Objective To analyze the influencing factors for postoperative anastomotic leak (AL) in carcinoma of the esophagus and gastroesophageal junction and construct a nomogram predictive model. Methods The patients who underwent radical esophagectomy at Jinling Hospital Affiliated to Nanjing University School of Medicine from January 2018 to June 2020 were included in this study. Relevant variables were screened using univariate and multivariate logistic regression analyses. A nomogram was then developed to predict the risk factors associated with postoperative AL. The predictive performance of the nomogram was validated using the receiver operating characteristic (ROC) curve. Results A total of 468 patients with carcinoma of the esophagus and gastroesophageal junction were included in the study, comprising 354 males and 114 females, with a mean age of (62.8±7.2) years. The tumors were predominantly located in the middle or lower esophagus, and 51 (10.90%) patients experienced postoperative AL. Univariate logistic regression analysis indicated that age, body mass index (BMI), tumor location, preoperative albumin levels, diabetes mellitus, anastomosis technique, anastomosis site, and C-reactive protein (CRP) levels were potentially associated with AL (P<0.05). Multivariate logistic regression analysis identified age, BMI, tumor location, diabetes mellitus, anastomosis technique, and CRP levels as independent risk factors for AL (P<0.05). A nomogram was developed based on the findings from the multivariate logistic regression analysis. The area under the receiver operating characteristic (ROC) curve was 0.803, indicating a strong concordance between the actual observations and the predicted outcomes. Furthermore, decision curve analysis demonstrated that the newly established nomogram holds significant value for clinical decision-making. Conclusion The predictive model for postoperative AL in patients with carcinoma of the esophagus and gastroesophageal junction demonstrates strong predictive validity and is essential for guiding clinical monitoring, early detection, and preventive strategies.

OBJECTIVE: To explore the potential effects and mechanisms of Liang-Ge-San (LGS) for the treatment of acute respiratory distress syndrome (ARDS) through network pharmacology analysis and to verify LGS activity through biological experiments. METHODS: The key ingredients of LGS and related targets were obtained from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform. ARDS-related targets were selected from GeneCards and DisGeNET databases. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed using the Metascape Database. Molecular docking analysis was used to confirm the binding affinity of the core compounds with key therapeutic targets. Finally, the effects of LGS on key signaling pathways and biological processes were determined by in vitro and in vivo experiments. RESULTS: A total of LGS-related targets and 496 ARDS-related targets were obtained from the databases. Network pharmacological analysis suggested that LGS could treat ARDS based on the following information: LGS ingredients luteolin, wogonin, and baicalein may be potential candidate agents. Mitogen-activated protein kinase 14 (MAPK14), recombinant V-Rel reticuloendotheliosis viral oncogene homolog A (RELA), and tumor necrosis factor alpha (TNF-α) may be potential therapeutic targets. Reactive oxygen species metabolic process and the apoptotic signaling pathway were the main biological processes. The p38MAPK/NF-κ B signaling pathway might be the key signaling pathway activated by LGS against ARDS. Moreover, molecular docking demonstrated that luteolin, wogonin, and baicalein had a good binding affinity with MAPK14, RELA, and TNF α. In vitro experiments, LGS inhibited the expression and entry of p38 and p65 into the nucleation in human bronchial epithelial cells (HBE) cells induced by LPS, inhibited the inflammatory response and oxidative stress response, and inhibited HBE cell apoptosis (P<0.05 or P<0.01). In vivo experiments, LGS improved lung injury caused by ligation and puncture, reduced inflammatory responses, and inhibited the activation of p38MAPK and p65 (P<0.05 or P<0.01). CONCLUSION LGS could reduce reactive oxygen species and inflammatory cytokine production by inhibiting p38MAPK/NF-κ B signaling pathway, thus reducing apoptosis and attenuating ARDS.
Drugs, Chinese Herbal/pharmacology* ; Respiratory Distress Syndrome/enzymology* ; p38 Mitogen-Activated Protein Kinases/metabolism* ; NF-kappa B/metabolism* ; Animals ; Signal Transduction/drug effects* ; Molecular Docking Simulation ; Humans ; Male ; Network Pharmacology ; Apoptosis/drug effects* ; Mice

