Natural-killer group 2 (NKG2), a natural killer (NK) cell receptor, plays a critical role in regulating NK cytotoxicity. In this study, we investigated the expression levels of natural killer group 2 member A (NKG2A) and natural killer group 2 member D (NKG2D) in NK cells as well as the regulatory function of NKG2D in patients with colorectal cancer (CRC). Sixty-two CRC patients and 32 healthy controls were enrolled in this study. The expression levels of NKG2A and NKG2D mRNA in peripheral blood mononuclear cells (PBMCs) were investigated using real-time PCR. Flow cytometry was performed to assay the levels of NKG2A and NKG2D proteins in NK cells. The levels of NKG2D mRNA in PBMCs in the patients were significantly lower than those in the controls [mean ± SD, 1.11±0.60 (CRC patients) vs. 1.65±0.71 (healthy controls); p<0.01], whereas the 2 groups showed no apparent difference in the levels of NKG2A mRNA (p>0.05). In addition, the patients showed significantly lower NKG2D levels in NK cells than the controls did (71.23%±8.31% [CRC patients] vs. 79.39%±5.58% [healthy controls]; p<0.01). However, we observed no distinct difference in the NKG2A expression levels in NK cells between the 2 groups (p>0.05). Notably, blockage of NKG2D signaling with anti-NKG2D antibodies ex vivo resulted in decreased cytotoxicity and CD107a degranulation. Our data revealed that the decrease in NKG2D expression levels may have been associated with suppression of NK cell activity in CRC patients.

Background Lactobacilli are often recognized as beneficial partners in human microbial environments. However, lactobacilli also cause diseases in human, e.g. infective endocarditis (IE), septicaemia, rheumatic vascular disease, and dental caries. Therefore, the identification of potential pathogenic traits associated with lactobacilli will facilitate the prevention and treatment of the diseases caused by lactobacilli. Herein, we investigated the genomic traits and pathogenic potential of a novel bacterial strain Lactobacillus paracasei LP10266 which has caused a case of IE. We isolated L. paracasei LP10266 from an IE patient’s blood to perform high-throughput sequencing and compared the genome of strain LP10266 with those of closely related lactobacilli to determine genes associated with its infectivity. We performed the antimicrobial susceptibility testing on strain LP10266. We assessed its virulence by mouse lethality and serum bactericidal assays as well as its serum complement- and platelet-activating ability. The biofilm formation and adherence of strain LP10266 were also studied. Results Phylogenetic analysis revealed that strain LP10266 was allied with L. casei and L. paracasei. Genomic studies revealed two spaCBA pilus clusters and one novel exopolysaccharides (EPS) cluster in strain LP10266, which was sensitive to ampicillin, penicillin, levofloxacin, and imipenem, but resistant to cefuroxime, cefazolin, cefotaxime, meropenem, and vancomycin. Strain LP10266 was nonfatal and sensitive to serum, capable of activating complement 3a and terminal complement complex C5b-9 (TCC). Strain LP10266 could not induce platelet aggregation but displayed a stronger biofilm formation ability and adherence to human vascular endothelial cells (HUVECs) compared to the standard control strain L. paracasei ATCC25302. Conclusion The genome of a novel bacterial strain L. paracasei LP10266 was sequenced. Our results based on various types of assays consistently revealed that L. paracasei LP10266 was a potential pathogen to patients with a history of cardiac disease and inguinal hernia repair. Strain LP10266 showed strong biofilm formation ability and adherence, enhancing the awareness of L. paracasei infections.

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Purpose: Leukemia is a lethal disease that is harmful to bone marrow and overall blood health. The classification of white blood cell images is crucial for leukemia diagnosis. The purpose of this study is to classify white blood cells by extracting discriminative information from cell segmentation and combining it with the fine-grained features. We propose a hybrid adversarial residual network with support vector machine (SVM), which utilizes the extracted features to improve the classification accuracy for human peripheral white cells. Methods: Firstly, we segment the cell and nucleus by utilizing an adversarial residual network, which contains a segmentation network and a discriminator network. To extract features that can handle the inter-class consistency problem effectively, we introduce the adversarial residual network. Then, we utilize convolutional neural network (CNN) features and histogram of oriented gradient (HOG) features, which can extract discriminative features from images of segmented cell nuclei. To utilize the representative features fully, a discriminative network is introduced to deal with neighboring information at different scales. Finally, we combine the vectors of HOG features with those of CNN features and feed them into a linear SVM to classify white blood cells into six types. Results: We used three methods to evaluate the effect of leukocyte classification based on 5000 leukocyte images acquired from a local hospital. The first approach is to use the CNN features as the input of SVM to classify leukocytes, which achieved 94.23% specificity, 95.10% sensitivity, and 94.41% accuracy. The use of the HOG features for SVM achieved 83.50% specificity, 87.50% sensitivity, and 85.00% accuracy. The use of combined CNN and HOG features achieved 94.57% specificity, 96.11% sensitivity, and 95.93% accuracy. Conclusions: We propose a novel hybrid adversarial-discriminative network for the classification of microscopic leukocyte images. It improves the accuracy of cell classification, reduces the difficulty and time pressure of doctors' work, and economizes the valuable time of doctors in daily clinical diagnosis.

