Serum Stimulates Growth of and Proteinase Secretion by<i>Aspergillus fumigatus</i>
Bibliographic record
Abstract
Serum contains iron-binding proteins, which inhibit the growth of most pathogenic microorganisms, including fungi. The purpose of this research was to investigate the effect of serum on growth of the opportunistic fungal pathogen Aspergillus fumigatus. Supplementing minimal essential medium (MEM) with up to 80% human serum or up to 80% fetal bovine serum (FBS) stimulated growth and increased the amount of A. fumigatus dry biomass approximately fourfold. In addition, a 100-fold increase in proteinase secretion, as measured by azocasein hydrolysis, was observed when 10% human serum or 10% FBS was added to MEM. The fungal proteinases secreted in serum-containing media were shown to degrade (3)H-labeled basal lamina proteins. The factor in serum that stimulated proteinase secretion was larger than 10 kDa and was 85% inactivated when the serum was heated for 30 min at 66 degrees C. The proportions of proteinases of each catalytic class secreted by A. fumigatus in the presence of serum were different from the proportions secreted in media containing single proteins. Proteinase secretion did not result from increased protein concentration in the medium per se because bovine serum albumin (BSA) at a concentration equivalent to the concentration of serum produced only 20% of the proteinase activity per milligram (dry weight) that was produced by FBS. Addition of BSA plus 100 microM FeCl(3) to MEM resulted in the same level of growth as addition of serum, indicating that a combination of nutritional factors in serum may stimulate growth. However, the level of proteinase secretion was still only 30% of the level observed with FBS. These data indicate that serum does not inhibit the growth of A. fumigatus and that the nutrients in serum result in high levels of proteinase secretion, potentially increasing the invasiveness of this species.
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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) | 0.001 | 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 | 0.000 |
| 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 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".