Similarities and divergences in the metabolism of immune cells in cancer and helminthic infections
Bibliographic record
Abstract
Energetic and nutritional requirements play a crucial role in shaping the immune cells that infiltrate tumor and parasite infection sites. The dynamic interaction between immune cells and the microenvironment, whether in the context of tumor or helminth infection, is essential for understanding the mechanisms of immunological polarization and developing strategies to manipulate them in order to promote a functional and efficient immune response that could aid in the treatment of these conditions. In this review, we present an overview of the immune response triggered during tumorigenesis and establishment of helminth infections, highlighting the transition to chronicity in both cases. We discuss the energetic demands of immune cells under normal conditions and in the presence of tumors and helminths. Additionally, we compare the metabolic changes that occur in the tumor microenvironment and the infection site, emphasizing the alterations that are induced to redirect the immune response, thereby promoting the survival of cancer cells or helminths. This emerging discipline provides valuable insights into disease pathogenesis. We also provide examples of novel strategies to enhance immune activity by targeting metabolic pathways that shape immune phenotypes, with the aim of achieving positive outcomes in cancer and helminth infections.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".