Objective To investigate the expression patterns of serine proteases Adsp1 and Adsp3 in the main tissue of Anopheles dirus and the effects of blood feeding and Plasmodium infection on the transcript abundance of Adsp1 and Adsp3 from Anopheles dirus haemocytes. Methods Haemocytes were collected by centrifugation. The midguts and salivary glands were dissected from 50 adult female Anopheles dirus of 3-5 d old for the extraction of total RNA for amplification by RT PCR. Anopheles dirus of 3-5 d old were divided into normal group (N), blood feeding group (B), and Plasmodium yoelii infection group (I). Haemocytes of 50 adult female Anopheles dirus from each group after blood feeding were also collected, and the total RNA was isolated at 1, 2, 3, 4, 7, and 11 d, respectively. Then, the same volume (10 ?l) of total RNA was analyzed by semi quantitative RT PCR with Ads7 as the internal control. Agarose gel analysis was performed on PCR products from each group. Results Expression of both Adsp1 and Adsp3 mRNA was found in the haemocytes and salivary glands, but not in the midguts. Semi quantitative RT PCR indicated that transcript abundance of AdsP1 and AdsP3 from Anopheles dirus haemocytes was enhanced after blood feeding and Plasmodium yoelii infection, especially during melanotic encapsulation of Plasmodium yoelii . Conclusion AdsP1 and AdsP3 may be involved in the melanotic encapsulation response of Plasmodium yoelii by Anopheles dirus , and may be the prophenoloxidase activating enzyme of Anopheles dirus .

OBJECTIVEExtraction of cepstral coefficients combined with Gaussian Mixture Model (GMM) is used to propose a biometric method based on heart sound signal.

METHODSFirstly, the original heart sounds signal was preprocessed by wavelet denoising. Then, Linear Prediction Cepstral Coefficients (LPCC) and Mel Frequency Cepstral Coefficients (MFCC) are compared to extract representative features and develops hidden Markov model (HMM) for signal classification. At last, the experiment collects 100 heart sounds from 50 people to test the proposed algorithm.

RESULTSThe comparative experiments prove that LPCC is more suitable than MFCC for heart sound biometric, and by wavelet denoising in each piece of heart sound signal, the system achieves higher recognition rate than traditional GMM.

CONCLUSIONThose results show that this method can effectively improve the recognition performance of the system and achieve a satisfactory effect.

Algorithms ; Biometry ; Heart ; physiology ; Humans ; Markov Chains ; Models, Biological ; Phonocardiography ; methods ; Wavelet Analysis

Objective To create multi-parameter analysis model by conventional two-dimensional echorcardiography( 2DE) and three-dimensional speckle tracking imaging( 3D-STI) in order to improve the early diagnosis in MYH7G + P- mutation carriers of familiar hypertrophic cardiomyopathy( HCM ) . Methods Twenty-eight MYH7 mutation carriers without left ventricular hypertrophy ( MYH7G + P- ) were enrolled as the research group , while 27 MYBPC3 mutation carriers without left ventricular hypertrophy ( MYBPC3G + P- ) were selected as the control group . The clinical data ,conventional 2DE and tissue Doppler imaging ( TDI) parameters of the two groups were acquired ,including the maximum of the thickness of interventricular septum( IVS) and left ventricular posterior ventricular wall thickness( LVPWT ) in end of diastole ,mitral flow E and A velocities ,E peak deceleration time( EDT ) ,atrioventricular filling time ,mitral annulus velocity of interventricular septum IVS-e' and lateral wall L-e' ,interventricular septum IVS-a' and lateral wall L-a' peak in diastole ,calculate mean value e' and a' ,and calculate E/ e' ,E/A ,left atrial volume index( LAVI) ,left ventricular mass index( LVMI) ,left ventricular ejection fraction( LVEF) and isovolumic relaxation time( IVRT ) ,isovolumic contraction time( IVCT ) . The global longitudinal peak strain( GLS) , global radial peak strain( GRS) ,global circumferential peak strain( GCS) and global area peak strain( GAS) were acquired by 3D-STI . All parameters were compared between research group and control group . Results Compared with the control group ,research group significantly increased in LAVI , maximum IVS and LVPWT ,LVMI and GAS( P < 0 .01) . There were no significant difference in TDI parameters( P > 0 .05) . For single parameter ,the area under ROC curve( AUC) were successively LVPWT > LAVI > IVS > LVMI >GAS( 0 .772 ,0 .738 ,0 .733 ,0 .719 ,0 .714 ,respectively) . AUC of multi-parameters was 0 .912 ,the sensitivity and specificity were 85 .2％ and 96 .3％ ,respectively . Conclusions Multi-parameter analysis model by conventional 2DE and 3D-STI can significantly improve the recognition efficiency of early diagnosis MYH7G + P- of familiar HCM . With further ,maximum LVPWT is an independent predictor of distinction MYH7G + P- and MYBPC3G + P- .

