Objective To evaluate the advantages of plasma cell enrichment combined with fluorescence in situ hybridization (FISH) based on a panel of probes by the conventional cytogenetic (CC) analysis.Methods Fresh heparinized bone marrow samples were collected by bone marrow biopsy.Plasma cells were enriched in BM samples using a magnetic cell-sorting procedure to select CD138+ cells.The common chromosome abnormalities of MM were detected by FISH based on a panel of probes and CC analysis after short-term culture of the BM cells,in order to compare the differences between these two methods for the frequency of common cytogenetic abnormalities.Results 72 of 95 (75.8％) MM patients were found to carry clonal chromosome abnormalities by FISH.And RB 1 deletion was the highest at 44.2％ (42/95) followed by CKS1B (1q21) amplification (42.1％).The frequencies of CDKN2C (1p32) deletion,TP53 deletion,IGH/CCND1 and IGH/FGFR3 were 8.4％ (8/95),12.6％ (12/95),14.7％ (14/ 95) and 14.7％ (14/95),respectively.IGH/MAF was negative.Thirty-two of 95 (33.7％) patients were found to carry clonal aberrations by CC analysis.The frequency of chromosome abnormalities detected by FISH was significantly higher than CC analysis (75.8％ vs 33.7％,P =0.000).Conclusion Plasma cell enrichment combined with FISH based on a panel of probes can greatly increase the frequency of chromosome abnormalities,which provides cytogenetic basis for risk stratification and prognosis of MM patients.

AIM: To observe the therapeutic action of Guizhifuling Capsule (Ramuluo Cinnamomi, Poria, Cortex moutan, Semen Persicae, etc.)on hyperplasia of mammary glands in rast, and to explore the new therapeutic effectiveness and its mechanism. METHODS: To inject extrinsic E2 and Pt into muscle to reproduce rats model of hyperplasia of mammary glands, to observe the morphologic changes of nipple in every group, determine extradiol and progesterone content on plasma in experimental rats by radio immunoassay, and study the changes of ER and PR by immunohistochemical method. RESULTS: Compared with the model group, Guizhi Fuling Capsule can markedly abate papilledema, proliferation of lobule, acinus and duct in mammary glands, reduce obviously changes of blood, estradiol on plasma, the ER and PR content of mammary glands and increase progesterone. CONCLUSION:Cuizhi Fuling Capsules have the good therapeutic action on hyperplasia of mammary glands in rats, its mechanism is related to correcting changes of blood, adjusting the blood concentration of sexual hormone in experimental rats and its expression of receptor.

Objective To study the genome molecular characteristics of Getah virus(DY0824)which isolated in Shandong province,2008 by molecular biology methods.Methods Reverse transcriptasepolymerase chain reaction(RT-PCR)was used to amplify the structural gene and 3'UTR fragments then the RT-PCR products were inserted into PGEM-T easy to be sequenced.Computer software was used to analyze the nucleotide and deduced amino acid sequence,and draw phylogenetic trees,including Clustal X1.83 and MegaAlign and Mega4.Results The capsid protein of DY0824 consists of 804 nucleotides,encoding 268 amino acids and the full-length of E2 protein is 1266 nucleotides,encoding 422 amino acids.The nucleotide homology of the capsid protein and the E2 protein with other strains were 95.4%-99.9%and 94.8%-99.5%,and the amino acid were 97.4%-100%and 97.6%-100%.The 3'UTR of the virus include 401 nucleotides and there are three repeat sequence elements.Conclusion Compared with the prototype virus,the Getah virus isolated in Shandong province had 7 amino acid differences in capsid protein genes and 10 amino acid differences in E protein genes.The 3'UTR region had multi-nucleotide changes.

