A Novel Multiinstance Learning Approach for Liver Cancer Recognition on Abdominal CT Images Based on CPSO-SVM and IO

A novel multi-instance learning (MIL) method is proposed to recognize liver cancer with abdominal CT images based on instance optimization (IO) and support vector machine with parameters optimized by a combination algorithm of particle swarm optimization and local optimization (CPSO-SVM). Introducing MIL into liver cancer recognition can solve the problem of multiple regions of interest classification. The images we use in the experiments are liver CT images extracted from abdominal CT images. The proposed method consists of two main steps: (1) obtaining the key instances through IO by texture features and a classification threshold in classification of instances with CPSO-SVM and (2) predicting unknown samples with the key instances and the classification threshold. By extracting the instances equally based on the entire image, the proposed method can ignore the procedure of tumor region segmentation and lower the demand of segmentation accuracy of liver region. The normal SVM method and two MIL algorithms, Citation-kNN algorithm and WEMISVM algorithm, have been chosen as comparing algorithms. The experimental results show that the proposed method can effectively recognize liver cancer images from two kinds of cancer CT images and greatly improve the recognition accuracy

Impact of body dose parameters on circulating immune cells in locally advanced nasopharyngeal carcinoma patients: a retrospective cohort study

Background and purpose: The implementation of intensity-modulated radiotherapy (IMRT) has significantly enhanced the survival outcomes for patients with nasopharyngeal carcinoma (NPC). However, this therapeutic approach still falls short in meeting the prognostic requirements of individuals with locally advanced NPC (LANPC). Therefore, it is imperative to identify effective prognostic markers to enhance the efficacy of radiotherapy and achieve personalized treatment. Given the potential predictive value demonstrated in previous studies regarding radiotherapy-related body dose parameters and immune blood cells, this study aimed to investigate the correlation between body dose parameters and reduced immune cells and patient prognosis during radiotherapy in LANPC patients. Methods: Clinical data of 423 patients with LANPC (stage Ⅲ-Ⅳa) treated in Fudan University Shanghai Cancer Center from Jan.1, 2012 to Dec. 31, 2016 were retrospectively analyzed. Percentage changes of different immune blood cells during radiotherapy were also collected. Cox proportional hazard model was used to determine prognostic factors for overall survival (OS), locoregional recurrence-free survival (LRFS) and distant metastasis-free survival (DMFS). Body dose-based parameters were extracted from dose-volume histograms (DVHs). Logistic regression was applied to determine parameters that could predict white blood cells reduction. Results: High ΔLYM% (ΔLYM%≥7 7.0%) and high ΔMONO% (ΔMONO%≥2 8.5%) were identified as two adverse prognostic factors in LANPC patients. In multivariable analysis, high ΔLYM% was found to be a significant predictor of worse OS (HR = 1.672, P = 0.012), LRFS (HR = 1.712, P = 0.006), and DMFS (HR = 1.971, P = 0.001). High ΔMONO% was associated with worse OS (HR = 1.355, P = 0.015) and DMFS (HR = 1.704, P = 0.003). The change of ΔLYM% was influenced by the integral body dose (IBD) (OR = 1.004, P = 0.037) and body V60 (OR = 1.046, P = 0.036). ΔMONO% was significantly affected by body V55 (OR = 1.144, P = 0.009) and V70 (OR = 0.734, P = 0.022). Conclusion: Integral body dose, V60, and V55, V70 can serve as dose-volume constraints to retain sufficient immune cell populations to improve prognosis

MLVA genotyping of Chinese human Brucella melitensis biovar 1, 2 and 3 isolates

<p>Abstract</p> <p>Background</p> <p>Since 1950, <it>Brucella melitensis </it>has been the predominant strain associated with human brucellosis in China. In this study we investigated the genotypic characteristics of <it>B. melitensis </it>isolates from China using a multiple-locus variable-number tandem-repeat analysis (MLVA) and evaluated the utility of MLVA with regards to epidemiological trace-back investigation.</p> <p>Results</p> <p>A total of 105 <it>B. melitensis </it>strains isolated from throughout China were divided into 69 MLVA types using MLVA-16. Nei's genetic diversity indices for the various loci ranged between 0.00 - 0.84. 12 out 16 loci were the low diversity with values < 0.2 and the most discriminatory markers were bruce16 and bruce30 with a diversity index of > 0.75 and containing 8 and 7 alleles, respectively. Many isolates were single-locus or double-locus variants of closely related <it>B. melitensis </it>isolates from different regions, including the north and south of China. Using panel 1, the majority of strains (84/105) were genotype 42 clustering to the 'East Mediterranean' <it>B. melitensis </it>group. Chinese <it>B. melitensis </it>are classified in limited number of closely related genotypes showing variation mainly at the panel 2B loci.</p> <p>Conclusion</p> <p>The MLVA-16 assay can be useful to reveal the predominant genotypes and strain relatedness in endemic or non-endemic regions of brucellosis. However it is not suitable for biovar differentiation of <it>B. melitensis</it>. Genotype 42 is widely distributed throughout China during a long time. Bruce 16 and bruce 30 in panel 2B markers are most useful for typing Chinese isolates.</p

Molecular Characteristics of Staphylococcus aureus From Food Samples and Food Poisoning Outbreaks in Shijiazhuang, China

