Editorial: Cross-talk and interaction between endocrinology and urology: challenges and opportunities
Bibliographic record
Abstract
Cross-talk and interaction between endocrinology and urology: challenges and opportunitiesThe urinary and reproductive systems are closely interconnected with the endocrine System (1, 2).Some of the urinary and reproductive organs themselves function as endocrine organs.If these organs undergo pathological changes, it can affect the endocrine system (3).Conversely, when the endocrine system experiences metabolic disorders, the urinary system can also be affected (4, 5).In recent decades, there have been rapid advances in the diagnosis and treatment of endocrine and urological diseases.Therefore, this Research Topic aims to study the endocrine metabolic disorders in patients with urinary and reproductive system diseases and their pathophysiological mechanisms.Through this interaction, we hope to promote cooperation and communication among experts in the fields of urinary and reproductive systems and endocrinology, and to carry out meaningful collaborative research, thereby proposing new ideas for diagnosis and treatment.Liu et al. investigates the impact of steroid hormones on retinal neurodegenerative diseases (RND) using genetic variations as instrumental variables.Glaucoma risk was found to be influenced by testosterone/17b-estradiol (T/E2) (OR = 1.11, 95% CI, 1.01-1.22,P = 0.03), which was validated by multiple methods.However, no impact of other steroid hormones (aldosterone, androstenedione, progesterone, and 17-hydroxyprogesterone) was observed on diabetic retinopathy (DR) and age-related macular degeneration (AMD) risk.The study suggests that T/E2 may have a suggestive effect on glaucoma risk, but further research is needed to explore steroid hormones as targets for prevention and treatment.An et al. investigates the relevance of metabolic syndrome (MetS) and metabolic scores to metastatic prostate cancer (PCa) occurrence, progression, and prognosis.Patients with MetS had higher T stage, Gleason score, and tumor load, with shorter time to progression to castration-resistant PCa stage.The median survival time was significantly shorter in the MetS group.Metabolic score correlated with survival time.The study concludes that MetS may promote metastatic PCa progression and affect prognosis, suggesting its role as a risk factor in metastatic PCa outcomes.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.014 | 0.016 |
| Insufficient payload (model declined to judge) | 0.026 | 0.017 |
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".