Enhancement of bioconversion gangliosides to monosialotetrahexosylganglioside by in situ sialic acid removal and recovery
Bibliographic record
Abstract
Monosialotetrahexosylganglioside (GM1) production via bioconversion from gangliosides is promising for industrial application because it has the advantages of a high GM1 yield and an environmentally friendly process. Sialidase hydrolyzes gangliosides to GM1 producing sialic acid as a by‐product, which inhibits the sialidase activity, while the incomplete conversion of gangliosides was indicated by thin‐layer chromatography (TLC) in the presence of sialic acid. The sialic acid showed competitive inhibition on the sialidase activity with an inhibition constant of 0.75 mmol/L. By harnessing the in situ product removal (ISPR) technique, 50 g/L of crude gangliosides was completely converted to GM1 after a 12 h conversion. The GM1 concentration increased from 0.42 to 10.88 g/L in the ISPR system, which was 59.1 % higher than that of the control (6.84 g/L GM1). In addition, sialic acid was recovered simultaneously with a yield of 74.7 %. In summary, the ISPR system improved the bioconversion from gangliosides to GM1 and recovered sialic acid within a one‐step bioprocess.
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 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) | 0.001 | 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 | 0.001 |
| 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 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".