Computational modeling predicts IL-10 control of lesion sterilization by balancing early host immunity-mediated antimicrobial responses with caseation during mycobacterium tuberculosis infection - Wikidata - wikimedia - TriplyDB

Computational modeling predicts IL-10 control of lesion sterilization by balancing early host immunity-mediated antimicrobial responses with caseation during mycobacterium tuberculosis infection

Computational modeling predicts IL-10 control of lesion sterilization by balancing early host immunity-mediated antimicrobial responses with caseation during mycobacterium tuberculosis infection

http://www.wikidata.org/entity/Q41596789

about

Interleukin-10 Family and Tuberculosis: An Old Story Renewed Versatile myeloid cell subsets contribute to tuberculosis-associated inflammation Agent-based model of angiogenesis simulates capillary sprout initiation in multicellular networks.Strategic Priming with Multiple Antigens can Yield Memory Cell Phenotypes Optimized for Infection with Mycobacterium tuberculosis: A Computational Study In silico evaluation and exploration of antibiotic tuberculosis treatment regimens A multi-scale approach to designing therapeutics for tuberculosis Systems Pharmacology Approach Toward the Design of Inhaled Formulations of Rifampicin and Isoniazid for Treatment of Tuberculosis Modeling Granulomas in Response to Infection in the Lung Inflammatory signaling in human tuberculosis granulomas is spatially organized.Strong Inference in Mathematical Modeling: A Method for Robust Science in the Twenty-First Century.Leukocyte Motility Models Assessed through Simulation and Multi-objective Optimization-Based Model Selection The Transcriptional Foundations of Sp110-mediated Macrophage (RAW264.7) Resistance to Mycobacterium tuberculosis H37Ra.Multiscale Model of Mycobacterium tuberculosis Infection Maps Metabolite and Gene Perturbations to Granuloma Sterilization Predictions Computational Modeling Predicts Simultaneous Targeting of Fibroblasts and Epithelial Cells Is Necessary for Treatment of Pulmonary Fibrosis.Comparing efficacies of moxifloxacin, levofloxacin and gatifloxacin in tuberculosis granulomas using a multi-scale systems pharmacology approach.A Novel Computational Model Predicts Key Regulators of Chemokine Gradient Formation in Lymph Nodes and Site-Specific Roles for CCL19 and ACKR4.Identifying mechanisms driving formation of granuloma-associated fibrosis during Mycobacterium tuberculosis infection.Mathematical Models for Immunology: Current State of the Art and Future Research Directions.Cytokines and Chemokines in Mycobacterium tuberculosis Infection.A complete categorization of multiscale models of infectious disease systems.Immune modulation by curcumin: The role of interleukin-10.Deletion of TGF-β1 Increases Bacterial Clearance by Cytotoxic T Cells in a Tuberculosis Granuloma Model.A Multi-Compartment Hybrid Computational Model Predicts Key Roles for Dendritic Cells in Tuberculosis Infection.Emergence and selection of isoniazid and rifampin resistance in tuberculosis granulomas.AmpliSeq transcriptome analysis of human alveolar and monocyte-derived macrophages over time in response to Mycobacterium tuberculosis infection.Low Levels of T Cell Exhaustion in Tuberculous Lung Granulomas

P2860

Q26748505-ED2A4C2F-1A9D-4DE0-82D8-7BD5D6F29A8B Q26997007-784653B0-0C8E-4B38-B3B6-3D95DE197708 Q27334865-7D779F78-AFEA-4433-BA9C-757B85F0504C Q28603191-1F6E9A9A-43CE-427F-A4D8-CED068395E3D Q28607283-B59339CB-7165-461E-8BC9-D65F4CAF09DD Q28647309-A530B8BC-7645-4AA2-B181-609C3C452013 Q28649769-C9C6932C-491E-4926-99BA-278E6AC2D3D0 Q35961242-9EF9CB0D-2202-4ECF-BEC9-FAB977C721AB Q35979217-57FEB214-8519-44AA-B5C7-BE98A596E18E Q36097151-5FF427AF-53F2-42BB-A80C-A2684CFF9F8F Q36121811-E829806A-55EE-48EB-B747-06E854DFD4A9 Q36616162-B975BF3D-7573-4DDB-87F7-ABA4615BE600 Q36888550-4C594EAC-600F-496E-B6A0-57D65F4476C0 Q37030035-D3D5DDE9-D19E-4F46-950A-D7945D97A856 Q38619965-DA0F4C99-E7EC-4509-8EA9-E97CA4AB2A73 Q38625030-FA706323-4F40-4E98-85AC-68984B526BFF Q38713268-4715391B-67B2-417A-8DEE-28555A30A81F Q38809377-8223B87E-BC73-4096-B5EB-5D5C690C8C5A Q38986062-850240FB-3188-41BA-B8E3-BE4BA3B50EA1 Q40076329-2E3BF9B8-7F11-45C4-B327-22C71C3EEC42 Q40097338-E89FCB90-A5A9-4C56-9DAD-E563D05269B8 Q49501894-A4DCDD47-7B54-486B-81A0-09193707BC20 Q51574513-2BE0432C-D3C4-4455-AB6B-41D4A685E219 Q54996259-0390B0AE-F36E-4166-AFA0-D24AAD485125 Q55216937-B257DEEE-318F-4B15-9B27-B1AB6853FAC7 Q56889855-04B42D7A-39D1-425A-B28D-0FD0CE8C8CF6

