OBJECTIVE: HbS/β cases having clinical, hematologic and electrophoretic similarities cannot be sufficiently distinguished from sickle cell anemia cases, and are misdiagnosed as sickle cell anemia. This study will investigate the congruence between the HPLC thalassemia scanning tests and the laboratory findings in comparison with the DNA sequence analysis results of the patients diagnosed with SCA between 2016 and 2020. This study also aims to indicate the current status to accurately diagnose sickle cell anemia and HbS/β in the light of hematologic, electrophoretic and molecular studies. MATERIAL METHOD: Fourteen patients who were diagnosed with SCA in hospitals at different cities in Turkey and followed by the Thalassemia Diagnosis, Treatment and Research Center, Muğla Sıtkı Koçman University were included in this retrospective study. The socio demographic characteristics, hemogram, hemoglobin variant analysis results and DNA chain analysis results of the patients were taken from the database of the center and then examined. The informed consents were taken from the patients. The patients were administered a survey containing questions about transfusion history and diagnostic awareness. The Beta-Thalassemia mutations were analyzed using DNA sequencer (Dade Behring, Germany) based on the Sanger method. RESULTS: According to the DNA sequence analysis results of these patients diagnosed with SCA in hospitals in different cities of Turkey: Of 14 patients, 8 had Hb S/β0 and Hb S/β+ and one had Hb S carrier, and one had Hb-O, and three had SCA. The patient with HbS carrier status also contains three additional mutations all of which are heterozygous. We discovered that although two of three mutations, which are c.315+16G>C and c.316-185C>T, are previously reported as benign, at least one of the two mentioned mutations, when combined with Hb S, causes transfusion-dependent Hb S/β. CONCLUSION: Briefly, HbSS and HbS/β thalassemia genotypes cannot be definitely characterized by electrophoretic and hematologic data, resulting in misdiagnosis. c.315+16G>C and c.316-185C>T, are previously reported as benign, at least one of the two mentioned mutations, when combined with Hb S, causes transfusion-dependent Hb S/β.  In undeveloped or some developing countries, molecular diagnosis methods and genetic analyses cannot be used. If mutation analyses could be performed, then such differential diagnosis errors would reduce. However, if mutation analysis cannot be performed, other methods such as HPLC, capillary electrophoresis absolutely be sought to have insight into the parental carriage status.

Objectives This article presents the use of machine learning techniques such as artificial neural networks, K-nearest neighbors (KNN), naive Bayes, and decision trees in the prediction of hemoglobin variants. To the best of our knowledge, this is the first study using machine learning models to predict suspicious cases with HbS or HbD Los Angeles carriers state. Methods We had a dataset of 238 observations, of which 128 were HbD carriers, and 110 were HbS carriers. The features were age, sex, RBC, Hb, HTC, MCV, MCH, RDW, serum iron, TIBC, ferritin, HbA2, HbF, HbA0, retention time (RT) of the abnormal peak, and the area under the peak of the abnormal peak. KNN, naive Bayes, decision tree models, and artificial neural network models were trained. Model performances were estimated using 7-fold cross-validation. Results When RT, the key point of differentiation used in high-performance liquid chromatography (HPLC), was included as a feature, all models performed well. When RT was excluded (eliminated), the deep learning model performed the best (Accuracy: 0.99; Specificity: 0.99; Sensitivity: 0.99; F1 score: 0.99), while the naive Bayes model performed the worst (Accuracy: 0.94; Specificity: 0.97; Sensitivity: 0.90; F1 score: 0.93). Conclusions Deep learning and decision tree models have demonstrated high performance and have the potential to be integrated into medical laboratory work practices as a tool for hemoglobinopathy detection. These outcomes suggest that when machine learning models are fed enough data, they can detect a wide range of hemoglobin variants. However, more comprehensive studies with data from a larger number of patients and hemoglobinopathies will be useful for validating our models.

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Background and aims: Alpha-thalassaemia is a group of disorders characterised by wide phenotypic variation caused by mutations in the α-globin genes (α1 and α2) of chromosome 16. The aim of this study was to investigate the molecular profile of α-thalassemia variants and to compare and characterise the chromatographic behaviour and haematological properties of α-thalassemia minor (-α/-α, --/ααα) and α-silent carriers (-α/ααα) on HPLC. Materials & Methods: A dataset of 200 individuals consisting of 42 alpha thalassemia minor (-α/-α, -- /ααα), 103 alpha silent carriers (-α/αα) and 55 normal participants from the Human Genetics Unit (HGU) of the Faculty of Medicine, Colombo, Srilanka was included. Blood samples from each patient were analysed by PCR for genotyping, haemogram and high-performance liquid chromatography (HPLC) for characterisation. These data were then comparatively analysed using standard descriptive statistics. Results: It was analysed in three sections as haematological, biochemical and molecular. Haematologically, alpha thalassaemia silent carrier was completely normal; alpha thalassaemia minor: decreased haemoglobin level; decreased MCV and MCH, normal RBC count. Alpha Thalassaemia Minor patients show a statistically significant difference from alpha silent carriers and normal population in terms of MCV, MCH, HGB, PCV, RDW. Biochemically, alpha thalassaemia silent carriers were found to have normal alpha-globin chain production, alpha thalassaemia minor: low HBA2 as determined by HPLC. In terms of HPLC tests, similar results were observed between alpha thalassaemia minor patients and alpha silent carriers and between alpha thalassaemia minor patients and the normal group, with no statistical significance for all parameters except HBA2. Molecularly, the most common mutations in both variants are _α3.7 and _α4.2 mutations. Conclusion: In summary, despite the haematological and biochemical differences between α-thalassaemia minor and normal individuals, both variants of alpha thalassaemia present a diagnostic conundrum, as CBC and HPLC results for individuals are comparable to normal humans. Although MCV, MCH, HGB, PCV and HBA2 levels differ between alpha thalassaemia minor carriers, alpha silent carriers and the normal group, conventional haemoglobin electrophoresis and haemogram alone have been found insufficient for the diagnosis of alpha thalassaemia. Therefore, although HPLC does not seem to be sufficient to distinguish between normal individuals and the two variants, the decrease in HBA2 levels is an important finding. Identifying a mutated alpha globin gene requires newer molecular diagnostic tests such as next-generation sequencing (NGS) and quantitative PCR (qPCR). It should be noted that a given genotype can greatly alter the clinical manifestation by the presence of additional mutations, making the relationship between genotype and phenotype highly variable.