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Record W2216485355 · doi:10.1007/s00018-015-2119-4

Exploring NAD+ metabolism in host–pathogen interactions

2015· review· en· W2216485355 on OpenAlexafffund
Inês Mesquita, Patrícia Varela, Ana Belinha, Joana Gaifem, Mireille Laforge, Baptiste Vergnes, Jérôme Estaquier, Ricardo Silvestre

Bibliographic record

VenueCellular and Molecular Life Sciences · 2015
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCalcium signaling and nucleotide metabolism
Canadian institutionsUniversité LavalCentre hospitalier de l'Université Laval
FundersSeventh Framework ProgrammeFundação para a Ciência e a TecnologiaCanada Research ChairsInstitut de Recherche pour le DéveloppementProvidence Health CareAgence Nationale de la RechercheBusiness Events Perth
KeywordsNAD+ kinasePathogenIntracellularNicotinamide adenine dinucleotideCatabolismBiologyMetabolismBiochemistryEnzymeCofactorCell biologyMicrobiology

Abstract

fetched live from OpenAlex

Nicotinamide adenine dinucleotide (NAD(+)) is a vital molecule found in all living cells. NAD(+) intracellular levels are dictated by its synthesis, using the de novo and/or salvage pathway, and through its catabolic use as co-enzyme or co-substrate. The regulation of NAD(+) metabolism has proven to be an adequate drug target for several diseases, including cancer, neurodegenerative or inflammatory diseases. Increasing interest has been given to NAD(+) metabolism during innate and adaptive immune responses suggesting that its modulation could also be relevant during host-pathogen interactions. While the maintenance of NAD(+) homeostatic levels assures an adequate environment for host cell survival and proliferation, fluctuations in NAD(+) or biosynthetic precursors bioavailability have been described during host-pathogen interactions, which will interfere with pathogen persistence or clearance. Here, we review the double-edged sword of NAD(+) metabolism during host-pathogen interactions emphasizing its potential for treatment of infectious diseases.

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 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 score0.994
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.130
GPT teacher head0.335
Teacher spread0.205 · 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

Citations52
Published2015
Admission routes2
Has abstractyes

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