Objective:To investigate the value of a preoperatively MRI-based deep learning (DL) radiomics machine learning model to distinguish low-grade and high-grade soft tissue sarcomas (STS).Methods:From November 2007 to May 2019, 151 patients with STS confirmed by pathology in the Affiliated Hospital of Qingdao University were enrolled as training sets, and 131 patients in the Affiliated Hospital of Shandong First Medical University and the Third Hospital of Hebei Medical University were enrolled as external validation sets. According to the French Federation Nationale des Centres de Lutte Contre le Cancer classification (FNCLCC) system, 161 patients with FNCLCC grades Ⅰ and Ⅱ were defined as low-grade and 121 patients with grade Ⅲ were defined as high-grade. The hand-crafted radiomic (HCR) and DL radiomic features of the lesions were extracted respectively. Based on HCR features, DL features, and HCR-DL combined features, respectively, three machine-learning models were established by decision tree, logistic regression, and support vector machine (SVM) classifiers. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of each machine learning model and choose the best one. The univariate and multivariate logistic regression were used to establish a clinical-imaging factors model based on demographics and MRI findings. The nomogram was established by combining the optimal radiomics model and the clinical-imaging model. The AUC was used to evaluate the performance of each model and the DeLong test was used for comparison of AUC between every two models. The Kaplan-Meier survival curve and log-rank test were used to evaluate the performance of the optimal machine learning model in the risk stratification of progression free survival (PFS) in STS patients.Results:The SVM radiomics model based on HCR-DL combined features had the optimal predicting power with AUC values of 0.931(95%CI 0.889-0.973) in the training set and 0.951 (95%CI 0.904-0.997) in the validation set. The AUC values of the clinical-imaging model were 0.795 (95%CI 0.724-0.867) and 0.615 (95%CI 0.510-0.720), and of the nomogram was 0.875 (95%CI 0.818-0.932) and 0.786 (95%CI 0.701-0.872) in the training and validation sets, respectively. In validation set, the performance of SVM radiomics model was better than those of the nomogram and clinical-imaging models ( Z=3.16, 6.07; P=0.002,<0.001). Using the optimal radiomics model, there was statistically significant in PFS between the high and low risk groups of STS patients (training sets: χ2=43.50, P<0.001; validation sets: χ2=70.50, P<0.001). Conclusion:Preoperative MRI-based DL radiomics machine learning model has accurate prediction performance in differentiating the histopathological grading of STS. The SVM radiomics model based on HCR-DL combined features has the optimal predicting power and was expected to undergo risk stratification of prognosis in STS patients.

Objective:To explore the characteristics of echocardiographic parameters among the many parameters of two-dimensional transthoracic echocardiography(2D-TTE) and three-dimensional speckle tracking imaging (3D-STI) that can be used for early identification of familial hypertrophic cardiomyopathy(FHCM) mutation gene carriers, and construct a Nomogram prediction model, in order to provide a diagnostic method for early identification of G+ P- patients for clinical practice.Methods:A total of 15 FHCM families admitted to the General Hospital of Ningxia Medical University from November 2017 to August 2022 were enrolled.Whole exome sequencing and Sanger sequencing technology were used for gene detection, among which 54 were G+ P- and 75 were G-P-. Stratified random sampling was used to divide the subjects into training set ( n=90) and test set ( n=39) according to the ratio of 7∶3. Philips iE33 ultrasonic diagnostic instrument and TomTec offline software were used to obtain relevant ultrasonic parameters. Lasso regression and Logistic regression were used to screen echocardiographic parameters and obtain independent risk factors for early prediction of G+ P-, based on which a Nomogram prediction model was established. Results:①Lasso-Logistic regression showed that global longitudinal strain(GLS) ( OR=1.739, 95% CI=1.305-2.316) and left ventricular outflow trac velocity time integral(LVOT-VTI) ( OR=1.358, 95% CI=1.072-1.722) could be used as independent risk factors for early prediction of G+ P-. ②The Nomogram prediction model was established based on the above indicators. After 1000 internal verifications of Bootstrap self-sampling, the C-indices of the training set and the test set were 0.885 (95% CI=0.816-0.954), 0.878 (95% CI=0.764-0.992), which had good internal consistency. ③The results of the calibration curve showed that the risk of G+ P- predicted by the Nomogram model was basically consistent with the actual risk (training set P=0.990, test set P=0.961); the clinical decision curve shows that under different threshold probabilities, using this prediction model to provide patients with clinical decision-making could bring benefits to patients. Conclusions:Echocardiographic parameters GLS and LVOT-VTI can be used as independent risk factors to predict FHCM mutation gene carriers. The Nomogram prediction model has good discrimination, goodness of fit and clinical benefit in identifying whether the family members of FHCM patients carry the mutation gene, and it can provide a new idea and evaluation method for the early identification of FHCM mutation gene carriers by echocardiography.

