Types Of Leukemia Research Articles

Promoter methylation might shift the balance of Galectin-3 & 12 expression in de novo adult acute myeloid leukemia patients.

Acute myeloid leukemia (AML) was reported as the most common type of leukemia among adults. Galectins constitute a family of galactose-binding proteins reported to play a critical role in many malignancies including AML. Galectin-3 and -12 are members of the mammalian galectin family. To understand the contribution of galectin-3 and -12 promoter methylation to their expression, we performed bisulfite methylation-specific (MSP)-PCR and bisulfite genomic sequencing (BGS) of primary leukemic cells in patients with de novo AML before receiving any therapy. Here, we show a significant loss of LGALS12 gene expression in association with promoter methylation. The lowest degree of expression was found in the methylated (M) group while the highest degree was in the unmethylated (U) group and the partially methylated (P) group expression lies in between. This was not the case with galectin-3 in our cohort unless the CpG sites analyzed were outside the frame of the studied fragment. We were also able to identify four CpG sites (CpG number 1, 5, 7& 8) in the promoter region of galectin-12; these sites must be unmethylated so that expression can be induced. As far as the authors know, these findings were not previously concluded in earlier studies.

Research progress on molecular biomarkers of acute myeloid leukemia.

Acute myeloid leukemia (AML) is the most common type of adult acute leukemia. The pathophysiology of the disease has been studied intensively at the cellular and molecular levels. At present, cytogenetic markers are an important basis for the early diagnosis, prognostic stratification and treatment of AML. However, with the emergence of new technologies, the detection of other molecular markers, such as gene mutations and epigenetic changes, began to play important roles in evaluating the occurrence and development of diseases. Recent evidence shows that identifying new AML biomarkers contributes to a better understanding of the molecular mechanism of the disease and is essential for AML screening, diagnosis, prognosis monitoring, and individualized treatment response. In this review, we summarized the promising AML biomarkers from four aspects, which contributing to a better understanding of the disease. Of course, it must be soberly aware that we have not listed all biomarkers of AML. Anyway, the biomarkers we mentioned are representative. For example, mutations in TP53, FLT3, and ASXL1 suggest poor prognosis, low remission rate, short survival period, and often require allogeneic hematopoietic stem cell transplantation. The CEBPA double mutation, NPM1 and CBF mutation suggest that the prognosis is good, the remission rate is high, the survival period is long, and the effect of chemotherapy or autotherapy is good. As for other mutations mentioned in the article, they usually predict a moderate prognosis. All in all, we hope it could provide a reference for the precise diagnosis and treatment of AML.

Tumour‑derived exosomes and their emerging roles in leukaemia (Review).

Exosomes are small vesicles with a diameter of ~40-100 nm that are secreted by the majority of endogenous cells under normal and pathological conditions. They contain abundant proteins, lipids, microRNAs, and biomolecules such as signal transduction molecules, adhesion factors and cytoskeletal proteins, and play an important role in exchanging materials and transmitting information between cells. Recent studies have shown that exosomes are involved in the pathophysiology of leukaemia by affecting the bone marrow microenvironment, apoptosis, tumour angiogenesis, immune escape and chemotherapy resistance. Furthermore, exosomes are potential biomarkers and drug carriers for leukaemia, impacting the diagnosis and treatment of leukaemia. The present study describes the biogenesis and general characteristics of exosomes, and then highlight the emerging roles of exosomes in different types of leukaemia. Finally, the value of clinical application of exosomes as biomarkers and drug carriers is discussed with the aim to provide novel strategies for the treatment of leukaemia.

The molecular and clinical significance of the Tie/angiopoietin system in leukemia.

