Detecting Glycogen in Peripheral Blood Mononuclear Cells with Periodic Acid Schiff Staining
Bibliographic record
Abstract
Periodic acid Schiff (PAS) staining is an immunohistochemical technique used on muscle biopsies and as a diagnostic tool for blood samples. Polysaccharides such as glycogen, glycoproteins, and glycolipids stain bright magenta making it easy to enumerate positive and negative cells within the tissue. In muscle cells PAS staining is used to determine the glycogen content in different types of muscle cells, while in blood cell samples PAS staining has been explored as a diagnostic tool for a variety of conditions. Blood contains a proportion of white blood cells that belong to the immune system. The notion that cells of the immune system possess glycogen and use it as an energy source has not been widely explored. Here, we describe an adapted version of the PAS staining protocol that can be applied on peripheral blood mononuclear immune cells from human venous blood. Small cells with PAS-positive granules and larger cells with diffuse PAS staining were observed. Treatment of samples with amylase abrogates these patterns confirming the specificity of the stain. An alternate technique based on enzymatic digestion confirmed the presence and amount of glycogen in the samples. This protocol is useful for hematologists or immunologists studying polysaccharide content in blood-derived lymphocytes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".