> Disorders > Neoplastic Process > Burkitt Lymphoma

Burkitt Lymphoma

Burkitt Lymphoma is an aggressive type of non-Hodgkin lymphoma that originates from B cells.
It is characterized by rapidly growing tumors, often in the abdomen, head, and neck.
The disease progresses quickly and requires prompt treatment.
PubCompare.ai is an AI-driven tool that helps optimize Burkitt Lymphoma research by enhancing reproducibility and accuracy.
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This improves research effeciency and supports advancements in the understanding and treatment of this challenging disease.

Most cited protocols related to «Burkitt Lymphoma»

Global Burden of Disease 2019 Analysis

Cited 223 times

GBD 2019 estimated each epidemiological quantity of interest—incidence, prevalence, mortality, years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs)—for 23 age groups; males, females, and both sexes combined; and 204 countries and territories that were grouped into 21 regions and seven super-regions. For GBD 2019, nine countries and territories (Cook Islands, Monaco, San Marino, Nauru, Niue, Palau, Saint Kitts and Nevis, Tokelau, and Tuvalu) were added, such that the GBD location hierarchy now includes all WHO member states. GBD 2019 includes subnational analyses for Italy, Nigeria, Pakistan, the Philippines, and Poland, and 16 countries previously estimated at subnational levels (Brazil, China, Ethiopia, India, Indonesia, Iran, Japan, Kenya, Mexico, New Zealand, Norway, Russia, South Africa, Sweden, the UK, and the USA). All subnational analyses are at the first level of administrative organisation within each country except for New Zealand (by Māori ethnicity), Sweden (by Stockholm and non-Stockholm), the UK (by local government authorities), and the Philippines (by province). In this publication, we present subnational estimates for Brazil, India, Indonesia, Japan, Kenya, Mexico, Sweden, the UK, and the USA; given space constraints, these results are presented in appendix 2. At the most detailed spatial resolution, we generated estimates for 990 locations. The GBD diseases and injuries analytical framework generated estimates for every year from 1990 to 2019.
Diseases and injuries were organised into a levelled cause hierarchy from the three broadest causes of death and disability at Level 1 to the most specific causes at Level 4. Within the three Level 1 causes—communicable, maternal, neonatal, and nutritional diseases; non-communicable diseases; and injuries—there are 22 Level 2 causes, 174 Level 3 causes, and 301 Level 4 causes (including 131 Level 3 causes that are not further disaggregated at Level 4; see appendix 1 sections 3.4 and 4.12 for the full list of causes). 364 total causes are non-fatal and 286 are fatal. For GBD 2019, 12 new causes were added to the modelling framework: pulmonary arterial hypertension, eye cancer, soft tissue and other extraosseous sarcomas, malignant neoplasm of bone and articular cartilage, and neuroblastoma and other peripheral nervous cell tumours at Level 3, and hepatoblastoma, Burkitt lymphoma, other non-Hodgkin lymphoma, retinoblastoma, other eye cancers, and two sites of osteoarthritis (hand and other joints) at Level 4.

Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. (2020). Lancet (London, England), 396(10258), 1204-1222.

Publication 2020

Bone Cancer Burkitt Lymphoma Cancer of Eye Cartilages, Articular Cells Degenerative Arthritides Disabled Persons Ethnicity Females Hepatoblastoma Idiopathic Pulmonary Arterial Hypertension Infant, Newborn Injuries Joints Lymphoma, Non-Hodgkin, Familial Males Neuroblastoma Noncommunicable Diseases Nutrition Disorders Peripheral Nervous System Neoplasms Retinoblastoma Sarcoma Tissues