Angiogenesis and factors affecting it are one of the most critical elements in vascular proliferation. Although they are essential in generating new vessels, their potential to generate solid tumors is accepted as a pathological condition. Leukemia can be an appropriate example of this condition. This study aims to evaluate the Tie/angiopoietin system effect in Leukemia. Leukemia is a pathological condition in which the uncontrolled proliferation of abnormal cells occurs in the bone marrow or lymphatic system. Based on severity and speed of development, many different types of Leukemia have been discovered through years of studying. Acute lymphocytic Leukemia (ALL), acute myeloid Leukemia (AML), chronic lymphocytic Leukemia (CLL), and chronic myeloid Leukemia (CML) are the four main types of Leukemia. Leukemia's function, effects, and medication have been a great concern over the years. Angiogenic factors such as angiopoietin-1 (Ang1), angiopoietin-2 (Ang2), angiopoietin-4 (Ang4), a combination of them and their receptors and their effect on Leukemia are the main purposes discussed in this article.

Survival in Elderly Patients Diagnosed With Acute Myeloid Leukemia: A Hospital-Based Study.

Acute myeloid leukemia (AML) is a hematological neoplasm that is more frequent in elderly patients. The objective of this study was to evaluate elderly patients' survival with de novo AML and acute myeloid leukemia myelodysplasia-related (AML-MR), treated with intensive and less-intensive chemotherapy and supportive care. A retrospective cohort study was conducted in Fundacion Valle del Lili (Cali, Colombia), between 2013 and 2019. We included patients ≥ 60 years old diagnosed with AML. The statistical analysis considered the leukemia type (de novo vs. myelodysplasia-related) and treatment (intensive chemotherapy regimen, less-intensive chemotherapy regimen, and without chemotherapy). Survival analysis was performed using Kaplan-Meier method and Cox regression models. A total of 53 patients were included (31 de novo and 22 AML-MR). Intensive chemotherapy regimens were more frequent in patients with de novo leukemia (54.8%), and 77.3% of patients with AML-MR received less-intensive regimens. Survival was higher in the chemotherapy group (P = 0.006), but with no difference between chemotherapy modalities. Additionally, patients without chemotherapy were 10 times more likely to die than those who received any regimen, independent of age, sex, Eastern Cooperative Oncology performance status, and Charlson comorbidity index (adjusted hazard ratio (HR) = 11.6, 95% confidence interval (CI) 3.47 - 38.8). Elderly patients with AML had longer survival time when receiving chemotherapy, regardless of the type of regimen.

Jak2/STAT6/c-Myc pathway is vital to the pathogenicity of Philadelphia-positive acute lymphoblastic leukemia caused by P190BCR-ABL

BackgroundThe Philadelphia chromosome encodes the BCR-ABL fusion protein, which has two primary subtypes, P210 and P190. P210 and P190 cause Philadelphia-positive chronic myeloid leukemia (Ph+ CML) and Philadelphia-positive acute lymphoblastic leukemia (Ph+ ALL), respectively. The Ph+ ALL is more malignant than Ph+ CML in disease phenotype and progression. This implies the key pathogenic molecules and regulatory mechanisms caused by BCR-ABL in two types of leukemia are different. It is reported that STAT6 was significantly activated only in P190 transformed cells. However, the potential role and the mechanism of STAT6 activation in Ph+ ALL and its activation mechanism by P190 are still unknown.MethodsThe protein and mRNA levels of STAT6, c-Myc, and other molecules were measured by western blot and quantitative real-time PCR. The STAT6 inhibitor AS1517499 was used to specifically inhibit p-STAT6. The effect of p-STAT6 inhibition on Ph+ CML and Ph+ ALL cells was identified by CCK-8 and FCM assay. Dual luciferase reporter and ChIP assay were performed to confirm the direct binding between STAT6 and c-Myc. The impact of STAT6 inhibition on tumor progression was detected in Ph+ CML and Ph+ ALL mouse models.ResultsOur results demonstrated that P210 induced CML-like disease, and P190 caused the more malignant ALL-like disease in mouse models. STAT6 was activated in P190 cell lines but not in P210 cell lines. Inhibition of STAT6 suppressed the malignancy of Ph+ ALL in vitro and in vivo, whereas it had little effect on Ph+ CML. We confirmed that p-STAT6 regulated the transcription of c-Myc, and STAT6 was phosphorylated by p-Jak2 in P190 cell lines, which accounted for the discrepant expression of p-STAT6 in P190 and P210 cell lines. STAT6 inhibition synergized with imatinib in Ph+ ALL cells.ConclusionsOur study suggests that STAT6 activation plays an essential role in the development of Ph+ ALL and may be a potential therapeutic target in Ph+ ALL.-58AP5UCfdMzXqQFXTpZyFVideo abstract

