Comparison between Macro and Trace Element Concentrations in Human Semen and Blood Serum in Highly Polluted Areas in Italy
Bibliographic record
Abstract
Macro and trace elements are important regulators of biological processes, including those ones linked to reproduction. Among them, Ca, Cu, Fe, K, Mg, Mn, Na, Se, and Zn ensure normal spermatic functions. Hence, the aim of this study was to evaluate the concentrations of 26 macro and trace elements (Al, As, Ba, Be, Ca, Cd, Co, Cr, Cu, Fe, Hg, K, Li, Mg, Mn, Na, Ni, Pb, Rb, Sb, Se, Sn, Sr, U, V, and Zn) in blood serum and also in semen of healthy young men, homogeneous for age, anthropometric characteristics, and lifestyle, living in three highly polluted areas in Italy. Furthermore, a comparison among three geographical areas was performed to highlight any difference in the investigated parameters and, overall, to speculate any correlations between chemical elements and semen quality. Statistically significant differences (p < 0.05) among the three areas were found for each investigated element, in both semen and serum samples, where inter-area differences were more evident in semen than in blood serum, suggesting human semen as an early environmental marker. Considering the homogeneity of three cohorts, these differences could be due more to environmental conditions in the recruiting areas, suggesting that variations in those involved in reproductive-associated pathways can have an impact on male fertility. Nevertheless, more research is needed to evaluate threshold values for sperm dysfunction and male infertility. Actually, the role of different dietary intake and environmental exposure underlying the observed differences in the recruiting areas is under further investigation for the same cohort.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".