OBJECTIVES: To investigate the therapeutic mechanism of Thesium chinense Turcz. (TCT) for antibiotic-associated diarrhea (AAD). METHODS: Network pharmacology, KEGG pathway enrichment analysis and molecular docking were used to identify the shared targets and genes of TCT and AAD, the key signaling pathways and the binding between the active components in TCT and the core protein targets. In a Kunming mouse model of AAD established by intragastric administration of lincomycin hydrochloride, the effects of daily gavage of 1% carboxymethyl cellulose sodium or TCT gel solutions at 1.5 g/kg and 3 g/kg (n=10) on body weight and diarrhea were observed. HE staining, ELISA, 16S rRNA sequencing, and Western blotting were used to examine pathologies, expression levels of IL-6 and TNF-α, changes in gut microbiota, and protein expressions of EGFR, p-EGFR, PI3K, p-PI3K, Akt, and p-Akt in the colon tissues of the mice. RESULTS: We identified a total of 66 active components of TCT and 68 core targets including EGFR, STAT3 and PIK3CA. KEGG pathway enrichment analysis suggested that the therapeutic effects of TCT was mediated primarily through the PI3K/Akt signaling pathway. Molecular docking showed that EGFR had the highest binding affinity with coniferin, and the EGFR-coniferin complex maintained a stable conformation at 10 ns, whose stability was also confirmed by Gibbs free energy analysis. In the mouse models of AAD, treatment with TCT significantly improved colonic tissue morphology, decreased colonic levels of TNF-α and IL-6, increased gut microbiota diversity, and modulated the relative abundances of the key genera including Lactobacillus and Bacteroides. TCT treatment also markedly reduced protein expressions of p-EGFR, p-PI3K and p-Akt in the colon tissues of the mice. CONCLUSIONS TCT can alleviate AAD in mice by modulating gut microbiota composition, regulating the EGFR/PI3K/Akt signaling pathway, and reducing TNF‑α and IL-6 expressions.
Animals ; Gastrointestinal Microbiome/drug effects* ; Signal Transduction/drug effects* ; Mice ; ErbB Receptors/metabolism* ; Proto-Oncogene Proteins c-akt/metabolism* ; Diarrhea/drug therapy* ; Phosphatidylinositol 3-Kinases/metabolism* ; Anti-Bacterial Agents/adverse effects* ; Drugs, Chinese Herbal/therapeutic use* ; Molecular Docking Simulation

Objective:To analyze the clinical efficacy and learning curve of robotic-assisted Warshaw procedure using the da Vinci system.Methods:Clinical data of 91 consecutive patients with pancreatic body and tail lesions undergoing robotic-assisted Warshaw procedure at Qingdao University Affiliated Hospital from October 2021 to April 2023 were retrospectively analyzed, including 21 males and 70 females, aged (50.2±14.3) years. Patient characteristics, operative time, and intraoperative blood loss were recorded. The learning curve was constructed using cumulative sum (CUSUM) analysis, with case number on the x-axis and CUSUM values on the y-axis. Linear fitting was performed, and the model with the highest determination coefficient was selected as the optimal fitting model. The learning process was divided into two phases based on the inflection point of the CUSUM learning curve: the learning phase and the proficiency phase. Perioperative outcomes were compared between these two phases.Results:All 91 procedures were successfully completed using the da Vinci robotic system with R0 resection margins. There were no perioperative mortalities or reoperations due to postoperative splenic infarction. The operative time was (227.84±76.68) min. The optimal fitting equation for the CUSUM learning curve was: CUSUM=0.005 640X 3-1.501X 2+ 92.59X-183.1. The CUSUM learning curve showed an inflection point at case 39, dividing the learning process into the learning phase (cases 1-39) and proficiency phase (cases 40-91). Compared to the learning phase, the proficiency phase demonstrated significantly shorter operative time [(203.0±75.6) min vs. (260.9±65.5) min], less intraoperative blood loss [50.0 (20.0, 50.0) ml vs. 100.0 (50.0, 100.0) ml], and reduced postoperative drainage duration [(8.7±2.4) d vs. (10.8±3.2) d] (all P<0.05). Conclusion:The robotic-assisted Warshaw procedure feasible for patients with pancreatic body and tail lesions. Surgeons require approximately 39 cases to complete the learning curve and achieve proficiency in this procedure.

