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Differential mechanisms of neutrophil and monocyte adhesion on neuroblastoma cells: CD18 and VLA-4 integrins mediate adhesion to SK-N-SH, but not to SK-n-MC cell line

2000· article· en· W2084670809 on OpenAlexaff
Eduardo Chuluyán, Bianca J. Lang, Andrew C. Issekutz

Bibliographic record

VenueJournal of Neuroscience Research · 2000
Typearticle
Languageen
FieldMedicine
TopicCell Adhesion Molecules Research
Canadian institutionsGrace (Canada)Izaak Walton Killam Health CentreDalhousie University
Fundersnot available
KeywordsCD18MonocyteIntegrinAdhesionTumor necrosis factor alphaMolecular biologyCell adhesionCell cultureCell adhesion moleculeChemistryCell biologyImmunologyBiologyCellBiochemistry

Abstract

fetched live from OpenAlex

We examined the adhesion of monocytes and polymorphonuclear leukocytes (PMNLs) to the neuroblastoma (NB) cell lines SK-N-SH and SK-N-MC, which have some distinct differentiation characteristics. Monocytes adhered to SK-N-SH and SK-N-MC to the same extent (20 +/- 1.4% and 24 +/- 0.8% of monocytes added). Monocyte adhesion to SK-N-SH but not SK-N-MC was partially inhibited by treating monocytes with a mAb to the CD18 (beta2) integrin chain. The adhesion was further inhibited when monocytes were treated with a combination of mAb to CD18 and VLA-4. Treatment of both NB cell lines with interleukin-1alpha (0.5 ng/ml), tumor necrosis factor alpha (100 U/ml), interferon gamma (200 U/ml), or their combinations increased monocyte adhesion to SK-N-SH and SK-N-MC. With each condition, monocyte adhesion to SK-N-SH was partially blocked by mAb to CD18. The inhibition of adhesion to IL-1alpha- or TNFalpha-treated SK-N-SH cells was greater when the monocytes were treated with mAb to both CD18 and VLA-4. In contrast, monocyte adhesion to IL-1alpha or IFNgamma treated SK-N-MC was only slightly inhibited with a combination of mAb to CD18 + VLA-4 and there was no inhibition at all to TNFalpha-treated SK-N-MC. Spontaneous PMNL adhesion to SK-N-SH was almost negligible but increased by treating the cell line with IL-1alpha, TNFalpha, IFNgamma or their combinations. A mAb to CD18 blocked this increase in each case. The pattern of adhesion of PMNLs to SK-N-MC was totally different. PMNL adhesion to unstimulated SK-N-MC was very high (24 +/- 1.3%), was not inhibited by mAb to CD18, and did not increase by stimulating the cell line with IL-1alpha, TNFalpha, IFNgamma or their combinations. Overall, these results suggest two distinct patterns of monocyte and PMNL interaction with neural cells, such as the SK-N-SH and MC cell lines. While monocyte and PMNL adhesion to SK-N-SH is mainly via CD18/VLA-4 or the CD18 mechanisms, respectively, leukocyte adhesion to SK-N-MC is CD18- and VLA-4-independent. Thus, leukocyte-neural cell interactions share some mechanisms common also to leukocyte-endothelium interaction, but there are also unique mechanisms which may be neural cell and differentiation specific.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.021
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.064
GPT teacher head0.367
Teacher spread0.303 · 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 teacher head, not a consensus.

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".

Quick stats

Citations3
Published2000
Admission routes1
Has abstractyes

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