INTEGRATED BIOINFORMATIC ANALYSIS TO EVALUATE TARGET GENES AND PATHWAYS IN CHRONIC LYMPHOCYTIC LEUKEMIA

Objective: The most common type of leukemia, chronic lymphocytic leukemia (CLL), is characterized by progressive accumulation of monoclonal B cells with a specific immunophenotype in the blood, bone marrow, and lymphoid organ. The goal of this research was to use bioinformatic analysis to comprehend the molecular mechanisms causing CLL and to investigate potential targets for the diagnosis and therapy of CLL.
 Material and Method: Expression data from CLL patients with accession numbers GSE22529 and GSE26725 were downloaded from the GEO database for bioinformatic analysis. GSE22529 data was studied with samples from 41 CLL patients and 11 healthy groups, while GSE26725 data was studied with blood samples from 12 CLL patients and 5 healthy groups. GEO2R was used to find differentially expressed genes (DEGs) in CLL patient samples and healthy control samples. The DAVID program was used to perform GO and KEGG enrichment analyses on DEGs. Using the Cytoscape software, a protein-protein interaction (PPI) network was created, and hub genes associated with CLL were identified.
 Result and Discussion: DEGs with p 0.05 and log2FC 0, log2FC>0 were chosen after analysis with GEO2R. In the GSE22529 dataset, 942 genes had higher expression levels in CLL patients compared with controls, while the expression of 1007 genes decreased. In the GSE26725 dataset, CLL patients had lower expression levels for 916 genes compared with controls, while 939 genes showed an increase in expression. 229 DEGs with higher expression levels and 308 DEGs with lower expression levels were found in both sets of data. It has been observed that these common genes, whose expression has changed, are enriched in protein processing in the ER, Chemokine, B-cell receptor, T-cell receptor, protein export pathways. Additionally, DDOST, RPL18, RPL18A, RPL19, RPL31, GNB3, GNB4, GNG11, GNGT1, NEDD8, UBE2M RBX1, FBXO21, SKP1, KLHL9 and CAND1 were identified as the most important genes. Our study's findings demonstrated that newly discovered genes and pathways may be candidates for CLL biomarkers that can be used for both the diagnosis and drug treatment of the disease.

Comparative Expression Analysis of Breakpoint Cluster Region-Abelson Oncogene in Leukemia Patients.

Leukemia is a proliferative disorder of myeloid and lymphoid cells that may lead to death. Different types of leukemia have been reported, and several genetic and environmental factors are involved in their development. The Philadelphia chromosome causes the most common mutation known as breakpoint cluster region-Abelson oncogene (BCR-ABL1), which shows abnormal protein tyrosine kinase (PTK) activity. Basically, this activity is accountable for activating multiple pathways, including the inhibition of cell differentiation, controlled proliferation, and cell death. As a result of the absence of kinase activity, this mutation leads to the uncontrolled proliferation of leukocytes, causing chronic myeloid leukemia (CML), acute myeloid leukemia (AML), acute lymphoid leukemia (ALL), and chronic lymphocytic leukemia (CLL). This study aimed to evaluate the level of BCR-ABL1 expression in patients with these types of leukemias through qPCR. In brief, PBMCs were isolated from blood samples of patients, RNA was extracted from PBMCs, cDNA was synthesized, and the transcript levels of BCR-ABL1 in patients with each type of leukemia were determined by qPCR. The clinical, demographical, and experimental data were analyzed among CML, AML, and ALL patients. Results: The BCR-ABL1 expression levels are variable in all studied groups and are 90, 30-35, and 1-2.5% in CML, ALL, and AML, respectively. Demographic characteristics such as gender, BMI, age, family history, and clinical parameters along with CBC are also associated with the prevalence and diagnosis of leukemia. In a comparative expression analysis, the expression of BCR-ABL1 is onefold high in AML, but four- and sevenfold high in ALL and CML, respectively, as compared with normal levels. Conclusions: In this study, a significant difference was observed in the expression levels of BCR-ABL1 between CML (p = 0.0043) and ALL (p = 0.0006) and between CML and AML groups, and a high expression of BCR-ABL1 was noted in CML as compared with ALL and AML.

