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Purified Peritoneal Macrophages Do Not Promote Angiogenesis In Vivo.

2005· article· en· W2571114168 on OpenAlexaff
Patricia Pelletier, Sabrina R. Perri, Moïra François, Ian B. Copland, Jacques Galipeau

Bibliographic record

VenueBlood · 2005
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAngiogenesis and VEGF in Cancer
Canadian institutionsMcGill University
Fundersnot available
KeywordsMatrigelAngiogenesisNeovascularizationIn vivoPathologyMedicineMacrophageImmunologyBone marrowBiologyCancer researchIn vitro

Abstract

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Abstract A large number of patients suffer from peripheral vascular disease not amenable to surgery, thus making medical therapies that promote neovascularization, including cell-based therapies, of interest. It is known that macrophages play an important role in angiogenesis in the context of wound healing and tumors, and that bone marrow (BM)-derived and peripheral blood (PB) mononuclear cells injected in ischemic limbs of animal models can favor development of collateral vessels. In order to determine the specific contribution of monocytes/macrophages to angiogenesis in an in vivo mouse model, we studied new vessel formation in a subcutaneously implanted Matrigel matrix plug in which peritoneal macrophages were embedded. We also tested whether macrophage exposure to GM-CSF would enhance their pro-angiogenic effect. A cohort of C57Bl/6 mice was sacrificed 6 days after intraperitoneal injection of 10% thioglycollate. Stimulated peritoneal macrophages were collected and plated in RPMI 1640 supplemented with 10% FBS (R10). Adherent macrophages were trypsinized, resuspended in 500μL of Matrigel, and injected subcutaneously in C57Bl/6 mice. The 4 experimental groups consisted of: Matrigel alone (n=5), with 5.7x106 macrophages (n=4), with murine GM-CSF 500u/mL (n=5), and with both 5.7x106 macrophages and murine GM-CSF 500u/mL (n=6). Matrigel plugs were resected at 21 days and endothelial cells on histological sections were stained with anti-vWF antibody. For each implant, blood vessels were counted on one entire section, excluding the periphery. The mean number of blood vessels per mm2 (±SEM) was: Matrigel alone: 4.0 (±1.4), Matrigel + GM-CSF: 2.6 (±1.1), Matrigel + macrophages: 5.3 (±1.2), Matrigel + GM-CSF + macrophages: 4.7 (±0.9). There was no statistically significant difference in new vessel formation among the four groups (p>0.1 by Student T test). In a separate experiment, adherent peritoneal macrophages were cultured in R10 with or without GM-CSF 500u/mL for 24 hours. The cells were then washed thoroughly and cultured in serum-free Opti media for 18 hours. A RayBiotech antibody array testing 24 pro- and anti-angiogenic cytokines was performed using concentrated conditioned media. It demonstrated that peritoneal macrophages secrete a number of pro-angiogenic/arteriogenic cytokines such as MCP-1, VEGF, bFGF and GM-CSF, as well as anti-angiogenic cytokines such as TIMP-1 and IL-12. However, the cytokine profile was not significantly altered by stimulation of cells with GM-CSF. In summary, a purified population of peritoneal macrophages failed to significantly alter host-derived angiogenesis in a Matrigel subcutaneous implant, despite the detectable presence of pro-angiogenic cytokines. This may be explained in part by the concomitant secretion of anti-angiogenic factors. Some clinical trials have successfully achieved angiogenesis in cardiovascular disease using unfractionated BM-derived or PB mononuclear cells. Considering that the proportion of monocytes in these preparations by far outnumbers that of stem and progenitor cells with hemangioblast potential, it might be worthwhile exploring whether removing monocytes from unfractionated mononuclear cell collections would promote a distinct and possibly enhanced proangiogenic effect. Figure Figure

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.245
Teacher spread0.236 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2005
Admission routes1
Has abstractyes

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