Complexity of Determining Cause and Effect In Vivo After Antisense Gene Therapy
Bibliographic record
Abstract
Injuries to joint tissues are major clinical problems occurring with significant frequency and resulting in the formation of scar tissue or in some tissues with no healing at all. Such scar tissue has compromised biomechanical integrity, which leads to impaired function, increased risk of reinjury, induction of remodeling in other joint tissues and increases the risk of diseases such as ostheoarthritis. Development of new therapies, such as gene therapy, to enhance repair could have a significant impact on quality of life for patients. The well-characterized rabbit medial collateral ligament injury model was used to transiently modulate the expression of specific molecules during early stages of healing. The small matrix proteoglycan decorin, known to influence matrix assembly and to bind and growth factors, was targeted in vivo using decorin-specific antisense oligodeoxynucleotides and Hemagglutinating Virus of Japan-Liposome method. After 4 weeks of healing, scar tissue was assessed after antisense exposure by reverse transcription polymerase chain reaction, Western Blot analysis, light and transmission electron microscopy, and biomechanically for low and high load behavior. Ligament scar messenger ribonucleic acid and protein levels for decorin decreased and collagen fibril diameter size increased after antisense treatment. Creep and stress at failure improved after antisense treatment indicating a functional improvement in the scar tissue. However, messenger ribonucleic acid levels for multiple genes were affected by the decorin-specific antisense treatment and therefore all of the observed improvements in the scar tissue cannot be directly ascribed to depressing decorin levels.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".