Pathological images of gastric cancer serve as the gold standard for diagnosing this malignancy. However, the recurrence prediction task often encounters challenges such as insignificant morphological features of the lesions, insufficient fusion of multi-resolution features, and inability to leverage contextual information effectively. To address these issues, a three-stage recurrence prediction method based on pathological images of gastric cancer is proposed. In the first stage, the self-supervised learning framework SimCLR was adopted to train low-resolution patch images, aiming to diminish the interdependence among diverse tissue images and yield decoupled enhanced features. In the second stage, the obtained low-resolution enhanced features were fused with the corresponding high-resolution unenhanced features to achieve feature complementation across multiple resolutions. In the third stage, to address the position encoding difficulty caused by the large difference in the number of patch images, we performed position encoding based on multi-scale local neighborhoods and employed self-attention mechanism to obtain features with contextual information. The resulting contextual features were further combined with the local features extracted by the convolutional neural network. The evaluation results on clinically collected data showed that, compared with the best performance of traditional methods, the proposed network provided the best accuracy and area under curve (AUC), which were improved by 7.63% and 4.51%, respectively. These results have effectively validated the usefulness of this method in predicting gastric cancer recurrence.
ObjectiveTo explore the risk factors affecting occurrence of arteriosclerosis obliterans (ASO) for patients with type 2 diabetes mellitus (T2DM) and to develop a nomogram predictive model using these risk factors. MethodsA case-control study was conducted. The patients with T2DM accompanied with ASO and those with T2DM alone, admitted to the First Affiliated Hospital of Xinjiang Medical University from January 2017 to December 2022, were retrospectively collected according to the inclusion and exclusion criteria. The basic characteristics, blood, thyroid hormones, and other relevant indicators of the paitents in two groups were compared. The multivariate logistic regression analysis was used to identify the risk factors for the occurrence of ASO in the patients with T2DM, and then a nomogram predictive model was developed. ResultsThere were 119 patients with T2DM alone and 114 patients with T2DM accompanied with lower extremity ASO in this study. The significant differences were observed between the two groups in terms of smoking history, white blood cell count, neutrophil count, lymphocyte count, platelet count, systemic immune-inflammation index, systemic inflammatory response index (SIRI), high-density lipoprotein cholesterol, apolipoprotein A1 (ApoA1), apolipoprotein α (Apoα), serum cystatin C, free-triiodothyronine (FT3), total triiodothyronine, FT3/total triiodothyronine ratio, fibrinogen (Fib), fibrinogen degradation products, and plasma D-dimer (P<0.05). Further the results of the multivariate logistic regression analysis revealed that the history of smoking, increased Fib level and SIRI value increased the probabilities of ASO occurrence in the patients with T2DM [OR (95%CI)=2.921 (1.023, 4.227), P=0.003; OR (95%CI)=2.641 (1.810, 4.327), P<0.001; OR (95%CI)=1.020 (1.004, 1.044), P=0.018], whereas higher levels of ApoA1 and FT3 were associated with reduced probabilities of ASO occurrence in the patients with T2DM [OR (95%CI)=0.231 (0.054, 0.782), P=0.021; OR (95%CI)=0.503 (0.352, 0.809), P=0.002]. The nomogram predictive model based on these factors demonstrated a good discrimination for predicting the ASO occurrence in the T2DM patients [area under the receiver operating characteristic curve (95%CI)=0.788 (0.730, 0.846)]. The predicted curve closely matched the ideal curve (Hosmer-Lemeshow goodness-of-fit test, χ2=5.952, P=0.653). The clinical decision analysis curve showed that the clinical net benefit of intervention based on the nomogram model was higher within a threshold probability range of 0.18 to 0.80 compared to no intervention or universal intervention. ConclusionsThe analysis results indicate that T2DM patients with a smoking history, elevated Fib level and SIRI value, as well as decreased ApoA1 and FT3 levels should be closely monitored for ASO risk. The nomogram predictive model based on these features has a good discriminatory power for ASO occurrence in T2DM patients, though its value warrants further investigation.
