Chemokines in hepatocellular carcinoma: a meta-analysis
Bibliographic record
Abstract
Accumulating evidence suggests that chemokines may play an important role in the formation and mediating of the immune microenvironment of hepatocellular carcinoma (HCC). The purpose of this meta-analysis was to explore the differences in blood or tissues chemokines concentrations between HCC patients and controls. Online databases, namely PubMed, Web of Science, Embase and Cochrane Library, were systematically searched for relevant articles published on or before 15 January 2020. Standardized mean differences (SMDs) with corresponding 95% confidence intervals of the chemokines concentrations were calculated as group differences between the HCC patients and the controls. Sixty-five studies met the inclusion criteria for the meta-analysis. Altogether they consisted of 26 different chemokines compared between 5828 HCC patients and 4909 controls; and 12 different chemokines receptors compared between 2053 patients and 2285 controls. The results of meta-analysis indicated that concentrations of CCL20, CXCL8 and CXCR4 in the HCC patients were significantly higher than those in the controls (SMD of 6.18, 1.81 and 1.04, respectively). Therefore, higher concentration levels of CCL20, CXCL8 and CXCR4 may indicate the occurrence of HCC Future research should explore the putative mechanisms underlying this linkage. Meanwhile, attempts can be made to replicate the existing findings in prospective cohort populations and explore the cause-and-effect relationships pertaining to this linkage in order to develop new diagnostic and therapeutic strategies for HCC.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.027 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".