Objectives:To explore the efficacy of robotic-assisted retroperitoneal benign tumor resection and to analyze its learning curve.Methods:This is a retrospective case series study. The data of patients who underwent robotic-assisted retroperitoneal benign tumor resection from August 2015 to February 2023 at the Department of Retroperitoneal Tumor Surgery was analyzed retrospectively. There were 24 males and 45 females, with an age of (46.3±10.6) years (range: 19 to 76 years). The perioperative data, postoperative pathological results, and follow-up data were recorded. The cumulative sum (CUSUM) method was used to analyze the robotic system setup time and operative time to plot the learning curve. A linear regression model was applied to determine the best-fit curve, selecting the model with the highest R2 value. Based on the vertex of the learning curve for surgical time, the patients were divided into a learning group and a mastery group. The general data and perioperative conditions of the two groups were compared. Independent sample t-tests, Mann-Whitney U tests, and χ2 tests were used for comparisons. Results:All 69 patients successfully completed the surgery without intraoperative complications. The diameter of tumors was (49.7±18.6) mm (range: 16 to 131 mm). The setup time for the robotic surgical system was (35.3±9.8) minutes (range: 20 to 61 minutes); the surgical time was (169.2±36.5) minutes (range: 70 to 305 minutes); intraoperative blood loss ( M(IQR)) was 10.0 (15.0) ml (range: 2.0 to 200.0 ml). The tumors in 32 patients (46.4%) were adherent to major blood vessels. All patients were discharged without complications. The follow-up period lasted until February 2024, and no patients required reoperation, readmission, or died due to retroperitoneal benign tumors. There were no severe long-term complications, and no radiological evidence of tumor recurrence was found. The best-fit equation for the learning curve based on surgical time was CUSUM=0.010X3-1.648X2-68.573X-61.091, and the best-fit equation for the learning curve based on robotic system setup time was CUSUM=0.0018X3-0.285X2+10.460X+57.541 (where X represents the number of surgeries). The R2 values of 2 learning curve models were 0.953 and 0.957, respectively, and the fit model tests had P<0.05. The inflection point of the learning curve based on surgical time was the 28th case, which is considered the minimum number of surgeries required to achieve proficiency in robotic-assisted retroperitoneal benign tumor resection. Based on this, the patients were divided into a learning group (cases 1 to 28) and a mastery group (cases 29 to 69). The surgical time for the learning group was significantly longer than that of the mastery group ((194.7±30.0) minutes vs. (151.9±31.4) minutes, t=4.126, P<0.01). No statistically significant differences were found for other parameters (all P>0.05). Conclusions:Robotic-assisted retroperitoneal benign tumor resection is feasible. The minimum number of surgeries required to achieve proficiency in overcoming the learning curve is about 28 cases.

