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Record W4400540305 · doi:10.1021/acsinfecdis.4c00315

Infection and the Glycome─New Insights into Host Response

2024· review· en· W4400540305 on OpenAlexafffund
Firaz Mohideen, Lara K. Mahal

Bibliographic record

VenueACS Infectious Diseases · 2024
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGlycosylation and Glycoproteins Research
Canadian institutionsUniversity of Alberta
FundersCanada Excellence Research Chairs, Government of Canada
KeywordsGlycomeHost (biology)Host responseBiologyHost factorsComputational biologyImmunologyGlycanGeneticsImmune systemGlycoprotein

Abstract

fetched live from OpenAlex

Glycans play critical roles in the host-pathogen interactions leading to infection. However, we still understand very little about the dynamic nature of glycosylation in response to infection and its function in modulating host immunity. Many of the host proteins involved in immune defense are glycoproteins. Furthermore, the innate immune system recognizes glycans. The glycoform of a protein can impact proteolytic stability, receptor interactions, serum half-life, and other aspects. New, cutting-edge chemical biology tools are shedding light on the interplay between infection and the host glycome. In this review, we highlight new work on the importance of dynamic glycosylation of host proteins in the innate and adaptive immune pathways in response to infection. These include recent findings on altered glycoprofiles of mucins, complement components, and antibodies.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.970
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.016
GPT teacher head0.332
Teacher spread0.315 · 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 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

Citations10
Published2024
Admission routes2
Has abstractyes

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