Extracellular DEK Treatment Increases Mitochondrial Dysfunction in the Mouse AML Cell line MLL-AF9


 
 
 Acute myeloid leukemia (AML) is the most common kind of acute leukemia and the second most common type of leukemia in adults. Poor outcomes resulting from AML are thought to occur due to the inability to target the small pool of cancer-initiating cells that develop from cells within hematopoietic stem (HSC) and progenitor (HPC) cell compartments in bone marrow. DEK, a nuclear protein that can be secreted under stress conditions, plays a role in regulating HSC and HPC function. Extracellular DEK has been found to improve functional HSC expansion in in-vivo and ex-vivo mouse studies. Moreover, RNAseq experiments suggest that recombinant human DEK treatment causes the upregulation of the antioxidant gene programs. Indeed, DEK treatment reduces total reactive oxygen species (ROS) in human umbilical cord blood HSCs and HPCs. Thus, extracellular DEK enhances normal HSC function through antioxidant programs, but the role of the extracellular DEK in AML is unclear. We hypothesized that recombinant mouse (rm)DEK treatment of the mouse-derived AML cell line MLL-AF9 would affect mitochondrial function since mitochondria are an important source of ROS production. Since ROS production can contribute to mitochondrial dysfunction by causing damage to the organelles, we investigated the effects of DEK signaling on mitochondrial metabolism in MLL-AF9 cells using the Seahorse XF instrument, which can measure changes in metabolic flux. Compared to vehicle-treated control, cells treated with DEK demonstrated a decrease in basal and maximal mitochondrial respiration, proton leak, and non-mitochondrial oxygen consumption. Experiments to explore the effects of DEK treatment on the glycolytic function of MLL-AF9 cells are ongoing. Our data shows DEK treatment of MLL-AF9 cells alters mitochondrial function. In the future, we wish to investigate DEK’s effect on proliferation and colony formation.
 
 

Computer-Aided Diagnosis System for Blood Diseases Using EfficientNet-B3 Based on a Dynamic Learning Algorithm.

The immune system's overproduction of white blood cells (WBCs) results in the most common blood cancer, leukemia. It accounts for about 25% of childhood cancers and is one of the primary causes of death worldwide. The most well-known type of leukemia found in the human bone marrow is acute lymphoblastic leukemia (ALL). It is a disease that affects the bone marrow and kills white blood cells. Better treatment and a higher likelihood of survival can be helped by early and precise cancer detection. As a result, doctors can use computer-aided diagnostic (CAD) models to detect early leukemia effectively. In this research, we proposed a classification model based on the EfficientNet-B3 convolutional neural network (CNN) model to distinguish ALL as an automated model that automatically changes the learning rate (LR). We set up a custom LR that compared the loss value and training accuracy at the beginning of each epoch. We evaluated the proposed model on the C-NMC_Leukemia dataset. The dataset was pre-processed with normalization and balancing. The proposed model was evaluated and compared with recent classifiers. The proposed model's average precision, recall, specificity, accuracy, and Disc similarity coefficient (DSC) were 98.29%, 97.83%, 97.82%, 98.31%, and 98.05%, respectively. Moreover, the proposed model was used to examine microscopic images of the blood to identify the malaria parasite. Our proposed model's average precision, recall, specificity, accuracy, and DSC were 97.69%, 97.68%, 97.67%, 97.68%, and 97.68%, respectively. Therefore, the evaluation of the proposed model showed that it is an unrivaled perceptive outcome with tuning as opposed to other ongoing existing models.