The drug-target protein interaction prediction can be used for the discovery of new drug effects. Recent studies often focus on the prediction of an independent matrix filling algorithm, which apply a single algorithm to predict the drug-target protein interaction. The single-model matrix-filling algorithms have low accuracy, so it is difficult to obtain satisfactory results in the prediction of drug-target protein interaction. AdaBoost algorithm is a strong multiple classifier combination framework, which is proved by the past researches in classification applications. The drug-target interaction prediction is a matrix filling problem. Therefore, we need to adjust the matrix filling problem to a classification problem before predicting the interaction among drug-target protein. We make full use of the AdaBoost algorithm framework to integrate several weak classifiers to improve performance and make accurate prediction of drug-target protein interaction. Experimental results based on the metric datasets show that our algorithm outperforms the other state-of-the-art approaches and classical methods in accuracy. Our algorithm can overcome the limitations of the single algorithm based on machine learning method, exploit the hidden factors better and improve the accuracy of prediction effectively.
Acute lung injury is one of the common and serious complications of acute aortic dissection, and it greatly affects the recovery of patients. Old age, overweight, hypoxemia, smoking history, hypotension, extensive involvement of dissection and pleural effusion are possible risk factors for the acute lung injury before operation. In addition, deep hypothermia circulatory arrest and blood product infusion can further aggravate the acute lung injury during operation. In this paper, researches on risk factors, prediction model, prevention and treatment of acute aortic dissection with acute lung injury were reviewed, in order to provide assistance for clinical diagnosis and treatment.
Aiming at the problem of scaffold degradation in bone tissue engineering, we studied the feasibility that controlls bone defect repair effect with the inhomogeneous structure of scaffold. The prediction model of bone defect repair which contains governing equations for bone formation and scaffold degradation was constructed on the basis of analyzing the process and main influence factors of bone repair in bone tissue engineering. The process of bone defect repair and bone structure after repairing can be predicted by combining the model with finite element method (FEM). Bone defect repair effects with homogenous and inhomogeneous scaffold were simulated respectively by using the above method. The simulation results illustrated that repair effect could be impacted by scaffold structure obviously and it can also be controlled via the inhomogeneous structure of scaffold with some feasibility.
This study introduced the construction of individualized risk assessment model based on Bayesian networks, comparing with traditional regression-based logistic models using practical examples. It evaluates the model's performance and demonstrates its implementation in the R software, serving as a valuable reference for researchers seeking to understand and utilize Bayesian network models.
ObjectiveTo summarise the application research progress of clinical prediction models in postoperative complications of gastric cancer, in order to reduce the risk of complications after gastric cancer surgery. MethodThe literature on the study of postoperative complications of gastric cancer at home and abroad was read and reviewed. ResultsAt present, the main way of treating gastric cancer was still radical resection, and the occurrence of complications after surgical treatment seriously affected the recovery and survival quality of patients. With the deepening of research, the prediction models of postoperative complications in gastric cancer were constantly constructed, and these models provided strong evidence for the early judgement of postoperative complications in gastric cancer, and provided a scientific basis for the improvement of patients’ life quality. ConclusionClinical predictive models are expected to become risk screening tools for predicting the risk of postoperative complications of gastric cancer with clinical utility.
ObjectiveTo systematically evaluate the risk prediction model of anastomotic fistula after radical resection of esophageal cancer, and to provide objective basis for selecting a suitable model. MethodsA comprehensive search was conducted on Chinese and English databases including CNKI, Wanfang, VIP, CBM, PubMed, EMbase, Web of Science, The Cochrane Library for relevant studies on the risk prediction model of anastomotic fistula after radical resection of esophageal cancer from inception to April 30, 2023. Two researchers independently screened literatures and extracted data information. PROBAST tool was used to assess the risk of bias and applicability of included literatures. Meta-analysis was performed on the predictive value of common predictors in the model with RevMan 5.3 software. ResultsA total of 18 studies were included, including 11 Chinese literatures and 7 English literatures. The area under the curve (AUC) of the prediction models ranged from 0.68 to 0.954, and the AUC of 10 models was >0.8, indicating that the prediction performance was good, but the risk of bias in the included studies was high, mainly in the field of research design and data analysis. The results of the meta-analysis on common predictors showed that age, history of hypertension, history of diabetes, C-reactive protein, history of preoperative chemotherapy, hypoproteinemia, peripheral vascular disease, pulmonary infection, and calcification of gastric omental vascular branches are effective predictors for the occurrence of anastomotic leakage after radical surgery for esophageal cancer (P<0.05). ConclusionThe study on the risk prediction model of anastomotic fistula after radical resection of esophageal cancer is still in the development stage. Future studies can refer to the common predictors summarized by this study, and select appropriate methods to develop and verify the anastomotic fistula prediction model in combination with clinical practice, so as to provide targeted preventive measures for patients with high-risk anastomotic fistula as soon as possible.
