Enhancing thalassemia gene carrier identification in non-anemic populations using artificial intelligence erythrocyte morphology analysis and machine learning.

BACKGROUND: Non-anemic thalassemia trait (TT) accounted for a high proportion of TT cases in South China. OBJECTIVE: To use artificial intelligence (AI) analysis of erythrocyte morphology and machine learning (ML) to identify TT gene carriers in a non-anemic population. METHODS: Digital morphological data from 76 TT gene carriers and 97 controls were collected. The AI technology-based Mindray MC-100i was used to quantitatively analyze the percentage of abnormal erythrocytes. Further, ML was used to construct a prediction model. RESULTS: Non-anemic TT carriers accounted for over 60% of the TT cases. Random Forest was selected as the prediction model and named TT@Normal. The TT@Normal algorithm showed outstanding performance in the training, validation, and external validation sets and could efficiently identify TT carriers in the non-anemic population. The top three weights in the TT@Normal model were the target cells, microcytes, and teardrop cells. Elevated percentages of abnormal erythrocytes should raise a strong suspicion of being a TT gene carrier. TT@Normal could be promoted and used as a visualization and sharing tool. It is accessible through a URL link and can be used by medical staff online to predict the possibility of TT gene carriage in a non-anemic population. CONCLUSIONS: The ML-based model TT@Normal could efficiently identify TT carriers in non-anemic people. Elevated percentages of target cells, microcytes, and teardrop cells should raise a strong suspicion of being a TT gene carrier.

A Clustering Multi-Criteria Decision-Making Method for Large-Scale Discrete and Continuous Uncertain Evaluation.

In recent years, Dempster-Shafer (D-S) theory has been widely used in multi-criteria decision-making (MCDM) problems due to its excellent performance in dealing with discrete ambiguous decision alternative (DA) evaluations. In the general framework of D-S-theory-based MCDM problems, the preference of the DAs for each criterion is regarded as a mass function over the set of DAs based on subjective evaluations. Moreover, the multi-criteria preference aggregation is based on Dempster's combination rule. Unfortunately, this an idea faces two difficulties in real-world applications: (i) D-S theory can only deal with discrete uncertain evaluations, but is powerless in the face of continuous uncertain evaluations. (ii) The generation of the mass function for each criterion relies on the empirical judgments of experts, making it time-consuming and laborious in terms of the MCDM problem for large-scale DAs. To the best of our knowledge, these two difficulties cannot be addressed with existing D-S-theory-based MCDM methods. To this end, this paper proposes a clustering MCDM method combining D-S theory with the analytic hierarchy process (AHP) and the Silhouette coefficient. By employing the probability distribution and the D-S theory to represent discrete and continuous ambiguous evaluations, respectively, determining the focal element set for the mass function of each criterion through the clustering method, assigning the mass values of each criterion through the AHP method, and aggregating preferences according to Dempster's combination rule, we show that our method can indeed address these two difficulties in MCDM problems. Finally, an example is given and comparative analyses with related methods are conducted to illustrate our method's rationality, effectiveness, and efficiency.

Secondary lymphoblastic leukemia occurring 38 months after the primary diagnosis of multiple myeloma: A case report.

Multiple myeloma (MM) is a malignant plasma cell neoplasm characterized by the accumulation of plasma cells in the bone marrow, the subsequent destruction of bone and organ dysfunction. The present study describes the case of a 66-year-old male patient who presented with the typical clinical manifestations of MM. The patient was administered a bortezomib and dexamethasone regimen for 2 cycles and achieved complete remission. Lenalidomide, vincristine, pirarubicin, dexamethasone, melphalan and thalidomide was used successively in consolidation therapy and maintenance therapy. The patient developed secondary B-cell lymphoblastic leukemia 38 months after the primary MM diagnosis was made. Owing to the exposure of the patient to a variety of therapeutic agents, it could be inferred that multiple immune defects may have played an important role in the secondary lymphoblastic leukemia of the patient. Microscopic examination and flow cytometry detection were important in identifying the secondary malignancy in this MM case.