Metabolomic Profiling of Bacterial and Cancer Cells

Cited 27 times

Detailed preparation of bacteria (Shewanella oneidensis MR-1 and Escherichia coli W3110), human cancer cells (Raji Burkitt’s lymphoma), and human plasma samples is included in the Supporting Information. Bacteria cell pellets (~10⁹ cells) and human cancer cell pellets (~10⁷ cells) were extracted using a MeOH:ACN:H₂O (2:2:1, v/v) solvent mixture. A volume of 1 mL of cold solvent was added to each pellet, vortexed for 30 s, and incubated in liquid nitrogen for 1 min. The samples were then allowed to thaw at room temperature and sonicated for 10 min. This cycle of cell lysis in liquid nitrogen combined with sonication was repeated three times. To precipitate proteins, the samples were incubated for 1 h at −20 °C, followed by 15 min centrifugation at 13,000 rpm and 4 °C. The resulting supernatant was removed and evaporated to dryness in a vacuum concentrator. The dry extracts were then reconstituted in 100 µL of ACN:H₂O (1:1, v/v), sonicated for 10 min and centrifuged 15 min at 13000 rpm and 4 °C to remove insoluble debris. The supernatants were transferred to HPLC vials and stored at −80 °C prior to LC/MS analysis.
Standard human plasma samples (200 µL) were extracted with 800 µL of cold MeOH:ACN (1:1, v/v) to keep the same MeOH:ACN:H₂O (2:2:1, v/v) ratio. The samples were then vortexed for 30 s, and sonicated for 10 min. The rest of the procedure was the same as described for bacteria and cancer cell pellets.

Ivanisevic J., Zhu Z.J., Plate L., Tautenhahn R., Chen S., O’Brien P.J., Johnson C.H., Marletta M.A., Patti G.J, & Siuzdak G. (2013). Toward ‘Omic Scale Metabolite Profiling: A Dual Separation – Mass Spectrometry Approach for Coverage of Lipids and Central Carbon Metabolism. Analytical chemistry, 85(14), 6876-6884.

Publication 2013

Bacteria Burkitt Lymphoma Cell Cycle Cells Centrifugation Cold Temperature Escherichia coli High-Performance Liquid Chromatographies Homo sapiens Malignant Neoplasms Nitrogen Pellets, Drug Plasma Proteins Shewanella oneidensis Solvents Vacuum

Cell line-based shRNA screening for DLBCL

Cited 16 times

Cell lines possessing the ecotropic retroviral receptor and the tetracycline repressor were generated and used in RNA interference library screening, shRNA toxicity assays and cDNA complementation studies as described4 (link). DLBCL cell lines were assigned to the ABC or GCB subtypes by gene expression profiling (ref.4 (link); Supplemental Fig. 14). shRNA screening results are given in Supplemental Tables 1 and 3, and shRNA sequences are listed in Supplemental Tables 2 and 3. Specific shRNA-mediated mRNA and protein knockdown was documented (Fig. 2C; Supplemental Fig. 6A, 15). IKK reporter lines were engineered to express an IκBα-Photinus luciferase fusion and Renilla luciferase27 (link). TIRF imaging of the BCR was based on previously described techniques12 .
Tumor biopsies were obtained prior to treatment from patients with de novo DLBCL28 (link), gastric MALT lymphoma, and Burkitt’s lymphoma. All samples were studied according to a protocol approved by the National Cancer Institute Institutional Review Board.

Davis R.E., Ngo V.N., Lenz G., Tolar P., Young R., Romesser P.B., Kohlhammer H., Lamy L., Zhao H., Yang Y., Xu W., Shaffer A.L., Wright G., Xiao W., Powell J., Jiang J.K., Thomas C.J., Rosenwald A., Ott G., Muller-Hermelink H.K., Gascoyne R.D., Connors J.M., Rimsza L.M., Campo E., Jaffe E.S., Delabie J., Smeland E.B., Fisher R.I., Braziel R.M., Tubbs R.R., Cook J.R., Weisenburger D.D., Chan W.C., Pierce S.K, & Staudt L.M. (2010). Chronic Active B Cell Receptor Signaling in Diffuse Large B Cell Lymphoma. Nature, 463(7277), 88-92.

