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Record W3122921496 · doi:10.1128/mbio.03226-20

Exploring the Impact of Ketodeoxynonulosonic Acid in Host-Pathogen Interactions Using Uptake and Surface Display by Nontypeable Haemophilus influenzae

2021· article· en· W3122921496 on OpenAlexaff
Sudeshna Saha, Alison Coady, Aniruddha Sasmal, Kunio Kawanishi, Biswa Choudhury, Hai Yu, Ricardo U. Sorensen, Jaime Inostroza, Ian C. Schoenhofen, Xi Chen, Anja Münster-Kühnel, Chihiro Sato, Ken Kitajima, Sanjay Ram, Victor Nizet, Ajit Varki

Bibliographic record

VenuemBio · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEscherichia coli research studies
Canadian institutionsNational Research Council Canada
FundersNational Institute of Allergy and Infectious DiseasesNational Institute of General Medical SciencesUniversity of California, San DiegoNational Institutes of HealthUniversity of Oxford
KeywordsHaemophilus influenzaeHost (biology)PathogenMicrobiologyHost factorsPathogenic organismBiologyVirologyVirusGeneticsAntibiotics

Abstract

fetched live from OpenAlex

All cells in vertebrates are coated with a dense array of glycans often capped with sugars called sialic acids. Sialic acids have many functions, including serving as a signal for recognition of "self" cells by the immune system, thereby guiding an appropriate immune response against foreign "nonself" and/or damaged cells. Several pathogenic bacteria have evolved mechanisms to cloak themselves with sialic acids and evade immune responses. Here we explore a type of sialic acid called "Kdn" (ketodeoxynonulosonic acid) that has not received much attention in the past and compare and contrast how it interacts with the immune system. Our results show potential for the use of Kdn as a natural intervention against pathogenic bacteria that take up and coat themselves with external sialic acid from the environment.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.228
Threshold uncertainty score0.360

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.060
GPT teacher head0.331
Teacher spread0.271 · 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 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

Citations22
Published2021
Admission routes1
Has abstractyes

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