Bibliographic record
Abstract
In 2010, an estimated 173 800 cases of cancer were diagnosed in Canada and 76 200 of these cases were expected to be fatal. Current treatment options are often very invasive, harmful, and ineffective. The purpose of this experiment was to mitigate tumour growth by manipulation of micro-RNA (mRNA) levels. mRNAs are small proteins, and levels of specific mRNAs are deregulated in cancer cells. In this experiment, levels of micro-RNA 449, which is deregulated in breast cancer cell lines, were returned to their baseline levels. Numerous tests were then conducted to test the viability of the resulting cells. Replicable experiments showed that the strength, motility and invasiveness of the breast cancer cells was greatly diminished after mRNA-449 levels returned to baseline levels. Furthermore, research indicated that 4 potential genes (CRIP2, XBP1, TAF4B, and SFXN2) can be manipulated in future experiments to further diminish the viability of the breast cancer tumours. En 2015, 25220 cas estimés de cancer du sein ont été diagnostiqués au Canada et 5060 de ces cas étaient prévus d’être fatales. Les options de traitement courantes sont souvent très envahissantes, nuisibles et inefficaces. L’objet de cette expérience était d’atténuer la croissance de la tumeur en manipulant les niveaux d’ARN-Micro. ARNm sont de petites molécules qui codent pour des protéines et en cellules de cancer, les niveaux de certains ARNms sont dérégulés. Les niveaux d’ARN-micro 449, qui sont dérégulés dans les lignées cellulaires de cancer du sein, ont été retournées à leurs niveaux de base dans cette expérience. La viabilité des cellules ainsi obtenues a été analysé par plusieurs tests. Les expériences reproductibles ont indiqué que la force, la mobilité et le caractère invasif des cellules de cancer du sein ont été diminués après que les niveaux d’ARN-micro 449 sont retournés à leurs niveaux de base. En outre, l’étude a révélé que quatre gènes potentiels (CRIP2, XBP1, TAF4B, et SFXN2) pourraient être manipulés à l’avenir pour réduire davantage la viabilité des tumeurs du cancer du sein.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".