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Record W2338409449 · doi:10.1002/jlb.67.4.464

Exploitation of host cell signaling machinery: activation of macrophage phosphotyrosine phosphatases as a novel mechanism of molecular microbial pathogenesis

2000· review· en· W2338409449 on OpenAlexaff
Devki Nandan, Keith L. Knutson, Raymond Lo, Neil E. Reiner

Bibliographic record

VenueJournal of Leukocyte Biology · 2000
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Tyrosine Phosphatases
Canadian institutionsVancouver Hospital and Health Sciences CentreUniversity of British Columbia
Fundersnot available
KeywordsBiologyPathogenesisIntracellularIntracellular parasiteCell biologySignal transductionMycobacterium tuberculosisProtein tyrosine phosphataseMacrophageLeishmania donovaniCell signalingMicrobiologyImmunologyTuberculosisIn vitroLeishmaniasisGeneticsVisceral leishmaniasis

Abstract

fetched live from OpenAlex

Intracellular pathogens, particularly those that target host mononuclear phagocytes, have evolved strategies to either evade or inhibit cellular mechanisms of host defense. Mycobacterium tuberculosis and Leishmania donovani exemplify a diverse group of microorganisms that have developed the ability to invade and replicate within host macrophages, leading to disease expression. Recent studies have suggested that the pathogenesis of intracellular infection may involve interference with host cell signaling. Drawing upon examples from in vitro models that focused on M. tuberculosis and L. donovani, we review evidence that activation of host cell phosphotyrosine phosphatases may contribute to pathogenesis. A leading candidate appears to be the Src homology 2 domain containing phosphotyrosine phosphatase SHP-1, the activation of which may contribute to the development of infection and disease progression.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.004

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 head0.018
GPT teacher head0.283
Teacher spread0.264 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations46
Published2000
Admission routes1
Has abstractyes

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