The Expression of Beclin-1 in Hepatocellular Carcinoma and Non-Tumor Liver Tissue: A Meta-Analysis
Bibliographic record
Abstract
Background Recently, Beclin-1 expression in hepatocellular carcinoma (HCC) and non-tumor liver tissue have been investigated by several studies. However, the results are controversial. The aim of this study was to clarify the role of Beclin-1 in the occurrence of HCC by comparing the difference of Beclin-1 expression between HCC and non-tumor liver tissue. Methods An electronic retrieve for relevant studies was performed in PubMed, EMBASE, China National Knowledge Infrastructure (CNKI), Wan Fang and Chinese VIP databases updated to December 31, 2017. Newcastle-Ottawa-Scale (NOS) was used to assess the quality of the eligible studies. Sensitivity, subgroup, and publication bias analyses were also carried out in this meta-analysis. Statistical analysis was performed by Review Manager 5.3 and STATA 12.0. Results Six high-quality studies with 357 HCC patients were eligible. There was no significant difference of Beclin-1 expression between HCC and non-tumor liver tissue (OR = 2.48, 95%CI = 0.64-9.58, P = 0.19). However, sensitivity analysis showed that Beclin-1 was lower in HCC than in non-tumor liver tissue after omitting Kang et al.'s study (OR = 4.14, 95% CI = 1.75-9.81, P = 0.001), and heterogeneity was not evident (P = 0.14, I² = 43%). Subgroup analysis suggested that heterogeneity may stem from ethnicity. The funnel plot and Egger's test (P = 0.900) demonstrated that no significant publication bias was present in this meta-analysis. Conclusion This meta-analysis indicated that there was no significant difference of Beclin-1 expression between HCC and non-tumor liver tissue.Read Complete Article at ijSciences: V72018031625 AND DOI: http://dx.doi.org/10.18483/ijSci.1625
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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 | 0.015 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.048 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".