The value of SDC2 and Septin9 combined with serum tumor markers in early diagnosis of colorectal cancer.

OBJECTIVE: The aim of this study is to evaluate the significance of combined detection of Septin9 and syndecan-2 (SDC2) methylation markers and serum tumor markers for the early diagnosis of colorectal cancer. METHODS: A total of 116 patients diagnosed with colorectal cancer between December 2022 and February 2024 were designated as the colorectal cancer group. Additionally, 31 patients with colorectal adenoma were assigned to the adenoma group, while 44 individuals undergoing routine physical examinations were included in the control group. Concentrations of Septin9, SDC2, fecal occult blood (FOB), and four tumor markers-carcinoembryonic antigen (CEA), carbohydrate antigen 199 (CA199), carbohydrate antigen 125 (CA125), and carbohydrate antigen 724 (CA724)-were measured. Diagnostic performance was assessed using receiver operating characteristic (ROC) curves for Septin9, SDC2, the four tumor markers, FOB, the combination of Septin9 and SDC2, and the combined use of all seven indicators (CEA, CA19-9, CA125, CA72-4, FOB, Septin9, and SDC2). RESULTS: The colorectal cancer group exhibited the highest positive rates for Septin9, SDC2, the four tumor markers, the combined detection of Septin9 and SDC2, and the combined detection of all seven indicators, compared to both the adenoma and control groups (P < 0.05). The adenoma group also showed higher positive rates than the control group (P < 0.05). For patients with stage I-III colorectal cancer, the positive rates for the combined detection of Septin9 and SDC2 were 81.3%, 78.9%, and 90.2%, respectively, surpassing those for the combined detection of the four tumor markers (43.8%, 55.3%, and 61.0%). Additionally, the positive rates for the two-gene combination in stage III colorectal cancer were higher than those for FOB (P < 0.05). The sensitivity and area under the curve (AUC) for SDC2 were 73.3% and 0.855, respectively, exceeding the sensitivity and AUC for the combined four tumor markers, which were 60.3% and 0.734 (P < 0.05). The combined detection of the two methylated genes demonstrated a sensitivity of 86.2% and an AUC of 0.908, outperforming both FOB and the combined detection of the four tumor markers (P < 0.05). CONCLUSION: The detection of SDC2 exhibits high sensitivity for colorectal cancer, and when combined with Septin9, it significantly enhances the diagnostic accuracy for early-stage colorectal cancer, offering substantial clinical value.

Risk prediction and prognostic analysis of post-implantation syndrome after thoracic endovascular aortic repair.

This study aimed to establish a predictive model for the risk of post-thoracic endovascular aortic repair (TEVAR) post-implantation syndrome (PIS) in type B aortic dissection (TBAD) patients, assisting clinical physicians in early risk stratification and decision management for high-risk PIS patients. This study retrospectively analyzed the clinical data of 547 consecutive TBAD patients who underwent TEVAR treatment at our hospital. Feature variables were selected through LASSO regression and logistic regression analysis to construct a nomogram predictive model, and the model's performance was evaluated. The optimal cutoff value for the PIS risk nomogram score was calculated through receiver operating characteristic (ROC) curve analysis, further dividing patients into high-risk group (HRG) and low-risk group (LRG), and comparing the short to midterm postoperative outcomes between the two groups. In the end, a total of 158 cases (28.9%) experienced PIS. Through LASSO regression analysis and multivariable logistic regression analysis, variables including age, emergency surgery, operative time, contrast medium volume, and number of main prosthesis stents were selected to construct the nomogram predictive model. The model achieved an area under the curve (AUC) of 0.86 in the training set and 0.82 in the test set. Results from calibration curve, decision curve analysis (DCA) and clinical impact curve (CIC) demonstrated that the predictive model exhibited good performance and clinical utility. Furthermore, after comparing the postoperative outcomes of HRG and LRG patients, we found that the incidence of postoperative PIS significantly increased in HRG patients. The duration of ICU stay and mechanical assistance time was prolonged, and the incidence of postoperative type II entry flow and acute kidney injury (AKI) was higher. The risk of aortic-related adverse events (ARAEs) and major adverse events (MAEs) at the first and twelfth months of follow-up also significantly increased. However, there was no significant difference in the mortality rate during hospitalization. This study established a nomogram model for predicting the risk of PIS in patients with TBAD undergoing TEVAR. It serves as a practical tool to assist clinicians in early risk stratification and decision-making management for patients.