P2860

Computational modeling predicts IL-10 control of lesion sterilization by balancing early host immunity-mediated antimicrobial responses with caseation during mycobacterium tuberculosis infection

http://www.wikidata.org/entity/Q41596789

description

2014 nî lūn-bûn

@nan

2014年の論文

@ja

2014年論文

@yue

2014年論文

@zh-hant

2014年論文

@zh-hk

2014年論文

@zh-mo

2014年論文

@zh-tw

2014年论文

@wuu

2014年论文

@zh

2014年论文

@zh-cn

name

Computational modeling predict ...... cterium tuberculosis infection

@en

type

label

Computational modeling predict ...... cterium tuberculosis infection

@en

prefLabel

Computational modeling predict ...... cterium tuberculosis infection

@en

P1433

Q41596789-E43E629C-67EC-4EA9-BC1F-F42ECCF953AA

P1476

Q41596789-FD534FEA-6381-4D9D-B38D-3DB691459241

P2093

Q41596789-3EB2B14C-B23C-4205-9C0B-3FCE8D839F5C

Q41596789-75FF879E-DF02-4521-91B4-D8D3430491DE

Q41596789-775019F9-9E32-4DE7-A6D9-EF8FD14C87D6

Q41596789-A39F9FEF-8F2A-4FED-B41C-0AB4C7E0ACDD

Q41596789-A4CB530E-30C1-4BCA-BD25-82352FD989A7

Q41596789-EFCB09B2-FD4C-40FB-A56A-DF952868CCAE

P2860

Q41596789-01DD00EB-2688-4219-A8E0-D389D5D6E0F0

Q41596789-03E89BA3-8348-4C29-A463-A5F4918030B3

Q41596789-050BC291-523D-4109-8940-A710D9E03C09

Q41596789-0AD08F85-C65F-4FD3-BB52-CAE87912DD70

Q41596789-100A73DC-DD11-4791-AD93-3483E479B151

Q41596789-12B785C5-4071-4AE9-866C-31AC0DBC8802

Q41596789-12CAE818-2522-4DF3-AEF9-A7C130D4AF7C

Q41596789-15910968-233B-4F31-A284-85E88BCA2BE6

Q41596789-18A4542A-EA3F-4951-8016-FF0063B58C5E

Q41596789-1AB62A41-BE1F-4F3B-A8C3-4F589C68DF87

P304

Q41596789-6DD83003-A604-4695-9A20-1DEB3FDADCD1

P31

Q41596789-5E8F9B81-1602-4854-9EAF-296EABC9D7A0

P356

Q41596789-1A6C2002-D94F-4ED7-B0EF-25911E925997

P407

Q41596789-E0C53EE0-7335-4CF4-A87B-BB48FBAF3A33

P433

Q41596789-6C21498F-E126-4FE0-9273-82F74D39F5A7

P478

Q41596789-B16AACB4-FD87-4981-8AF4-2FBDC69937E9

P50

Q41596789-B15584D3-2A32-4093-BBEC-E782C8376948

Q41596789-D9669CCD-B01D-4A72-9995-F98B277690CC

P577

Q41596789-110524B2-0C33-4EF7-992B-CCCE735A1C45

P698

Q41596789-25CB0084-203C-47D6-A1F9-0C2B4D034FF0

P921

Q41596789-3B4947AF-C7CF-4A82-BF69-0F00F82727ED

P932

Q41596789-2EB298D7-73DF-4555-A843-46DB7780F1C6

P356

JIMMUNOL.1400734

P1433

Journal of Immunology

P1476

Computational modeling predict ...... cterium tuberculosis infection

@en

P2093

Christopher B Ford

http://www.w3.org/2001/XMLSchema#string

Denise E Kirschner

http://www.w3.org/2001/XMLSchema#string

Jennifer J Linderman

http://www.w3.org/2001/XMLSchema#string

JoAnne L Flynn

http://www.w3.org/2001/XMLSchema#string

Joshua T Mattila

http://www.w3.org/2001/XMLSchema#string

Nicholas A Cilfone

http://www.w3.org/2001/XMLSchema#string

P2860

Mathematical modeling of tuberculosis bacillary counts and cellular populations in the organs of infected mice.

Interleukin-10 and the interleukin-10 receptor

The PPE18 of Mycobacterium tuberculosis interacts with TLR2 and activates IL-10 induction in macrophage

TNF-alpha induces two distinct caspase-8 activation pathways

Receptor interacting protein kinase-3 determines cellular necrotic response to TNF-alpha

Apoptosis, pyroptosis, and necrosis: mechanistic description of dead and dying eukaryotic cells.

Apoptosis, oncosis, and necrosis. An overview of cell death

Alternative activation of macrophages

Fluctuations in HIV-1 viral load are correlated to CD4+ T-lymphocyte count during the natural course of infection

Differential virulence and disease progression following Mycobacterium tuberculosis complex infection of the common marmoset (Callithrix jacchus)

P304

664-677

http://www.w3.org/2001/XMLSchema#string

P31

scholarly article

P356

10.4049/JIMMUNOL.1400734

http://www.w3.org/2001/XMLSchema#string

P407

P433

2

http://www.w3.org/2001/XMLSchema#string

P478

194

http://www.w3.org/2001/XMLSchema#string

P50

Hannah Priyadarshini Gideon

P577

2014-12-15T00:00:00Z

http://www.w3.org/2001/XMLSchema#dateTime

P698

25512604

http://www.w3.org/2001/XMLSchema#string

P921

Mycobacterium tuberculosis

P932

4283220

http://www.w3.org/2001/XMLSchema#string