Objective To explore the molecular mechanism of Jianfu mixture in the treatment of erectile dysfunction （ED） by network pharmacology and molecular docking techniques, and validate its core targets and mechanisms through in vitro experiments. Methods The active components and corresponding molecular targets of Jianfu mixture were searched by searching TCMSP and Batman-TCM databases, and the disease targets of ED were searched by using GeneCards database. Find the intersection of drug ingredient target and disease target. The interaction between intersected targets was described and analyzed by String database, and the analysis results were visualized by Cytoscape software to determine the core target and the corresponding active components. GO functional enrichment analysis and KEGG pathway enrichment analysis were performed for intersection targets; the core target within the intersection were found through MCODE plug-in on Cytoscape software and molecular docking was performed with the corresponding active ingredients. An endothelial dysfunction model was established by transfecting HUVECs with si-eNOS. Intervene with different concentrations of the Jianfu mixture for the model cells for 24 h. QPCR was used to detect mRNA expression of core targets （MAPK1, MAPK3, JUN, ESR1, MAPK8）; Western blot was used to analyze protein expression （eNOS, JUN, p-JUN, MAPK, p-MAPK） and phosphorylation levels. Results 144 effective active components and 168 active components target-disease targe intersection of Jianfu mixture were obtained. GO analysis revealed 200 5 biological processes, 151 molecular functions, and 63 cellular components. KEGG analysis yielded 181 pathways. 5 core targets including MAPK1, MAPK3, JUN, ESR1 and MAPK8 were screened out. The active components such as β-sitosterol, kaempferol, astapterocarpan had good binding affinity with the core target. In vitro experiments confirmed successful construction of the endothelial dysfunction model （eNOS expression significantly decreased after si-eNOS transfection）. Jianfu mixture dose-dependently inhibited mRNA expression of MAPK1, MAPK3, JUN, ESR1, and MAPK8. Additionally, it reduced phosphorylation levels of JUN and MAPK, indicating inhibition of the JNK/c-Jun and ERK/MAPK signaling pathways to improve endothelial function. Conclusion Jianfu mixture treats ED by suppressing abnormal activation of multi-target signaling pathways （MAPK/JUN/ESR1）, reducing endothelial apoptosis, and promoting NO synthesis. This mechanism aligns with the traditional Chinese medicine principle of “activating blood circulation, resolving stasis, tonifying Qi, and strengthening cardiovascular function.” The study provided molecular-level evidence for the therapeutic efficacy of Jianfu mixture in ED management.

Objective To explore the molecular mechanism of Jianfu mixture in the treatment of erectile dysfunction （ED） by network pharmacology and molecular docking techniques, and validate its core targets and mechanisms through in vitro experiments. Methods The active components and corresponding molecular targets of Jianfu mixture were searched by searching TCMSP and Batman-TCM databases, and the disease targets of ED were searched by using GeneCards database. Find the intersection of drug ingredient target and disease target. The interaction between intersected targets was described and analyzed by String database, and the analysis results were visualized by Cytoscape software to determine the core target and the corresponding active components. GO functional enrichment analysis and KEGG pathway enrichment analysis were performed for intersection targets; the core target within the intersection were found through MCODE plug-in on Cytoscape software and molecular docking was performed with the corresponding active ingredients. An endothelial dysfunction model was established by transfecting HUVECs with si-eNOS. Intervene with different concentrations of the Jianfu mixture for the model cells for 24 h. QPCR was used to detect mRNA expression of core targets （MAPK1, MAPK3, JUN, ESR1, MAPK8）; Western blot was used to analyze protein expression （eNOS, JUN, p-JUN, MAPK, p-MAPK） and phosphorylation levels. Results 144 effective active components and 168 active components target-disease targe intersection of Jianfu mixture were obtained. GO analysis revealed 200 5 biological processes, 151 molecular functions, and 63 cellular components. KEGG analysis yielded 181 pathways. 5 core targets including MAPK1, MAPK3, JUN, ESR1 and MAPK8 were screened out. The active components such as β-sitosterol, kaempferol, astapterocarpan had good binding affinity with the core target. In vitro experiments confirmed successful construction of the endothelial dysfunction model （eNOS expression significantly decreased after si-eNOS transfection）. Jianfu mixture dose-dependently inhibited mRNA expression of MAPK1, MAPK3, JUN, ESR1, and MAPK8. Additionally, it reduced phosphorylation levels of JUN and MAPK, indicating inhibition of the JNK/c-Jun and ERK/MAPK signaling pathways to improve endothelial function. Conclusion Jianfu mixture treats ED by suppressing abnormal activation of multi-target signaling pathways （MAPK/JUN/ESR1）, reducing endothelial apoptosis, and promoting NO synthesis. This mechanism aligns with the traditional Chinese medicine principle of “activating blood circulation, resolving stasis, tonifying Qi, and strengthening cardiovascular function.” The study provided molecular-level evidence for the therapeutic efficacy of Jianfu mixture in ED management.

