Analysis and decision based on specialist self-assessment for prognosis factors of acute leukemia integrating data-driven Bayesian network and fuzzy cognitive map.

The purpose of the present study is to analyze the prognostic factors of acute leukemia and to construct a decision model based on a causal relationship between the factors of this disease to assist medical specialists. In medical decisions, to reach effective, quick, and reliable results, there is a need for a simple decision-making model based on a specialist's self-assessment. It may help the medical team before final diagnosis by costly and time-consuming procedures such as bone marrow sampling and pathological test as well as provide an appropriate prognosis and diagnosis tool. Because of the complex and not the well-defined structure of medical data, the use of intelligent methods must be considered. For this purpose, first, a data-driven Bayesian network (BN) and Greedy algorithm are employed to determine causal relationships and probability between nodes using the real set of data. Then, these causal relationships will form based on the fuzzy cognitive map (FCM). Finally, according to scenarios defined, the results are analyzed. These analyses are also repeated for each type of acute leukemia including acute lymphocytic leukemia (ALL) and acute myelocytic leukemia (AML). Graphical abstract.

Design of an integrated model for diagnosis and classification of pediatric acute leukemia using machine learning.

Applying artificial intelligence techniques for diagnosing diseases in hospitals often provides advanced medical services to patients such as the diagnosis of leukemia. On the other hand, surgery and bone marrow sampling, especially in the diagnosis of childhood leukemia, are even more complex and difficult, resulting in increased human error and procedure time decreased patient satisfaction and increased costs. This study investigates the use of neuro-fuzzy and group method of data handling, for the diagnosis of acute leukemia in children based on the complete blood count test. Furthermore, a principal component analysis is applied to increase the accuracy of the diagnosis. The results show that distinguishing between patient and non-patient individuals can easily be done with adaptive neuro-fuzzy inference system, whereas for classifying between the types of diseases themselves, more pre-processing operations such as reduction of features may be needed. The proposed approach may help to distinguish between two types of leukemia including acute lymphoblastic leukemia and acute myeloid leukemia. Based on the sensitivity of the diagnosis, experts can use the proposed algorithm to help identify the disease earlier and lessen the cost.

The spatio-temporal expression of some genes involved in the biosynthetic pathways of terpenes/phenylpropanoids in yarrow (Achillea millefolium).

Yarrow (Achillea millefolium) is a medicinal plant from the Asteracea which biosynthesize different secondary metabolites especially terpenes and phenylpropanoids. To improve our understanding of the regulatory mechanisms behind the biosynthesis of these compounds we analyzed the expression of some genes associated with the biosynthesis of terpenes and phenylpropanoids in different tissues and in response to trans-cinnamic acid (tCA) as an inhibitor of PAL activity. Isolation and expression analysis of DXR, GPPS, PAL and CHS genes together with linalool synthase (LIS) as monoterpene synthase was conducted in different developmental stages of leaves, flowers and in response to trans-cinnamic acid (tCA). Differential expression of these genes observed in different tissues. tCA up-regulated the biosynthetic genes of monterpenes and down-regulated the biosynthetic genes of phenylpropanoids. Gene expression analysis in intact leaves and leaves without glandular trichomes showed that DXR, LIS, PAL and CHS are highly expressed in glandular trichomes while GPPS expressed ubiquitously. Analysis of essential oils composition showed that sesquiterpenes and monoterpenes are main compounds; in which from 57 identified compounds the highest were germacreneD (% 11.5), guaiol (%10.38), spatulenol (%8.73) and caryophyllene oxide (%7.48).