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Record W2900203733 · doi:10.1186/s12929-018-0482-9

The monocyte-macrophage-mast cell axis in dengue pathogenesis

2018· review· en· W2900203733 on OpenAlexafffund
Shu-Wen Wan, Betty A. Wu‐Hsieh, Yee-Shin Lin, Wen-Yu Chen, Yan Huang, Robert Anderson

Bibliographic record

VenueJournal of Biomedical Science · 2018
Typereview
Languageen
FieldMedicine
TopicMosquito-borne diseases and control
Canadian institutionsDalhousie University
FundersCanadian Institutes of Health ResearchMinistry of Science and Technology, Taiwan
KeywordsDengue virusDengue feverAntibody-dependent enhancementImmunologyVirusMonocyteMacrophageBiologyPathogenesisVirologyIn vitro

Abstract

fetched live from OpenAlex

Dengue virus, the causative agent of dengue disease which may have hemorrhagic complications, poses a global health threat. Among the numerous target cells for dengue virus in humans are monocytes, macrophages and mast cells which are important regulators of vascular integrity and which undergo dramatic cellular responses after infection by dengue virus. The strategic locations of these three cell types, inside blood vessels (monocytes) or outside blood vessels (macrophages and mast cells) allow them to respond to dengue virus infection with the production of both intracellular and secretory factors which affect virus replication, vascular permeability and/or leukocyte extravasation. Moreover, the expression of Fc receptors on the surface of monocytes, macrophages and mast cells makes them important target cells for antibody-enhanced dengue virus infection which is a major risk factor for severe dengue disease, involving hemorrhage. Collectively, these features of monocytes, macrophages and mast cells contribute to both beneficial and harmful responses of importance to understanding and controlling dengue infection and disease.

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)0.001
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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.024
GPT teacher head0.348
Teacher spread0.325 · 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

Citations62
Published2018
Admission routes2
Has abstractyes

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