METHODS: A total of 120 healthy volunteers were enrolled (55 adult males, 32 adult females, and 33 children). The volunteers were interviewed for any bleeding history or drug intake which affects coagulation. Kaolin-activated TEG was performed on citrated whole blood, and parameters including R-time, K-time, angle, MA, LY30, and CI were analyzed.
RESULTS: Derived reference range for total volunteers irrespective of age and sex were as follows: R-time: 3.8-10.6, K-time: 1.2-3.1, angle: 44.9-72.0, MA: 41.2-64.5, LY30: 0-9.9, and CI: -3.7 to 3.4. Statistically significant difference was observed in different age and sex groups for R-time, K-time, and angle. About 40% of the volunteers had at least one abnormal parameter according to the manufacturer's reference range which decreased to 12.5% when the derived reference ranges were considered.
CONCLUSION: Gender- and age-related variances were observed in reference ranges of our population and which was also differed from the other ethnic population. Many of our healthy volunteers were categorized as coagulopathic when manufacturer's reference range was considered. So, it is important to derive the reference range of the target population before using the TEG into clinical practice.
METHODS: The purpose of this study was to design an assay for the detection of triplications, common and rare deletional alpha thalassaemia using droplet digital PCR (ddPCR).
RESULTS: This is a quantitative detection method to measure the changes of copy number which can detect deletions, duplications and triplications of the alpha globin gene simultaneously.
CONCLUSION: In conclusion, ddPCR is an alternative method for rapid detection of alpha thalassaemia variants in Malaysia.
MATERIALS AND METHODS: A total of 170 blood donors were recruited into the study. Blood donors were classified into three groups: normal, latent iron deficiency and iron deficiency anaemia based on their Hb, serum ferritin and transferrin saturation (TSAT) levels. The diagnostic performance of %Hypo-He was evaluated with a validation group comprising 160 blood donors.
RESULTS: Receiver operating characteristic (ROC) curve analysis showed that %Hypo-He is an excellent parameter for detecting iron deficiency, with an area under the curve (AUC) of 0.906, a confidence interval (CI) of 0.854-0.957 at a cut-off of 0.6%, and 74.51% sensitivity and 88.24% specificity. A moderate negative correlation between %Hypo-He and TSAT (ρ = -0.576 [P
METHODS: Forty BCR-ABL1-negative MPN patients' DNA: 19 polycythemia vera (PV), 7 essential thrombocytosis (ET) and 14 primary myelofibrosis (PMF), were screened for CALR mutations by CSGE. PCR primers were designed to amplify sequences spanning between exons 8 and 9 to target the mutation hotspots in CALR. Amplicons displaying abnormal CSGE profiles by electrophoresis were directly sequenced, and results were analysed by BioEdit Sequence Alignment Editor v7.2.6. CSGE results were compared with AS-PCR and confirmed by Sanger sequencing.
RESULTS: CSGE identified 4 types of mutations; 2 PMF patients with either CALR type 1 (c.1099_1150del52) or type 2 (c.1155_1156insTTGTC), 1 ET patient with nucleotide deletion (c.1121delA) and insertion (c.1190insA) and 1 PV patient with p.K368del (c.1102_1104delAAG) and insertion (c.1135insA) inframe mutations. Three patients have an altered KDEL motif at the C-terminal of CALR protein. In comparison, AS-PCR only able to detect two PMF patients with mutations, either type 1 and type 2.
CONCLUSION: CSGE is inexpensive, sensitive and reliable alternative method for the detection of CALR mutations in BCR-ABL1-negative MPN patients.
METHODS: To overcome these limitations, our research introduces the ALLDet classifier, an innovative tool employing deep transfer learning for the automated analysis and categorization of ALL from White Blood Cell (WBC) nuclei images. Our investigation encompassed the evaluation of nine state-of-the-art pre-trained convolutional neural network (CNN) models, namely VGG16, VGG19, ResNet50, ResNet101, DenseNet121, DenseNet201, Xception, MobileNet, and EfficientNetB3. We augmented this approach by incorporating a sophisticated contour-based segmentation technique, derived from the Chan-Vese model, aimed at the meticulous segmentation of blast cell nuclei in blood smear images, thereby enhancing the accuracy of our analysis.
RESULTS: The empirical assessment of these methodologies underscored the superior performance of the EfficientNetB3 model, which demonstrated exceptional metrics: a recall specificity of 98.5%, precision of 95.86%, F1-score of 97.16%, and an overall accuracy rate of 97.13%. The Chan-Vese model's adaptability to the irregular shapes of blast cells and its noise-resistant segmentation capability were key to capturing the complex morphological changes essential for accurate segmentation.
CONCLUSION: The combined application of the ALLDet classifier, powered by EfficientNetB3, with our advanced segmentation approach, emerges as a formidable advancement in the early detection and accurate diagnosis of ALL. This breakthrough not only signifies a pivotal leap in leukemia diagnostic methodologies but also holds the promise of significantly elevating the standards of patient care through the provision of timely and precise diagnoses. The implications of this study extend beyond immediate clinical utility, paving the way for future research to further refine and enhance the capabilities of artificial intelligence in medical diagnostics.