Objective:To explore the predictive factors for ureteral stone impaction preoperatively and to construct a nomogram prediction model for impacted ureteral stones.Methods:A retrospective analysis was conducted on the clinical data of 209 patients with ureteral stones treated at The Second Affiliated Hospital of Zhengzhou University from July 2023 to June 2024. There were 164 males（78.5%）and 45 females（21.5%）. The age was 49（47，57）years，and the body mass index（BMI）was 25.10（23.55，27.24）kg/m2. Of the patients，85（40.7%）had comorbid hypertension and 85（40.7%）had comorbid diabetes. Stones were located on the left side in 124 patients（59.3%）and on the right side in 85 patients（40.7%）. Hydronephrosis was present in 169 patients（80.9%），and urine culture was positive in 29 patients（13.9%）. Patients were divided into impacted and non-impacted groups based on the presence or absence of ureteral stone impaction. Univariate and multivariate logistic regression analyses were performed to determine independent predictive factors for impacted ureteral stones. A nomogram model was constructed based on these results. The performance of the predictive model was evaluated using receiver operating characteristic（ROC）curves，calibration plots，and decision curve analysis（DCA）.Results:Among the 209 patients in this study，85（40.7%）experienced ureteral stone impaction. The impacted group had a significantly higher neutrophil-to-lymphocyte ratio（NLR）than the non-impacted group（3.91 ± 2.05 vs. 3.25 ± 2.10， P = 0.024），a higher rate of hydronephrosis［81.2%（69/85）vs. 80.6%（100/124）， P = 0.002］，larger stone surface area［（64.96 ± 39.96）mm2 vs.（51.86 ± 39.80）mm2， P = 0.021］，greater ureteral wall thickness（UWT）［（3.96 ± 1.37）mm vs.（3.06 ± 1.33）mm， P < 0.001］，and a higher ratio of the upper ureter diameter（D1）to the lower ureter diameter（D2）（DDR）（2.87 ± 1.58 vs. 2.00 ± 0.99， P < 0.001）. Univariate analysis showed that NLR，hydronephrosis，stone length，stone surface area，UWT，D1，D2，and DDR were statistically significant（ P < 0.05）. After multivariate logistic regression analysis，the following items were identified as independent predictors of impacted ureteral stones：NLR（ OR = 1.205，95% CI 1.026 - 1.415， P = 0.023），hydronephrosis（ OR = 1.840，95% CI 1.236 - 2.740， P = 0.003），stone length（ OR = 1.587，95% CI 1.142 - 2.206， P = 0.006），ureteral wall thickness（UWT）（ OR = 1.643，95% CI 1.263 - 2.136， P < 0.001），and DDR（ OR = 2.907，95% CI 1.040 - 8.130， P = 0.042）.Based on these independent predictive factors，a nomogram prediction model for impacted ureteral stones was constructed. The area under the ROC curve was 0.797（95% CI 0.737 - 0.858），and the calibration curve showed good consistency. The decision curve suggested that the model had good clinical net benefit. Conclusions:NLR，hydronephrosis，stone length，UWT，and DDR are all independent predictors for impacted ureteral stones. The nomogram model constructed based on these factors has good predictive performance.

