The in-vivo electron paramagnetic resonance (EPR) method can be used for on-site, rapid, and non-invasive detection of radiation dose to casualties after nuclear and radiation emergencies. For in-vivo EPR spectrum analysis, manual labeling of peaks and calculation of signal intensity are often used, which have problems such as large workload and interference by subjective factors. In this study, a method for automatic classification and identification of in-vivo EPR spectra was established using support vector machine (SVM) technology, which can in-batch and automatically identify and screen out invalid spectra due to vibration and dental surface water interference during in-vivo EPR measurements. In this study, a spectrum analysis method based on genetic algorithm optimization neural network (GA-BPNN) was established, which can automatically identify the radiation-induced signals in in-vivo EPR spectra and predict the radiation doses received by the injured. The experimental results showed that the SVM and GA-BPNN spectrum processing methods established in this study could effectively accomplish the automatic spectra classification and radiation dose prediction, and could meet the needs of dose assessment in nuclear emergency. This study explored the application of machine learning methods in EPR spectrum processing, improved the intelligence level of EPR spectrum processing, and would help to enhance the efficiency of mass EPR spectra processing.
In order to improve the motion fluency and coordination of lower extremity exoskeleton robots and wearers, a pace recognition method of exoskeleton wearer is proposed base on inertial sensors. Firstly, the triaxial acceleration and triaxial angular velocity signals at the thigh and calf were collected by inertial sensors. Then the signal segment of 0.5 seconds before the current time was extracted by the time window method. And the Fourier transform coefficients in the frequency domain signal were used as eigenvalues. Then the support vector machine (SVM) and hidden Markov model (HMM) were combined as a classification model, which was trained and tested for pace recognition. Finally, the pace change rule and the human-machine interaction force were combined in this model and the current pace was predicted by the model. The experimental results showed that the pace intention of the lower extremity exoskeleton wearer could be effectively identified by the method proposed in this article. And the recognition rate of the seven pace patterns could reach 92.14%. It provides a new way for the smooth control of the exoskeleton.
Because of the diversity and complexity of clinical indicators, it is difficult to establish a comprehensive and reliable prediction model for induction of labor (IOL) outcomes with existing methods. This study aims to analyze the clinical indicators related to IOL and to develop and evaluate a prediction model based on a small-sample of data. The study population consisted of a total of 90 pregnant women who underwent IOL between February 2023 and January 2024 at the Shanghai First Maternity and Infant Healthcare Hospital, and a total of 52 clinical indicators were recorded. Maximal information coefficient (MIC) was used to select features for clinical indicators to reduce the risk of overfitting caused by high-dimensional features. Then, based on the features selected by MIC, the support vector machine (SVM) model based on small samples was compared and analyzed with the fully connected neural network (FCNN) model based on large samples in deep learning, and the receiver operating characteristic (ROC) curve was given. By calculating the MIC score, the final feature dimension was reduced from 55 to 15, and the area under curve (AUC) of the SVM model was improved from 0.872 before feature selection to 0.923. Model comparison results showed that SVM had better prediction performance than FCNN. This study demonstrates that SVM successfully predicted IOL outcomes, and the MIC feature selection effectively improves the model’s generalization ability, making the prediction results more stable. This study provides a reliable method for predicting the outcome of induced labor with potential clinical applications.
ObjectiveTo explore the effect of full nutritional management pattern on perioperative nutritional status in patients with head and neck malignancies. MethodsSixty-four patients with head and neck cancer treated in our department between March 2012 and June 2013 were randomly divided into control group and study group with 32 in each. The control group received conventional dietary guidance, while patients in the study group were given full nutritional management. Nutritional Risk Screening Scale 2002 (NRS-2002) was used for nutrition screening and assessment before surgery (after admission) and after surgery (3 days after surgery). The study group received full nutritional support, along with nutrition-related physical examination and biochemical tests, and observation of postoperative complications, and hospital stay and costs were also observed. ResultsNutritional risk existed in 29.7%-48.4% of the head and neck cancer patients during various stages of the perioperative period. Through the full nutritional support, patients in the study group had a significantly lower risk than those in the control group (P<0.01). Body mass index, triceps skinfold thickness, mid-arm muscle circumference, prealbumin, and creatinine in the study group were significantly more improved compared with the control group (P<0.01). No significant difference was detected in blood urea and serum albumin between the two groups. Postoperative complications in the study group was significantly lower (P<0.05), and hospital stay and costs were significantly lower than the control group (P<0.001). ConclusionFull nutritional management pattern can significantly improve the perioperative nutritional status in head and neck cancer patients. Early detection of nutritional risk and malnutrition (foot) in the patients and carrying out normal and scientific nutrition intervention are helpful in the rehabilitation of these patients. We suggest that qualified hospitals should carry out the full nutritional management model managed by a Nutrition Support Team for patients with malignancies.
