Immunophenotypic changes of monocytes in myelodysplastic syndrome and clinical significance

Li, Lijuan; Yu, Shunjie; Hu, Xian; Liu, Zhaoyun; Tian, Xiaoying; Ren, Xiaotong; Guo, Xinyu; Fu, Rong

doi:10.1007/s10238-022-00856-7

Cited by 4 publications

(1 citation statement)

References 29 publications

(28 reference statements)

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“…It is also a common laboratory procedure in clinical workflows [1,2]. Although a host of other methods are now available, such as cytogenetics [3], immunophenotyping [4], and, increasingly, molecular genetics [5], these methods are very sophisticated and highly dependent on experimental manipulation [6]. Furthermore, not all institutions have the corresponding facilities and capabilities.…”

Section: Introductionmentioning

confidence: 99%

Improved YOLOv7 Algorithm for Detecting Bone Marrow Cells

Cheng,

2023

Sensors

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The detection and classification of bone marrow (BM) cells is a critical cornerstone for hematology diagnosis. However, the low accuracy caused by few BM-cell data samples, subtle difference between classes, and small target size, pathologists still need to perform thousands of manual identifications daily. To address the above issues, we propose an improved BM-cell-detection algorithm in this paper, called YOLOv7-CTA. Firstly, to enhance the model’s sensitivity to fine-grained features, we design a new module called CoTLAN in the backbone network to enable the model to perform long-term modeling between target feature information. Then, in order to cooperate with the CoTLAN module to pay more attention to the features in the area to be detected, we integrate the coordinate attention (CoordAtt) module between the CoTLAN modules to improve the model’s attention to small target features. Finally, we cluster the target boxes of the BM cell dataset based on K-means++ to generate more suitable anchor boxes, which accelerates the convergence of the improved model. In addition, in order to solve the imbalance between positive and negative samples in BM-cell pictures, we use the Focal loss function to replace the multi-class cross entropy. Experimental results demonstrate that the best mean average precision (mAP) of the proposed model reaches 88.6%, which is an improvement of 12.9%, 8.3%, and 6.7% compared with that of the Faster R-CNN model, YOLOv5l model, and YOLOv7 model, respectively. This verifies the effectiveness and superiority of the YOLOv7-CTA model in BM-cell-detection tasks.

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Section: Introductionmentioning

confidence: 99%

Improved YOLOv7 Algorithm for Detecting Bone Marrow Cells

Cheng,

2023

Sensors

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Immune‐monitoring of myelodysplastic neoplasms: Recommendations from the i4MDS consortium

Tentori,

Zhao,

Tinterri

et al. 2024

HemaSphere

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Advancements in comprehending myelodysplastic neoplasms (MDS) have unfolded significantly in recent years, elucidating a myriad of cellular and molecular underpinnings integral to disease progression. While molecular inclusions into prognostic models have substantively advanced risk stratification, recent revelations have emphasized the pivotal role of immune dysregulation within the bone marrow milieu during MDS evolution. Nonetheless, immunotherapy for MDS has not experienced breakthroughs seen in other malignancies, partly attributable to the absence of an immune classification that could stratify patients toward optimally targeted immunotherapeutic approaches. A pivotal obstacle to establishing “immune classes” among MDS patients is the absence of validated accepted immune panels suitable for routine application in clinical laboratories. In response, we formed International Integrative Innovative Immunology for MDS (i4MDS), a consortium of multidisciplinary experts, and created the following recommendations for standardized methodologies to monitor immune responses in MDS. A central goal of i4MDS is the development of an immune score that could be incorporated into current clinical risk stratification models. This position paper first consolidates current knowledge on MDS immunology. Subsequently, in collaboration with clinical and laboratory specialists, we introduce flow cytometry panels and cytokine assays, meticulously devised for clinical laboratories, aiming to monitor the immune status of MDS patients, evaluating both immune fitness and identifying potential immune “risk factors.” By amalgamating this immunological characterization data and molecular data, we aim to enhance patient stratification, identify predictive markers for treatment responsiveness, and accelerate the development of systems immunology tools and innovative immunotherapies.

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