Development of a machine learning-based model to predict major adverse events after surgery for type A aortic dissection complicated by malnutrition.

Objective: This study aims to develop a predictive model for the risk of major adverse events (MAEs) in type A aortic dissection (AAAD) patients with malnutrition after surgery, utilizing machine learning (ML) algorithms. Methods: We retrospectively collected clinical data from AAAD patients with malnutrition who underwent surgical treatment at our center. Through least absolute shrinkage and selection operator (LASSO) regression analysis, we screened for preoperative and intraoperative characteristic variables. Based on the random forest (RF) algorithm, we constructed a ML predictive model, and further evaluated and interpreted this model. Results: Through LASSO regression analysis and univariate analysis, we ultimately selected seven feature variables for modeling. After comparing six different ML models, we confirmed that the RF model demonstrated the best predictive performance in this dataset. Subsequently, we constructed a model using the RF algorithm to predict the risk of postoperative MAEs in AAAD patients with malnutrition. The test set results indicated that this model has excellent predictive efficacy and clinical applicability. Finally, we employed the Shapley additive explanations (SHAP) method to further interpret the predictions of this model. Conclusion: We have successfully constructed a risk prediction model for postoperative MAEs in AAAD patients with malnutrition using the RF algorithm, and we have interpreted the model through the SHAP method. This model aids clinicians in early identification of high-risk patients for MAEs, thereby potentially mitigating adverse clinical outcomes associated with malnutrition.

Quality of life in young patients with acute type a aortic dissection in China: comparison with Marfan syndrome and non-Marfan syndrome.

BACKGROUND: There is a paucity of Chinese studies evaluating the quality of life (QoL) in young acute type A aortic dissection (AAAD) patients with Marfan syndrome. METHODS: Young adult AAAD patients (younger than 45 years old) underwent surgical treatment at our institution from January 2017 to December 2020 were consecutive enrolled. The hospital survivors completed 1 year of follow up. Patients were divided into two groups according to the presence or absence of Marfan syndrome (MFS). A 1:1 propensity score matching (PSM) with a caliper 0.2 was conducted to balance potential bias in baseline. The follow-up data were analyzed primarily for change in quality of life and anxiety status. RESULTS: After PSM, 32 comparable pairs were matched. The baseline data were comparable and postoperative complications were similar between groups. In terms of SF-36 scale, the role physical, bodily pain, role emotional and mental health subscales were no significantly improved in MFS patients over time. At 1 year after discharged, the subscale of mental health and bodily pain were significantly lower in the MFS group than in the non-MFS group. In terms of HADS assessments, the level of anxiety in MFS patients was significantly higher than in non-MFS patients at 1 year after discharged. CONCLUSIONS: The QoL in young AAAD patients with MFS is lower than those without MFS after surgery. This may be associated with the uncontrollable persistent chronic pain and the uncertainty and concerns for the disease's progression.

A predictive model for postoperative adverse outcomes following surgical treatment of acute type A aortic dissection based on machine learning.

Acute type A aortic dissection (AAAD) has a high probability of postoperative adverse outcomes (PAO) after emergency surgery, so exploring the risk factors for PAO during hospitalization is key to reducing postoperative mortality and improving prognosis. An artificial intelligence approach was used to build a predictive model of PAO by clinical data-driven machine learning to predict the incidence of PAO after total arch repair for AAAD. This study included 380 patients with AAAD. The clinical features that are associated with PAO were selected using the LASSO regression analysis. Six different machine learning algorithms were tried for modeling, and the performance of each model was analyzed comprehensively using receiver operating characteristic curves, calibration curve, precision recall curve, and decision analysis curves. Explain the optimal model through Shapley Additive Explanation (SHAP) and perform an individualized risk assessment. After comprehensive analysis, the authors believe that the extreme gradient boosting (XGBoost) model is the optimal model, with better performance than other models. The authors successfully built a prediction model for PAO in AAAD patients based on the XGBoost algorithm and interpreted the model with the SHAP method, which helps to identify high-risk AAAD patients at an early stage and to adjust individual patient-related clinical treatment plans in a timely manner.

A Predictive Model for Prolonged Mechanical Ventilation After Triple-Branched Stent Graft for Acute Type A Aortic Dissection.

