Bibliographic record
Abstract
Purpose: This study aims to investigate whether SDH1, the active ingredient of the Chinese medicine compound Selaginella Doederleinii Hieron, can effectively inhibit anaplastic lymphoma kinase (ALK) activity in non-small cell lung cancer (NSCLC). NSCLC is a significant and challenging disease, which has limited treatment options with widely existing adverse events (AEs). The research will involve both in vitro and in vivo experiments to assess the potential of SDH1 to target and restrain ALK kinase activity, potentially leading to the remission of NSCLC. Methods: Materials for this study included human NSCLC cell lines cultured in specific conditions and various reagents. In vitro, the level of protein is pressured by Western blot, and cell growth is assessed by MTT assay. In vivo, experiments used a xenograft tumor mouse model injected with NSCLC cells and treated with different reagents, with tumor growth and other observations recorded. Possible Results: There are three most possible results: (1)SDH1 can inhibit ALK kinase to control the growth of NSCLC cells; (2) SDH1 cannot target ALK kinase but it can control the growth of NSCLC cells; (3) SDH1 cannot restrain the activity of ALK kinase neither in vitro nor in vivo, thus it cannot kill NSCLC cells. Conclusion: The result of our study investigates the impact of SDH1 on anaplastic lymphoma kinase (ALK) and A549 NSCLC. The findings could reveal the potential of targeting ALK to induce remission in non-small cell lung cancer both in laboratory and animal models, which tends to provide a possible avenue for future clinical and pharmaceutical approaches to combat NSCLC.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".