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Record W3123533981 · doi:10.1084/jem.20201831

Large pH oscillations promote host defense against human airways infection

2020· article· en· W3123533981 on OpenAlexaff
Dusik Kim, Jie Liao, Nathan Scales, C. Martini, Xiaojie Luan, Asmahan Abu‐Arish, Renaud Robert, Yishan Luo, Geoffrey A. McKay, Dao Nguyen, Marc A. Tewfik, Charles Poirier, Elias Matouk, Juan P. Ianowski, Saul Frenkiel, John W. Hanrahan

Bibliographic record

VenueThe Journal of Experimental Medicine · 2020
Typearticle
Languageen
FieldMedicine
TopicCystic Fibrosis Research Advances
Canadian institutionsMcGill University Health CentreUniversity of SaskatchewanCentre Hospitalier de l’Université de MontréalMcGill University
Fundersnot available
KeywordsCystic fibrosisBicarbonateMucusAirwayCarbonic anhydraseMicrobiologyRespiratory systemInhalationImmunologyMucociliary clearanceChemistryBiologyMedicineLungBiochemistryInternal medicineEnzymeAnesthesiaAnatomy

Abstract

fetched live from OpenAlex

The airway mucosal microenvironment is crucial for host defense against inhaled pathogens but remains poorly understood. We report here that the airway surface normally undergoes surprisingly large excursions in pH during breathing that can reach pH 9.0 during inhalation, making it the most alkaline fluid in the body. Transient alkalinization requires luminal bicarbonate and membrane-bound carbonic anhydrase 12 (CA12) and is antimicrobial. Luminal bicarbonate concentration and CA12 expression are both reduced in cystic fibrosis (CF), and mucus accumulation both buffers the pH and obstructs airflow, further suppressing the oscillations and bacterial-killing efficacy. Defective pH oscillations may compromise airway host defense in other respiratory diseases and explain CF-like airway infections in people with CA12 mutations.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.039
GPT teacher head0.359
Teacher spread0.320 · 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 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

Citations32
Published2020
Admission routes1
Has abstractyes

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