OBJECTIVE: To identify the underlying mechanism by which quercetin (Que) alleviates sepsis-related acute respiratory distress syndrome (ARDS). METHODS: In vivo, C57BL/6 mice were assigned to sham, cecal ligation and puncture (CLP), and CLP+Que (50 mg/kg) groups (n=15 per group) by using a random number table. The sepsisrelated ARDS mouse model was established using the CLP method. In vitro, the murine alveolar macrophages (MH-S) cells were classified into control, lipopolysaccharide (LPS), LPS+Que (10 μmol/L), and LPS+Que+acetylcysteine (NAC, 5 mmol/L) groups. The effect of Que on oxidative stress, inflammation, and apoptosis in mice lungs and MH-S cells was determined, and the mechanism with reactive oxygen species (ROS)/p38 mitogen-activated protein kinase (MAPK) pathway was also explored both in vivo and in vitro. RESULTS: Que alleviated lung injury in mice, as reflected by a reversal of pulmonary histopathologic changes as well as a reduction in lung wet/dry weight ratio and neutrophil infiltration (P<0.05 or P<0.01). Additionally, Que improved the survival rate and relieved gas exchange impairment in mice (P<0.01). Que treatment also remarkedly reduced malondialdehyde formation, superoxide dismutase and catalase depletion, and cell apoptosis both in vivo and in vitro (P<0.05 or P<0.01). Moreover, Que treatment diminished the release of inflammatory factors interleukin (IL)-1β, tumor necrosis factor-α, and IL-6 both in vivo and in vitro (P<0.05 or P<0.01). Mechanistic investigation clarifified that Que administration led to a decline in the phosphorylation of p38 MAPK in addition to the suppression of ROS expression (P<0.01). Furthermore, in LPS-induced MH-S cells, ROS inhibitor NAC further inhibited ROS/p38 MAPK pathway, as well as oxidative stress, inflammation, and cell apoptosis on the basis of Que treatment (P<0.05 or P<0.01). CONCLUSION Que was found to exert anti-oxidative, anti-inflammatory, and anti-apoptotic effects by suppressing the ROS/p38 MAPK pathway, thereby conferring protection for mice against sepsis-related ARDS.
Animals ; Sepsis/drug therapy* ; Quercetin/therapeutic use* ; Respiratory Distress Syndrome/enzymology* ; p38 Mitogen-Activated Protein Kinases/metabolism* ; Mice, Inbred C57BL ; Reactive Oxygen Species/metabolism* ; Apoptosis/drug effects* ; Male ; Oxidative Stress/drug effects* ; MAP Kinase Signaling System/drug effects* ; Lung/drug effects* ; Mice ; Lipopolysaccharides ; Macrophages, Alveolar/pathology* ; Inflammation/pathology* ; Protective Agents/therapeutic use*

Objective:To analyze the risk factors of cardiac arrest during pre-hospital emergency treatment,and to construct the corresponding nomogram model.Methods:A retrospective analysis was performed on 159 patients who were dispatched by our center and received pre-hospital emergency treatment from Jan 2021 to Jan 2023.As the modeling group,these patients were divided into the occurrence group(n=54)and the non-occurrence group(n=105)according to whether they had cardiac arrest.According to the ratio of modeling group∶validation group=1.2∶1.0,132 patients who were dispatched and received pre-hospital emergency treatment by our center from Feb 2023 to Oct 2024 were selected as the validation group.They were divided into the occurrence group(n=45)and the non-occurrence group(n=87).Multivariable Logistic regression analysis was employed to investigate the factors influencing pre-hospital cardiac arrest and to construct a predictive model.The value of the predictive model was evaluated using receiver operating characteristic(ROC)curve,calibration curve,and decision curve.Results:Multivariate Logistic regression analysis showed that age＞59 years old(OR=0.658,95%CI:0.559-0.773),rescue arrival time＞10 min(OR=7.699,95%CI:3.013-19.675)and non-standard pre-hospital emergency measures(OR=2.807,95%CI:1.150-6.853)were independent risk factors for pre-hospital emergency cardiac arrest(P＜0.05).The nomogram model constructed according to the above factors was verified,and the C-index of the modeling group and verification group model was 0.798(95%CI:0.749-0.847)and 0.794(95%CI:0.740-0.848),respectively,which proved that the prediction efficiency of this model was good,and the decision curve showed good clinical benefit.Conclusion:Age＞59 years old,rescue arrival time＞10 min,and non-standard pre-hospital emergency measures are independent risk factors for pre-hospital emergency cardiac arrest,and the constructed prediction model has good clinical application.

