Bibliographic record
Abstract
Lipids play diverse roles in sustaining life, including energy storage, hormonal balance, and cellular communication. Alterations in lipid metabolism can lead to various disorders, including diabetes, atherosclerosis, cancer, and neurodegenerative diseases. Among these disorders, lysosomal storage disorders (LSDs) related to glycosphingolipids metabolism present significant challenges. This review systematically analyzes the current literature on LSDs, focusing on classification, clinical presentations, diagnostic advancements, available treatments, and emerging therapeutic strategies. Glycosphingolipids biosynthesis, particularly its role in viral dissemination and melanin synthesis, underscores its significance in health and disease. Additionally, the review delves into specific LSDs, such as Fabry disease, Gaucher disease, Sandhoff disease, Tay-Sachs disease, and Krabbe disease, highlighting their pathophysiology, prevalence, and treatment options. Enzyme replacement therapy and hematopoietic stem cell transplantation are mainstays in LSD treatment, but gene therapy shows promise. Furthermore, the review explores the role of glycosphingolipids in non-communicable diseases like diabetes, cancer, atherosclerosis, lupus, Alzheimer's, Parkinson's disease, and influenza. Understanding glycosphingolipid metabolism offers insights into disease mechanisms and therapeutic targets, paving the way for improved treatments and ultimately enhancing patient outcomes.
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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".