underwent systematic prostate biopsy followed by RP. MpMRI included conventional diffusion-weighted and dynamic contrast-enhanced imaging. Multivariable logistic regression analysis was performed to analyze the factors associated with GS upgrading and downgrading after RP. Receiver operating characteristic curve analysis was used to estimate the area under the curve (AUC) to indicate the performance of the multivariable logistic regression models in predicting GS upgrade and downgrade after RP. Results: The GS after RP was upgraded, downgraded, and unchanged in 92, 43, and 84 patients, respectively. The AUCs of the clinical (percentage of positive biopsy cores [PBCs], time from biopsy to RP) and mpMRI models (prostate cancer [PCa] location, Prostate Imaging Reporting and Data System [PI-RADS] v2.1 score) for predicting GS upgrading after RP were 0.714 and 0.749, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, tPSA, PCa location, and PIRADS v2.1 score) was 0.816, which was larger than that of the clinical factors alone (P < 0.001). The AUCs of the clinical (age, percentage of PBCs, ratio of free/total PSA [F/T]) and mpMRI models (PCa diameter, PCa location, and PI-RADS v2.1 score) for predicting GS downgrading after RP were 0.749 and 0.835, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, F/T, PCa diameter, PCa location, and PI-RADS v2.1 score) was 0.883, which was larger than that of the clinical factors alone (P < 0.001). Conclusion Combining clinical factors and mpMRI findings can predict GS upgrade and downgrade after RP more accurately than using clinical factors alone.

Objective: This study investigated the value of multiparametric MRI (mpMRI) in predicting Gleason score (GS) upgrading and downgrading in radical prostatectomy (RP) compared with presurgical biopsy. Materials and Methods: Clinical and mpMRI data were retrospectively collected from 219 patients with prostate disease between January 2015 and December 2021. All patients underwent systematic prostate biopsy followed by RP. MpMRI included conventional diffusion-weighted and dynamic contrast-enhanced imaging. Multivariable logistic regression analysis was performed to analyze the factors associated with GS upgrading and downgrading after RP. Receiver operating characteristic curve analysis was used to estimate the area under the curve (AUC) to indicate the performance of the multivariable logistic regression models in predicting GS upgrade and downgrade after RP. Results: The GS after RP was upgraded, downgraded, and unchanged in 92, 43, and 84 patients, respectively. The AUCs of the clinical (percentage of positive biopsy cores [PBCs], time from biopsy to RP) and mpMRI models (prostate cancer [PCa] location, Prostate Imaging Reporting and Data System [PI-RADS] v2.1 score) for predicting GS upgrading after RP were 0.714 and 0.749, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, tPSA, PCa location, and PIRADS v2.1 score) was 0.816, which was larger than that of the clinical factors alone (P < 0.001). The AUCs of the clinical (age, percentage of PBCs, ratio of free/total PSA [F/T]) and mpMRI models (PCa diameter, PCa location, and PI-RADS v2.1 score) for predicting GS downgrading after RP were 0.749 and 0.835, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, F/T, PCa diameter, PCa location, and PI-RADS v2.1 score) was 0.883, which was larger than that of the clinical factors alone (P < 0.001). Conclusion Combining clinical factors and mpMRI findings can predict GS upgrade and downgrade after RP more accurately than using clinical factors alone.

ObjectiveMaxing Shigan Tang， as a traditional prescription for treating pneumonia， has a remarkable clinical effect. This study aims to systematically investigate the molecular mechanisms of Maxing Shigan Tang in treating pneumonia by integrating its structural and transcriptomic data at the target level. MethodsNP-TCMtarget， a developed systematic network pharmacological model focusing on drug targets， was used to mine the effect targets of Maxing Shigan Tang for treating pneumonia based on the transcriptome data. The structural targets of chemical components in Maxing Shigan Tang were predicted based on the structural information. The intersection of effect targets and structural targets was taken as the direct targets of Maxing Shigan Tang for treating pneumonia， and the remaining effect targets except direct targets were taken as indirect targets. Finally， functional enrichment analysis was performed on these targets to explore the molecular mechanism of Maxing Shigan Tang in treating pneumonia. ResultsA total of 1 604 effect targets and 816 structural targets of Maxing Shigan Tang for treating pneumonia were identified. Maxing Shigan Tang exerted its therapeutic effects through 164 direct targets and 1 440 indirect targets. The functional analysis of 1 604 effect targets predicted 19 significantly enriched pathways. Comprehensive analysis of these pathways showed that these targets were mainly linked to immune and inflammatory responses， such as cytokine-cytokine receptor interaction， necrosis factor （NF）-κB signaling pathway， and helper T cell 17 differentiation. ConclusionFocusing on the hierarchical feature of drug targets and the structural and transcriptomic data， this study systematically reveals the path of herbal component-direct target-indirect target-biological effects of Maxing Shigan Tang in treating pneumonia.

