Log In Sign up
The largest database of trusted experimental protocols
> Disorders > Neoplastic Process > Hematopoietic Neoplasms

Hematopoietic Neoplasms

Hematopoietic Neoplasms are a diverse group of cancers affecting blood, bone marrow, and lymph node tissues.
These malignancies arise from abnormal growth and proliferation of hematopoietic cells, including leukemias, lymphomas, and myelodysplastic syndromes.
Careful study of hematopoietic neoplasms is crucial for developing effective treatments and improving patient outcomes.
PubCompare.ai offers a cutting-edge solution to streamline this critical research process, providing AI-powered protocol comparisons and product recommendations from the latest literature, preprints, and patents.
Experiance the future of hematopoietic neoplasm research today with this innovative tool.

Most cited protocols related to «Hematopoietic Neoplasms»

1
Global Burden of Disease Cause Hierarchy
Cited 43 times
The GBD study attributes each death to a single underlying cause that began the series of events leading to death, in accordance with ICD principles. The GBD study organises causes of death in a hierarchical list containing four levels (appendix 1 section 7). At the highest level (Level 1), all disease burden is divided among three mutually exclusive and collectively exhaustive categories: communicable, maternal, neonatal, and nutritional (CMNN) diseases; non-communicable diseases (NCDs); and injuries. Level 2 distinguishes these Level 1 categories into 21 cause groups, such as cardiovascular diseases; diarrhoeal diseases, lower respiratory infections (LRIs), and other common infectious diseases; or transport injuries. Level 3 disaggregates these causes further; in most cases this disaggregation represents the finest level of detail by cause, such as stroke, ischaemic heart disease, or road injuries. Where data are sufficiently available or specific policy relevance has been sought, selected causes are further disaggregated at Level 4, such as drug-susceptible tuberculosis, multidrug-resistant tuberculosis without extensive drug resistance, and extensively drug-resistant tuberculosis. For GBD 2017, the cause hierarchy was further refined to separately estimate causes with substantial policy interest or high levels of burden. Specific changes included separate estimation of non-rheumatic calcific aortic and degenerative mitral valve diseases, and myelodysplastic, myeloproliferative, and other haemopoietic neoplasms, resulting in a reduction in the estimates of some residual causes. Disaggregation of residual causes also allowed separate estimation of type 1 and type 2 diabetes, chronic kidney disease due to type 1 and type 2 diabetes, poisoning by carbon monoxide, liver cancer due to non-alcoholic steatohepatitis (NASH), subarachnoid haemorrhage, ectopic pregnancy, and invasive non-typhoidal salmonella. Maternal and neonatal disorders, previously estimated as separate cause groupings at Level 2 of the hierarchy, were estimated for GBD 2017 at Level 3 of the hierarchy, and then aggregated up to Level 2 to better capture the epidemiological connections and linked burden between them. The complete hierarchy of causes included in GBD 2017 and their corresponding ICD9 and ICD10 codes are described in appendix 1 (section 7).
Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980–2017: a systematic analysis for the Global Burden of Disease Study 2017. (2018). Lancet (London, England), 392(10159), 1736-1788.
Publication 2018
Aorta Cancer of Liver Carbon Monoxide Poisoning Cardiovascular Diseases Cerebrovascular Accident Chronic Kidney Diseases Communicable Diseases Diabetes Mellitus, Non-Insulin-Dependent Diarrhea Ectopic Pregnancy Extensively Drug-Resistant Tuberculosis Hematopoietic Neoplasms Infant, Newborn Injuries Mitral Valve Mothers Myocardial Ischemia Neonatal Diseases Nonalcoholic Steatohepatitis Noncommunicable Diseases Nutrition Disorders Pharmaceutical Preparations Resistance, Drug Respiratory Tract Infections Salmonella Subarachnoid Hemorrhage Tuberculosis Tuberculosis, Multidrug-Resistant Typhoid Fever
2
Global Burden of Disease Cancer Estimates
Cited 28 times
