Potential of Pre-Operative Serum Interleukin-6 as a Biomarker for Colorectal Cancers
Bibliographic record
Abstract
The diagnosis of colorectal cancers (CRC) at its early stage is challenging due to lack of early markers. Current diagnostic tests are either invasive or show low sensitivity. Interleukins are known to elevate and play important roles in the development and progression of the CRC. The studies on interleukin profiles of CRC patients are mainly confined to Caucasian populations while South Asian data are sparse. Therefore, the aim of this study was to investigate the serum IL-6 and IL-10 levels in a cohort of Sri Lankan CRC patients and explore their potential to be used as markers for early diagnosis/prognosis of CRC. Blood samples from 35 CRC patients and 35 healthy volunteers were obtained after informed consent. Their clinical findings and carcinoembryonic antigen (CEA) levels were recorded. Concentrations of IL-6 and IL-10 were measured using ELISA according to manufacturer’s protocols. Mean serum [IL-6] was found to be significantly higher in CRC patients than controls (p<0.05). The mean [IL-10]showed no difference to that of controls. (p>0.05). Interestingly, the [IL-6] in CRC patients were correlated with the disease stage (Stage I-0.16pg/ml; stage II-7.01pg/ml; stage III-15.8pg/ml and stage IV-35.48pg/ml). CEA levels were not correlated with the disease stage or withIL-6 levels. This study provided preliminary evidence to use IL-6 as a potential biochemical marker for the diagnosis of CRC inaddition to CEA. Furthermore, IL-6 could be a marker for prognosis of CRC. Further studies with higher patient samples are needed to validate the results of this study.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".