Publication 2010

alpha, NF-KappaB Inhibitor Biological Assay Biopsy Burkitt Lymphoma cDNA Library Cell Lines DNA, Complementary Ethics Committees, Research Gastric lymphoma luciferase, Photinus Neoplasms Patients Proteins Retroviridae RNA, Messenger RNA Interference Sea Pansy Short Hairpin RNA Tetracycline

Cell Culture and Transfection Protocols

Cited 10 times

HEK293T cells (ATCC; CRL-11268) were maintained in DMEM medium (Invitrogen) supplemented with 10% FBS (Premium Select; Atlanta Biologicals), GlutaMAX (Invitrogen), and PSG (penicillin, streptomycin, and L-glutamine, Invitrogen). Human Raji Burkitt lymphoma cells (ATCC; CCL-86) were transduced with concentrated RIEP retroviral particles (plasmid was kindly provided by C. Miething, Uniklinikum Freiburg, Freiburg in Breisgau, Germany) in the presence of Ecotropic Receptor Booster (Clontech). Cells were then selected with puromycin (1μg/ml) and maintained in RPMI 1640 media supplemented with 10% FBS and PSG. All cell lines procured from ATCC were characterized by Short Tandem Repeat (STR) profiling. NSCLC A549-pBABE and A549-LKB1 cells were previously characterized and kindly provided by Dr. Frederic J. Kaye (31 (link)) and maintained in RPMI 1640 media supplemented with 10% FBS and PSG antibiotics. Mouse lymphoma cell lines were generated by crossing the Eu-Myc transgenic mouse with the Rosa(26 (link))rtTA transgenic mouse (JAX#006965). At the age of 8 weeks, offspring carrying Eu-Myc and Rosa(26 (link))rtTA alleles were closely monitored for tumor development. Lymphomas were then harvested and homogenized in PBS with 10% FBS and the erythrocytes were lysed. The cells were then filtered through a 40 μm nylon filter and plated in 45% IMDM (with 25 mM HEPES) (GIBCO), 45% DMEM (high glucose, GIBCO), 10% FBS with 1% penicillin/streptomycin, 4 mM L-glutamine, 25 μM β-mercaptoethanol, 1× sodium pyruvate and 10 ng/ml mouse IL7 (R&D Systems). The cells were passaged several times to create a stably growing cell line. All cells were cultured in standard, humidified conditions (37°C, 5% CO₂).
Transfection of HEK293T cells for luciferase assays was carried out using Lipofectamine 2000 (Life Technologies). Luminescence was measured 24 hr post-transfection on an Envision plate reader (Perkin Elmer) following addition of Nano-Glo Luciferase Assay Substrate (Promega).

Schaub F.X., Reza M.S., Flaveny C.A., Li W., Musicant A.M., Hoxha S., Guo M., Cleveland J.L, & Amelio A.L. (2015). Fluorophore-NanoLuc BRET Reporters Enable Sensitive In Vivo Optical Imaging and Flow Cytometry for Monitoring Tumorigenesis. Cancer research, 75(23), 5023-5033.

Publication 2015

2-Mercaptoethanol A549 Cells Alleles Antibiotics Biological Assay Biological Factors Burkitt Lymphoma Cell Lines Cells Erythrocytes Glucose Glutamine HEPES Homo sapiens lipofectamine 2000 Luciferases Luminescence Lymphoma Mice, Transgenic Mus Neoplasms Non-Small Cell Lung Carcinoma Nylons Penicillins Plasmids Promega Puromycin Pyruvate Retroviridae Rosa Secondary Immunization Short Tandem Repeat Sodium STK11 protein, human Streptomycin Transfection