Methods have remained similar to the GBD 2016 study.5 (link) Detailed descriptions of the methods can be found in the GBD 2017 publications6 (link),7 (link),8 (link),9 (link) as well as in the eAppendix, eFigures, and eTables in the Supplement. For each GBD study, the entire time series is re-estimated. This study therefore supersedes prior GBD iterations. The GBD study is compliant with the Guidelines for Accurate and Transparent Health Estimates Reporting statement (eTable 1 in the Supplement). Compared with the prior GBD study (GBD 2016), the neoplasms category for GBD 2017 also includes benign and in situ neoplasms (International Statistical Classification of Diseases and Related Health Problems, Tenth Revision [ICD-10] codes D00-D49). Because disability associated with benign neoplasms is most often very small, we only estimated disability for the new cause: myelodysplastic, myeloproliferative, and other hematopoietic neoplasms. The terms malignant neoplasms or cancer in this article only include ICD-10 codes C00 through C96. Other changes since GBD 2016 are the addition of new data sources (eTable 3 in the Supplement) for GBD 2017 and improvements in the way we estimated cancer survival by using the mortality-to-incidence ratio (MIR). In this study, estimates are presented for 29 cancer categories and 195 countries and territories. Estimates for benign neoplasms as well as selected subnational estimates are available online (https://vizhub.healthdata.org/gbd-compare/ and http://ghdx.healthdata.org/gbd-results-tool). All rates are reported per 100 000 person-years. The GBD world population standard was used for the calculation of age-standardized rates.9 (link) We report 95% uncertainty intervals for all estimates.
Fitzmaurice C., Abate D., Abbasi N., Abbastabar H., Abd-Allah F., Abdel-Rahman O., Abdelalim A., Abdoli A., Abdollahpour I., Abdulle A.S., Abebe N.D., Abraha H.N., Abu-Raddad L.J., Abualhasan A., Adedeji I.A., Advani S.M., Afarideh M., Afshari M., Aghaali M., Agius D., Agrawal S., Ahmadi A., Ahmadian E., Ahmadpour E., Ahmed M.B., Akbari M.E., Akinyemiju T., Al-Aly Z., AlAbdulKader A.M., Alahdab F., Alam T., Alamene G.M., Alemnew B.T., Alene K.A., Alinia C., Alipour V., Aljunid S.M., Bakeshei F.A., Almadi M.A., Almasi-Hashiani A., Alsharif U., Alsowaidi S., Alvis-Guzman N., Amini E., Amini S., Amoako Y.A., Anbari Z., Anber N.H., Andrei C.L., Anjomshoa M., Ansari F., Ansariadi A., Appiah S.C., Arab-Zozani M., Arabloo J., Arefi Z., Aremu O., Areri H.A., Artaman A., Asayesh H., Asfaw E.T., Ashagre A.F., Assadi R., Ataeinia B., Atalay H.T., Ataro Z., Atique S., Ausloos M., Avila-Burgos L., Avokpaho E.F., Awasthi A., Awoke N., Ayala Quintanilla B.P., Ayanore M.A., Ayele H.T., Babaee E., Bacha U., Badawi A., Bagherzadeh M., Bagli E., Balakrishnan S., Balouchi A., Bärnighausen T.W., Battista R.J., Behzadifar M., Behzadifar M., Bekele B.B., Belay Y.B., Belayneh Y.M., Berfield K.K., Berhane A., Bernabe E., Beuran M., Bhakta N., Bhattacharyya K., Biadgo B., Bijani A., Bin Sayeed M.S., Birungi C., Bisignano C., Bitew H., Bjørge T., Bleyer A., Bogale K.A., Bojia H.A., Borzì A.M., Bosetti C., Bou-Orm I.R., Brenner H., Brewer J.D., Briko A.N., Briko N.I., Bustamante-Teixeira M.T., Butt Z.A., Carreras G., Carrero J.J., Carvalho F., Castro C., Castro F., Catalá-López F., Cerin E., Chaiah Y., Chanie W.F., Chattu V.K., Chaturvedi P., Chauhan N.S., Chehrazi M., Chiang P.P., Chichiabellu T.Y., Chido-Amajuoyi O.G., Chimed-Ochir O., Choi J.Y., Christopher D.J., Chu D.T., Constantin M.M., Costa V.M., Crocetti E., Crowe C.S., Curado M.P., Dahlawi S.M., Damiani G., Darwish A.H., Daryani A., das Neves J., Demeke F.M., Demis A.B., Demissie B.W., Demoz G.T., Denova-Gutiérrez E., Derakhshani A., Deribe K.S., Desai R., Desalegn B.B., Desta M., Dey S., Dharmaratne S.D., Dhimal M., Diaz D., Dinberu M.T., Djalalinia S., Doku D.T., Drake T.M., Dubey M., Dubljanin E., Duken E.E., Ebrahimi H., Effiong A., Eftekhari A., El Sayed I., Zaki M.E., El-Jaafary S.I., El-Khatib Z., Elemineh D.A., Elkout H., Ellenbogen R.G., Elsharkawy A., Emamian M.H., Endalew D.A., Endries A.Y., Eshrati B., Fadhil I., Fallah V., Faramarzi M., Farhangi M.A., Farioli A., Farzadfar F., Fentahun N., Fernandes E., Feyissa G.T., Filip I., Fischer F., Fisher J.L., Force L.M., Foroutan M., Freitas M., Fukumoto T., Futran N.D., Gallus S., Gankpe F.G., Gayesa R.T., Gebrehiwot T.T., Gebremeskel G.G., Gedefaw G.A., Gelaw B.K., Geta B., Getachew S., Gezae K.E., Ghafourifard M., Ghajar A., Ghashghaee A., Gholamian A., Gill P.S., Ginindza