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Record W1936337345 · doi:10.1080/21505594.2015.1062211

A tug-of-war between the host and the pathogen generates strategic hotspots for the development of novel therapeutic interventions against infectious diseases

2015· review· en· W1936337345 on OpenAlexaff
Aarti Rana, Mushtaq Ahmed, Abdur Rub, Yusuf Akhter

Bibliographic record

VenueVirulence · 2015
Typereview
Languageen
FieldImmunology and Microbiology
Topicinterferon and immune responses
Canadian institutionsInstitute of Infection and Immunity
Fundersnot available
KeywordsBiologyPathogenHost (biology)Pattern recognition receptorHost–pathogen interactionTug of warComputational biologyPathogen-associated molecular patternImmunologyImmunityImmune systemVirulenceGeneGenetics

Abstract

fetched live from OpenAlex

Microbial pathogens are known to express an array of specific signaling molecules referred as Pathogen Associated Molecular Patterns (PAMPs), which are recognized by Pattern Recognition Receptors (PRRs), present on the surface of the host cells. Interactions between PAMPs and PRRs on the surface of the host cells lead to signaling events which could culminate into either successful infection or clearance of the pathogens. Here, we summarize how these events may generate novel host based as well as pathogen based molecular targets for designing effective therapeutic strategies against infections.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.987
Threshold uncertainty score0.529

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.133
GPT teacher head0.358
Teacher spread0.225 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations31
Published2015
Admission routes1
Has abstractyes

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