Objective To investigate the therapeutic mechanism of Thesium chinense Turcz.(TCT)for antibiotic-associated diarrhea(AAD).Methods Network pharmacology,KEGG pathway enrichment analysis and molecular docking were used to identify the shared targets and genes of TCT and AAD,the key signaling pathways and the binding between the active components in TCT and the core protein targets.In a Kunming mouse model of AAD established by intragastric administration of lincomycin hydrochloride,the effects of daily gavage of 1%carboxymethyl cellulose sodium or TCT gel solutions at 1.5 g/kg and 3 g/kg(n=10)on body weight and diarrhea were observed.HE staining,ELISA,16S rRNA sequencing,and Western blotting were used to examine pathologies,expression levels of IL-6 and TNF-α,changes in gut microbiota,and protein expressions of EGFR,p-EGFR,PI3K,p-PI3K,Akt,and p-Akt in the colon tissues of the mice.Results We identified a total of 66 active components of TCT and 68 core targets including EGFR,STAT3 and PIK3CA.KEGG pathway enrichment analysis suggested that the therapeutic effects of TCT was mediated primarily through the PI3K/Akt signaling pathway.Molecular docking showed that EGFR had the highest binding affinity with coniferin,and the EGFR-coniferin complex maintained a stable conformation at 10 ns,whose stability was also confirmed by Gibbs free energy analysis.In the mouse models of AAD,treatment with TCT significantly improved colonic tissue morphology,decreased colonic levels of TNF-α and IL-6,increased gut microbiota diversity,and modulated the relative abundances of the key genera including Lactobacillus and Bacteroides.TCT treatment also markedly reduced protein expressions of p-EGFR,p-PI3K and p-Akt in the colon tissues of the mice.Conclusion TCT can alleviate AAD in mice by modulating gut microbiota composition,regulating the EGFR/PI3K/Akt signaling pathway,and reducing TNF-α and IL-6 expressions.

Objective:To analyze the clinical efficacy and learning curve of robotic-assisted Warshaw procedure using the da Vinci system.Methods:Clinical data of 91 consecutive patients with pancreatic body and tail lesions undergoing robotic-assisted Warshaw procedure at Qingdao University Affiliated Hospital from October 2021 to April 2023 were retrospectively analyzed, including 21 males and 70 females, aged (50.2±14.3) years. Patient characteristics, operative time, and intraoperative blood loss were recorded. The learning curve was constructed using cumulative sum (CUSUM) analysis, with case number on the x-axis and CUSUM values on the y-axis. Linear fitting was performed, and the model with the highest determination coefficient was selected as the optimal fitting model. The learning process was divided into two phases based on the inflection point of the CUSUM learning curve: the learning phase and the proficiency phase. Perioperative outcomes were compared between these two phases.Results:All 91 procedures were successfully completed using the da Vinci robotic system with R0 resection margins. There were no perioperative mortalities or reoperations due to postoperative splenic infarction. The operative time was (227.84±76.68) min. The optimal fitting equation for the CUSUM learning curve was: CUSUM=0.005 640X 3-1.501X 2+ 92.59X-183.1. The CUSUM learning curve showed an inflection point at case 39, dividing the learning process into the learning phase (cases 1-39) and proficiency phase (cases 40-91). Compared to the learning phase, the proficiency phase demonstrated significantly shorter operative time [(203.0±75.6) min vs. (260.9±65.5) min], less intraoperative blood loss [50.0 (20.0, 50.0) ml vs. 100.0 (50.0, 100.0) ml], and reduced postoperative drainage duration [(8.7±2.4) d vs. (10.8±3.2) d] (all P<0.05). Conclusion:The robotic-assisted Warshaw procedure feasible for patients with pancreatic body and tail lesions. Surgeons require approximately 39 cases to complete the learning curve and achieve proficiency in this procedure.