T.T., Girmay A., Gizaw M., Gomez R.S., Gopalani S.V., Gorini G., Goulart B.N., Grada A., Ribeiro Guerra M., Guimaraes A.L., Gupta P.C., Gupta R., Hadkhale K., Haj-Mirzaian A., Haj-Mirzaian A., Hamadeh R.R., Hamidi S., Hanfore L.K., Haro J.M., Hasankhani M., Hasanzadeh A., Hassen H.Y., Hay R.J., Hay S.I., Henok A., Henry N.J., Herteliu C., Hidru H.D., Hoang C.L., Hole M.K., Hoogar P., Horita N., Hosgood H.D., Hosseini M., Hosseinzadeh M., Hostiuc M., Hostiuc S., Househ M., Hussen M.M., Ileanu B., Ilic M.D., Innos K., Irvani S.S., Iseh K.R., Islam S.M., Islami F., Jafari Balalami N., Jafarinia M., Jahangiry L., Jahani M.A., Jahanmehr N., Jakovljevic M., James S.L., Javanbakht M., Jayaraman S., Jee S.H., Jenabi E., Jha R.P., Jonas J.B., Jonnagaddala J., Joo T., Jungari S.B., Jürisson M., Kabir A., Kamangar F., Karch A., Karimi N., Karimian A., Kasaeian A., Kasahun G.G., Kassa B., Kassa T.D., Kassaw M.W., Kaul A., Keiyoro P.N., Kelbore A.G., Kerbo A.A., Khader Y.S., Khalilarjmandi M., Khan E.A., Khan G., Khang Y.H., Khatab K., Khater A., Khayamzadeh M., Khazaee-Pool M., Khazaei S., Khoja A.T., Khosravi M.H., Khubchandani J., Kianipour N., Kim D., Kim Y.J., Kisa A., Kisa S., Kissimova-Skarbek K., Komaki H., Koyanagi A., Krohn K.J., Bicer B.K., Kugbey N., Kumar V., Kuupiel D., La Vecchia C., Lad D.P., Lake E.A., Lakew A.M., Lal D.K., Lami F.H., Lan Q., Lasrado S., Lauriola P., Lazarus J.V., Leigh J., Leshargie C.T., Liao Y., Limenih M.A., Listl S., Lopez A.D., Lopukhov P.D., Lunevicius R., Madadin M., Magdeldin S., El Razek H.M., Majeed A., Maleki A., Malekzadeh R., Manafi A., Manafi N., Manamo W.A., Mansourian M., Mansournia M.A., Mantovani L.G., Maroufizadeh S., Martini S.M., Mashamba-Thompson T.P., Massenburg B.B., Maswabi M.T., Mathur M.R., McAlinden C., McKee M., Meheretu H.A., Mehrotra R., Mehta V., Meier T., Melaku Y.A., Meles G.G., Meles H.G., Melese A., Melku M., Memiah P.T., Mendoza W., Menezes R.G., Merat S., Meretoja T.J., Mestrovic T., Miazgowski B., Miazgowski T., Mihretie K.M., Miller T.R., Mills E.J., Mir S.M., Mirzaei H., Mirzaei H.R., Mishra R., Moazen B., Mohammad D.K., Mohammad K.A., Mohammad Y., Darwesh A.M., Mohammadbeigi A., Mohammadi H., Mohammadi M., Mohammadian M., Mohammadian-Hafshejani A., Mohammadoo-Khorasani M., Mohammadpourhodki R., Mohammed A.S., Mohammed J.A., Mohammed S., Mohebi F., Mokdad A.H., Monasta L., Moodley Y., Moosazadeh M., Moossavi M., Moradi G., Moradi-Joo M., Moradi-Lakeh M., Moradpour F., Morawska L., Morgado-da-Costa J., Morisaki N., Morrison S.D., Mosapour A., Mousavi S.M., Muche A.A., Muhammed O.S., Musa J., Nabhan A.R., Naderi M., Nagarajan A.J., Nagel G., Nahvijou A., Naik G., Najafi F., Naldi L., Nam H.S., Nasiri N., Nazari J., Negoi I., Neupane S., Newcomb P.A., Nggada H.A., Ngunjiri J.W., Nguyen C.T., Nikniaz L., Ningrum D.N., Nirayo Y.L., Nixon M.R., Nnaji C.A., Nojomi M., Nosratnejad S., Shiadeh M.N., Obsa M.S., Ofori-Asenso R., Ogbo F.A., Oh I.H., Olagunju A.T., Olagunju T.O., Oluwasanu M.M., Omonisi A.E., Onwujekwe O.E., Oommen A.M., Oren E., Ortega-Altamirano D.D., Ota E., Otstavnov S.S., Owolabi M.O., P A M., Padubidri J.R., Pakhale S., Pakpour A.H., Pana A., Park E.K., Parsian H., Pashaei T., Patel S., Patil S.T., Pennini A., Pereira D.M., Piccinelli C., Pillay J.D., Pirestani M., Pishgar F., Postma M.J., Pourjafar H., Pourmalek F., Pourshams A., Prakash S., Prasad N., Qorbani M., Rabiee M., Rabiee N., Radfar A., Rafiei A., Rahim F., Rahimi M., Rahman M.A., Rajati F., Rana S.M., Raoofi S., Rath G.K., Rawaf D.L., Rawaf S., Reiner R.C., Renzaho A.M., Rezaei N., Rezapour A., Ribeiro A.I., Ribeiro D., Ronfani L., Roro E.M., Roshandel G., Rostami A., Saad R.S., Sabbagh P., Sabour S., Saddik B., Safiri S., Sahebkar A., Salahshoor M.R., Salehi F., Salem H., Salem M.R., Salimzadeh H., Salomon J.A., Samy A.M., Sanabria J., Santric Milicevic M.M., Sartorius B., Sarveazad A., Sathian B., Satpathy M., Savic M., Sawhney M., Sayyah M., Schneider I.J., Schöttker B., Sekerija M., Sepanlou S.G., Sepehrimanesh M., Seyedmousavi S., Shaahmadi F., Shabaninejad H., Shahbaz M., Shaikh M.A., Shamshirian A., Shamsizadeh M., Sharafi H., Sharafi Z., Sharif M., Sharifi A., Sharifi H., Sharma R., Sheikh A., Shirkoohi R., Shukla S.R., Si S., Siabani S., Silva D.A., Silveira D.G., Singh A., Singh J.A., Sisay S., Sitas F., Sobngwi E., Soofi M., Soriano J.B., Stathopoulou