A potential area of use for immune checkpoint inhibitors: Targeting bone marrow microenvironment in acute myeloid leukemia.

Acute myeloid leukemia (AML) arises from the cells of myeloid lineage and is the most frequent leukemia type in adulthood accounting for about 80% of all cases. The most common treatment strategy for the treatment of AML includes chemotherapy, in rare cases radiotherapy and stem cell and bone marrow transplantation are considered. Immune checkpoint proteins involve in the negative regulation of immune cells, leading to an escape from immune surveillance, in turn, causing failure of tumor cell elimination. Immune checkpoint inhibitors (ICIs) target the negative regulation of the immune cells and support the immune system in terms of anti-tumor immunity. Bone marrow microenvironment (BMM) bears various blood cell lineages and the interactions between these lineages and the noncellular components of BMM are considered important for AML development and progression. Administration of ICIs for the AML treatment may be a promising option by regulating BMM. In this review, we summarize the current treatment options in AML treatment and discuss the possible application of ICIs in AML treatment from the perspective of the regulation of BMM.

An efficient EGWO algorithm as feature selection for B-ALL diagnoses and its subtypes classification using peripheral blood smear images

Acute lymphoblastic leukemia (ALL) is the most serious type of leukemia that develops because it causes an abnormal increase in the production of immature white blood cells in the bone marrow. ALL spreads rapidly in children's bodies and leads to their death. The main objective of this paper is to introduce an enhanced methodology based on the k-means clustering algorithm for classifying all subtypes of ALL. Image preprocessing is the first step of the proposed methodology. For obtaining the images' descriptive features, feature extraction is the second step that has been used. To select the most vital features that can characterize the histology of the blood cells, Enhanced Grey Wolf Optimization (EGWO) algorithm has been used in the third step. Hence, EGWO will start to select the search agents based on certain criteria as the best cluster center by using the k-means clustering algorithm. Several supervised classifiers as Random Forest (RF), K Nearest Neighbors (KNN), Support Vector Machine (SVM), and Naïve Bayes (NB) have been compared. The proposed methodology achieves a high degree of accuracy of99.22%,precision of99%,and sensitivity of99%.A comparative study has been established in order to verify the effectiveness of the proposed methodology.

Investigating the Expression Pattern of the SETMAR Gene Transcript Variants in Childhood Acute Leukemia: Revisiting an Old Gene.

The chimeric enzyme SETMAR (or Metnase) has been associated with several DNA processes, including DNA damage repair through the non-homologous joining pathway and suppression of chromosomal translocation in mouse fibroblasts. SETMAR overexpression has been reported in certain cancers suggesting that it might contribute to the establishment or progression of these cancers. In leukemia, the SETMAR gene transcript variants have not been widely studied. Therefore, this study aimed to quantify 3 predominant SETMAR variants in 2 types of childhood acute leukemia, acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL). In this study, using reverse transcription-quantitative polymerase chain reaction (RT-qPCR), the relative expression of 3 SETMAR transcript variants (Var 1, Var 2, and Var A) were evaluated in the bone marrow samples collected from 30 newly diagnosed patients with AML, 65 newly diagnosed patients with ALL, and 15 healthy individuals. The expression of SETMAR variants 1 and A were significantly higher in AML patients compared with controls ( P =0.02, and P =0.009, respectively). Variant A expression was significantly higher in ALL compared with controls ( P =0.003). When comparing the expression in translocation-positive and negative subgroups, the expression of variant 1 was significantly higher in translocation-positive ALL patients ( P =0.03). The variants' distribution patterns differed concerning translocation status ( P =0.041), as variants 1 and A were dominant in the translocation-positive ALL group, and variant 2 was more prevalent in translocation-negative ones. According to the results, SETMAR showed increased expression in pediatric acute leukemia's bone marrow samples, indicating a role for this molecule in leukemia pathogenesis. As this is the first report of SETMAR expression in pediatric leukemias, further studies are needed to investigate the causality of this association.

