Objective To explore the risk factors for postoperative respiratory failure (RF) in patients with esophageal cancer, construct a predictive model based on the least absolute shrinkage and selection operator (LASSO)-logistic regression, and visualize the constructed model. Methods A retrospective analysis was conducted on patients with esophageal cancer who underwent surgical treatment in the Department of Thoracic Surgery, Sun Yat-sen University Cancer Center Gansu Hospital from 2020 to 2023. Patients were divided into a RF group and a non-RF (NRF) group according to whether RF occurred after surgery. Clinical data of the two groups were collected, and LASSO-logistic regression was used to optimize feature selection and construct the predictive model. The model was internally validated by repeated sampling 1000 times based on the Bootstrap method. Results A total of 217 patients were included, among which 24 were in the RF group, including 22 males and 2 females, with an average age of (63.33±9.10) years; 193 were in the NRF group, including 161 males and 32 females, with an average age of (62.14±8.44) years. LASSO-logistic regression analysis showed that the percentage of forced expiratory volume in one second/forced vital capacity (FEV1/FVC) to predicted value (FEV1/FVC%pred) [OR=0.944, 95%CI (0.897, 0.993), P=0.026], postoperative anastomotic fistula [OR=4.106, 95%CI (1.457, 11.575), P=0.008], and postoperative lung infection [OR=3.776, 95%CI (1.373, 10.388), P=0.010] were risk factors for postoperative RF in patients with esophageal cancer. Based on the above risk factors, a predictive model was constructed, with an area under the receiver operating characteristic curve of 0.819 [95%CI (0.737, 0.901)]. The Hosmer-Lemeshow test for the calibration curve showed that the model had good goodness of fit (P=0.527). The decision curve showed that the model had good clinical net benefit when the threshold probability was between 5% and 50%. Conclusion FEV1/FVC%pred, postoperative anastomotic fistula, and postoperative lung infection are risk factors for postoperative RF in patients with esophageal cancer. The predictive model constructed based on LASSO-logistic regression analysis is expected to help medical staff screen high-risk patients for early individualized intervention.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Background/Aims: Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1–3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy. Methods: We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment. Results: The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset. Conclusions Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.

Objective:To explore the potential mechanism of Tianshui Dichang Decoction in the treatment of ulcerative colitis through network pharmacology and experimental verification.Methods:The active components and targets of Tianshui Dichang Decoction were screened by TCMSP. The related targets of ulcerative colitis were screened by OMIM, GeneCard and TTD databases, and the effective component targets of Tianshui Dichang Decoction were intersected with the potential targets of ulcerative colitis. The PPI network was constructed by STRING database to screen the core targets, and the "Chinese materia medica-disease-active components-target" network was constructed by Cytoscape 3.8.0 software. GO function and KEGG pathway enrichment analysis were carried out using Metascape database. 48 mice were divided into control group, model group, mesalazine group (0.3 g/kg) and Tianshui Dichang Decoction low-, medium-, and high-dosage groups (7.5,15 and 30 g/kg) according to random number table method, with 8 mice in each group. Except the control group, the ulcerative colitis model was established in other groups. After 7 days of intervention with corresponding drugs, the disease activity index (DAI) was scored, the pathological changes of colon were observed by HE staining, and the expressions of IL-6, STAT3mRNA and protein in colon tissue were detected by PCR and Western blot methods.Results:Totally 127 active components in Tianshui Dichang Decoction and 560 targets of ulcerative colitis were obtained. 89 intersecting targets of Tianshui Dichang Decoction and ulcerative colitis were obtained, and the core targets included IL6, TNF, IL1B, AKT1, TP53, VEGFA, JUN, PTGS2, CXCL8, CCL2, STAT3, MMP9 and so on. Oxidative stress response, lipopolysaccharide metabolism, bacterial response, signal transduction and other biological processes were mainly involved, mainly through the cancer pathway, IL17, TNF, MAPK and other signal pathways to play a role in the treatment of ulcerative colitis. The results of experimental verification showed that the DAI score, the expressions of IL-6 and STAT3 protein in colon tissue of Tianshui Dichang Decoction medium- and high-dosage groups.decreased ( P<0.05). The levels of IL-6 and STAT3 mRNA in colon tissue decreased in the Tianshui Dichang Decoction low-, medium- and high-dosage groups.groups ( P<0.05). Conclusion:Tianshui Dichang Decoction has a certain therapeutic effect on UC through component-multitarget-signal pathway, and its mechanism is related to regulating IL-6/STAT3 signal pathway and inhibiting intestinal mucosal inflammation.

