Analysis of Seminal Plasma from Patients with Non-obstructive Azoospermia and Identification of Candidate Biomarkers of Male Infertility
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Infertility affects approximately 15% of couples with equivalent male and female contribution. Absence of sperm in semen, referred to as azoospermia, accounts for 5-20% of male infertility cases and can result from pretesticular azoospermia, non-obstructive azoospermia (NOA), and obstructive azoospermia (OA). The current clinical methods of differentiating NOA cases from OA ones are indeterminate and often require surgical intervention for a conclusive diagnosis. We catalogued 2048 proteins in seminal plasma from men presented with NOA. Using spectral-counting, we compared the NOA proteome to our previously published proteomes of fertile control men and postvasectomy (PV) men and identified proteins at differential abundance levels among these clinical groups. To verify spectral counting ratios for candidate proteins, extracted ion current (XIC) intensities were also used to calculate abundance ratios. The Pearson correlation coefficient between spectral counting and XIC ratios for the Control-NOA and NOA-PV data sets is 0.83 and 0.80, respectively. Proteins that showed inconsistent spectral counting and XIC ratios were removed from analysis. There are 34 proteins elevated in Control relative to NOA, 18 decreased in Control relative to NOA, 59 elevated in NOA relative to PV, and 16 decreased in NOA relative to PV. Many of these proteins have expression in the testis and the epididymis and are linked to fertility. Some of these proteins may be useful as noninvasive biomarkers in discriminating NOA cases from OA.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it