In recent years, robot-assisted esophagectomy has become increasingly widespread, but the esophagogastric anastomosis step remains relatively complex and cumbersome. Currently, commonly used gastrointestinal reconstruction anastomosis techniques include end-to-end anastomosis, end-to-side anastomosis, and side-to-side anastomosis. Depending on the anastomosis method, they can be further divided into manual anastomosis and mechanical anastomosis, with common instruments including circular staplers and linear staplers. In esophageal cancer surgery, the choice of esophagogastric anastomosis technique is typically based on the tumor’s location and size as well as the surgeon’s preference. Each anastomosis technique has its advantages and disadvantages. With continuous improvements in anastomosis techniques and updates in stapling instruments, the incidence of complications after esophagogastric anastomosis has been effectively reduced. However, safely and efficiently completing gastrointestinal reconstruction during surgery remains a significant challenge. Scholars have made extensive explorations in this field, actively proposing and achieving various reconstruction methods, leading to significant progress. This article reviews the research progress of robot-assisted esophagogastric anastomosis techniques from both the anastomosis techniques and methods perspectives.

Objective:To screen risk factors for ICU-acquired weakness in patients with mechanical ventilation and construct a predictive model, so as to provide a basis for the health management of patients with mechanical ventilation.Methods:Convenience sampling was used to select 312 ICU patients with mechanical ventilation admitted to the Tenth People's Hospital of Tongji University from October 2019 to August 2020 for the study. Patients were divided into training set ( n=220) and test set ( n=92) in a 7∶3 ratio. Based on machine learning algorithms, decision random forest (DRF), extremely-randomized trees (XRT) and generalized linear model (GLM) were used to construct three ICU-acquired weakness risk prediction models for patients with mechanical ventilation, respectively. The performance of the prediction model was evaluated using the area under the receiver operating characteristic curve ( AUC), the area under the precision-recall curve ( AUPRC), and the root mean square error ( RMSE) . Results:There were 7 predictors of risk of ICU-acquired weakness in patients with mechanical ventilation, including age, gender, braking, duration of mechanical ventilation, blood glucose, lactic acid, and parenteral nutrition. Test set and training set validation showed that AUC and AUPRC of GLM prediction model were greater than those of DRF, XRT prediction model. Test set validation indicated that the RMSE, logarithmic loss of GLM prediction model was less than those of DRF, XRT prediction model. Conclusions:Machine learning algorithm based GLM prediction model has good prediction performance. Healthcare professionals can construct evidence-based decisions for interventions in areas such as braking, duration of mechanical ventilation, and blood glucose management.

Objective:To screen risk factors for ICU-acquired weakness in patients with mechanical ventilation and construct a predictive model, so as to provide a basis for the health management of patients with mechanical ventilation.Methods:Convenience sampling was used to select 312 ICU patients with mechanical ventilation admitted to the Tenth People's Hospital of Tongji University from October 2019 to August 2020 for the study. Patients were divided into training set ( n=220) and test set ( n=92) in a 7∶3 ratio. Based on machine learning algorithms, decision random forest (DRF), extremely-randomized trees (XRT) and generalized linear model (GLM) were used to construct three ICU-acquired weakness risk prediction models for patients with mechanical ventilation, respectively. The performance of the prediction model was evaluated using the area under the receiver operating characteristic curve ( AUC), the area under the precision-recall curve ( AUPRC), and the root mean square error ( RMSE) . Results:There were 7 predictors of risk of ICU-acquired weakness in patients with mechanical ventilation, including age, gender, braking, duration of mechanical ventilation, blood glucose, lactic acid, and parenteral nutrition. Test set and training set validation showed that AUC and AUPRC of GLM prediction model were greater than those of DRF, XRT prediction model. Test set validation indicated that the RMSE, logarithmic loss of GLM prediction model was less than those of DRF, XRT prediction model. Conclusions:Machine learning algorithm based GLM prediction model has good prediction performance. Healthcare professionals can construct evidence-based decisions for interventions in areas such as braking, duration of mechanical ventilation, and blood glucose management.