A Customized Efficient Deep Learning Model for the Diagnosis of Acute Leukemia Cells Based on Lymphocyte and Monocyte Images

The production of blood cells is affected by leukemia, a type of bone marrow cancer or blood cancer. Deoxyribonucleic acid (DNA) is related to immature cells, particularly white cells, and is damaged in various ways in this disease. When a radiologist is involved in diagnosing acute leukemia cells, the diagnosis is time consuming and needs to provide better accuracy. For this purpose, many types of research have been conducted for the automatic diagnosis of acute leukemia. However, these studies have low detection speed and accuracy. Machine learning and artificial intelligence techniques are now playing an essential role in medical sciences, particularly in detecting and classifying leukemic cells. These methods assist doctors in detecting diseases earlier, reducing their workload and the possibility of errors. This research aims to design a deep learning model with a customized architecture for detecting acute leukemia using images of lymphocytes and monocytes. This study presents a novel dataset containing images of Acute Lymphoblastic Leukemia (ALL) and Acute Myeloid Leukemia (AML). The new dataset has been created with the assistance of various experts to help the scientific community in its efforts to incorporate machine learning techniques into medical research. Increasing the scale of the dataset is achieved with a Generative Adversarial Network (GAN). The proposed CNN model based on the Tversky loss function includes six convolution layers, four dense layers, and a Softmax activation function for the classification of acute leukemia images. The proposed model achieved a 99% accuracy rate in diagnosing acute leukemia types, including ALL and AML. Compared to previous research, the proposed network provides a promising performance in terms of speed and accuracy; and based on the results, the proposed model can be used to assist doctors and specialists in practical applications.

Artificial intelligence-assisted diagnosis of hematologic diseases based on bone marrow smears using deep neural networks

Background and objectivesThe morphological examination of bone marrow (BM) cells is essential in both diagnosing and treating various hematologic diseases. However, it is still done manually with a heavy workload. An artificial intelligence-assisted diagnosis support system of BM cells is highly required to reduce the workloads of examiners and improve the reproducibility of the results. MethodsIn this paper, we proposed an artificial intelligence-assisted diagnosis support system of morphological examination based on bone marrow smears including cells detection, classification and prediction of leukemia types. For cell detection, we trained the novel YOLOX-s model to locate cells precisely and obtain single cell images. For cell classification, we regarded it as a fine- grained classification task and proposed a novel architecture called MLFL-Net utilizing multi-level features. Furthermore, we predicted the leukemia types on a dataset including 40 normal people (BM transplantation donors) and 40 patients of different kinds of acute leukemia according to the World Health Organization (WHO) standard. ResultsWe constructed a large-scale data set of 11,788 fully-annotated micrographs from 728 smears and 131,300 expert-annotated single cell images. With the data set, the detection model achieved 0.9797 AUC and 4.33% box placement error. For cell classification, the total accuracy of our proposed MLFL-Net reached 89.53% which outperformed all the other related models in identifying cell categories. In the meantime, we took acute leukemia as an example to explore the leukemia types prediction procedure of hematological disease. It generated the same diagnostic prediction as the experts gave for 92.5 percent of the cohort. ConclusionThis Artificial Intelligence-assisted system can be implemented to aid in clinical decision making and accelerate diagnosis. The method will contribute to promote the intelligence and modernization of BM cytomorphology, which has vital significance of the development of the medical career.