Comprehensive Human Body Atlas Protocol

Cited 10 times

The nine data sets used to compare the gene-set activation metrics were selected from eight studies in the GEO database. Each data set contained two relatively homogeneous subsets of samples. One study (GDS1329) provided two data sets. These subsets consisted of a baseline type and pathological samples or, in some cases, two different but related disease types. (Samples not in either subset were omitted from the comparisons.) We treated these single-channel data sets as ratio data sets by computing the median for each gene over all the baseline samples and dividing all expression values by the corresponding median and taking the base-10 logarithm. For each data set, Table 1 contains the GEO identifier and nature and sizes (in parentheses) of the two sample subgroups. In each data set the samples in Subgroup 1 constitute the baseline set.
To create the human body atlas, oligonucleotide probes were placed at each exon-exon junction of 11,138 RefSeq transcripts [35 (link)]. Purchased mRNA from 44 tissues in normal physiological state, pooled from multiple individuals, and 8 cell lines were amplified and labeled using a full-length amplification protocol and hybridized in duplicate in a two-color dye swap experiment[54 (link)]. In Johnson et al. [35 (link)], six of 52 tissues contained data for only 80% of the genes. For five of these tissues (pancreas, kidney, Burkitt's lymphoma (Raji), lung carcinoma (A549), and melanoma (G361)), new hybridizations were performed here to fill in the missing data. After background normalization, the intensity value of each probe in each tissue was divided by the average intensity across all 52 tissues to determine a ratio, and then the log10 of that ratio used for further analysis. Standard deviations (SDs) for each intensity measurement were calculated using the equation:

SD = \sqrt{a + b * i n t e n s i t y}

where a = 100 and b = 0.2 were empirically derived from individual same-versus-same and same-versus-different hybridization experiments and represent single-hybridization, single-probe estimates of background (a) and fractional error (b). As we used multiple probes per gene and two hybridizations per sample pair (a dye-swap), final error estimates for gene expression are a combination of both propagation of this model measurement error and variance over the repeat measurements. These error estimates were then propagated to ratio and log10 ratio error estimates (Supplemental Tables T10 and T11 in Additional data file 2). Since the initial array design, NCBI has removed over 300 of the RefSeq transcripts from their databases. After removing these transcripts and any other transcripts currently unmapped to Entrez gene identifiers from our data set, the remaining 10,815 RefSeq transcripts map to 9,982 genes. Finally, using all gene-associated probes, we calculated an error-weighted average of log10 ratios for each gene in each tissue. Probe-level expression data have been deposited in the GEO database [35 (link)] (GSE740), and all gene and pathway expression data are available online [21 ].

Levine D.M., Haynor D.R., Castle J.C., Stepaniants S.B., Pellegrini M., Mao M, & Johnson J.M. (2006). Pathway and gene-set activation measurement from mRNA expression data: the tissue distribution of human pathways. Genome Biology, 7(10), R93.

Publication 2006

Acid Hybridizations, Nucleic Burkitt Lymphoma Cell Lines Chromosome Mapping Exons Gene Activation Gene Expression Genes Human Body Kidney Lung Cancer Melanoma Multiple Birth Offspring Oligonucleotide Probes Pancreas physiology RNA, Messenger Tissues

Most recents protocols related to «Burkitt Lymphoma»

Retrospective Analysis of Mature B-cell Neoplasms

Flow cytometric results from patients who were diagnosed with mature B-cell neoplasms from October 2015 to October 2020, were reviewed retrospectively. Each case represented a primary diagnosis of lymphoma that was made based on an incisional or excisional tissue biopsy or fine-needle aspiration biopsy specimens. Histologic slides including immunohistochemical slides, were reviewed without knowledge of the flow cytometric results to confirm the initial diagnoses in all available cases. The diagnosis was made according to the World Health Organization (WHO) 2008 classification (12 (link)), WHO 2017 classification,and WHO 2022 classification (2 , 3 (link), 13 (link)). These patients included 119 patients with DLBCL, 25 patients with Burkitt lymphoma, 67 patients with MCL, 76 patients with follicular lymphoma (FL), 30 patients with marginal zone lymphoma (MZL), 32 patients with lymphoplasmacytic lymphoma (LPL)/Waldenstrom’s macroglobulinemia (WM), 159 patients with chronic lymphocytic leukemia (CLL)/small lymphocytic lymphoma (SLL), 5 patients with hairy cell leukemia, 4 patients with mucosa-associated lymphoid tissue lymphoma (MALT-L), and 42 patients with transformed lymphoma. For the diagnosis, Ki67 expression in lymphoma cells was detected in the bone marrow, pleural effusion, and ascites or lymph node samples. The present study was approved by the Ethical Committee of Tongji Hospital, Tongji Medical College, and Huazhong University of Science and Technology (permit number TJ-IRB20200716), and all procedures conducted followed the protocols of the Declaration of Helsinki.