V., Sufiyan M.B., Tabarés-Seisdedos R., Tabuchi T., Takahashi K., Tamtaji O.R., Tarawneh M.R., Tassew S.G., Taymoori P., Tehrani-Banihashemi A., Temsah M.H., Temsah O., Tesfay B.E., Tesfay F.H., Teshale M.Y., Tessema G.A., Thapa S., Tlaye K.G., Topor-Madry R., Tovani-Palone M.R., Traini E., Tran B.X., Tran K.B., Tsadik A.G., Ullah I., Uthman O.A., Vacante M., Vaezi M., Varona Pérez P., Veisani Y., Vidale S., Violante F.S., Vlassov V., Vollset S.E., Vos T., Vosoughi K., Vu G.T., Vujcic I.S., Wabinga H., Wachamo T.M., Wagnew F.S., Waheed Y., Weldegebreal F., Weldesamuel G.T., Wijeratne T., Wondafrash D.Z., Wonde T.E., Wondmieneh A.B., Workie H.M., Yadav R., Yadegar A., Yadollahpour A., Yaseri M., Yazdi-Feyzabadi V., Yeshaneh A., Yimam M.A., Yimer E.M., Yisma E., Yonemoto N., Younis M.Z., Yousefi B., Yousefifard M., Yu C., Zabeh E., Zadnik V., Moghadam T.Z., Zaidi Z., Zamani M., Zandian H., Zangeneh A., Zaki L., Zendehdel K., Zenebe Z.M., Zewale T.A., Ziapour A., Zodpey S, & Murray C.J. (2019). Global, Regional, and National Cancer Incidence, Mortality, Years of Life Lost, Years Lived With Disability, and Disability-Adjusted Life-Years for 29 Cancer Groups, 1990 to 2017: A Systematic Analysis for the Global Burden of Disease Study. JAMA Oncology, 5(12), 1749-1768.
Publication 2019
Benign Neoplasm Dietary Supplements Disabled Persons Hematopoietic Neoplasms Malignant Neoplasms Neoplasms
3
Comprehensive Cancer Sample Profiling
Cited 13 times
We tested 250 primary cancer samples spanning 26 human malignancies, which included: lung cancer (n = 87), breast cancer (n = 33), colorectal cancer (n = 30), pancreatic cancer (n = 23), prostate cancer (n = 20), melanoma (n = 11), chronic myeloproliferative disease (n = 10), cholangiocarcinoma (n = 6), gastric cancer (n = 4), ovarian cancer (n = 3), salivary gland cancer (n = 3) and thyroid cancer (n = 3) among others. Sixty-two of these primary tumour samples were evaluated for official clinical testing, and included 52 lung adenocarcinomas, most of them small core biopsies with very limited tissue. For haematopoietic malignancies, spare DNA that had been previously extracted from patient blood for clinical testing was obtained from the Massachusetts General Hospital (MGH) Molecular Diagnostics Laboratory. For solid tumours, formalin-fixed paraffin-embedded (FFPE) tumour blocks were obtained from the MGH archives. All samples were collected with institutional review board approval. Histological examination of haematoxylin and eosin-stained slides derived from FFPE samples was performed by a pathologist (AJI) and assessed for the presence of tumour. Available tumour tissue was manually macrodissected from serial 5 µm unstained sections, or cored from the paraffin block using a 1.5 mm dermal punch. Total nucleic acid was extracted from FFPE material using a modified FormaPure System (Agencourt Bioscience Corporation, Beverly, MA) on a custom Beckman Coulter Biomek NXP workstation. Blood-derived DNA was extracted using the QIAamp Blood kit (Qiagen, Inc., Valencia, CA).
Dias-Santagata D., Akhavanfard S., David S.S., Vernovsky K., Kuhlmann G., Boisvert S.L., Stubbs H., McDermott U., Settleman J., Kwak E.L., Clark J.W., Isakoff S.J., Sequist L.V., Engelman J.A., Lynch T.J., Haber D.A., Louis D.N., Ellisen L.W., Borger D.R, & Iafrate A.J. (2010). Rapid targeted mutational analysis of human tumours: a clinical platform to guide personalized cancer medicine. EMBO Molecular Medicine, 2(5), 146-158.
Publication 2010
Adenocarcinoma of Lung BLOOD Cancer of Salivary Gland Carcinoma, Thyroid Cholangiocarcinoma Colorectal Carcinoma Core Needle Biopsy Disease, Chronic Eosin Ethics Committees, Research Formalin Gastric Cancer Hematopoietic Neoplasms Hematoxylin Homo sapiens Lung Cancer Malignant Neoplasm of Breast Malignant Neoplasms Melanoma Molecular Diagnostics Neoplasms Nucleic Acids Ovarian Cancer Pancreatic Cancer Paraffin Paraffin Embedding Pathologists Patients Prostate Cancer Tissues
4
Stratified Survival Analysis of Atomic Bomb Survivors
Cited 8 times