Clinicohaematological Study of Leukaemias at a Tertiary Care Centre, Vindhya Region of India: A Cross-sectional Study

Introduction: Leukaemia is characterised by widespread proliferation of leucocytes and their precursors in the body tissues and is usually associated with qualitative and quantitative changes in circulating white blood cells. The global burden of leukaemia is increasing. Aim: To study the morphology of leukaemia from Peripheral Blood Smears (PBS) and/or bone marrow smears and evaluate the clinical and haematological profile of leukaemia patients. Materials and Methods: This prospective observational study was conducted in a Tertiary Care Centre of the Vindhya region, in the Department of Pathology at Shyam Shah Medical College, Rewa, Madhya Pradesh, India. The study was carried out over a period of one year, from June 2019 to June 2020. A total of 90 newly diagnosed cases of all types of leukaemia during the study period. Detailed history, physical examination, complete blood counts, and bone marrow examinations were performed to confirm the diagnosis. Cytochemistry was used for the diagnosis and classification of acute leukaemia according to the French-American-British (FAB) classification. The data was compiled and analysed using appropriate statistical methods, and the mean was described with Standard Deviation (SD). Statistical significance was assessed using Analysis of Variance (ANOVA) test conducted with Statistical Package for Social Sciences (SPSS) version 22.0. Results: The study included 90 cases of all types of leukaemia, with a mean age of 36.5±22.5 years. There was a male preponderance in the study, with 65 (72.2%) males and 25 (27.8%) females, resulting in a male-to-female ratio of 2.6:1. Chronic Myeloid Leukaemia (CML) was the most common malignancy, accounting for total 40 (44.4%) cases. Non specific symptoms such as fever in 50 (55.6%), weight loss 49 (54.4%), and loss of appetite 42 (46.7%) cases were the most common symptoms across all types of leukaemia. Acute leukaemia also presented with leukopenia and normal leucocyte counts, while chronic leukaemia mostly showed markedly elevated Total Leucocyte Count (TLC) with a mean count of 1,30,912±63,792 per mm3. Conclusion: Light microscopic features of peripheral smear and bone marrow examination still play an important role in the diagnosis of leukaemia, especially in resource-poor health centres lacking immunotyping and cytogenetics.

Bioactive properties of Persea lingue Ness (Lauraceae) fruit and leaf extracts.

Persea lingue Ness is a tree species that lives mainly in temperate forests of south-central Chile. Its leaves are used in ethnomedicine, the fruit is a drupe similar to that of the avocado and has not been studied. The aim of this study was to determine the cytotoxicity in leukemia cell and antibacterial activity, along with some chemical content characteristics of P. lingue fruit and leaf extracts. The antibacterial activity was determined by the inhibition of bacterial growth in liquid medium assay against Gram-positive and Gram-negative bacteria. The leukemia cell lines Kasumi-1 and Jurkat were used to evaluate the cytotoxic activity by using propidium iodide and AlamarBlue assays. Total phenolic, flavonoid, condensed tannin, alkaloid and lipid contents were evaluated in the fruit and in the leaf extracts. The antioxidant activity of both extracts were also elavaluated. Leaf extract presented the highest content of total phenols, condensed tannins and flavonoids, and also the highest antioxidant activity. While the fruit extract has a higher amount of lipids and alkaloids and the high antibacterial activity against Escherichia coli, Pseudomonas aeruginosa, Staphylococcus aureus, Bacillus megaterium and Micrococcus luteus. The leaf extract only showed activity against M. luteus. Concerning the cytotoxic activity, only the fruit extract showed cytotoxicity against the cell lines Jurkat and Kasumi-1. P. lingue fruit extract is a potential source of biologically active molecules for the development of new drugs to be used in some types of leukemia, as well as antibacterial agent.

Typing of acute leukemia by intelligent optical time-stretch imaging flow cytometry on a chip.

