WITHDRAWN: Mera: A scalable high throughput automated micro-physiological system
Post-publication record
OpenAlex flags this work as retracted, but it carries no matching Retraction Watch record in this frame.
Bibliographic record
Abstract
Background: Eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) have been suggested to play roles in various diseases, yet there is little data on their changes in patients with non-small-cell lung cancer (NSCLC). A simple LC-MS/MS method for EPA and DHA determination is critical to exploring EPA and DHA level changes in NSCLC patients. Method: 25 μL of serum was mixed with 25 μL of internal standard working solution, and then 450 μL of acetonitrile for protein precipitation. After vortex and centrifugation, the supernatant was directly used for LC-MS/MS analysis. The method was well validated with linearity, precision, recovery, and matrix effect. The concentrations of EPA and DHA in serum samples from 211 NSCLC patients and 227 healthy controls were determined by this LC-MS/MS method. Results: Good separation and reliable quantification of EPA and DHA in serum samples were achieved by our method. Compared with healthy controls, serum EPA and DHA were significantly reduced in both adenocarcinoma and squamous cell carcinoma. The concentrations of EPA and DHA showed a progressive decrease in healthy controls, early- and advanced-stage NSCLC patients. Conclusions: This study identified significant reductions in serum EPA and DHA in NSCLC patients through the development of an LC-MS/MS method.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.129 | 0.111 |
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".