Evidence map›Paper›PMID 39375288›Full record

ArticleAndrology2025

Rapid detection of mouse spermatogenic defects by testicular cellular composition analysis via enhanced deep learning model.

Nianfei Ao, Min Zang, Yue Lu, Yiping Jiao, Haoda Lu, Chengfei Cai, Xiangxue Wang, Xin Li, Minge Xie, Tingting Zhao and 2 more

Abstract read
In one paragraph

Article in Andrology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Nianfei AoJiangsu Key Laboratory of Intelligent Medical Image Computing, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China.
Min ZangState Key Laboratory of Reproductive Medicine and Offspring Health, Nanjing Medical University, Nanjing, Jiangsu, China.
Yue LuState Key Laboratory of Reproductive Medicine and Offspring Health, Nanjing Medical University, Nanjing, Jiangsu, China.
Yiping JiaoJiangsu Key Laboratory of Intelligent Medical Image Computing, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China.
Haoda LuJiangsu Key Laboratory of Intelligent Medical Image Computing, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China.
Chengfei CaiJiangsu Key Laboratory of Intelligent Medical Image Computing, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China.
Xiangxue WangJiangsu Key Laboratory of Intelligent Medical Image Computing, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China.
Xin LiState Key Laboratory of Reproductive Medicine and Offspring Health, Nanjing Medical University, Nanjing, Jiangsu, China.
Minge XieDepartment of Statistics, Rutgers University, Piscataway, New Jersey, USA.
Tingting ZhaoState Key Laboratory of Reproductive Medicine and Offspring Health, Nanjing Medical University, Nanjing, Jiangsu, China.
Jun XuJiangsu Key Laboratory of Intelligent Medical Image Computing, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China.
Eugene Yujun XuState Key Laboratory of Reproductive Medicine and Offspring Health, Nanjing Medical University, Nanjing, Jiangsu, China.ORCID 0000-0002-4344-6606

Funding

National Key R&D Program of China 2023YFC3402800National Natural Science Foundation of China 31970792National Natural Science Foundation of China 62101365National Natural Science Foundation of China 62171230National Natural Science Foundation of China 92159301
6 · The paper itself

Abstract

backgroundHistological analysis of the testicular sections is paramount in infertility research but tedious and often requires months of training and practice.

objectivesEstablish an expeditious histopathological analysis of mutant mice testicular sections stained with commonly available hematoxylin and eosin (H&E) via enhanced deep learning model MATERIALS AND

methodsAutomated segmentation and cellular composition analysis on the testes of six mouse reproductive mutants of key reproductive gene family, DAZ and PUMILIO gene family via H&E-stained mouse testicular sections.

resultsWe improved the deep learning model with human interaction to achieve better pixel accuracy and reduced annotation time for histologists; revealed distinctive cell composition features consistent with previously published phenotypes for four mutants and novel spermatogenic defects in two newly generated mutants; established a fast spermatogenic defect detection protocol for quantitative and qualitative assessment of testicular defects within 2.5-3 h, requiring as few as 8 H&E-stained testis sections; uncovered novel defects in AcDKO and a meiotic arrest defect in HDBKO, supporting the synergistic interaction of Sertoli Pum1 and Pum2 as well as redundant meiotic function of Dazl and Boule. DISCUSSION: Our testicular compositional analysis not only could reveal spermatogenic defects from staged seminiferous tubules but also from unstaged seminiferous tubule sections.

conclusionOur SCSD-Net model offers a rapid protocol for detecting reproductive defects from H&E-stained testicular sections in as few as 3 h, providing both quantitative and qualitative assessments of spermatogenic defects. Our analysis uncovered evidence supporting the synergistic interaction of Sertoli PUM1 and PUM2 in maintaining average testis size, and redundant roles of DAZ family proteins DAZL and BOULE in meiosis.

Indexed as

Deep LearningSpermatogenesisTestisAnimalsMaleMiceRNA-Binding ProteinsRNA-Binding ProteinsBOULEDAZLdeep learningPUMILIOseminiferous tubules testis histology

Identifiers

PMID39375288
PMCPMC12368930

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.