Exploring the Molecular Link Between Diabetes and Erectile Dysfunction Through Single-Cell Transcriptome Analysis
Bibliographic record
Abstract
Erectile dysfunction (ED) is a pathophysiological condition in which the patients cannot achieve an erection during sexual activity, and it is often overlooked yet prevalent among diabetic men, globally affecting approximately 35-75% of diabetic individuals. The precise mechanisms through which diabetes contributes to ED remain elusive, but the existing literature suggests the potential involvement of nerve and vascular damage that affects the penile supply. In the present review, we reanalyze the existing human single-cell transcriptomic data from patients having diabetes mellitus-associated ED with normal erections. The analysis validates the expression of genes associated with antioxidative pathways, growth factors, adipokines, angiogenesis, vascular functions, penile erection, sexual function, and inflammation in diverse cell types from healthy individuals and those with ED. Our transcriptomic analysis reveals alterations in the expression of adiponectin receptors in the pathogenesis of ED compared to their counterparts in healthy subjects. This comprehensive review sheds light on the molecular underpinnings of ED in the context of diabetes, providing an in-depth understanding of the biological and cellular alterations involved and paving the way for possible targeted therapeutic discoveries in the field of diabetes-associated male infertility.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".