Mutations of JAK2V617F, CALR, and MPL genes confirm the diagnosis of myeloproliferative neoplasm (MPN). This study aims to determine the genetic profile of JAK2V617F, CALR exon 9 Type 1 (52 bp deletion) and Type 2 (5 bp insertion), and MPL W515 L/K genes among Malaysian patients and correlate these mutations with clinical and hematologic parameters in MPN. Mutations of JAK2V617F, CALR, and MPL were analyzed in 159 Malaysian patients using allele-specific polymerase chain reaction, including 76 polycythemia vera (PV), 41 essential thrombocythemia (ET), and 42 primary myelofibrosis (PMF) mutations, and the demographics of the patients were retrieved. The result showed that 73.6% JAK2V617F, 5.66% CALR, and 27.7% were triple-negative mutations. No MPL W515L/K mutation was detected. In ET and PMF, the predominance type was the CALR Type 1 mutation. In JAK2V617F mutant patients, serum LDH was significantly higher in PMF compared to PV and ET. PV has a higher risk of evolving to post PV myelofibrosis compared to ET. A thrombotic event at initial diagnosis of 40.9% was high compared to global incidence. Only one PMF patient had a CALR mutation that transformed to acute myeloid leukemia. JAK2V617F and CALR mutations play an important role in diagnostics. Hence, every patient suspected of having a myeloproliferative neoplasm should be screened for these mutations.

Thalassemia is identified as a prevalent disease in Malaysia, known to be one of the developing countries. Fourteen patients with confirmed cases of thalassemia were recruited from the Hematology Laboratory. The molecular genotypes of these patients were tested using the multiplex-ARMS and GAP-PCR methods. The samples were repeatedly investigated using the Devyser Thalassemia kit (Devyser, Sweden), a targeted NGS panel targeting the coding regions of hemoglobin genes, namely the HBA1, HBA2, and HBB genes, which were used in this study. There were many different genetic variants found in 14 unrelated cases. Out of all fourteen cases, NGS was able to determine an additional -50 G>A (HBB:c.-100G>A) that were not identified by the multiplex-ARMS method, including HBA2 mutations, namely CD 79 (HBA2:c.239C>G). Other than that, CD 142 (HBA2:c.427T>C) and another non-deletional alpha thalassemia and alpha triplication were also not picked up by the GAP-PCR methods. We illustrated a broad, targeted NGS-based test that proposes benefits rather than using traditional screening or basic molecular methods. The results of this study should be heeded, as this is the first report on the practicality of targeted NGS concerning the biological and phenotypic features of thalassemia, especially in a developing population. Discovering rare pathogenic thalassemia variants and additional secondary modifiers may facilitate precise diagnosis and better disease prevention.

BACKGROUND: Chronic Myeloid Leukemia (CML) is a myeloproliferative neoplasm characterized by the presence of the Philadelphia chromosome (BCR-ABL1 fusion gene). CML primarily progresses through chronic, accelerated, and blast phases. While global studies on BCR-ABL1 fusion transcript types and their associations with clinical, laboratory, and prognostic profiles exist, such data is scarce in Malaysia. OBJECTIVES: This study aimed to determine the distribution of BCR-ABL1 fusion transcript types and evaluate their associations with demographic, clinical, laboratory, prognostic profiles, and disease outcomes among Malaysian CML patients. PATIENTS, MATERIALS AND METHODS: A total of 98 patients diagnosed with CML were identified at East Coast Hospital, Malaysia. This 12-year cross-sectional study was carried out using data extracted from patients’ medical records. Molecular results for BCR-ABL1 fusion genes were obtained using one-step multiplex reverse transcriptase polymerase chain reaction. RESULTS: Out of the 98 patients, 56% had e14a2, 41% had e13a2 fusion transcripts, while the remaining 2 patients had e14a3 transcripts. Additionally, 1 patient co-expressed both e13a2 and e14a2. Among patients with the major BCR-ABL1 transcript type, those with e14a2 fusion transcripts showed an older median age (P = 0.025), while patients with e13a2 fusion transcripts had significantly higher white blood cell (WBC) counts (P = 0.014). Furthermore, there were significantly more patients with blastic transformation in the e13a2 group (P = 0.038). The median latency period of CML was 12 months. The blast cell lineages consisted of myeloid (68.4%), followed by B-lymphoid (26.3%) and mixed phenotypic (5.3%). The differences in fusion transcript expression might influence certain parameters; for instance, older patients were associated with the e14a2 fusion transcript. Meanwhile, patients exhibiting e13a2 might have a higher WBC count at diagnosis and be more vulnerable to blastic transformation of CML. CONCLUSIONS: This study highlights the predominance of e14a2 fusion transcripts in Malaysian CML patients and its association with older age. The e13a2 transcript was linked to higher tumor burden and a higher rate of blastic transformation, suggesting potential prognostic significance. These findings underscore the importance of baseline molecular profiling for optimizing disease management.

In the medical field, the segmentation of white blood cells is an important process in machine learning techniques. The conventional process requires an expert, a hematopathologist, to manually analyse blood smear samples from patients. The sample is pre-processed on a slide using a staining procedure that provides the components of platelets, red blood cells and white blood cells. White blood cell images obtained by a microscope are important in guiding haematology imaging to diagnose blood cancers such as leukemia and lymphoma. However, there are some challenges while conducting manual segmentation of white blood cells, which requires expert labour to observe each blood sample individually to diagnose the patients. This process is also very iterative, time-consuming, and relatively expensive. Besides that, this procedure has medical and scientific drawbacks, including inaccurate output due to interobserver disagreement and inadequate sensitivity, specificity, and predictive value. Thus, new methodologies were proposed throughout the years to aid the white blood cell segmentation process. The objective of this study is to review thresholding and K-means clustering methods, which are two widely used segmentation approaches, to segment the microscopic image of white blood cells. The effectiveness of these approaches was then evaluated. The proposed methodology was tested on five types of white blood cells: neutrophils, eosinophils, basophils, monocytes, and lymphocytes, to compare their performances. The result shows that the K-means clustering technique's performance is more accurate than the thresholding, where the dice similarity is 93.88% and 93.23%, respectively. Contrarily, the thresholding technique runs much quicker than the K-means clustering algorithm, requiring 0.3238 seconds as compared to 6.4292 seconds.