Keloids are benign skin tumors resulting from the excessive proliferation of connective tissue in wound skin. Precise prediction of keloid risk in trauma patients and timely early diagnosis are of paramount importance for in-depth keloid management and control of its progression. This study analyzed four keloid datasets in the high-throughput gene expression omnibus (GEO) database, identified diagnostic markers for keloids, and established a nomogram prediction model. Initially, 37 core protein-encoding genes were selected through weighted gene co-expression network analysis (WGCNA), differential expression analysis, and the centrality algorithm of the protein-protein interaction network. Subsequently, two machine learning algorithms including the least absolute shrinkage and selection operator (LASSO) and the support vector machine-recursive feature elimination (SVM-RFE) were used to further screen out four diagnostic markers with the highest predictive power for keloids, which included hepatocyte growth factor (HGF), syndecan-4 (SDC4), ectonucleotide pyrophosphatase/phosphodiesterase 2 (ENPP2), and Rho family guanosine triphophatase 3 (RND3). Potential biological pathways involved were explored through gene set enrichment analysis (GSEA) of single-gene. Finally, univariate and multivariate logistic regression analyses of diagnostic markers were performed, and a nomogram prediction model was constructed. Internal and external validations revealed that the calibration curve of this model closely approximates the ideal curve, the decision curve is superior to other strategies, and the area under the receiver operating characteristic curve is higher than the control model (with optimal cutoff value of 0.588). This indicates that the model possesses high calibration, clinical benefit rate, and predictive power, and is promising to provide effective early means for clinical diagnosis.
Hypertension is the primary disease that endangers human health. A convenient and accurate blood pressure measurement method can help to prevent the hypertension. This paper proposed a continuous blood pressure measurement method based on facial video signal. Firstly, color distortion filtering and independent component analysis were used to extract the video pulse wave of the region of interest in the facial video signal, and the multi-dimensional feature extraction of the pulse wave was preformed based on the time-frequency domain and physiological principles; Secondly, an integrated feature selection method was designed to extract the universal optimal feature subset; After that, we compared the single person blood pressure measurement models established by Elman neural network based on particle swarm optimization, support vector machine (SVM) and deep belief network; Finally, we used SVM algorithm to build a general blood pressure prediction model, which was compared and evaluated with the real blood pressure value. The experimental results showed that the blood pressure measurement results based on facial video were in good agreement with the standard blood pressure values. Comparing the estimated blood pressure from the video with standard blood pressure value, the mean absolute error (MAE) of systolic blood pressure was 4.9 mm Hg with a standard deviation (STD) of 5.9 mm Hg, and the MAE of diastolic blood pressure was 4.6 mm Hg with a STD of 5.0 mm Hg, which met the AAMI standards. The non-contact blood pressure measurement method based on video stream proposed in this paper can be used for blood pressure measurement.
ST segment morphology is closely related to cardiovascular disease. It is used not only for characterizing different diseases, but also for predicting the severity of the disease. However, the short duration, low energy, variable morphology and interference from various noises make ST segment morphology classification a difficult task. In this paper, we address the problems of single feature extraction and low classification accuracy of ST segment morphology classification, and use the gradient of ST surface to improve the accuracy of ST segment morphology multi-classification. In this paper, we identify five ST segment morphologies: normal, upward-sloping elevation, arch-back elevation, horizontal depression, and arch-back depression. Firstly, we select an ST segment candidate segment according to the QRS wave group location and medical statistical law. Secondly, we extract ST segment area, mean value, difference with reference baseline, slope, and mean squared error features. In addition, the ST segment is converted into a surface, the gradient features of the ST surface are extracted, and the morphological features are formed into a feature vector. Finally, the support vector machine is used to classify the ST segment, and then the ST segment morphology is multi-classified. The MIT-Beth Israel Hospital Database (MITDB) and the European ST-T database (EDB) were used as data sources to validate the algorithm in this paper, and the results showed that the algorithm in this paper achieved an average recognition rate of 97.79% and 95.60%, respectively, in the process of ST segment recognition. Based on the results of this paper, it is expected that this method can be introduced in the clinical setting in the future to provide morphological guidance for the diagnosis of cardiovascular diseases in the clinic and improve the diagnostic efficiency.
