Lectin affinity chromatography using porous polymer monolith assisted nanoelectrospray MS/MS
Bibliographic record
Abstract
An affinity porous polymer monolith is utilized as a nanoelectrospray emitter as well as an online affinity capture column for the preconcentration of glycans. Porous polymer monolith (PPM) assisted electrospray provides a facile methodology for coupling microfluidics to mass spectrometry that is sheathless and with zero dead volume. Affinity PPM was photopolymerized using glycidyl methacrylate/ethylene dimethacrylate utilizing different porogenic solvents based on aliphatic alcohols to provide PPMs with a variety of pore sizes. The use of longer alkyl chain alcohols decreased the pore size of the formed PPM as indicated by the higher flow back pressure generated. The effect of the pore size on the stability of the electrospray was tested showing higher stability of the TIC with lower pore size. A lectin, namely Concanavaline A, was immobilized on glycidyl methacrylate/ethylene dimethacrylate using the Schiff base method to provide an affinity monolith for high mannose glycans. The amount of the lectin immobilized was studied as a function of the porogenic solvent used in the polymerization. The glycopeptides of the glycoprotein Ribonuclease B was preconcentrated on the affinity PPM sprayer and detected by tandem MS.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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".