Invasive aspergillosis promotes tumor growth and severity in a tumor-bearing mouse model
Bibliographic record
Abstract
Invasive aspergillosis increases in chronic immunosuppressive diseases such as cancer. There is little information about the mechanisms by which Aspergillus infection affects the immune regulation and microenvironment of cancer cells. Hence, this study was aimed at investigating the effect of invasive aspergillosis on immunosurveillance, metastasis, and prognosis of cancer in tumor-bearing mice. After implantation of mouse mammary tumor in BALB/c mice, they were infected with Aspergillus conidia intravenously. For comparison, groups of mice were experimentally infected with Aspergillus conidia or implanted with tumor cells separately. Seven days after Aspergillus infection, the serum levels of tissue inhibitor of metalloproteinase-1 (TIMP-1) were measured by ELISA, and subsequently regulatory T lymphocytes were analyzed by flow cytometry. The survival of animals and mean tumor size were then determined. Our results indicated that tumor sizes in mice increased significantly after infection with Aspergillus conidia. Moreover, invasive aspergillosis enhanced the population of regulatory lymphocytes and level of TIMP-1. This study supports the idea that massive Aspergillus infection could stimulate tumor growth and increases the possibility of a bad prognosis. As a result, treatment of Aspergillus infection could be considered an important issue for efficient cancer therapy.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".