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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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Since December 2019, after the declaration of new cases regarding novel coronavirus disease, many variants have emerged as a consequence of the viral evolution. Though the SARS-CoV-2 variants have been studied for molecular basis, the clinical and pathologic disparities of them have been understood inadequately. The aim of this research was to figure out the differences between the SARS-CoV-2 Alpha (B1.1.7) variant and the classical Wuhan groups on the clinical basis and laboratory results of the COVID-19 patients who had positive PCR test.The study was done retrospectively inclusive of epidemiological, laboratory data and clinical symptoms of patients who were admitted to the emergency service between February 15 and March 15, 2021 and had positive COVID-19 PCR test results. Though there was no statistically significant difference in symptoms between SARS-CoV-2 Alpha variant and classical variant (Wuhan type) groups; C-reactive protein (CRP), lymphocyte and leukocyte counts were statistically significantly higher in the Wuhan type group; prothrombin time (PT), International Normalized Ratio (INR) and serum creatinine values were statistically significantly higher in the Alpha group. Studies such as ours that investigate both the clinical features and laboratory data of SARS-CoV-2 variants will close the knowledge gaps, so better decisions may be made by health policy makers. Additional studies in this area will increase the understanding of the topic.