As an opportunistic pathogen worldwide, Staphylococcus aureus can cause food poisoning and human infections. This study investigated the sequence typing, the penicillin (blaZ) and methicillin (mec) resistance profiles of S. aureus from food samples and food poisoning outbreaks in Shijiazhuang City, and the staphylococcal enterotoxin (SE) types of the S. aureus isolates from food poisoning. A total of 138 foodborne S. aureus isolates were distributed into 8 clonal complexes (CCs) and 12 singletons. CC1, CC5, CC8, CC15, CC97, CC59, CC398, CC88, and CC7 were the predominant CCs of foodborne S. aureus isolates. Moreover, CC59, CC15, and CC5 were the most prevalent CCs in food poisoning outbreaks. SEE was the most commonly detected SE in food poisoning isolates. One hundred thirty-three S. aureus isolates harbored the penicillin-resistant gene blaZ, and nine isolates carried the mec gene. The present study further explained the relationship between S. aureus and foods and food poisoning and indicated the potential risk of S. aureus infection

The Epitope and Neutralization Mechanism of AVFluIgG01, a Broad-Reactive Human Monoclonal Antibody against H5N1 Influenza Virus

The continued spread of highly pathogenic avian influenza (HPAI) H5N1 virus underscores the importance of effective antiviral approaches. AVFluIgG01 is a potent and broad-reactive H5N1-neutralizing human monoclonal antibody (mAb) showing great potential for use either for therapeutic purposes or as a basis of vaccine development, but its antigenic epitope and neutralization mechanism have not been finely characterized. In this study, we first demonstrated that AVFluIgG01 targets a novel conformation-dependent epitope in the globular head region of H5N1 hemagglutinin (HA). By selecting mimotopes from a random peptide library in combination with computational algorithms and site-directed mutagenesis, the epitope was mapped to three conserved discontinuous sites (I-III) that are located closely at the three-dimensional structure of HA. Further, we found that this HA1-specific human mAb can efficiently block both virus-receptor binding and post-attachment steps, while its Fab fragment exerts the post-attachment inhibition only. Consistently, AVFluIgG01 could inhibit HA-mediated cell-cell membrane fusion at a dose-dependent manner and block the acquisition of pH-induced protease sensitivity. These results suggest a neutralization mechanism of AVFluIgG01 by simultaneously blocking viral attachment to the receptors on host cells and interfering with HA conformational rearrangements associated with membrane fusion. The presented data provide critical information for developing novel antiviral therapeutics and vaccines against HPAI H5N1 virus

The Hybrid Feature Selection Algorithm Based on Maximum Minimum Backward Selection Search Strategy for Liver Tissue Pathological Image Classification

We propose a novel feature selection algorithm for liver tissue pathological image classification. To improve the efficiency of feature selection, the same feature values of positive and negative samples are removed in rough selection. To obtain the optimal feature subset, a new heuristic search algorithm, which is called Maximum Minimum Backward Selection (MMBS), is proposed in precise selection. MMBS search strategy has the following advantages. (1) For the deficiency of Discernibility of Feature Subsets (DFS) evaluation criteria, which makes the class of small samples invalid for unbalanced samples, the Weighted Discernibility of Feature Subsets (WDFS) evaluation criteria are proposed as the evaluation strategy of MMBS, which is also available for unbalanced samples. (2) For the deficiency of Sequential Forward Selection (SFS) and Sequential Backward Selection (SBS), which can only add or only delete feature, MMBS decides whether to add the feature to feature subset according to WDFS criteria for each feature firstly; then it decides whether to remove the feature from feature subset according to SBS algorithm. In this way, the better feature subset can be obtained. The experiment results show that the proposed hybrid feature selection algorithm has good classification performance for liver tissue pathological image

How to select a proper early warning threshold to detect infectious disease outbreaks based on the China infectious disease automated alert and response system (CIDARS)

Abstract Background China Centre for Diseases Control and Prevention (CDC) developed the China Infectious Disease Automated Alert and Response System (CIDARS) in 2005. The CIDARS was used to strengthen infectious disease surveillance and aid in the early warning of outbreak. The CIDARS has been integrated into the routine outbreak monitoring efforts of the CDC at all levels in China. Early warning threshold is crucial for outbreak detection in the CIDARS, but CDCs at all level are currently using thresholds recommended by the China CDC, and these recommended thresholds have recognized limitations. Our study therefore seeks to explore an operational method to select the proper early warning threshold according to the epidemic features of local infectious diseases. Methods The data used in this study were extracted from the web-based Nationwide Notifiable Infectious Diseases Reporting Information System (NIDRIS), and data for infectious disease cases were organized by calendar week (1–52) and year (2009–2015) in Excel format; Px was calculated using a percentile-based moving window (moving window [5 week*5 year], x), where x represents one of 12 centiles (0.40, 0.45, 0.50….0.95). Outbreak signals for the 12 Px were calculated using the moving percentile method (MPM) based on data from the CIDARS. When the outbreak signals generated by the ‘mean + 2SD’ gold standard were in line with a Px generated outbreak signal for each week during the year of 2014, this Px was then defined as the proper threshold for the infectious disease. Finally, the performance of new selected thresholds for each infectious disease was evaluated by simulated outbreak signals based on 2015 data. Results Six infectious diseases were selected in this study (chickenpox, mumps, hand foot and mouth diseases (HFMD), scarlet fever, influenza and rubella). Proper thresholds for chickenpox (P75), mumps (P80), influenza (P75), rubella (P45), HFMD (P75), and scarlet fever (P80) were identified. The selected proper thresholds for these 6 infectious diseases could detect almost all simulated outbreaks within a shorter time period compared to thresholds recommended by the China CDC. Conclusions It is beneficial to select the proper early warning threshold to detect infectious disease aberrations based on characteristics and epidemic features of local diseases in the CIDARS