Objective: To explore the improvement rate of liver fibrosis in patients with chronic hepatitis B virus infection who received entecavir alone or in combination with anluohuaxianwan for 78 weeks. Methods: Patients with chronic HBV infection were randomly treated with entecavir alone or in combination with anluohuaxian for 78 weeks. Ishak fibrosis score was used for blind interpretation of liver biopsy specimens. The improvement in liver fibrosis condition before and after the treatment was compared. Student's t test and non-parametric test (Mann-Whitney U-Test and Kruskal-Wallis test) were used to analyze the measurement data. The categorical variables were analyzed by Chi-square test method and Spearman’s rank correlation coefficient was used to test bivariate associations. Results: Liver fibrosis improvement rate after 78 weeks of treatment was 36.53% (80/219) and the progression rate was 23.29% (51/219). The improvement of liver fibrosis was associated to the degree of baseline fibrosis and treatment methods (P < 0.05). The improvement rate of hepatic fibrosis in patients treated with anluohuaxianwan combined with entecavir at baseline F < 3 (54.74%, 52/95) was significantly higher than that in patients treated only with entecavir (33.33%, 16/48), P = 0.016 and the progression rate of hepatic fibrosis (13.68%, 13/95) was lower than that in patients treated alone (18.75%, 9/48), P = 0.466. In patients with baseline F < 3, the proportion of patients with improved and stable liver fibrosis in the combined treatment group (68.1%, 32/47) was higher than that in the treatment group alone (51.7%, 15/29). Conclusion Combined anluohuaxianwan and entecavir treatment can significantly improve the improvement rate of liver fibrosis in patients with chronic hepatitis B virus infection. Furthermore, it has the tendency to improve the stability rate and reduce the rate of progression of liver fibrosis.

BACKGROUNDIt is important to choose an appropriate antiepileptic drug (AED) to manage partial epilepsy. Traditional AEDs, such as carbamazepine (CBZ) and valproate (VPA), have been proven to have good therapeutic effects. However, in recent years, a variety of new AEDs have increasingly been used as first-line treatments for partial epilepsy. As the studies regarding the effectiveness of new drugs and comparisons between new AEDs and traditional AEDs are few, it is determined that these are areas in need of further research. Accordingly, this study investigated the long-term effectiveness of six AEDs used as monotherapy in patients with partial epilepsy.

METHODSThis is a retrospective, long-term observational study. Patients with partial epilepsy who received monotherapy with one of six AEDs, namely, CBZ, VPA, topiramate (TPM), oxcarbazepine (OXC), lamotrigine (LTG), or levetiracetam (LEV), were identified and followed up from May 2007 to October 2014, and time to first seizure after treatment, 12-month remission rate, retention rate, reasons for treatment discontinuation, and adverse effects were evaluated.

RESULTSA total of 789 patients were enrolled. The median time of follow-up was 56.95 months. CBZ exhibited the best time to first seizure, with a median time to first seizure of 36.06 months (95% confidential interval: 30.64-44.07). CBZ exhibited the highest 12-month remission rate (85.55%), which was significantly higher than those of TPM (69.38%, P = 0.006), LTG (70.79%, P = 0.001), LEV (72.54%, P = 0.005), and VPA (73.33%, P = 0.002). CBZ, OXC, and LEV had the best retention rate, followed by LTG, TPM, and VPA. Overall, adverse effects occurred in 45.87% of patients, and the most common adverse effects were memory problems (8.09%), rashes (7.76%), abnormal hepatic function (6.24%), and drowsiness (6.24%).

CONCLUSIONThis study demonstrated that CBZ, OXC, and LEV are relatively effective in managing focal epilepsy as measured by time to first seizure, 12-month remission rate, and retention rate.

Adolescent ; Adult ; Anticonvulsants ; therapeutic use ; Carbamazepine ; analogs & derivatives ; therapeutic use ; China ; Epilepsies, Partial ; drug therapy ; Female ; Fructose ; analogs & derivatives ; therapeutic use ; Humans ; Male ; Middle Aged ; Piracetam ; analogs & derivatives ; therapeutic use ; Retrospective Studies ; Treatment Outcome ; Triazines ; therapeutic use ; Valproic Acid ; therapeutic use ; Young Adult