Preparation and application of biochip for detection of various antigens in stool.
Bibliographic record
Abstract
Aim To prepare and apply a concise biochip for detection of various antigens in stool. Methods Based on the principle of antigen-antibody affinity, we developed a concise biochip for detection of various antigens in stool including hemoglobin, albumin, the whole bacterial antigen of H, pylori, K-12 E. coli, CEA, p53, Ras21, QYC. bellyworm egg, hookworm egg and white cell in pyroxylin membrane. Results The protein biochips were diverse on sensitivity of various antigens, that of hemoglobin, albumin was 50ng/ml. 1000ng/ml, H. pylori and K-12 antigens were 20.0ng/ml, CEA was 12.5ng/ml, P53, Ras21 and QYC were 10.0ng/ml, and genes of bellyworm egg, hookworm egg and white cell were 50.0ng/ml, respectively. Furthermore, the biochip was applied to detection of the clinical stool samples for assessment of clinical value, The results indicated the biochip had excellent work in difference clinical stool samples. We obtained the coincident rate of 95% and 92% in hemoglobin and H. pylori antigen. Conclusions Biochip technique for detection of stool proteins is a new method and should have broad future market.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".