The analyses were based on a highly stratified table of person-time and numbers of cases by city (Hiroshima or Nagasaki), sex (male or female), age at exposure (14 five-year categories from 0 to 69 and one of ≥70), attained age (15 five-year categories from 10 to 84 and one of ≥85–<110), time period of cancer diagnosis [13 categories: 1958–1960, 1961–1965, 1966–1970, 1971–1975, 1976–1980, 1981–1985, 1986–1987, 1988–1990, 1991–1995, 1996–1998 (cutoff for the previously reported study), 1999–2000, 2001–2004 and 2005–2009], NIC status (>10,000 m from the hypocenter), DS02R1 weighted absorbed colon dose (22 categories with dose cutoff points at 0, 0.005, 0.02, 0.04, 0.06, 0.08, 0.1, 0.125, 0.150, 0.175, 0.2, 0.25, 0.3, 0.5, 0.75, 1, 1.25, 1.5, 1.75, 2, 2.5 and 3 Gy) and an indicator of high dose (unweighted gamma plus neutron shielded kerma >4 Gy).
Further time-dependent stratification was also performed for smoking. Smoking history was considered unknown for all cohort members prior to the time they first provided smoking history information. Individual smoking histories were considered as known thereafter. In addition to smoking status categories, the smoking data were stratified by average cigarettes per day (seven categories with cutoff points at 0, >0, 7.5, 12.5, 17.5, 22.5, 27.5), duration (6 categories with cutoff points at 0, >0, 5, 10, 20 and 30 years) and years since quitting (5 categories with cutoff points at 0, >0, 5, 10 and 15). Approximately 40% of the person-years and 60% of the cases in these analyses were accumulated after ascertainment of smoking status. For those with smoking history information, smoking status (never, current or past smoker) was considered to remain unchanged from the latest survey on which they provided information until the end of follow-up. Males who did not provide smoking information were analyzed in an “unknown” category while females who did not provide information were considered nonsmokers due to the high prevalence of smoking among males and the low prevalence of smoking among females. Almost 70% of the person-years for people with known smoking status were accumulated after the last date at which their smoking status was known. Total pack-years of smoking at the time of the first questionnaire were calculated from the intensity and number of years reportedly smoked. Pack-years accrued with additional years of smoking after the time of the first questionnaire.
Person-years of observation were computed from January 1, 1958 until the earliest date of diagnosis of any cancer (including hematopoietic cancers and cancers diagnosed outside of the catchment area, but excluding in situ and intramucosal colorectal carcinomas), date at which the subject reached 110 years of age, date of death or December 31, 2009, whichever occurred first. Since cancers that were diagnosed outside of the catchment areas could generally not be detected, person-years were adjusted for migration into and out of the catchment areas, as discussed in Appendix B.
Bösl M.R., Takaku K., Oshima M., Nishimura S, & Taketo M.M. (1997). Early embryonic lethality caused by targeted disruption of the mouse selenocysteine tRNA gene (Trsp). Proceedings of the National Academy of Sciences of the United States of America, 94(11), 5531-5534.
Publication 2017
Colon Colorectal Carcinoma Early Diagnosis of Cancer Females Gamma Rays Hematopoietic Neoplasms Males Malignant Neoplasms Non-Smokers Woman
5
Filtering Signature Genes for Tumor Deconvolution
Cited 7 times
We filtered the signature genes that were highly expressed also in tumor cells by discarding the genes having a median log2 expression larger than 7 in all non-hematopoietic cancer cell lines assayed in the Cancer Cell Line Encyclopedia (CCLE) [26 (link)], as done in [17 (link)]. Moreover, RNA-seq data from 8243 TCGA solid tumors were used to remove genes that provide little support for bulk-tissue deconvolution because their expression in tumor samples is generally low or null. More precisely, we discarded the genes having an average expression across all TCGA samples lower than 1 TPM.
Finotello F., Mayer C., Plattner C., Laschober G., Rieder D., Hackl H., Krogsdam A., Loncova Z., Posch W., Wilflingseder D., Sopper S., Ijsselsteijn M., Brouwer T.P., Johnson D., Xu Y., Wang Y., Sanders M.E., Estrada M.V., Ericsson-Gonzalez P., Charoentong P., Balko J., de Miranda N.F, & Trajanoski Z. (2019). Molecular and pharmacological modulators of the tumor immune contexture revealed by deconvolution of RNA-seq data. Genome Medicine, 11, 34.
Publication 2019
Cell Lines Cells Dietary Fiber Genes Hematopoietic Neoplasms Malignant Neoplasms Neoplasms RNA-Seq Tissues