Objective:To analyze the risk factors of cardiac arrest during pre-hospital emergency treatment,and to construct the corresponding nomogram model.Methods:A retrospective analysis was performed on 159 patients who were dispatched by our center and received pre-hospital emergency treatment from Jan 2021 to Jan 2023.As the modeling group,these patients were divided into the occurrence group(n=54)and the non-occurrence group(n=105)according to whether they had cardiac arrest.According to the ratio of modeling group∶validation group=1.2∶1.0,132 patients who were dispatched and received pre-hospital emergency treatment by our center from Feb 2023 to Oct 2024 were selected as the validation group.They were divided into the occurrence group(n=45)and the non-occurrence group(n=87).Multivariable Logistic regression analysis was employed to investigate the factors influencing pre-hospital cardiac arrest and to construct a predictive model.The value of the predictive model was evaluated using receiver operating characteristic(ROC)curve,calibration curve,and decision curve.Results:Multivariate Logistic regression analysis showed that age＞59 years old(OR=0.658,95%CI:0.559-0.773),rescue arrival time＞10 min(OR=7.699,95%CI:3.013-19.675)and non-standard pre-hospital emergency measures(OR=2.807,95%CI:1.150-6.853)were independent risk factors for pre-hospital emergency cardiac arrest(P＜0.05).The nomogram model constructed according to the above factors was verified,and the C-index of the modeling group and verification group model was 0.798(95%CI:0.749-0.847)and 0.794(95%CI:0.740-0.848),respectively,which proved that the prediction efficiency of this model was good,and the decision curve showed good clinical benefit.Conclusion:Age＞59 years old,rescue arrival time＞10 min,and non-standard pre-hospital emergency measures are independent risk factors for pre-hospital emergency cardiac arrest,and the constructed prediction model has good clinical application.

Background The threshold limit values (TLVs) established and regularly updated by the American Conference of Governmental Industrial Hygienists (ACGIH) are widely adopted and referenced globally, serving as a crucial reference for China's occupational exposure limits (OELs). It is necessary to track it regularly and compare it with China's OELs. Objective To compare the OELs stipulated in Occupational exposure limits for hazardous agents in the workplace—Part 1: Chemical hazardous agents (GBZ 2.1—2019) and the ACGIH TLVs (2024) and to provide references for subsequent formulation and revision of OELs in China. Methods The OELs specified in GBZ 2.1—2019 and the TLVs issued by ACGIH were used to establish a database using Microsoft Excel 2019 software. Cross verification was conducted through matching Chemical Abstracts Service Registry Numbers (CAS Rn) and both Chinese and English names to ensure accuracy. Then, comparisons and analyses were carried out based on the type of limit values, which were matched as follows: permissible concentration-time weighted average (PC-TWA) with threshold limit value-time weighted average (TLV-TWA), permissible concentration-short term exposure limit (PC-STEL) with threshold limit value-short term exposure limit (TLV-STEL), and maximum allowable concentration (MAC) with threshold limit value-ceiling (TLV-C). Comparisons included types, quantities, and sizes of limits. Results The GBZ 2.1—2019 OELs and the ACGIH TLVs (2024) were generally consistent in terms of types and definitions, but there were differences in the number and size of the limits. In terms of the number of limits, GBZ 2.1—2019 specified 365 OELs for 358 chemical hazardous agents, while ACGIH TLVs (2024) included 316 corresponding limits. Among these, 148 (46.9%) limits were consistent, 38 (12.0%) were basically consistent, and 130 (41.1%) were inconsistent. In terms of the size of the limits, out of the 130 inconsistent limits, 51 OELs were lower than the corresponding TLVs, 67 OELs were higher than the corresponding TLVs, and 12 were under different limit types. For some chemical hazardous agents, their OELs were significantly lower or higher than their TLVs. Conclusion Some of the OELs for chemical hazardous agents specified in GBZ 2.1—2019 are significantly lower or higher than the TLVs. For these chemical hazardous factors, it is recommended to prioritize their inclusion in research projects and to complete the revisions as soon as possible based on the latest scientific evidence.