Mao X., Li Y., Liu S., He C., Yi S., Kuang D., Xiao M., Zhu L, & Wang C. (2023). Multicolor flow cytometric assessment of Ki67 expression and its diagnostic value in mature B-cell neoplasms. Frontiers in Oncology, 13, 1108837.

Publication 2023

Ascites Aspiration Biopsy, Fine-Needle B-Cell Lymphomas Biopsy Bone Marrow Burkitt Lymphoma Cells Chronic Lymphocytic Leukemia Diagnosis Flow Cytometry Hairy Cell Leukemia Lymphoma Lymphoma, Follicular Mucosa-Associated Lymphoid Tissue Lymphoma Nodes, Lymph Patients Pleural Effusion Tissues Waldenstrom Macroglobulinemia

Characterization of EBV mutant cell lines

All cell lines were maintained in Dulbecco’s modified Eagle’s medium (DMEM) (Gibco) supplemented with 10% fetal bovine serum (FBS) (HyClone Laboratories) and appropriate antibiotics for selection (described below). All cell culture media were supplemented with 200 U/mL penicillin (MP Biomedicals) and 200 mg/mL streptomycin (MP Biomedicals). All cells were grown at 37°C in a 5% CO₂ humidified atmosphere. iD98/HR1 is a fusion between D98 cells and the Burkitt lymphoma cell line P3HR1. Both iD98/HR1 and iD98/HR1 eGFP-H2B cells have been previously described (13 (link)); inducible Z-ER (EBV’s immediate early protein Zta fused to the estrogen receptor ligand-binding domain) was selected and maintained in these cells with 1 μg/mL puromycin. The following 293 cells infected with mutant EBV BACmids were kind gifts from Ya-Fang Chiu (Chang-Gung University, Taoyuan, Taiwan) and have been previously described (26 (link)): 293/EBV ΔBBLF2/3 (MI-405), 293/EBV ΔBBLF4 (MI-80), 293/EBV ΔBSLF1 (MI-317), and 293/EBV ΔBMRF1 (D28). The following 293 cells infected with mutant EBV BACmids were kind gifts from Eric Johannsen (University of Wisconsin—Madison, USA) and have been previously described (27 (link)): 293/EBV ΔoriLyt and 293/EBV ΔBALF2/HA-BcRF1. The cell lines 293/EBV ΔBALF5, 293/EBV ΔBMLF1, and 293/EBV ΔBGLF4 were obtained from Eric Johannsen. The cell lines 293/EBV ΔBVLF1 and 293/EBV ΔBcRF1 were generated using EBV mutant BACmids obtained from Eric Johannsen and have been previously described (27 (link)). EBV-positive 293 cells were selected and maintained with 200 μg/mL hygromycin B.

Rosemarie Q., Kirschstein E, & Sugden B. (2023). How Epstein-Barr Virus Induces the Reorganization of Cellular Chromatin. mBio, 14(1), e02686-22.