ObjectiveMaxing Shigan Tang， as a traditional prescription for treating pneumonia， has a remarkable clinical effect. This study aims to systematically investigate the molecular mechanisms of Maxing Shigan Tang in treating pneumonia by integrating its structural and transcriptomic data at the target level. MethodsNP-TCMtarget， a developed systematic network pharmacological model focusing on drug targets， was used to mine the effect targets of Maxing Shigan Tang for treating pneumonia based on the transcriptome data. The structural targets of chemical components in Maxing Shigan Tang were predicted based on the structural information. The intersection of effect targets and structural targets was taken as the direct targets of Maxing Shigan Tang for treating pneumonia， and the remaining effect targets except direct targets were taken as indirect targets. Finally， functional enrichment analysis was performed on these targets to explore the molecular mechanism of Maxing Shigan Tang in treating pneumonia. ResultsA total of 1 604 effect targets and 816 structural targets of Maxing Shigan Tang for treating pneumonia were identified. Maxing Shigan Tang exerted its therapeutic effects through 164 direct targets and 1 440 indirect targets. The functional analysis of 1 604 effect targets predicted 19 significantly enriched pathways. Comprehensive analysis of these pathways showed that these targets were mainly linked to immune and inflammatory responses， such as cytokine-cytokine receptor interaction， necrosis factor （NF）-κB signaling pathway， and helper T cell 17 differentiation. ConclusionFocusing on the hierarchical feature of drug targets and the structural and transcriptomic data， this study systematically reveals the path of herbal component-direct target-indirect target-biological effects of Maxing Shigan Tang in treating pneumonia.

Education is a crucial element in the development of acupuncture as a discipline, providing essential talent support for its future advancement. A structured interview was conducted with renowned acupuncture expert Professor ZHAO Jiping, focusing on key topics such as the core of acupuncture education, the connotation and development of acupuncture textbooks, and acupuncture teaching models. Through in-depth discussion, the current problems in acupuncture education were analyzed, and possible solutions were explored, aiming to offer ideas for the innovative development of acupuncture education.
Acupuncture/trends* ; Humans ; Acupuncture Therapy ; China

Amber and subfossil resins are subjects of interdisciplinary research across multiple fields. However, due to their diverse origins and complex compositions, different disciplines vary in their definitions and functional interpretations. In traditional Chinese medicine(TCM), amber has been utilized as a medicinal material since ancient time, with extensive historical documentation. However, its classification, provenance, and nomenclature remain ambiguous, and authentic medicinal amber artifacts are exceedingly rare. This study employed Fourier-transform infrared spectroscopy(FTIR) to characterize amber and subfossil resins from various geological sources and commercially "medicinal amber". Additionally, historical literature and market surveys were analyzed to explore their provenance, composition, and functional attributes. The results indicate that amber and subfossil resins from different sources and with different compositions exhibit distinct fingerprint characteristics in the FTIR spectral range of 1 800-700 cm~(-1). "Medicinal amber" available in the market primarily consists of subfossil or modern resins, significantly differing in composition and structure from geological amber. This study highlights the importance of interdisciplinary research on amber identification and resource management. It is essential to establish a systematic database of amber and subfossil resin characteristics and integrate modern analytical techniques to enhance research on their composition, pharmacological mechanisms, and potential therapeutic effects, thereby promoting the standardized utilization of amber resources and advancing the modernization of TCM.
Amber/history* ; Archaeology ; Spectroscopy, Fourier Transform Infrared ; Medicine, Chinese Traditional