Objective To analyze the elevated blood pressure status and its influencing factors among manufacturing workers in Foshan City. Methods A total of 565 795 manufacturing workers who underwent occupational medical examinations in Foshan City from 2017 to 2023 were included. Data of workers were obtained from the Guangdong Provincial Key Occupational Disease Monitoring and Management Platform. The influencing factors of elevated blood pressure were analyzed using descriptive epidemiological methods. Results A total of 89 526 cases of elevated blood pressure were detected among the 565 795 workers, with a detection rate of 15.82%. From 2017 to 2023, the annual detection rate of elevated blood pressure was 14.11%, 15.00%, 14.69%, 15.94%, 17.00%, 16.90%, and 16.68%, respectively, showing an overall upward trend (P<0.01). The top three industries with the highest detection rates of elevated blood pressure were instrument and meter manufacturing; wood processing and the manufacture of wood, bamboo, rattan, palm, and straw products; as well as agricultural and sideline food processing. Binary logistic regression analysis showed that male workers had a higher risk of elevated blood pressure than female workers (P<0.01). The risk of elevated blood pressure in workers increased with age, years of occupational hazard exposure, and duration of dust exposure (all P<0.05). Workers exposed to noise for more than three years had a higher risk of elevated blood pressure than those without noise exposure (P<0.05). The risk of elevated blood pressure among workers in Nanhai, Shunde, and Gaoming districts was higher than that in Chancheng District (all P<0.01). Workers originating from Northeast and Northwest China had a higher risk of elevated blood pressure than those from North China (all P<0.05). Workers from smaller-scale enterprises had higher risk of elevated blood pressure (all P<0.01). Conclusion Targeted blood pressure health interventions in Foshan City should be strengthened for male manufacturing workers who are older, have longer working years, and originate from Northeast and Northwest China. The development of exemplary health enterprises should be promoted, particularly among micro- and small-sized enterprises and key industries. Enhanced occupational health management is especially needed for positions with high intensity noise exposure and high concentration dust exposure.

Professor LI Zhidao puts forward the application of "group acupoints" in his clinical practice by imitating the mutual reinforcement and mutual assistance of Chinese herbal medicine. It is based on the theory as "where is the acupoint located, what are the indications of this acupoint"; and consists with the specific actions of ancient needling techniques at acupoints. The distribution of "group acupoints" is in line with the "located by the region division of the head and trunk, and by the meridians on the four extremities", which is recorded in Zhenjiu Jiayi Jing (the Systematic Classic of Acupuncture and Moxibustion). It shows "the importance of the relationship between acupoints and zangfu", and "the emphasis on the distribution of nerves and muscles" respectively. In clinical practice, controlling needling sensation is the essence of this technique at "group acupoints", the integration of acupoints and needling technique is the basic requirement, and the step-by-step needling manipulation is critical for obtaining the therapeutic effect. "Group acupoints" combined with specific needling technique advance the application efficiency and the effect of acupoints.
Acupuncture Points ; Acupuncture Therapy/methods* ; Humans ; China ; History, 20th Century ; Meridians ; Medicine in Literature ; Acupuncture/history*

Objective To investigate the health status of high-temperature workers in Longhua District, Shenzhen City. Methods A total of 10 323 high-temperature workers in Longhua District from 2019 to 2022 were selected as the research subjects using the judgment sampling method. Their in-service occupational medical examination results during employment were collected and analyzed by grouping. Results The abnormal rates of urinalysis, blood pressure, serum alanine aminotransferase (ALT), electrocardiogram (ECG), and blood glucose among the research subjects were 34.9%, 12.5%, 8.5%, 7.6%, and 7.2%, respectively. Women had a higher rate of abnormal urinalysis than men (63.0% vs 25.9%, P<0.01), while men had higher rates of abnormal blood pressure and serum ALT than women (13.5% vs 9.4%, 10.2% vs 3.1%, both P<0.01). The abnormal rates of urinalysis, blood pressure, ECG, and blood glucose among the research subjects increased with age (all P<0.05), while the abnormal rate of serum ALT decreased with age (P<0.01). The abnormal rates of blood pressure and blood glucose increased with the length of high-temperature work time (all P<0.01). The most common occupation exposed to high temperatures was injection molding (accounting for 22.2%). The abnormal rates of urinalysis, blood pressure, serum ALT, ECG, and blood glucose among different occupations had significant differences (all P<0.01). Among them, injection molding workers had the highest rate of abnormal urinalysis (43.5%), while inflation film workers had the highest rate of abnormal blood pressure (24.2%). Conclusion The health status of high-temperature workers in Longhua District is concerning. Priority should be given to addressing abnormalities in urinalysis, ALT, ECG, blood pressure, and blood glucose. Occupational medical examinations should focus on elder workers and those with long high-temperature work time.

