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Record W2556813508 · doi:10.1139/cjc-2016-0416

Synthesis of a homologous series of galactofuranose-containing mycobacterial arabinogalactan fragments

2016· article· en· W2556813508 on OpenAlexafffundvenue
Maju Joe, Todd L. Lowary

Bibliographic record

VenueCanadian Journal of Chemistry · 2016
Typearticle
Languageen
FieldChemistry
TopicCarbohydrate Chemistry and Synthesis
Canadian institutionsAlberta Glycomics CentreUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaAlberta Glycomics Centre
KeywordsArabinogalactanChemistryGalactanPeptidoglycanGlycoconjugateCell wallBiochemistryMycobacterium tuberculosisMycobacteriumGlycosphingolipidEnzymeBacteriaTuberculosisBiologyGenetics

Abstract

fetched live from OpenAlex

Mycobacteria, including the human pathogen Mycobacterium tuberculosis, the causative agent of tuberculosis, produce a complex cell wall structure made of carbohydrates and lipids. The major structural element of the mycobacterial cell wall is a glycoconjugate called the mycolic acid – arabinogalactan – peptidoglycan (mAGP) complex. Inhibition of mAGP biosynthesis is a proven strategy for developing anti-mycobacterial drugs, and thus, understanding the pathways and enzymes involved in the assembly of this molecule is of interest. In this paper, we describe the chemical synthesis of a panel of nine oligosaccharide fragments (4–12) of the galactan domain of the mAGP complex designed as biosynthetic probes. These structures, ranging in size from a hexasaccharide to a tetradecasaccharide, are potential substrates for two biosynthetic enzymes, GlfT2 and AftA, and represent the largest mycobacterial galactan fragments synthesized to date. The route developed was iterative and provided multimilligram quantities of the target molecules 4–12 in good overall yield.

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)0.000
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 score0.006

Distilled classifier scores by category (both heads)

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)0.0020.000

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 head0.009
GPT teacher head0.188
Teacher spread0.179 · 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

Citations7
Published2016
Admission routes3
Has abstractyes

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