Most recents protocols related to «Hematopoietic Neoplasms»

1
Colocalization of Antibodies in B-cells
Not available on PMC !

Example 4

To determine where 2F2-grafted “humanized” antibodies and antibody variants are delivered upon internalization into the cell, colocalization studies of the anti-CD79b antibodies internalized into B-cell lines may be assessed in Ramos cell lines. LAMP-1 is a marker for late endosomes and lysosomes (Kleijmeer et al., Journal of Cell Biology, 139(3): 639-649 (1997); Hunziker et al., Bioessays, 18:379-389 (1996); Mellman et al., Annu. Rev. Dev. Biology, 12:575-625 (1996)), including MHC class II compartments (MIICs), which is a late endosome/lysosome-like compartment. HLA-DM is a marker for MIICs.

Ramos cells are incubated for 3 hours at 37° C. with 1 μg/ml 2F2-grafted “humanized” antibodies and antibody variants, FcR block (Miltenyi) and 25 μg/ml Alexa647-Transferrin (Molecular Probes) in complete carbonate-free medium (Gibco) with the presence of 10 μg/ml leupeptin (Roche) and 5 μM pepstatin (Roche) to inhibit lysosomal degradation. Cells are then washed twice, fixed with 3% paraformaldehyde (Electron Microscopy Sciences) for 20 minutes at room temperature, quenched with 50 mM NH4Cl (Sigma), permeabilized with 0.4% Saponin/2% FBS/1% BSA for 20 minutes and then incubated with 1 μg/ml Cy3 anti-mouse (Jackson Immunoresearch) for 20 minutes. The reaction is then blocked for 20 minutes with mouse IgG (Molecular Probes), followed by a 30 minute incubation with Image-iT FX Signal Enhancer (Molecular Probes). Cells are finally incubated with Zenon Alexa488-labeled mouse anti-LAMP1 (BD Pharmingen), a marker for both lysosomes and MIIC (a lysosome-like compartment that is part of the MHC class II pathway), for 20 minutes, and post-fixed with 3% PFA. Cells are resuspended in 20 μl saponin buffer and allowed to adhere to poly-lysine (Sigma) coated slides prior to mounting a coverglass with DAPI-containing VectaShield (Vector Laboratories). For immunofluorescence of the MIIC or lysosomes, cells are fixed, permeabilized and enhanced as above, then co-stained with Zenon labeled Alexa555-HLA-DM (BD Pharmingen) and Alexa488-Lamp1 in the presence of excess mouse IgG as per the manufacturer's instructions (Molecular Probes).

Accordingly, colocalization of 2F2-grafted “humanized” antibodies or antibody variants with MIIC or lysosomes of B-cell lines as assessed by immunofluorescence may indicate the molecules as excellent agents for therapy of tumors in mammals, including B-cell associated cancers, such as lymphomas (i.e. Non-Hodgkin's Lymphoma), leukemias (i.e. chronic lymphocytic leukemia), and other cancers of hematopoietic cells.

