Revue Scientifique et Technique: Diseases of poultry: World trade and public health implications
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
The identity of glycoproteins in stimulated normal human tears was investigated by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) of tears onto minigels, blotting, and subsequent incubation with different biotinylated lectins (concanavalin A [Con A], peanut agglutinin [PNA], glycine max agglutinin [SBA], Phaseolus vulgaris agglutinin, wheat germ agglutinin [WGA, native form], Artocarpus integrifolia agglutinin [Jacalin], and Pisum sativum agglutinin). Control proteins included purified secretory immunoglobulin A (sIgA) from human colostrum, human milk lactoferrin, and chicken-egg lysozyme. All samples were prepared in a denaturing (SDS) buffer under nonreducing and reducing conditions. The sIgA in tears and IgA (alpha) heavy chain fragments (reduced sample) were identified with most of the lectins tested. A particular high molecular weight (greater than 200 kD) protein fraction in tears that just entered the separation gel on SDS-PAGE was detected with WGA and Jacalin. This fraction stain poorly with silver. Tear lactoferrin was identified with all lectins used, although binding was low with SBA. Purified milk lactoferrin showed a poor reaction with Jacalin, but a protein in tears of similar mobility bound this lectin (nonreduced samples). Under both nonreducing and reducing conditions, tear-specific prealbumin in tears did not bind any of the lectins tested. Tear lysozyme only reacted with lectin after reduction. The techniques described may provide additional valuable information in addition to commonly used methods for tear protein analysis and further knowledge concerning the role of glycoproteins on the ocular surface.
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.
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) | 0.000 | 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