Immunometabolism at the crossroads of obesity and cancer—a Keystone Symposia report
Bibliographic record
Abstract
Immunometabolism considers the relationship between metabolism and immunity. Typically, researchers focus on either the metabolic pathways within immune cells that affect their function or the impact of immune cells on systemic metabolism. A more holistic approach that considers both these viewpoints is needed. On September 5-8, 2022, experts in the field of immunometabolism met for the Keystone symposium "Immunometabolism at the Crossroads of Obesity and Cancer" to present recent research across the field of immunometabolism, with the setting of obesity and cancer as an ideal example of the complex interplay between metabolism, immunity, and cancer. Speakers highlighted new insights on the metabolic links between tumor cells and immune cells, with a focus on leveraging unique metabolic vulnerabilities of different cell types in the tumor microenvironment as therapeutic targets and demonstrated the effects of diet, the microbiome, and obesity on immune system function and cancer pathogenesis and therapy. Finally, speakers presented new technologies to interrogate the immune system and uncover novel metabolic pathways important for immunity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.020 | 0.006 |
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".