Objective To evaluate the correlation between pelvic incidence (PI) angle, hip deflection angle (HDA), combined deflection angle (CDA) and osteonecrosis of the femoral head (ONFH) after femoral neck fracture, in order to explore early predictive indicators for ONFH occurrence after femoral neck fracture. Methods A study was conducted on patients with femoral neck fractures who underwent cannulated screw internal fixation between December 2018 and December 2020. Among them, 208 patients met the selection criteria and were included in the study. According to the occurrence of ONFH, the patients were allocated into ONFH group and non-NOFH group. PI, HDA, and CDA were measured based on the anteroposterior X-ray films of pelvis and axial X-ray films of the affected hip joint before operation, and the differences between the two groups were compared. The receiver operating characteristic curve (ROC) was used to evaluate the value of the above imaging indicators in predicting the occurrence of ONFH. ResultsAmong the 208 patients included in the study, 84 patients experienced ONFH during follow-up (ONFH group) and 124 patients did not experience ONFH (non-ONFH group). In the non-ONFH group, there were 59 males and 65 females, the age was 18-86 years (mean, 53.9 years), and the follow-up time was 18-50 months (mean, 33.2 months). In the ONFH group, there were 37 males and 47 females, the age was 18-76 years (mean, 51.6 years), and the follow-up time was 8-45 months (mean, 22.1 months). The PI, HDA, and CDA were significantly larger in the ONFH group than in the non-ONFH group (P<0.05). ROC curve analysis showed that the critical value of PI was 19.82° (sensitivity of 40.5%, specificity of 86.3%, P<0.05); the critical value of HDA was 20.94° (sensitivity of 77.4%, specificity of 75.8%, P<0.05); and the critical value of CDA was 39.16° (sensitivity of 89.3%, specificity of 83.1%, P<0.05). Conclusion There is a correlation between PI, HDA, CDA and the occurrence of ONFH after femoral neck fracture, in which CDA can be used as an important reference indicator. Patients with CDA≥39.16° have a higher risk of ONFH after femoral neck fracture.
ObjectiveTo analyze the predictive value of ensemble classification algorithm of random forest for delirium risk in ICU patients with cardiothoracic surgery. MethodsA total of 360 patients hospitalized in cardiothoracic ICU of our hospital from June 2019 to December 2020 were retrospectively analyzed. There were 193 males and 167 females, aged 18-80 (56.45±9.33) years. The patients were divided into a delirium group and a control group according to whether delirium occurred during hospitalization or not. The clinical data of the two groups were compared, and the related factors affecting the occurrence of delirium in cardiothoracic ICU patients were predicted by the multivariate logistic regression analysis and the ensemble classification algorithm of random forest respectively, and the difference of the prediction efficiency between the two groups was compared.ResultsOf the included patients, 19 patients fell out, 165 patients developed ICU delirium and were enrolled into the delirium group, with an incidence of 48.39% in ICU, and the remaining 176 patients without ICU delirium were enrolled into the control group. There was no statistical significance in gender, educational level, or other general data between the two groups (P>0.05). But compared with the control group, the patients of the delirium group were older, length of hospital stay was longer, and acute physiology and chronic health evaluationⅡ(APACHEⅡ) score, proportion of mechanical assisted ventilation, physical constraints, sedative drug use in the delirium group were higher (P<0.05). Multivariate logistic regression analysis showed that age (OR=1.162), length of hospital stay (OR=1.238), APACHEⅡ score (OR=1.057), mechanical ventilation (OR=1.329), physical constraints (OR=1.345) and sedative drug use (OR=1.630) were independent risk factors for delirium of cardiothoracic ICU patients. The variables in the random forest model for sorting, on top of important predictor variable were: age, length of hospital stay, APACHEⅡ score, mechanical ventilation, physical constraints and sedative drug use. The diagnostic efficiency of ensemble classification algorithm of random forest was obviously higher than that of multivariate logistic regression analysis. The area under receiver operating characteristic curve of ensemble classification algorithm of random forest was 0.87, and the one of multivariate logistic regression analysis model was 0.79.ConclusionThe ensemble classification algorithm of random forest is more effective in predicting the occurrence of delirium in cardiothoracic ICU patients, which can be popularized and applied in clinical practice and contribute to early identification and strengthening nursing of high-risk patients.