Germ cell-specific proteins ACRV1 and AKAP4 facilitate identification of rare spermatozoa in semen of non-obstructive azoospermia patients
Bibliographic record
Abstract
ABSTRACT Non-obstructive azoospermia (NOA), the most severe form of male infertility due to testicular failure, could be treated with intra-cytoplasmic sperm injection (ICSI), providing spermatozoa were retrieved with the microdissection testicular sperm extraction (mTESE). Here, we hypothesized that some testis- and germ cell-specific proteins would facilitate flow cytometry-assisted identification of rare spermatozoa in semen cell pellets of NOA patients, thus enabling non-invasive diagnostics prior to mTESE. Data mining and extensive verification by targeted proteomic assays and immunofluorescent microscopy revealed a panel of testis-specific proteins expressed at the continuum of germ cell differentiation, including the late germ cell-specific proteins AKAP4_HUMAN and ASPX_HUMAN (ACRV1 gene) with the exclusive expression in spermatozoa tails and acrosomes, respectively. A multiplex imaging flow cytometry assay revealed low numbers of the morphologically intact AKAP4 + /ASPX + /Hoechst + spermatozoa in semen pellet of NOA patients. While the previously suggested soluble markers for spermatozoa retrieval suffered from low diagnostic specificity, our multi-step gating strategy and visualization of AKAP4 + /ASPX + /Hoechst + cells bearing elongated tails and acrosome-capped nuclei facilitated fast and unambiguous identification of the mature intact spermatozoa. Pending further validation, our assay may emerge as a non-invasive test to predict the retrieval of morphologically intact spermatozoa by mTESE, thus improving diagnostics and treatment of the severe forms of 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".