Resistin, but not adiponectin is elevated in Caucasian prostate cancer patients
Bibliographic record
Abstract
Emerging evidence suggests that adipokines, such as resistin and adiponectin, play a role in modulating cancer risk. However, the role of resistin and adiponectin in prostate cancer (PCa) risk is poorly understood. 275 archived plasma samples consisting of 138 PCa cases and 137 controls were analyzed for resistin and total adiponectin protein using a Bio‐Plex human adipokine assay and xMAP technology. Overall, resistin, but not adiponectin, was significantly elevated (p=0.02) in PCa patients compared with controls. Future studies are warranted to determine the functional role of resistin in PCa. When examined by ethnicity, there was no difference between cases (n=16) and controls (n=8) in African American subjects for either adipokine. In contrast, among Caucasians, adiponectin (p=0.07) and resistin (p=0.03) were higher in PCa patients (n=114) compared with controls (n=110). Our data suggests that differences in circulating adipokines may exist between ethnic groups, but this requires further study in a larger population. (D. Ma. is funded by an NSERC Discovery grant. B.K. Smith is funded by an OGS and Sun Life Financial Research Fund) Grant Funding Source Ontario Graduate Scholarship and Sun Life Financial Research Fund
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".