Publication 2023

Antibiotics Atmosphere Burkitt Lymphoma Cell Culture Techniques Cell Lines Cells Culture Media Eagle estrogen receptor alpha, human Fetal Bovine Serum Fusions, Cell Gifts Hygromycin B Immediate-Early Proteins Ligands Penicillins Puromycin Streptomycin

In vivo Evaluation of NAMPT Inhibitors

The new NAMPT inhibitors (JJ08, FEI191 and FEI199 (in comparison with lead compound, FK866 (APO866))) were evaluated in vivo in a mouse xenograft model of human Burkitt lymphoma. Twenty non-leaky C.B.-17 SCID mice (8 to 10 weeks old; Iffa Credo, L’Arbresle, France) were housed in micro-isolator cages in a specific pathogen-free room in the animal facility at the University Hospital of Lausanne. Firstly, the mice spent one week alone to acclimatize to their new environment. All animals were handled according to the institutional regulations and with the prior approval of the animal ethic committee of the University of Lausanne. Manipulations were performed in sterile conditions under a laminar flow hood. Firstly, Namalwa cells (1 × 10⁷) were injected subcutaneously into the mouse flank side. Once the tumors became palpable and reached a size between 100 and 150 mm³, mice (n = five/ group) were randomized into control and treated groups. The drugs were administered intraperitoneally (10 mg/kg body weight) in 200 µL 0.9% saline twice a day for 4 days, repeated weekly over 3 weeks. The control group was treated only with 200 µL 0.9% saline. Every day, the animals were monitored for any signs of illness, and in cases where the tumor size reached a diameter of 15 mm, they were sacrificed immediately.

Biniecka P., Matsumoto S., Belotti A., Joussot J., Bai J.F., Majjigapu S.R., Thoueille P., Spaggiari D., Desfontaine V., Piacente F., Bruzzone S., Cea M., Decosterd L.A., Vogel P., Nencioni A., Duchosal M.A, & Nahimana A. (2023). Anticancer Activities of Novel Nicotinamide Phosphoribosyltransferase Inhibitors in Hematological Malignancies. Molecules, 28(4), 1897.

Publication 2023

Animals APO 866 Body Weight Burkitt Lymphoma Cells Ethics Committees Heterografts Homo sapiens inhibitors Mus Neoplasms nicotinamide phosphoribosyltransferase, human Normal Saline Pharmaceutical Preparations SCID Mice Specific Pathogen Free Sterility, Reproductive

Culturing Hematological Cell Lines

Four hematological cell lines (ML2—acute myeloid leukemia; Jurkat—acute lymphoblastic leukemia; Namalwa—Burkitt lymphoma; and RPMI8226—multiple myeloma) were purchased from DSMZ (German Collection of Microorganisms and Cell Cultures, Braunschweig, Germany) or ATCC.
All cells were cultured in RPMI medium (Invitrogen AG, 61870-01) supplemented with 10% heat inactivated fetal calf serum (Amimed, 2-01F30-I) and 1% penicillin/streptomycin at 37 °C (Amimed, 4-01F00-H) in a humidified atmosphere of 95% air and 5% CO₂.

Publication 2023

Atmosphere Burkitt Lymphoma Cell Culture Techniques Cell Lines Cells Culture Media Fetal Bovine Serum Leukemia, Myelocytic, Acute Multiple Myeloma Penicillins Precursor Cell Lymphoblastic Leukemia Lymphoma Streptomycin