Objective To study the serum expression of interleukin-36α(IL-36α),C1q/tumor necrosis factor-related protein 6(CTRP6)levels in patients with polycystic ovary syndrome(PCOS)and their diagnostic value.Methods A total of 98 PCOS patients diagnosed and treated in the Fifth Affiliated Hospital of Xinjiang Medical University from April 2019 to April 2022 were taken as the PCOS group,and 70 healthy women were taken as the control group.Enzyme-linked immunosorbent assay was applied to detect serum IL-36 α and CTRP6 level expression.The correlation between the expression of serum IL-36 α,CTRP6 levels and clinical indicators was analyzed by Pearson correlation analysis.Multivariate logistic regression analysis was used to analyze factors affecting the occurrence of PCOS.Receiver operating characteristic curve was used to evaluate the diagnostic efficacy of serum IL-36α,CTRP6 and combination in PCOS.Results Serum CTRP6(18.25±3.67μg/L),FPG(5.71±0.49nmol/L),FINS(18.96±2.68mIU/L),HOMA-IR(4.72±0.46),LH(6.17±1.44IU/L),T(1.32±0.42nmol/L),ovarian number(17.86±5.20)and ovarian volume(9.29±2.14cm3)in the PCOS group were higher,while serum IL-36 α(0.67±0.13ng/L)and FSH((4.27±1.33IU/L)were lower compared with those in the control group(5.14±1.28μg/L,4.76±0.54mmol/L,8.63±1.65mIU/L,1.83±0.33,4.92±1.39IU/L,0.86±0.28nmol/L,6.76±1.94 个,5.26±1.31cm3,2.11±0.38ng/L,5.42±1.67IU/L),with significant differences(t/x2=4.962～44.934,all P<0.05).Serum IL-36α(0.87±0.15ng/L,0.70±0.12ng/L,0.51±0.11ng/L,0.42±0.10ng/L)levels in patients with type 1,type 2,type 3,and type 4 PCOS were decreased sequentially,while CTRP6(14.07±3.35 μg/L,17.66±3.97 μg/L,21.16±3.67 μg/L,24.08±3.53 μg/L)were increased sequentially,with significant differences(F=61.281,33.854,all P<0.05).There was a negative correlation between serum IL-36 α and ovarian number,ovarian volume,FINS and HOMA-IR(r=-0.661,-0.621,-0.554,-0.671,all P<0.05).Serum CTRP6 was positively correlated with ovarian number,ovarian volume,FINS and HOMA-IR(r=0.625,0.631,0.537,0.738,all P<0.05).CTRP6(OR=1.327,95%CI:1.104～1.596)was an independent risk factor affecting the occurrence of PCOS,while IL-36 α(OR=0.707,95%CI:0.547～0.914)was a protective factor.The area under the curve(95%CI)of combination of IL-36 α and CTRP6 for the diagnosis of PCOS was 0.933(0.872～0.969),which was greater than that of IL-36 α,CTRP6 alone[0.870(0.821～0.926),0.898(0.854～0.940)],with significant differences(Z=4.258,4.119,all P<0.05).Conclusion The decrease of serum IL-36α and the increase of CTRP6 in PCOS patients are related to the severity of PCOS,and the combined detection of the two may have high diagnostic value for PCOS.