Acute leukemia (AL) is one of the top life-threatening diseases. Accurate typing of AL can significantly improve its prognosis. However, conventional methods for AL typing often require cell staining, which is time-consuming and labor-intensive. Furthermore, their performance is highly limited by the specificity and availability of fluorescent labels, which can hardly meet the requirements of AL typing in clinical settings. Here, we demonstrate AL typing by intelligent optical time-stretch (OTS) imaging flow cytometry on a microfluidic chip. Specifically, we employ OTS microscopy to capture the images of cells in clinical bone marrow samples with a spatial resolution of 780 nm at a high flowing speed of 1 m s-1 in a label-free manner. Then, to show the clinical utility of our method for which the features of clinical samples are diverse, we design and construct a deep convolutional neural network (CNN) to analyze the cellular images and determine the AL type of each sample. We measure 30 clinical samples composed of 7 acute lymphoblastic leukemia (ALL) samples, 17 acute myelogenous leukemia (AML) samples, and 6 samples from healthy donors, resulting in a total of 227 620 images acquired. Results show that our method can distinguish ALL and AML with an accuracy of 95.03%, which, to the best of our knowledge, is a record in label-free AL typing. In addition to AL typing, we believe that the high throughput, high accuracy, and label-free operation of our method make it a potential solution for cell analysis in scientific research and clinical settings.

An Overview of Cancer

Cancer is defined as one of the large groups of diseases characterized by the development of abnormal cells that grow beyond their boundaries, which can invade adjoining tissues via circulation, and spread to other organs in the body. Cancer can be initiated anywhere in the body, where damaged cells grow and multiply where they shouldn't. These cells form tumors, also called neoplasm- an abnormal mass of cells. Tumors can be non-cancerous (benign) and cancerous (malignant). Cancers are grouped according to their origin of tissue or organ. Four major types of cancers- • Carcinomas – are malignancies that begin in the epithelial cells, which make up the skin , and tissues that line other internal organs. Examples of carcinomas involved are prostate cancer, breast cancer, lung cancer, and colorectal cancer. • Sarcomas- sarcoma is a type of cancer that starts in tissues like bone or soft tissues and connective tissues . • Leukemias- leukemias are cancer of the white blood cells, which begins in the bone marrow as the bone marrow produces an excessive amount of abnormal white blood cells, that do not function properly. There are 4 major types of leukemia - acute lymphocytic leukemia, chronic lymphocytic leukemia, acute myelogenous leukemia, and chronic myelogenous leukemia . • Lymphoma- a cancer that begins in the lymphatic system, the lymphatic system includes the lymph nodes, spleen, thymus gland, and other network of vessels. There are main types of lymphomas- Hodgkin’s lymphoma and non-Hodgkin's lymphomas. • And there are also other types of cancer • In overview, cancer is a condition where cells multiply abnormally over time. The cells divide and grow rebelliously, they invade surrounding tissues and spread to distant parts of the body. In the course of time, the mass of the cancer cells can get massive enough to make lumps(tumors) that can be felt or seen. But not all tumors are cancer.

PLATELET INDICES IN LEUKEMIAS: - A CROSS SECTIONAL STUDY

Objective: To evaluate the pattern of changes in the platelet indices in different Leukemias. Material and method: This descriptive cross-sectional study was conducted in the Pathology Department of Khyber Teaching Hospital from January 2021 to December 2021 (1-year duration). Diagnosed acute and chronic leukemia cases were included while those receiving therapeutic chemotherapy were excluded. The platelet count was analyzed as a part of complete blood counts by the Sysmex Hematology analyzer in the Hematology section of the Pathology department. The changes observed in the platelet counts in different types of leukemia were recorded in a proforma. Mean, standard deviation, frequency and percentages were used to compute variables. Results: The age range of the 100 included cases of leukemia was 01 - 50 years. The mean age of the study population was 30 ± 10 SD years. Platelet count was low in acute Leukemias and chronic lymphocytic leukemia. Only in chronic myeloid leukemia, raised platelet count was seen (in 62% of cases). Mean platelet volume and platelet distribution width were decreased in all leukemias. Conclusion: Platelet indices are decreased in Leukemias, with the exception of chronic myeloid leukemia where platelet count is raised. Keywords: Platelet count, Acute lymphoid leukemia, Acute myelogenous leukemia, Chronic lymphocytic leukemia, Chronic myelogenous leukemia.