INTRODUCTION: The aim of this study is to develop a model for predicting the risk of prolonged mechanical ventilation (PMV) following surgical repair of acute type A aortic dissection (AAAD). METHODS: We retrospectively collected clinical data from 381 patients with AAAD who underwent emergency surgery. Clinical features variables for predicting postoperative PMV were selected through univariate analysis, least absolute shrinkage and selection operator regression analysis, and multivariate logistic regression analysis. A risk prediction model was established using a nomogram. The model's accuracy and reliability were evaluated using the area under the curve of the receiver operating characteristic curve and the calibration curve. Internal validation of the model was performed using bootstrap resampling. The clinical applicability of the model was assessed using decision curve analysis and clinical impact curve. RESULTS: Among the 381 patients, 199 patients (52.2%) experienced postoperative PMV. The predictive model exhibited good discriminative ability (area under the curve = 0.827, 95% confidence interval: 0.786-0.868, P < 0.05). The calibration curve confirmed that the predicted outcomes of the model closely approximated the ideal curve, indicating agreement between the predicted and actual results (with an average absolute error of 0.01 based on 1000 bootstrap resampling). The decision curve analysis curve demonstrated that the model has significant clinical value. CONCLUSIONS: The nomogram model established in this study can be used to predict the risk of postoperative PMV in patients with AAAD. It serves as a practical tool to assist clinicians in adjusting treatment strategies promptly and implementing targeted therapeutic measures.

A nomogram-based model to predict postoperative transient neurological dysfunctions in patients receiving acute type A aortic dissection surgery.

The purposes of this study were to develop and validate a nomogram for predicting postoperative transient neurological dysfunctions (TND) in patients with acute type A aortic dissection (AAAD) who underwent modified triple-branched stent graft implantation. This retrospective study developed a nomogram-based model in a consecutive cohort of 146 patients. Patient characteristics, preoperative clinical indices, and operative data were analyzed. Univariate and multivariable analyses were applied to identify the most useful predictive variables for constructing the nomogram. Discrimination and the calibration of the model was evaluated through the receiver operating characteristic curve (ROC), the Hosmer-Lemeshow goodness-of-fit test and the decision curve analysis (DCA). At the same time, to identify and compare long-term cumulative survival rate, Kaplan-Meier survival curve was plotted. The incidence rate of postoperative TND observed in our cohort were 40.9%. Supra-aortic dissection with or without thrombosis, creatinine >115 µmol and albumin <39.7 g/L, selective antegrade cerebral perfusion (SACP) time >7 min and total operation time >303 min, were confirmed as independent predictors that enhanced the likelihood of TND. Internal validation showed good discrimination of the model with under the ROC curve (AUC) of 0.818 and good calibration (Hosmer-Lemeshow test, p > .05). DCA revealed that the nomogram was clinically useful. In the long-term survival there was no significant difference between patients with or without TND history. The results showed the predict model based on readily available predictors has sufficient validity to identify TND risk in this population, that maybe useful for clinical decision-making.

Human Serum-derived Extracellular Vesicles Protect A549 from PM 2.5-induced Cell Apoptosis.

OBJECTIVE: Epidemiological studies reveal that exposure to fine particulate matter (aerodynamic diameter ≤ 2.5 µm, PM 2.5) increases the morbidity and mortality of respiratory diseases. Emerging evidence suggests that human circulating extracellular vesicles (EVs) may offer protective effects against injury caused by particulate matter. Currently, however, whether EVs attenuate PM 2.5-induced A549 cell apoptosis is unknown. METHODS: EVs were isolated from the serum of healthy subjects, quantified via nanoparticle tracking analysis, and qualified by the marker protein CD63. PM 2.5-exposed (50 µg/mL) A549 cells were pre-treated with 10 µg/mL EVs for 24 h. Cell viability, cell apoptosis, and AKT activation were assessed via Cell Counting Kit-8, flow cytometry, and Western blot, respectively. A rescue experiment was also performed using MK2206, an AKT inhibitor. RESULTS: PM 2.5 exposure caused a 100% increase in cell apoptosis. EVs treatment reduced cell apoptosis by 10%, promoted cell survival, and inhibited the PM 2.5-induced upregulation of Bax/Bcl2 and cleaved caspase 3/caspase 3 in PM 2.5-exposed A549 cells. Moreover, EVs treatment reversed PM 2.5-induced reductions in p-AKT Thr308 and p-AKT Ser473. AKT inhibition attenuated the anti-apoptotic effect of EVs treatment on PM 2.5-exposed A549 cells. CONCLUSIONS: EVs treatment promotes cell survival and attenuates PM 2.5-induced cell apoptosis via AKT phosphorylation. Human serum-derived EVs may be an efficacious novel therapeutic strategy in PM 2.5-induced lung injury.