Objective:To evaluate the diagnostic efficacy of artificial intelligence-assisted cytology in detecting esophageal cancer and precancerous lesions.Methods:Outpatients who were scheduled for endoscopic examination at the Second Affiliated Hospital of Baotou Medical College from June 21, 2021 to May 12, 2022 were selected. Artificial intelligence-assisted cytology and painless gastroscopy (add narrow-band imaging and biopsy pathology when need) (hereinafter referred to as gastroscopy) were performed. The detection rates of esophageal cancer and precancerous lesions by the two methods were compared. Taking gastroscopy as the standard, sensitivity, specificity, positive predictive value, negative predictive value of artificial intelligence-assisted cytology diagnosis were calculated to verify its diagnostic efficacy.Results:A total of 849 patients meeting the inclusion criteria were included. Artificial intelligence-assisted cytology detected 365 cases of esophageal lesions, with a detection rate of 42.99%. One hundred cases of esophageal cancer, precancerous lesions, and atypical squamous cell hyperplasia were detected, with a detection rate of 11.78%. Gastroscopy detected 281 cases of esophageal lesions, with a detection rate of 33.10%. Eighty cases of esophageal cancer and precancerous lesion were detected, with a detection rate of 9.42%. The sensitivity and specificity of artificial intelligence-assisted cytology diagnosis were 81.25% (65/80) and 95.45% (734/769), respectively. The positive predictive value was 65.00% (65/100), and the negative predictive value was 98.00% (734/749). The area under the receiver operating characteristic curve of artificial intelligence-assisted cytology in dectecting esophageal cancer and precancerous lesions was 0.785.Conclusion:Artificial intelligence-assisted cytology demonstrates relatively high sensitivity and specificity in diagnosing esophageal cancer and precancerous lesions, exhibiting good diagnostic performance for esophageal lesions.

Objective:To explore the independent factors influencing extraprostatic extension (EPE) after radical prostatectomy(RP) in patients with clinically localized prostate cancer by utilizing the Surveillance, Epidemiology, and End Results (SEER) database. A nomogram model was developed and externally validated.Methods:Clinical and pathological data of 20 916 clinically localized prostate cancer patients (T 1-2N 0M 0) who underwent RP between 2010 and 2021 were extracted from the SEER database. The mean age was (61.71±7.09) years old, and a total of 17 835 patients (85.3%) were married.There were 2 243 patients (10.7%) with prostate-specific antigen (PSA) <4 ng/ml, 14 831 patients (70.9%) with ≥4 and <10 ng/ml, and 2 965 patients (14.2%) with ≥10 and <20 ng/ml. There were 14 870 patients (71.1%) with clinical staging of stage T 1, and 6 046 patients (28.9%) with T 2. There were 48 patients (0.2%) with pathological staging of stage T 1, 15 794 (75.5%) with T 2, 5 001(23.9%) with T 3, and 73 (0.3%) with T 4 stage after radical surgery.The patients of SEER database were divided into training and internal validation groups in a 7∶3 ratio by using stratified sampling. Additionally, data were collected for 75 clinically localized prostate cancer patients who underwent RP at the Second Affiliated Hospital of Zhengzhou University from September 2019 to September 2024, serving as the external validation group.The mean age was(65.39±7.45) years old. Among them, 73 (97.3%) were married. There were 2 patients (2.7%) with PSA <4 ng/ml, 17 patients (22.7%) with ≥4 and <10 ng/ml, and 34 patients (45.3%) with ≥10 and <20 ng/ml. There were 47 patients (62.7%) with clinical staging of stage T 1, and 28 patients (37.3%) with T 2. There were 7 patients (9.3%) with pathological staging of stage T 1, 48 patients (64.0%)with T 2, 18 patients (24.0%) with T 3, and 2 patients (2.7%) with