Objectives:To explore the efficacy of robotic-assisted retroperitoneal benign tumor resection and to analyze its learning curve.Methods:This is a retrospective case series study. The data of patients who underwent robotic-assisted retroperitoneal benign tumor resection from August 2015 to February 2023 at the Department of Retroperitoneal Tumor Surgery was analyzed retrospectively. There were 24 males and 45 females, with an age of (46.3±10.6) years (range: 19 to 76 years). The perioperative data, postoperative pathological results, and follow-up data were recorded. The cumulative sum (CUSUM) method was used to analyze the robotic system setup time and operative time to plot the learning curve. A linear regression model was applied to determine the best-fit curve, selecting the model with the highest R2 value. Based on the vertex of the learning curve for surgical time, the patients were divided into a learning group and a mastery group. The general data and perioperative conditions of the two groups were compared. Independent sample t-tests, Mann-Whitney U tests, and χ2 tests were used for comparisons. Results:All 69 patients successfully completed the surgery without intraoperative complications. The diameter of tumors was (49.7±18.6) mm (range: 16 to 131 mm). The setup time for the robotic surgical system was (35.3±9.8) minutes (range: 20 to 61 minutes); the surgical time was (169.2±36.5) minutes (range: 70 to 305 minutes); intraoperative blood loss ( M(IQR)) was 10.0 (15.0) ml (range: 2.0 to 200.0 ml). The tumors in 32 patients (46.4%) were adherent to major blood vessels. All patients were discharged without complications. The follow-up period lasted until February 2024, and no patients required reoperation, readmission, or died due to retroperitoneal benign tumors. There were no severe long-term complications, and no radiological evidence of tumor recurrence was found. The best-fit equation for the learning curve based on surgical time was CUSUM=0.010X3-1.648X2-68.573X-61.091, and the best-fit equation for the learning curve based on robotic system setup time was CUSUM=0.0018X3-0.285X2+10.460X+57.541 (where X represents the number of surgeries). The R2 values of 2 learning curve models were 0.953 and 0.957, respectively, and the fit model tests had P<0.05. The inflection point of the learning curve based on surgical time was the 28th case, which is considered the minimum number of surgeries required to achieve proficiency in robotic-assisted retroperitoneal benign tumor resection. Based on this, the patients were divided into a learning group (cases 1 to 28) and a mastery group (cases 29 to 69). The surgical time for the learning group was significantly longer than that of the mastery group ((194.7±30.0) minutes vs. (151.9±31.4) minutes, t=4.126, P<0.01). No statistically significant differences were found for other parameters (all P>0.05). Conclusions:Robotic-assisted retroperitoneal benign tumor resection is feasible. The minimum number of surgeries required to achieve proficiency in overcoming the learning curve is about 28 cases.

Intrahepatic cholangiocarcinoma (ICC) is a highly malignant tumor with poor prognosis. In the process of clinical diagnosis and treatment, only a small number of patients can receive surgical treatment in time, and the rest often have local infiltration or distant metastasis at the time of presentation, which can only prolong the overall survival by adjuvant therapy. At present, the main adjuvant treatments in clinical practice include chemotherapy, targeted therapy and immunotherapy. This article reviews the progress of systemic therapy and some clinical trials in patients with advanced unresectable ICC, to provide a reference for the clinical diagnosis and treatment of ICC.

Objective:To establish a set of scientific and reasonable indicator system of common prosperity in the field of health, so as to promote the construction and evaluation of the demonstration area of common prosperity with high quality of health.Methods:According to the requirements of promoting common prosperity demonstration area with high quality of health in Zhejiang province, the initial indicator pool was established through literature research and theoretical analysis in July 2021, and experts were convened to carry out expert brainstorming to determine indicator system in the form of meetings. Delphi method was used to conduct two rounds of expert consultation on the indicator system.Finally, the analytic hierarchy process and percentage weight method were used to calculate the indicator weight value.Results:The final indicator system included 4 first-level indicators and 30 second-level indicators. Among the first-level indicators, the weight values of the development, equilibrium, inclusiveness, and sustainability were 0.326 4, 0.242 8, 0.245 8, and 0.185 0. There were 8 second-level indicators in developmental indicator dimension, of which the indicator with the highest weight was the per capita health expectancy. The balance indicator dimension included 6 second-level indicators, of which the indicator with the highest weight was the per capita financing difference of basic medical insurance between the urban workers with the urban-rural residents. The inclusive indicator dimension included 6 second-level indicators, and the proportion of personal health expenditure to total health expenditure had the highest weight. The sustainability indicator dimension included 10 second-level indicators, and the proportion of government health expenditure in fiscal expenditure had the highest weight.Conclusions:The indicator system constructed in this research could provide certain guidance and reference for promoting the construction of common prosperity in health, and provide some reference for follow-up research in this field.