US11866496B2. Humanized anti-CD79B antibodies and immunoconjugates and methods of use (2024-01-09). Genentech, Inc. [US]. Inventors: Yvonne Chen [US], Mark Dennis [US], Kristi Elkins [US], Jagath Reddy Junutula [US], Andrew Polson [US], Bing Zheng [US].
Patent 2024
Alexa Fluor 647 Anti-Antibodies Antibodies, Monoclonal, Humanized B-Lymphocytes Buffers Carbonates CD79B protein, human Cell Lines Cells Chronic Lymphocytic Leukemia Cloning Vectors DAPI Electron Microscopy Endosomes Genes, MHC Class II Hematopoietic Neoplasms Immunofluorescence Immunoglobulins Leukemia leupeptin Lymphoma Lymphoma, Non-Hodgkin Lysine lysosomal-associated membrane protein 1, human Lysosomes Malignant Neoplasms Mammals Molecular Probes Mus Neoplasms paraform pepstatin Poly A Saponin Therapeutics Transferrin
2
Neoplasm Prevalence in NAME Region
The data were extracted for 21 countries of the NAME region and 21 neoplasm groups (see Supplementary Table 1 for details on each variable). These 21 neoplasm groups comprised 19 specific malignant neoplasm types, as well as “other malignant neoplasms” and “other neoplasms”. “Other neoplasms” included myelodysplastic, myeloproliferative, and other hematopoietic neoplasms, benign and in situ intestinal neoplasms, benign and in situ cervical and uterine neoplasms, and other benign and in situ neoplasms. GBD code for each neoplasm is also mentioned in Supplementary Table 1.
To better understand the countries and their locations in order to better comprehend the figures that are presented as maps, please refer to Supplementary Fig. 1 that depicts a raw map of the NAME region.
Children and adolescents were defined as 0 to 19 years old. The utilized age subgroups based on the GBD data were: early neonatal (0–6 days), late neonatal (7–27 days), post-neonatal (28–364 days), 1–4 years, 5–9 years, 10–14 years, and 15–19 years.
Karimi A., Saeedi Moghaddam S., Azadnajafabad S., Esfahani Z., Sharifnejad Tehrani Y., Abbasi-Kangevari M., Shobeiri P., Ghamari S.H., Masinaei M., Rezaei N., Shahin S., Rayzan E., Rezaei N., Larijani B, & Kompani F. (2023). The burden of childhood and adolescent cancers in North Africa and the Middle East (NAME) region: findings from the Global Burden of Disease study 2019. BMC Pediatrics, 23, 113.
Publication 2023
Adolescent Child Hematopoietic Neoplasms Infant, Newborn Intestinal Neoplasms Malignant Neoplasms Microtubule-Associated Proteins Neck Neoplasms Uterine Neoplasms
3
Epidemiology of B-cell Non-Hodgkin Lymphoma in Pakistan
Study design
A cross-sectional study using non-probability consecutive sampling was conducted between January 2021 and September 2022 at the Chughtai Institute of Pathology with Institutional Review Board approval (CIP/IRB/1123). It is one of the largest private laboratories in Pakistan that accepts samples from all over the country. Chughtai Laboratories operate in multiple locations throughout the country. Because of its geographical location in the middle of the second largest city in Pakistan, approximately 70% of the total number of patients attending this clinic are residents of different areas of Pakistan.
Inclusion criteria
According to the World Health Organization (WHO) Classification of Tumors of Hematopoietic and Lymphoid Tissues, all diagnosed cases of B-cell NHL were included in the study. In this study, individuals of both genders, regardless of their age, were included.
Exclusion criteria
The study excluded biopsies with poor preservation, tru-cut biopsies that were inconclusive for diagnosis, unclassifiable lymphomas, cytological specimens, as well as bone marrow biopsies.
Data collection
The data for this study were retrieved from the archive of the institute using an electronic data system (Nexus Pro). Data regarding patient age, gender, location of involvement, and subtypes of B-cell NHL were also documented. The specific subtypes of the tumor were accompanied by specific information regarding the type of tumor, its location, and whether it had been described as a nodal or extra-nodal variant.
Statistical analysis
Statistical Package for the Social Sciences (SPSS) version 26.0 (IBM Corp., Armonk, NY, USA) was used to analyze the data. Qualitative variables were presented as frequencies and percentages. Quantitative variables were presented as mean and standard deviation. A chi-square test was applied, and a p-value of less than 0.05 was considered statistically significant.
Saleem R., Chughtai A., Zafar G., Chughtai O., Javeed S, & Chughtai A.S. (2023). Clinicopathological Spectrum of B-Cell Non-Hodgkin Lymphoma in Pakistan Population: A Single-Center Study. Cureus, 15(1), e34298.
Publication 2023
B-Lymphocytes Biologic Preservation Biopsy Bone Marrow Diagnosis Gap Junctions Gender Hematopoietic Neoplasms Lymphoid Tissue Lymphoma Neoplasms Patients
4
Retrospective Study of Naïve CLL Patients
In total, 113 patients diagnosed with naïve CLL at Seoul St. Mary’s Hospital, Catholic University of Korea, from March 2018 to December 2021 were included in the study. Patients were diagnosed according to the World Health Organization (WHO) Classification of Tumors of Hematopoietic and Lymphoid Tissues on the basis of their clinical features, laboratory findings (PB and/or BM morphology, and flow cytometric immunophenotyping), cytogenetics, and molecular genetics [1 (link)]. Morphologic characteristics of CLL were reviewed on PB smears and/or BM aspiration smears and hematoxylin and eosin-stained tissue sections and confirmed independently by two experienced hematopathologists to exclude other small B-cell lymphomas. CLL with atypical immunophenotype was regarded when either CD5 or CD23 were negative or dim positive (−/dim+), or when FMC7 is positive [60 ]. Extramedullary involvement and localization were assessed using positron emission tomography/computed tomography. Initial staging was performed according to the Rai and Binet systems [61 (link),62 (link)]. This study was approved by the institutional review board of Seoul St. Mary’s Hospital (KC21RISI0569).
Decision-making for treatment initiation was performed following the international workshop on CLL 2018 (iwCLL 2018) [63 (link)]. Most patients were monitored every 4–6 months without intervention because they did not exhibit any symptoms and signs of advanced disease or evidence of progressive disease (n = 73). The combination of fludarabine, cyclophosphamide, and rituximab (FCR) was the most commonly used treatment regimen (n = 22), followed by rituximab plus bendamustine (n = 6) and obinutuzumab plus chlorambucil (n = 5). Other regimens included acalabrutinib monotherapy (n = 4), acalabrutinib, venetoclax plus obinutuzumab (n = 1), venetoclax plus obinutuzumab (n = 1), and venetoclax plus acalabrutinib (n = 1).
Ahn A., Kim H.S., Kim T.Y., Lee J.M., Kang D., Yu H., Lee C.Y., Kim Y., Eom K.S, & Kim M. (2023). Genetic and Clinical Characteristics of Korean Chronic Lymphocytic Leukemia Patients with High Frequencies of MYD88 Mutations. International Journal of Molecular Sciences, 24(4), 3177.
Publication 2023
acalabrutinib B-Cell Lymphomas Bendamustine Chlorambucil Cyclophosphamide Eosin Ethics Committees, Research Flow Cytometry fludarabine Hematopoietic Neoplasms Hematoxylin Immunophenotyping Lymphoid Tissue obinutuzumab Patients Rituximab Roman Catholics Scan, CT PET Tissue Stains Treatment Protocols venetoclax
5
ITDectect: Detecting FLT3-ITD in AML
For the development of ITDectect, bone marrow samples from 77 patients with newly diagnosed, relapsed, or refractory AML were collected at the Seoul National University Hospital (SNUH) from June 2000 to December 2014. AML was diagnosed according to the WHO Classification of Hematopoietic Neoplasms, which requires the identification of 20% or more leukemic blasts in the bone marrow.
For clinical verification of ITDectect, fragment analysis of 789 AML patients was performed by the Department of Diagnostic Laboratory Medicine from May 2017 to May 2020, and the results of the targeted FiRST Hemic Panel based on targeted NGS were reviewed retrospectively. Both sets of results were available for 143 patients, and 23 patients (23/143, 16%) whose fragment analysis result was positive for FLT3-ITD were collected. This study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of SNUH (IRB No. 1611-020-805). All patients provided written informed consent at the time of sample collection.
Lee S., Sun C.H., Jang H., Kim D., Yoon S.S., Koh Y., Na S.C., Cho S.I., Kim M.J., Seong M.W., Byun J.M, & Yun H. (2023). ITDetect: a method to detect internal tandem duplication of FMS-like tyrosine kinase (FLT3) from next-generation sequencing data with high sensitivity and clinical application. BMC Bioinformatics, 24, 62.
Publication 2023
Bone Marrow Diagnosis FLT3 protein, human Grouping, Blood Hematopoietic Neoplasms Patients Pharmaceutical Preparations Specimen Collection

Top products related to «Hematopoietic Neoplasms»