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ABSTRACT. Killer cell immunoglobulin-like receptors (KIRs) are involved in the pathogenesis of a variety of diseases. However, whether KIR polymorphism is associated with susceptibility to pulmonary tuberculosis was unknown. We examined a possible association of KIR polymorphism with susceptibility to pulmonary tuberculosis in Chinese Han. We analyzed 15 KIR genes in 109 pulmonary tuberculosis patients and 110 healthy controls using sequence-specific primer PCR analysis of genomic DNA. We found that the frequencies of KIR2DS1, 2DS3 and 3DS1 were significantly higher in patients than in the control group. In addition, the number of subjects carrying more than two activating KIR genes in the patient group was significantly higher than in the control group. The gene cluster containing KIR3DS1-Association of KIR with pulmonary tuberculosis ©FUNPEC-RP www.funpecrp.com.br Genetics and Molecular Research 11 (2) 1370-1378 (2012) 13712DL5-2DS1-2DS5 was also significantly more frequent in the patient group. In conclusion, KIR genes 2DS1, 2DS3 and 3DS1 appear to be associated with resistance to pulmonary tuberculosis in the Chinese Han population. KIR genes apparently have a role in resistance to pulmonary tuberculosis.

The aim of the present study was to investigate the characteristics of the four subtypes of myelodysplastic/myeloproliferative neoplasms (MDS/MPNs) in order to improve current knowledge and to aid their diagnosis. A total of 53 cases of MDS/MPNs were analyzed using routine blood cell analysis and morphological, cytogenetic and molecular genetic characteristics were investigated. Numerical data for several groups were compared using a single-factor analysis of variance. The Student-Newman-Keuls test was used to compare the means of two groups. The proportions were compared using a Chi-square test or Fisher’s exact test. Analysis of the patients with MDS/MPNs revealed that 46 patients (86.8%) had paleness and fatigue, and blood analysis revealed hemoglobin (Hb) levels of 83.1±24.6 g/l, a white blood cell (WBC) count of 19.8±8.1×109/l and a platelet (PLT) count of 158.7±108.2×1012/l. Immature neutrophils and monocytes were identified in the peripheral blood at levels of 0.058±0.031 and 0.152±0.034%, respectively. There were 23 cases (43.4%) with dyserythropoiesis and 36 cases (67.9%) had dysgranulopoiesis. Fifteen cases were immunologically characterized using flow cytometry (FCM), of which 13 cases showed abnormalities on blasts and myelocytes. Karyotyping was performed in 27 cases of MDS/MPN and 12 (44.4%) were identified as abnormal. In 23 cases, testing for BCR/ABL1, AML-ETO, CBF-MYH11A, PML-RARA, E2A-PBX1, TEL-AML1, SIL-TAL1 returned negative results. The JAK2V617F mutation was positive in one of five cases. The majority of MDS/MPN cases had anemia, cytosis, low-grade blasts and immature neutrophils in the peripheral blood and dysplasia in the bone marrow. Immunological abnormalities and abnormal karyotypes occurred frequently in MDS/MPNs and although there were no statistical differences between the four subtypes, these were able to aid diagnosis. No specific molecular abnormalities were identified in MDS/MPNs.

Clinically, red blood cell abnormalities are closely related to tumor diseases, red blood cell diseases, internal medicine, and other diseases. Red blood cell classification is the key to detecting red blood cell abnormalities. Traditional red blood cell classification is done manually by doctors, which requires a lot of manpower produces subjective results. This paper proposes an Attention-based Residual Feature Pyramid Network (ARFPN) to classify 14 types of red blood cells to assist the diagnosis of related diseases. The model performs classification directly on the entire red blood cell image. Meanwhile, a spatial attention mechanism and channel attention mechanism are combined with residual units to improve the expression of category-related features and achieve accurate extraction of features. Besides, the RoI align method is used to reduce the loss of spatial symmetry and improve classification accuracy. Five hundred and eighty eight red blood cell images are used to train and verify the effectiveness of the proposed method. The Channel Attention Residual Feature Pyramid Network (C-ARFPN) model achieves an mAP of 86%; the Channel and Spatial Attention Residual Feature Pyramid Network (CS-ARFPN) model achieves an mAP of 86.9%. The experimental results indicate that our method can classify more red blood cell types and better adapt to the needs of doctors, thus reducing the doctor's time and improving the diagnosis efficiency.