RESUMEN
Recent studies have indicated that microRNA and VEGF are considered to be genetic modifiers and are associated with elevated levels of fetal haemoglobin HbF, and thus they reduce the clinical impact of sickle haemoglobin (HbS) patients. This cross-sectional study was performed on clinical confirmed subjects of SCD cases. miR-423-rs6505162 C>T and VEGF-2578 C>A genotyping was conducted by ARMS-PCR in SCD and healthy controls. A strong clinical significance was reported while comparing the association of miR-423 C>T genotypes between SCD patients and controls (p = 0.031). The microRNA-423 AA genotype was associated with an increased severity of SCD in codominant model with odd ratio (OR = 2.36, 95% CI, (1.15-4.84), p = 0.018) and similarly a significant association was observed in recessive inheritance model for microRNA-423 AA vs (CC+CA) genotypes (OR = 2.19, 95% CI, (1.32-3.62), p < 0.002). The A allele was associated with SCD severity (OR = 1.57, 95% CI, (1.13-2.19), p < 0.007). The distribution of VEGF-2578 C>A genotypes between SCD patients and healthy controls was significant (p < 0.013). Our results indicated that in the codominant model, the VEGF-2578-CA genotype was strongly associated with increased SCD severity with OR = 2.56, 95% CI, (1.36-4.82), p < 0.003. The higher expression of HbA1 (65.9%), HbA2 (4.40%), was reported in SCD patients carrying miR-423-AA genotype than miR-423 CA genotype in SCD patients carrying miR-423 CA genotype HbA1 (59.98%), HbA2 (3.74%) whereas SCD patients carrying miR-423 CA genotype has higher expression of HbF (0.98%) and HbS (38.1%) than in the patients carrying AA genotype HbF (0.60%), HbS (36.1%). ARMS-PCR has been proven to be rapid, inexpensive and is highly applicable to gene mutation screening in laboratories and clinical practices. This research highlights the significance of elucidating genetic determinants that play roles in the amelioration of the HbF levels that is used as an indicator of severity of clinical complications of the monogenic disease. Further well-designed studies with larger sample sizes are necessary to confirm our findings.
RESUMEN
Accurate blood smear quantification with various blood cell samples is of great clinical importance. The conventional manual process of blood smear quantification is quite time consuming and is prone to errors. Therefore, this paper presents automatic detection of the most frequently occurring condition in human blood-microcytic hyperchromic anemia-which is the cause of various life-threatening diseases. This task has been done with segmentation of blood contents, i.e., Red Blood Cells (RBCs), White Blood Cells (WBCs), and platelets, in the first step. Then, the most influential features like geometric shape descriptors, Gray Level Co-occurrence Matrix (GLCM), Gray Level Run Length Matrix (GLRLM), and Gabor features (mean squared energy and mean amplitude) are extracted from each of the RBCs. To discriminate the cells as hypochromic microcytes among other RBC classes, scanning is done at angles (0∘, 45∘, 90∘, and 135∘). To achieve high-level accuracy, Adaptive Synthetic (AdaSyn) sampling for imbalance learning is used to balance the datasets and locality sensitive discriminant analysis (LSDA) technique is used for feature reduction. Finally, upon using these features, classification of blood cells is done using the multilayer perceptual model and random forest learning algorithms. Performance in terms of accuracy was 96%, which is better than the performance of existing techniques. The final outcome of this work may be useful in the efforts to produce a cost-effective screening scheme that could make inexpensive screening for blood smear analysis available globally, thus providing early detection of these diseases.