A cytokine and angiogenic factor (CAF) analysis in plasma in testicular germ cell tumor patients (TGCTs).
Bibliographic record
Abstract
e15599 Background: We investigated cytokines and angiogenic factors (CAFs) in patients with testicular germ cell tumors (TGCTs). We aimed to link the CAF profile to type of response to chemotherapy and select candidate prognostic and predictive markers for further study. Methods: In the presented study, plasma from 99 patients (pt) with TGCTs treated with first line (95 pt) or salvage (4 pt) chemotherapy was collected. The concentrations of 51 plasma CAFs were measured pre-treatment (n = 80) and on day 22 (n = 60) using multiplex bead arrays (Human Group I and II cytokine panels and TGF beta by Bio-Plex 200 system (Bio-Rad Laboratories, Hercules, CA). We used unsupervised clustering with self-organizing map (SOM) and k-means clustering to analyze CAF expression profiles. Results: Unsupervised clustering with self-organizing maps and k-means identified characteristic plasma cytokine profiles in different subgroups of patients according to response to chemotherapy and other clinical variables. Several cytokines were differentially expressed in patients with favourable and unfavorable response in serum before chemotherapy including IL-1b, IL-15, M-CSF, IL-4, IL-5, b-NGF, IL-10, MCP-3, FGF basic, IL-6, MIP-1a, GM-CSF, IL-17, IL-13, MIP1b, IL-8, SCF, IFN alfa, SCGFb, IL-2RA, IP-10, IL-16, TGFb-3. Similarly, following cytokines were differentially expressed in serum after 1st cycle of chemotherapy IL-1b, TGFb-3, LIF, TGFb-2, IL-16, IL-18, IL-2RA, MCP-3, IFN alfa, HGF, IL1a, MIF, SCF, IL-3, SCGFb, b-NGF, MIG, CTACK. Conclusions: CAF profiling with unsupervised clustering revealed clinically relevant differences in subgroups of TGCTs patients. We suggest that this platform may provide valuable insights into TGCTs biology, and could help to identify plasma cytokine signature for predicting treatment resistance.
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) | 0.001 | 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".