The spectrum of lysosomal stress and damage responses: from mechanosensing to inflammation
Bibliographic record
Abstract
Cells and tissues turn over their aged and damaged components in order to adapt to a changing environment and maintain homeostasis. These functions rely on lysosomes, dynamic and heterogeneous organelles that play essential roles in nutrient redistribution, metabolism, signaling, gene regulation, plasma membrane repair, and immunity. Because of metabolic fluctuations and pathogenic threats, lysosomes must adapt in the short and long term to maintain functionality. In response to such challenges, lysosomes deploy a variety of mechanisms that prevent the breaching of their membrane and escape of their contents, including pathogen-associated molecules and hydrolases. While transient permeabilization of the lysosomal membrane can have acute beneficial effects, supporting inflammation and antigen cross-presentation, sustained or repeated lysosomal perforations have adverse metabolic and transcriptional consequences and can lead to cell death. This review outlines factors contributing to lysosomal stress and damage perception, as well as remedial processes aimed at addressing lysosomal disruptions. We conclude that lysosomal stress plays widespread roles in human physiology and pathology, the understanding and manipulation of which can open the door to novel therapeutic strategies.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".