Modulation of Inflammation and Regeneration in the Intervertebral Disc Using Enhanced Cell‐Penetrating Peptides for MicroRNA Delivery
Bibliographic record
Abstract
Back pain is a global epidemiological and socioeconomic problem affecting up to 80% of people at some stage during their life and is often due to degeneration of the intervertebral disc (IVD). Therapies aimed at restoring the intradiscal space have predominantly focused on delivery of biomaterials, cells, or growth factors, among others, with variable degrees of success. While viral gene delivery strategies have emerged as promising therapeutic options in recent years, these approaches often have off‐target effects and are associated with immunogenicity risks and other comorbidities. Consequently, nonviral methods have gained traction as potential avenues for gene delivery. Herein, enhanced cell‐penetrating peptide (CPP) systems are used to deliver microRNAs in an in vitro and ex vivo model of disc degeneration. The data suggest that nanoparticle complexation of CPPs with (miR‐221‐inhibitor + miR‐149‐mimic) promotes protective effects in nucleus pulposus cells challenged with inflammatory cytokines TNF‐α and IL‐1β. Specifically, increases in matrix deposition, significant decreases in the secretion of an array of inflammatory cytokines, and decreased expression of matrix degradation enzymes MMP13 and ADAMTS5 are observed. These miR‐CPP nanocomplexes can be further employed for targeting of the pericellular matrix space through homing, thus providing a promising approach for therapies of the intradiscal space.
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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 | 0.000 | 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".