Liver-qi stagnation and spleen deficiency syndrome can be seen in a variety of clinical diseases,such as chronic hepatitis,liver cirrhosis,chronic gastritis,peptic ulcer,irritable bowel syndrome (IBS),p.sychosis and so on.Disease characteristics determine the symptom characteristic and criterion of syndrome differentiation and treatment.Therefore,different diseases with liver-qistagnation and spleen deficiency syndrome have different clinical manifestation and diagnostic criteria.This paper summarized the modern biological basis of liver-qistagnation and spleen deficiency syndrome from the nervous system,endocrine system,digestive system,circulatory system,immune system and metabolic system,in order to provide reference for researches on modern biology basis of liver-qistagnation and spleen deficiency syndrome.

Objective To investigate the residents' acceptance and the influencing factors of the hierarchical medical system in Xuzhou, and to suggest on effective system implementation. Methods Xuzhou residents free of cognitive impairment and over 18 years old were sampled for questionnaire survey in July-August 2016, to study their acceptance of their basics and acceptance of the system. 1 550 questionnaires were distributed, and 1 473 valid ones were recovered. The count data were expressed as constituent ratio, and χ2test was used for single-factor analysis, with binary logistic regression analysis for multi-factor analysis. Results 71. 0% of the residents embraced this system. Their acceptance varies significantly with their age, place of residence, education, annual average monthly income, self-rated health status, physical examination experience, conditions of chronic diseases, medical visit experience at primary healthcare institutions, and their awareness of the system (P<0.05). Conclusions The acceptance of the system by Xuzhou residents needs to be elevated, by means of greater promotional efforts, capacity building for primary institutions, so as to fully leverage the system to serve the residents.

Objective:To evaluate the clinical value of 5.0 T ultra-high filed MRI system in assessing intracranial arteries segments and vessel branchers.Methods:This study was a prospective study. Totally 40 consecutive healthy volunteers were recruited from Zhongshan Hospital, Fudan University from September 1, 2021 to November 30, and all participants who underwent either 3.0 T or 5.0 T time-of-flight MR angiography (TOF-MRA) in random order were divided into 3.0 T MR group and 5.0 T MR group with 20 volunteers for each group. Image quality was assessed by Likert 5 scoring systems and signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR),and score in visualization of intracranial arteries [middle cerebral artery (MCA) and its segments, anterior cerebral artery (ACA) and its segments, posterior cerebral artery (PCA) and its segments, lenticulostriate arteries (LA) and pontine artery (PA)] were assessed from 0 to 3 (≥2: good depiction of vessel segment). Quantitative indicators were compared between 2 groups using independent t test or Mann-Whitney U test. Results:Among the 40 subjects, there were 29 males and 11 females, aged 20-69 (50±12) years. SNR and CNR were both significantly higher in 5.0 T MR group than those in 3.0 T MR group (SNR: 187±9 vs 91±4, t=31.59, P<0.001; CNR: 156±7 vs 70±4, t=31.45, P<0.001), but there was no significant difference in subjective scores of image quality between the 5.0 T MR and 3.0 T MR groups [5.0 (4.0, 5.0), 5.0 (5.0, 5.0) points, respectively, Z=-1.23, P=0.218]. In the evaluation of cerebral arteries, the visualizations of the proximal and middle segments of MCA, ACA and PCA was better than those in the 3.0 T MR group, and there was no significant difference in the scores ( P>0.05), while the visualizations of proximal arteries in the 5.0 T MR group were significantly better than those in the 3.0 T MR group ( P<0.05). Furthermore, small vessel branches such as LA and PA in 5.0 T MR group were visualized better than those in 3.0 T MR group ( P<0.001). Conclusion:TOF-MRA by ultra-high filed 5.0-T provides an optimal choice in visualization of distal large arteries and small vessel branches, which could be useful for the diagnosis on cerebral vascular disease.

