Generation of Free Oligosaccharides from Bacterial Protein N‐Linked Glycosylation Systems
Classification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
All Campylobacter species are capable of N-glycosylating their proteins and releasing the same oligosaccharides into the periplasm as free oligosaccharides (fOS). Previously, analysis of fOS production in Campylobacter required fOS derivatization or large culture volumes and several chromatography steps prior to fOS analysis. In this study, label-free fOS extraction and purification methods were developed and coupled with quantitative analysis techniques. Our method follows three simple steps: (1) fOS extraction from the periplasmic space, (2) fOS purification using silica gel chromatography followed by porous graphitized carbon purification and (3) fOS analysis and accurate quantitation using a combination of thin-layer chromatography, mass spectrometry, NMR, and high performance anion exchange chromatography with pulsed amperometric detection. We applied our techniques to analyze fOS from C. jejuni, C. lari, C. rectus, and C. fetus fetus that produce different fOS structures. We accurately quantified fOS in Campylobacter species that ranged from 7.80 (±0.84) to 49.82 (±0.46) nmoles per gram of wet cell pellet and determined that the C. jejuni fOS comprises 2.5% of the dry cell weight. In addition, a novel di-phosphorylated fOS species was identified in C. lari. This method provides a sensitive and quantitative method to investigate the genesis, biology and breakdown of fOS in the bacterial N-glycosylation systems.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it