Novel immunomodulatory properties of low dose cytarabine entrapped in a mannosylated cationic liposome
Bibliographic record
Abstract
Cancer treatment remains unsatisfactory with high rates of recurrence and metastasis. Immunomodulatory agents capable of promoting cellular antitumor immunity while inhibiting the local immunosuppressive tumor microenvironment could greatly improve cancer treatment. We have developed a multi-targeted mannosylated cationic liposome delivery system containing muramyl dipeptide (DS) and low doses of the chemotherapeutic agent cytarabine (Ara-C). Immunomodulation of primary immune cells and immortalized cancer cell lines by Ara-C/DS was assessed by measuring cytokine levels and surface marker expression. As a proof of concept, the generation of targeted cellular immunity was investigated in the context of responses to viral antigens. This report is the first demonstrating that Ara-C combined with DS can modulate immune responses and revert immunosuppression as evidenced by increased IFN-γ and IL-12p40 without changes in IL-10 in peripheral blood mononuclear cells, and increased CD80 and decreased CD163 on immunosuppressive macrophages. Furthermore, Ara-C/DS increased MHC class I expression on cancer cells while increasing the production of antigen-specific IFN-γ+ CD8+ T cells in viral peptide-challenged lymphocytes from both humans and vaccinated mice. Taken together, these results are the first to document immunomodulatory properties of Ara-C linked with recognition of antigens and potentially the generation of antitumor immune memory.
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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) | 0.000 | 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) | 0.001 | 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".