1
Penicillin by Merck Group
Sourced in United States, Germany, United Kingdom, Italy, France, Switzerland, Brazil, China, Poland, Macao, Spain, Canada, Japan, Australia, Austria, Belgium, Israel, Sao Tome and Principe, Netherlands, India, Sweden, Ireland, Argentina, Czechia, Denmark, New Zealand, Hungary, Mexico, Holy See (Vatican City State), Ukraine
Penicillin is a type of antibacterial drug that is widely used in medical and laboratory settings. It is a naturally occurring substance produced by certain fungi, and it is effective against a variety of bacterial infections. Penicillin works by inhibiting the growth and reproduction of bacteria, making it a valuable tool for researchers and medical professionals.
2
Streptomycin by Merck Group
Sourced in United States, Germany, United Kingdom, Italy, France, China, Macao, Poland, Switzerland, Spain, Sao Tome and Principe, Japan, Brazil, Canada, Australia, Belgium, Austria, Netherlands, Israel, India, Sweden, Denmark, Ireland, Czechia, Norway, Gabon, Argentina, Portugal, Hungary, Holy See (Vatican City State), Mexico, Ukraine, Slovakia
Streptomycin is a laboratory product manufactured by Merck Group. It is an antibiotic used in research applications.
3
α biotin magnetic beads by Miltenyi Biotec
α-biotin magnetic beads are designed for the separation and purification of biotinylated molecules or cells. The beads are coated with an anti-biotin antibody, enabling the efficient capture and isolation of biotinylated targets. This product is suitable for a variety of biomedical research applications that require the separation and enrichment of biotinylated samples.
4
Miseq ngs platform by Illumina
Sourced in United States
The MiSeq NGS platform is a benchtop next-generation sequencing (NGS) system designed for targeted resequencing, small genome sequencing, and amplicon-based studies. The system utilizes Illumina's proprietary sequencing-by-synthesis technology to generate high-quality sequencing data.
5
Envision system by Maixin Group
The EnVision system is a versatile piece of lab equipment designed for use in various scientific applications. It functions as a high-performance multi-mode microplate reader, capable of performing a range of detection methods such as absorbance, fluorescence, and luminescence measurements. The EnVision system is engineered to provide accurate and reliable data, enabling researchers to conduct a wide array of assays and experiments.
6
Sas version 9 by SAS Institute
Sourced in United States, Austria, Japan, Cameroon, Germany, United Kingdom, Canada, Belgium, Israel, Denmark, Australia, New Caledonia, France, Argentina, Sweden, Ireland, India
SAS version 9.4 is a statistical software package. It provides tools for data management, analysis, and reporting. The software is designed to help users extract insights from data and make informed decisions.
7
Cd45 magnetic beads by Miltenyi Biotec
Sourced in United States, Germany, Italy
CD45 magnetic beads are a laboratory product used for the isolation and enrichment of CD45-positive cells from biological samples. They consist of superparamagnetic particles coated with anti-CD45 antibodies. When a sample is exposed to these beads, the CD45-positive cells bind to the beads and can be separated magnetically.
8
Automacs pro separator by Miltenyi Biotec
Sourced in Germany, United States, Sweden, Japan, United Kingdom, Italy, France
The AutoMACS Pro Separator is a magnetic cell separation system designed for efficient and automated cell isolation. It utilizes magnetic beads and a magnetic field to separate target cells from a heterogeneous cell population.
9
Lymphocyte separation medium by Biosharp
Sourced in China
Lymphocyte separation medium is a laboratory reagent used to isolate lymphocytes from whole blood or other biological samples. It is a density gradient solution that allows the separation of different blood cell types based on their density.
10
Cd34 clone qbend 10 by Thermo Fisher Scientific
Sourced in United States
The CD34 (clone QBend/10) is a laboratory reagent used for the identification and enumeration of CD34-positive cells by flow cytometry. It is a mouse monoclonal antibody that specifically binds to the CD34 antigen expressed on the surface of hematopoietic stem and progenitor cells.

More about "Hematopoietic Neoplasms"

Hematopoietic neoplasms, also known as blood cancers or hematolymphoid malignancies, are a diverse group of cancers affecting the blood, bone marrow, and lymph node tissues.
These malignancies arise from the abnormal growth and proliferation of hematopoietic cells, including leukemias, lymphomas, and myelodysplastic syndromes.
Careful study of these conditions is crucial for developing effective treatments and improving patient outcomes.
Leukemias are characterized by the uncontrolled growth of immature blood cells, leading to the accumulation of abnormal cells in the bone marrow and blood.
Acute leukemias, such as acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML), progress rapidly, while chronic leukemias, such as chronic lymphocytic leukemia (CLL) and chronic myeloid leukemia (CML), develop more slowly.
Lymphomas are cancers that originate in the lymphatic system, which is responsible for the body's immune response.
These include Hodgkin lymphoma and non-Hodgkin lymphoma, which can be further classified into various subtypes based on the type of lymphocytes involved.
Myelodysplastic syndromes (MDS) are a group of disorders characterized by the abnormal production and maturation of blood cells in the bone marrow, leading to a reduction in the number of healthy blood cells.
Advancements in research, including the use of technologies like the MiSeq NGS platform, CD45 magnetic beads, and the AutoMACS Pro Separator, have significantly improved our understanding of hematopoietic neoplasms.
These tools, combined with AI-powered solutions like PubCompare.ai, have the potential to streamline the research process, enabling researchers to identify the most effective protocols and products from the latest literature, preprints, and patents.
By harnessing the power of these innovative technologies, researchers can accelerate the development of more effective treatments for hematopoietic neoplasms, ultimately improving patient outcomes and quality of life.
Experience the future of hematopoietic neoplasm research today with PubCompare.ai.