Objective To analyze the research status and trend of scarlet fever literature in China, and to provide reference for subsequent research. Methods Three major Chinese databases, CNKI, Wanfang, and VIP, as well as Web of Science English database, were used to search for literature related to scarlet fever from 2000 to 2023. Citespace6.2.R2 software was used to statistically analyze the number of publications, authors, institutions and journals, co-cited literature, keyword clustering, and other literature characteristics of the literature. Results From 2000 to 2023, a total of 1 011 Chinese literature were included in the three major Chinese databases. Since 2011, the number of publications had gradually increased, but in recent years, the number of publications had decreased. The organization with the most publications was the Shenyang Center for Disease Control and Prevention. The cluster analysis of key words mainly formed 9 cluster tags, and the high-frequency keywords mainly included epidemic characteristics, epidemiology, incidence rate, etc. A total of 84 English literature were included in the WOS database, with an overall upward trend in publication volume. The institution with the most publications was the China Center for Disease Control and Prevention, and the most frequently cited journal was ^“LANCET INFECT DIS^”.《Resurgence of scarlet fever in China: a 13-year population-based surveillance study》 was the most cited journal. After keyword cluster analysis, 9 cluster labels were mainly formed, and the keywords were mainly outbreak，Hong Kong, and Group A streptococcus. Conclusion Compared with the English literature, which mainly focuses on spatiotemporal aggregation, etiology and strain resistance, Chinese literature focuses more on epidemic surveillance, clinical features and quality nursing.

Objective In this study, a strain of colistin and tigecycline-resistant bacteria isolated in 2009 was analyzed, and the structure of drug-resistant plasmid and genetic environment were discussed, so as to provide basis for the prevention and control of multidrug-resistant bacteria. Methods A strain (GZ12244) with positive mcr and tet(M) was obtained by screening colistin and tigecycline resistance genes. Vitek-2 was used for strain identification, and the drug sensitivity test was carried out by broth dilution method. The molecular typing, drug resistance genes, insertion sequences, plasmid structure and genetic background were analyzed by genome-wide sequencing and bioinformatics. Results Strain GZ12244 is Klebsiella pneumoniae, which is resistant to colistin B, tigecycline, cefuroxime and tetracycline, and carries a variety of drug-resistant related genes such as mcr-1 and tet(M), and some of the drug-resistant genes with antibiotic efflux and antibiotic target change have amino acid substitution mutations. Mcr-1 and tet(M) coexist in a plasmid, and mcr-1 flanked by two insertion sequences ISApl1. There are insertion sequences such as IS15, IS1D and ISEc63 in the upstream and downstream of tet(M) gene. Conclusion Klebsiella pneumoniae GZ12244 is a multidrug-resistant strain. The drug-resistant gene exists in plasmid, and the mobile elements in upstream and downstream may spread the drug-resistant gene.

Objective To explore the research progress, research hotspot and development trend of tigecycline resistance based on the quantitative analysis and visualization function of CiteSpace. Methods The data were collected from 4,263 Chinese and English articles on tigecycline resistance in CNKI, Wanfang, VIP and Web of Science (WOS) databases from 2012 to 2022. CiteSpace 5.8.R3 software was used to analyze the cooperative network of authors, the cooperative network of countries and institutions, the total citation times of journals, and keywords included in the literature, to reveal the hotspots and trends of tigecycline resistance research. Results The number of articles published in English literature was higher than that in Chinese literature. China had the largest number of published documents, showing a significant international academic influence in this research field. Countries all over the world were concerned about the resistance of tigecycline, but Chinese literatures focused more on the clinical infection and prevention of tigecycline resistance, while English literatures placed special emphasis on the research about the drug resistance mechanism of tigecycline. Conclusion The research direction at home and abroad is basically the same, but the research focus has gradually shifted from the clinical treatment and monitoring of tigecycline to the molecular level of drug resistance mechanism.