Abstract： Objective To assess the effects of three different palliative procedures including modified BlalockTaussig (B-T) shunt, Waterston shunt, and reconstruction of right ventricularpulmonary artery (RV-PA) continuity for pulmonary atresia with ventricular septal defect (PAVSD). Methods We retrospectively analyzed the clinical data of 93 patients with PAVSD who had undergone palliative surgical procedures including modifie BT shunt, Waterston shunt, and RVPA econstruction in Fu Wai Hospital from September 1998 to September 2008. There were 53 males and 40 females, aged from 14.0 days to 14.4 years, with the body weight ranged from 3.6 to 33.0 kg (9.9±6.3 kg). According to International Congenital Heart Surgery Nomenclature and Database Project, these patients were categorized into 2 groups: 64 of type Ⅰ and 29 of type Ⅱ. The most common associated anomaly is rightsided aortic arch (except for ventricular septal defect). The application of the three kinds of palliative surgical procedures in staged management of PAVSD and the followup results were statistically analyzed. Results The corrective rate of the three palliative procedures were 28.12% (18/64) for modified BT shunt, 7.14%(1/14) for Waterston shunt, and 66.67% (10/15) for RV-PA reconstruction, respectively. RV-PA reconstruction had a significantly higher corrective 〖CM(1585mm〗rate than the other two surgical procedures (P=0.016). The percutaneous oxygen saturation (SpO2) increased by 4%59% and Nakata index by 31-104 mm2/m2. No tortuous pulmonary artery was found under echocardiogram or angiocardiography after palliative operation. The perioperative mortality of both surgical stages was 10 patients. Twostage radical surgery was successfully performed for 25 patients, among whom 20 were followed up till May 2009. During the followup, one died suddenly, 15 were classified as New York Heart Association (NYHA) Ⅰ, and 4 as NYHA Ⅱ. Conclusion The surgical management of PAVSD needs to be improved continuously. Compared with shunting procedures, the RVPA reconstruction is a better palliative operation method, and the modified B-T shunt is preferred in younger patients.
ObjectiveTo predict the total hospitalization expenses of bronchopneumonia inpatients in a tertiay hospital of Sichuan Province through BP neural network and support vector machine models, and analyze the influencing factors.MethodsThe home page information of 749 cases of bronchopneumonia discharged from a tertiay hospital of Sichuan Province in 2017 was collected and compiled. The BP neural network model and the support vector machine model were simulated by SPSS 20.0 and Clementine softwares respectively to predict the total hospitalization expenses and analyze the influencing factors.ResultsThe accuracy rate of the BP neural network model in predicting the total hospitalization expenses was 81.2%, and the top three influencing factors and their importances were length of hospital stay (0.477), age (0.154), and discharge department (0.083). The accuracy rate of the support vector machine model in predicting the total hospitalization expenses was 93.4%, and the top three influencing factors and their importances were length of hospital stay (0.215), age (0.196), and marital status (0.172), but after stratified analysis by Mantel-Haenszel method, the correlation between marital status and total hospitalization expenses was not statistically significant (χ2=0.137, P=0.711).ConclusionsThe BP neural network model and the support vector machine model can be applied to predicting the total hospitalization expenses and analyzing the influencing factors of patients with bronchopneumonia. In this study, the prediction effect of the support vector machine is better than that of the BP neural network model. Length of hospital stay is an important influencing factor of total hospitalization expenses of bronchopneumonia patients, so shortening the length of hospital stay can significantly lighten the economic burden of these patients.
Objective To determine whether lymph node-targeted chemotherapy with carbon nanoparticles absorbing 5-FU affects expressions of bcl-2, bax and caspase-3 in gastric cancer tissues, metastatic lymph nodes and normal gastric mucosa. Methods Twenty-eight patients with gastric cancer in our department were divided into lymph node-targeted chemotherapy (LNTC) group and control group from October 2005 to August 2006. The patients were treated with carbon nanoparticles absorbing 5-FU before operation in LNTC group and those were operated directly in control group. The gastric cancer tissues, metastatic lymph nodes and normal gastric mucosa were collected after operation. The expressions of bcl-2, bax and caspase-3 in those tissues were determined by immunohistochemical technique. Results In LNTC group, the positive expression rate of bcl-2 in gastric cancer tissues and metastatic lymph nodes was significantly lower than those in control group (28.6% vs . 78.6% , 25.0% vs . 70.0% , P ＜ 0.05), the positive expression rate of bax (85.7% vs . 28.6% , 80.0% vs . 30.0% ) and caspase-3 (57.1% vs . 14.3% , 55.0% vs . 15.0% ) in gastric cancer tissues and metastatic lymph nodes was significantly higher than those in control group ( P ＜ 0.05). The positive expression rate of bcl-2, bax and caspase-3 in normal gastric mucosa was not significantly different between two groups ( P ＞ 0.05). Conclusion The lymph node-targeted chemotherapy with carbon nanoparticles absorbing 5-FU can down-regulate the expression of bcl-2 and up-regulate the expression of bax and caspase-3 in gastric cancer tissues and metastatic lymph nodes, and therefore by affecting the expression levels of these apoptosis molecules may be one of the ways to induce tumor cell apoptosis.