T 4 stage after radical surgery. All patients were categorized into organ-confined (OC) and EPE groups based on post-surgical pathology. Univariate and multivariate logistic regression analyses, with a stepwise backward selection, were performed on the training group to identify independent risk factors of EPE, which were used to construct a nomogram model. Model performance was assessed using receiver operating characteristic (ROC) curve area under the curve (AUC), calibration curves, and decision curve analysis (DCA) for the training group, internal validation group, and external validation group. Results:EPE was observed in 3 585 cases (24.5%), 1 489 cases (23.8%), and 20 cases (26.7%) in the training, internal validation, and external validation groups, respectively. Logistic regression analyses identified preoperative age ( OR=1.026, P<0.001), PSA levels (≥10 and <20 ng/ml: OR=1.790, P<0.001; ≥20 ng/ml: OR=2.683, P<0.001), tumor maximum diameter (10-20 mm: OR=2.051, P<0.001; >20 mm: OR=3.937, P<0.001), biopsy Gleason score (score 7: OR=1.911, P<0.001; score 8: OR=2.906, P<0.001; score 9: OR = 5.278, P<0.001; score 10: OR=4.421, P=0.003), number of positive biopsy cores (≥4 cores: OR=1.260, P<0.001), and their proportion of total cores ( OR=1.012, P<0.001) as independent predictors of EPE. The nomogram model demonstrated good predictive performance, with AUC of 0.741, 0.748, and 0.724 in the training, internal validation, and external validation groups, respectively. Calibration and DCA curves confirmed the model’s excellent stability and generalizability. Conclusions:Age, PSA levels, maximum tumor diameter, biopsy Gleason score, number of positive biopsy cores, and their proportion of total cores are independent predictors of EPE after RP in clinically localized prostate cancer. The constructed model effectively predicts the risk of EPE occurrence.

Objective:To evaluate the diagnostic efficacy of artificial intelligence-assisted cytology in detecting esophageal cancer and precancerous lesions.Methods:Outpatients who were scheduled for endoscopic examination at the Second Affiliated Hospital of Baotou Medical College from June 21, 2021 to May 12, 2022 were selected. Artificial intelligence-assisted cytology and painless gastroscopy (add narrow-band imaging and biopsy pathology when need) (hereinafter referred to as gastroscopy) were performed. The detection rates of esophageal cancer and precancerous lesions by the two methods were compared. Taking gastroscopy as the standard, sensitivity, specificity, positive predictive value, negative predictive value of artificial intelligence-assisted cytology diagnosis were calculated to verify its diagnostic efficacy.Results:A total of 849 patients meeting the inclusion criteria were included. Artificial intelligence-assisted cytology detected 365 cases of esophageal lesions, with a detection rate of 42.99%. One hundred cases of esophageal cancer, precancerous lesions, and atypical squamous cell hyperplasia were detected, with a detection rate of 11.78%. Gastroscopy detected 281 cases of esophageal lesions, with a detection rate of 33.10%. Eighty cases of esophageal cancer and precancerous lesion were detected, with a detection rate of 9.42%. The sensitivity and specificity of artificial intelligence-assisted cytology diagnosis were 81.25% (65/80) and 95.45% (734/769), respectively. The positive predictive value was 65.00% (65/100), and the negative predictive value was 98.00% (734/749). The area under the receiver operating characteristic curve of artificial intelligence-assisted cytology in dectecting esophageal cancer and precancerous lesions was 0.785.Conclusion:Artificial intelligence-assisted cytology demonstrates relatively high sensitivity and specificity in diagnosing esophageal cancer and precancerous lesions, exhibiting good diagnostic performance for esophageal lesions.

