CDC-NET: a cell detection and confirmation network of bone marrow aspirate images for the aided diagnosis of AML.

Standardized morphological evaluation in pathology is usually qualitative. Classifying and qualitatively analyzing the nucleated cells in the bone marrow aspirate images based on morphology is crucial for the diagnosis of acute myoid leukemia (AML), acute lymphoblastic leukemia (ALL), and Myelodysplastic syndrome (MDS), etc. However, it is time-consuming and difficult to accurately identify nucleated cells and calculate the percentage of the cells because of the complexity of bone marrow aspirate images. This paper proposed a deep learning analysis model of bone marrow aspirate images, termed Cell Detection and Confirmation Network (CDC-NET), for the aided diagnosis of AML by improving the accuracy of cell detection and recognition. Specifically, we take the nucleated cells in the bone marrow aspirate images as the detection objects to establish the model. Since some cells from different categories have similar morphology, classification error is inevitable. We design a confirmation network in which multiple trained classifiers work as pathologists to confirm the cell category by a voting method. To demonstrate the effectiveness of the proposed approach, experiments on clinical microscopic datasets are conducted. The Recall and Precision of CDC-NET are 78.54% and 91.74% respectively, and the missed rate of our method is lower than those of the other popular methods. The experimental results demonstrated that the proposed model has the potential for the pathological analysis of aspirate smears and the aided diagnosis of AML.

A benchmark bone marrow aspirate smear dataset and a multi-scale cell detection model for the diagnosis of hematological disorders.

Research on pathological diagnosis of hematopoietic disorders based on bone marrow aspirate smear images has attracted more and more attention with the development of deep learning methods. However, high quality bone marrow aspirate smear image datasets are not readily available because of the time, the efforts, and the medical knowledge required in the acquisition and manual annotation images. In order to facilitate the research of automated diagnosis of hematological disorders, we constructed a high quality Bone Marrow Aspirate Smear Image Dataset (BMASID), which contains 230 bone marrow aspirate smear images, all with the corresponding labeled images. We used additional clinical images as testing data, which are more challenging because of image noise, cell overlap, cell adhesion, blurred borders of cells and ambiguous types of cells. We also proposed a Cell Recognition Network (CRNet) that was trained on this benchmark dataset. CRNet is comprised of a cell detector to locate and recognize cells in the bone marrow aspirate images, and a cell classifier to classify the types of cells. New anchors and novel evaluation metrics are proposed and applied in CRNet. Benchmark evaluations of the proposed CRNet demonstrated the satisfactory performance of our state-of-the-art methods. Experimental results show that the detection precision by detector is more than 83%, and it is better when compared with other detection methods. After the cell type confirmation by the cell classifier, the precision is more than 95%. Compared with the most popular evaluation metrics Intersection over Union (IoU) and the newly proposed Generalized Intersection over Union (GIoU) used in the object detection benchmarks, our evaluation metrics are more suitable for the cell detection task with ambiguous cell boundaries. The proposed bone marrow aspirate smear image dataset and the proposed evaluation metrics can be used in the training and the evaluation of cell detection models, which contributes to future research in the pathological analysis and auxiliary diagnostic methods of hematological disorders. The codes are available at: https://github.com/SuJie-Med/hematolgical-disorders.

Lack of association between two key SNPs on chromosome 12p13 and ischemic stroke in Chinese Uyghur population.

Recent genome-wide association studies (GWAS) have identified two key SNPs (rs11833579 and rs12425791) on chromosome 12p13 that were significantly associated with stroke in Caucasians. However, the validity of the association has remained controversial. We performed genetic association analyses in a very unique population which has 60% European ancestry and 40% East Asian ancestry. No significant association between these two SNPs and ischemic stroke was detected in this Chinese Uyghur population.

[Optimization of high-cell-density fermentation process for S-adenosyl-L-methionine production].

Poor stability existed in the anaphase of the high-cell-density fermentation of Saccharomyces crevisiae for S-adenosyl-L-methionine (SAM) production in 5 L fermentor. To improve the fermentation stability, we studied the addition of diammonium hydrogen phosphate, sodium glutamate and adenosine disodium triphosphate into glucose feeding solution. Study of four fed-batch cultures showed that, after 34 h fermentation, when dry cell weight reached 100 g/L, the addition of 50 g pre-L-methionine and glucose feeding with 10 g/L adenosine disodium triphosphate was optimal for SAM production. Under this condition, after 65.7 h fermentation, both the dry cell weight and the yield of SAM reached the maximum, 180 g/L and 17.1 g/L respectively.