Predicting Factors for Lymphoma in Pediatric Patients

We collected data from electronic patient files. Two different researchers (E.A.M.Z. and L.A.W.) collected data independently. Uncertainties were discussed together and, if necessary, with the other researchers (A.B., R.J.N.) to reach a consensus. Due to the retrospective design, no standard formats were used for the description of the investigated variables. For example, involvement of the body regions was scored based on the radiology reports. Sometimes, one of the investigators was not certain how to interpret the radiology reports. This was discussed with the colleagues, and if necessary, the radiology investigations were re-analyzed by the involved radiologist of our study.
Data were processed anonymously and encrypted.
We identified predicting factors for lymphoma based on an extensive search of the literature using PubMed, Medline, and Embase. We searched for studies using Medical Subject Heading terms including “lymphadenopathy”, “child”, “adolescent”, and “lymphoma”. An overview of potential predicting factors based on this search of the literature and their results are given in Table S1 [8 (link),31 (link),32 (link),33 (link),34 (link),35 (link),36 (link),37 (link),38 (link),46 ,47 (link),48 (link),49 (link),50 (link),51 (link),52 (link)]. We identified 39 potential predictors and included these in our univariate analyses: age, gender, presence of B-symptoms, 11 laboratory parameters including TARC, and several imaging findings. These variables and their definitions are listed in Table S2.
The body regions of the involved areas were scored individually. An overview of the separately scored anatomical body regions and an explanation is provided in Table S3.
We used pathology reports primarily for defining the diagnosis; 158 out of 182 patients underwent biopsy, including all cases of lymphoma. Twenty-four patients were diagnosed without a biopsy, but based on clinical, radiological, microbiological, and laboratory results (twenty infectious/reactive lymphadenopathy, one venous malformation, one lymphangioma, one branchiogenic cyst, and one dermoid cyst).
We categorized the patients into 12 groups according to their diagnosis. The malignant diagnoses in the study population included: cHL, NLPHL, ALCL, primary mediastinal large B-cell lymphoma (PMBCL), diffuse large B-cell lymphoma (DLBCL), Burkitt lymphoma (BL), T-LBL, B-cell lymphoblastic lymphoma (B-LBL), and other malignancies (Langerhans cell histiocytosis (LCH)). Furthermore, there were three groups with benign causes of lymphadenopathy: reactive or infectious lymphadenopathy, progressive transformation of germinal centers (PTGC), and other non-malignant causes.
For the identification of predictive factors, we divided the outcome into the benign group and the malignant group for univariate analysis. However, the malignant group contained nine different diagnoses, which differ significantly in incidence and clinical presentation. Therefore, we subdivided the group into five categories for multivariate analysis: cHL, NLPHL, NHL, other malignancies, and the benign group. In brief, we collected data from electronic patient files. We identified 39 potential predictors based on an extensive search of the literature. We used pathology reports for defining the diagnosis.

Zijtregtop E.A., Winterswijk L.A., Beishuizen T.P., Zwaan C.M., Nievelstein R.A., Meyer-Wentrup F.A, & Beishuizen A. (2023). Machine Learning Logistic Regression Model for Early Decision Making in Referral of Children with Cervical Lymphadenopathy Suspected of Lymphoma. Cancers, 15(4), 1178.

Publication 2023

Adolescent B-Cell Lymphomas Biopsy Body Regions Burkitt Lymphoma CD30+ Anaplastic Large Cell Lymphoma Child Congenital Abnormality Cyst Dermoid Cyst Diagnosis Differential Diagnosis Diffuse Large B-Cell Lymphoma Gender Histiocytosis, Langerhans-Cell Infection Lymphadenopathy Lymphangioma Lymphoma Malignant Neoplasms Mediastinum Outpatients Patients Radiologist Tests, Diagnostic Veins X-Rays, Diagnostic

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More about "Burkitt Lymphoma"

Burkitt lymphoma is a highly aggressive form of non-Hodgkin's lymphoma, a type of blood cancer that originates from B cells.
This fast-growing malignancy is characterized by rapidly proliferating tumors, often located in the abdomen, head, and neck regions.
Prompt treatment is crucial due to the disease's swift progression.
PubCompare.ai is an innovative AI-powered tool that optimizes Burkitt lymphoma research by enhancing reproducibility and accuracy.
This platform allows researchers to locate relevant protocols from the literature, pre-prints, and patents, while performing intelligent comparisons to identify the most effective protocols and products.
This improves research efficiency and supports advancements in the understanding and treatment of this challenging disease.
In cell culture studies, Burkitt lymphoma cells are commonly grown in media such as RPMI 1640, supplemented with fetal bovine serum (FBS), L-glutamine, and antibiotics like penicillin and streptomycin.
The Daudi cell line is a well-established Burkitt lymphoma model, while DMEM medium is also used for some Burkitt lymphoma research.
By leveraging the capabilities of PubCompare.ai, researchers can optimize their experimental approaches and accelerate progress in the fight against this aggressive form of non-Hodgkin's lymphoma.