Objective:To evaluate the performance of an artificial intelligent (AI)-based automated digital cell morphology analyzer (hereinafter referred as AI morphology analyzer) in detecting peripheral white blood cells (WBCs).Methods:A multi-center study. 1. A total of 3010 venous blood samples were collected from 11 tertiary hospitals nationwide, and 14 types of WBCs were analyzed with the AI morphology analyzers. The pre-classification results were compared with the post-classification results reviewed by senior morphological experts in evaluate the accuracy, sensitivity, specificity, and agreement of the AI morphology analyzers on the WBC pre-classification. 2. 400 blood samples (no less than 50% of the samples with abnormal WBCs after pre-classification and manual review) were selected from 3 010 samples, and the morphologists conducted manual microscopic examinations to differentiate different types of WBCs. The correlation between the post-classification and the manual microscopic examination results was analyzed. 3. Blood samples of patients diagnosed with lymphoma, acute lymphoblastic leukemia, acute myeloid leukemia, myelodysplastic syndrome, or myeloproliferative neoplasms were selected from the 3 010 blood samples. The performance of the AI morphology analyzers in these five hematological malignancies was evaluated by comparing the pre-classification and post-classification results. Cohen′s kappa test was used to analyze the consistency of WBC pre-classification and expert audit results, and Passing-Bablock regression analysis was used for comparison test, and accuracy, sensitivity, specificity, and agreement were calculated according to the formula.Results:1. AI morphology analyzers can pre-classify 14 types of WBCs and nucleated red blood cells. Compared with the post-classification results reviewed by senior morphological experts, the pre-classification accuracy of total WBCs reached 97.97%, of which the pre-classification accuracies of normal WBCs and abnormal WBCs were more than 96% and 87%, respectively. 2. The post-classification results reviewed by senior morphological experts correlated well with the manual differential results for all types of WBCs and nucleated red blood cells (neutrophils, lymphocytes, monocytes, eosinophils, basophils, immature granulocytes, blast cells, nucleated erythrocytes and malignant cells r>0.90 respectively, reactive lymphocytes r=0.85). With reference, the positive smear of abnormal cell types defined by The International Consensus Group for Hematology, the AI morphology analyzer has the similar screening ability for abnormal WBC samples as the manual microscopic examination. 3. For the blood samples with malignant hematologic diseases, the AI morphology analyzers showed accuracies higher than 84% on blast cells pre-classification, and the sensitivities were higher than 94%. In acute myeloid leukemia, the sensitivity of abnormal promyelocytes pre-classification exceeded 95%. Conclusion:The AI morphology analyzer showed high pre-classification accuracies and sensitivities on all types of leukocytes in peripheral blood when comparing with the post-classification results reviewed by experts. The post-classification results also showed a good correlation with the manual differential results. The AI morphology analyzer provides an efficient adjunctive white blood cell detection method for screening malignant hematological diseases.

Objective: To explore the feasibility of high-throughput texture analysis in the distinction of single brain metastases (SBM) from high-grade gliomas (HGG) and validate the established model. Methods: A total of 86 patients who were histologically diagnosed with SBM or HGG were retrospectively collected, including 43 patients with SBM and 43 with HGG. All of patients were performed preoperative conventional head magnetic resonance imaging (MRI) scans. A total of 236 fluid-attenuated inversion recovery (FLALR) images containing the information of tumors were selected from the MRI images and each image was considered as an object. The training set had 200 images, including 106 from SBM group and 94 from HGG group, whereas the validation set had 36 images, including 19 from SBM group and 17 from HGG. After images preprocessing, images segmentation, features extraction, and features selection, a radiomic diagnostic model was finally established using the training set. The diagnostic performance of the diagnostic model was evaluated using a receiver operating characteristic (ROC) curve. Hierarchical clustering analysis was used to evaluate the quality of the extracted feature data and the classification effect of the model. The model was further validated using the independent validation set. Results: A total of 629 features were extracted and quantified from each sample, and 41 features were selected to establish feature subsets and the diagnostic model. The classification decision function of the model is f(x)=sign and the kernel function of the model is K(x, x_i)=exp. In the training set, the diagnostic accuracy, sensitivity, specificity, positive predictive value and negative predictive value were 0.845, 0.849, 0.840, 0.857 and 0.832, respectively. The area under the ROC curve reached to 0.939. Similar results were obtained in the validation set. Conclusion The high-throughput texture analysis shows high accuracy in differentiating SBM from HGG.