LIRTS Viewer: A Web-Based Resource to View the Transcriptional Response of Lens Epithelial Cells to Injury
Bibliographic record
Abstract
Purpose: Residual lens epithelial cells (LECs) respond to injury after cataract surgery, leading to posterior capsular opacification (PCO). Transcriptomic profiling of lens capsule-associated cells (CACs) post-cataract surgery (PCS) revealed that LECs quickly alter their transcriptome, producing numerous pro-inflammatory cytokines within a few hours PCS. In contrast, the significant activation of TGFβ signaling and fibrotic extracellular matrix deposition related to PCO only begins 1 to 3 days later. However, the global changes in gene expression in CACs, following the establishment of robust TGFβ signaling, remain unknown. Methods: Lens fiber cells were removed from wild-type mice, and CACs were isolated at 0, 72, or 120 hours PCS to perform bulk RNA sequencing (RNA-seq) to obtain estimates of RNA abundance. These data were combined with existing RNA-seq datasets to create a web-based visualization resource to explore the expression dynamics of most protein coding genes in CACs. Results: At 72 hours PCS, CACs differentially express genes consistent with a surge in proliferation and changes in actin filament organization while also robustly expressing fibrotic marker genes by 120 hours PCS. We developed a data visualization resource, the Lens Injury Response Time Series (LIRTS) Viewer, which integrates all data to gather valuable insights from gene expression in CACs over the first 5 days PCS. Conclusions: The LIRTS Viewer is useful for generating hypotheses related to PCO pathogenesis, as it reveals CAC gene expression dynamics, gene correlations, and biological pathways during the first 5 days following lens injury in an in vivo cataract surgery model.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.089 | 0.039 |
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".