Objective:To explore the independent factors influencing extraprostatic extension (EPE) after radical prostatectomy(RP) in patients with clinically localized prostate cancer by utilizing the Surveillance, Epidemiology, and End Results (SEER) database. A nomogram model was developed and externally validated.Methods:Clinical and pathological data of 20 916 clinically localized prostate cancer patients (T 1-2N 0M 0) who underwent RP between 2010 and 2021 were extracted from the SEER database. The mean age was (61.71±7.09) years old, and a total of 17 835 patients (85.3%) were married.There were 2 243 patients (10.7%) with prostate-specific antigen (PSA) <4 ng/ml, 14 831 patients (70.9%) with ≥4 and <10 ng/ml, and 2 965 patients (14.2%) with ≥10 and <20 ng/ml. There were 14 870 patients (71.1%) with clinical staging of stage T 1, and 6 046 patients (28.9%) with T 2. There were 48 patients (0.2%) with pathological staging of stage T 1, 15 794 (75.5%) with T 2, 5 001(23.9%) with T 3, and 73 (0.3%) with T 4 stage after radical surgery.The patients of SEER database were divided into training and internal validation groups in a 7∶3 ratio by using stratified sampling. Additionally, data were collected for 75 clinically localized prostate cancer patients who underwent RP at the Second Affiliated Hospital of Zhengzhou University from September 2019 to September 2024, serving as the external validation group.The mean age was(65.39±7.45) years old. Among them, 73 (97.3%) were married. There were 2 patients (2.7%) with PSA <4 ng/ml, 17 patients (22.7%) with ≥4 and <10 ng/ml, and 34 patients (45.3%) with ≥10 and <20 ng/ml. There were 47 patients (62.7%) with clinical staging of stage T 1, and 28 patients (37.3%) with T 2. There were 7 patients (9.3%) with pathological staging of stage T 1, 48 patients (64.0%)with T 2, 18 patients (24.0%) with T 3, and 2 patients (2.7%) with T 4 stage after radical surgery. All patients were categorized into organ-confined (OC) and EPE groups based on post-surgical pathology. Univariate and multivariate logistic regression analyses, with a stepwise backward selection, were performed on the training group to identify independent risk factors of EPE, which were used to construct a nomogram model. Model performance was assessed using receiver operating characteristic (ROC) curve area under the curve (AUC), calibration curves, and decision curve analysis (DCA) for the training group, internal validation group, and external validation group. Results:EPE was observed in 3 585 cases (24.5%), 1 489 cases (23.8%), and 20 cases (26.7%) in the training, internal validation, and external validation groups, respectively. Logistic regression analyses identified preoperative age ( OR=1.026, P<0.001), PSA levels (≥10 and <20 ng/ml: OR=1.790, P<0.001; ≥20 ng/ml: OR=2.683, P<0.001), tumor maximum diameter (10-20 mm: OR=2.051, P<0.001; >20 mm: OR=3.937, P<0.001), biopsy Gleason score (score 7: OR=1.911, P<0.001; score 8: OR=2.906, P<0.001; score 9: OR = 5.278, P<0.001; score 10: OR=4.421, P=0.003), number of positive biopsy cores (≥4 cores: OR=1.260, P<0.001), and their proportion of total cores ( OR=1.012, P<0.001) as independent predictors of EPE. The nomogram model demonstrated good predictive performance, with AUC of 0.741, 0.748, and 0.724 in the training, internal validation, and external validation groups, respectively. Calibration and DCA curves confirmed the model’s excellent stability and generalizability. Conclusions:Age, PSA levels, maximum tumor diameter, biopsy Gleason score, number of positive biopsy cores, and their proportion of total cores are independent predictors of EPE after RP in clinically localized prostate cancer. The constructed model effectively predicts the risk of EPE occurrence.