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Related Concept Videos

Infertility in Males01:23

Infertility in Males

Male infertility affects millions of couples worldwide, arising from various factors that impact different stages of the reproductive process. An endocrine imbalance resulting from conditions like hypogonadism, Klinefelter syndrome, or pituitary disorders can disrupt hormone levels and reduce sperm production. Testicular defects, such as tumors, cryptorchidism, atrophic testes, abnormal sperm morphology, and low sperm count or motility, may arise due to genetic factors, structural...
Infertility in Females01:28

Infertility in Females

Female infertility is defined as the inability to conceive after a year of regular, unprotected intercourse and affects about 10–15% of couples worldwide. The primary cause of female infertility is ovulatory disorders, which hinder the release of eggs. These disorders can be classified as hypothalamic amenorrhea, polycystic ovarian syndrome (PCOS), premature ovarian failure, and hyperprolactinemic anovulation disorders.
Endometriosis, a condition characterized by abnormal growth of endometrial...
In Vitro Fertilization01:24

In Vitro Fertilization

In vitro fertilization (IVF) is a form of assisted reproductive technology where an egg is fertilized with sperm in a controlled laboratory environment before transferring the resulting embryo into the uterus. This process is designed to help individuals and couples experiencing difficulties conceiving.
The IVF process begins with ovarian stimulation, during which reproductive endocrinologists prescribe hormonal medications to stimulate the ovaries to produce multiple eggs instead of the single...

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Related Experiment Video

Updated: Jun 25, 2026

Treatment Model for Young Patients with Psychogenic Erectile Dysfunction and Resultant Infertility
04:22

Treatment Model for Young Patients with Psychogenic Erectile Dysfunction and Resultant Infertility

Published on: May 30, 2025

E-InfertilityTest: Implementing an explainable AI framework for male infertility assessment.

Gourab Das1, Byapti Ghosh1, Zhumur Ghosh1

  • 1Department of Biological Sciences, Bose Institute, Unified Academic Campus, Kolkata, 700 091, India.

Biochemical and Biophysical Research Communications
|June 23, 2026
PubMed
Summary
This summary is machine-generated.

Genetic defects cause male infertility, impacting sperm function. A new AI tool, E-InfertilityTest, analyzes gene expression to predict infertility and identify contributing genes, aiding in understanding and potential treatment.

Keywords:
AsthenozoospermiaExplainable AIMachine learningMale infertilitySpermTeratozoospermia

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Flow Cytometric Analysis of Biomarkers for Detecting Human Sperm Functional Defects
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Flow Cytometric Analysis of Biomarkers for Detecting Human Sperm Functional Defects

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Last Updated: Jun 25, 2026

Treatment Model for Young Patients with Psychogenic Erectile Dysfunction and Resultant Infertility
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Flow Cytometric Analysis of Biomarkers for Detecting Human Sperm Functional Defects
08:48

Flow Cytometric Analysis of Biomarkers for Detecting Human Sperm Functional Defects

Published on: April 21, 2022

Area of Science:

  • Genomics
  • Reproductive Medicine
  • Bioinformatics

Background:

  • Male infertility is a growing concern, often linked to genetic factors affecting sperm motility and morphology.
  • Conditions like Asthenozoospermia and Teratozoospermia reduce fertility, with current assisted reproductive technologies (ART) having limited success rates.
  • Standard semen analysis offers a limited view, necessitating deeper genetic insights into sperm function.

Purpose of the Study:

  • To analyze transcriptomic data from infertile and fertile sperm to identify genetic markers of male infertility.
  • To develop a predictive model (E-InfertilityTest) for classifying sperm as fertile or infertile based on gene expression.
  • To provide patient-specific gene expression profiles linked to male infertility aberrations.

Main Methods:

  • Performed a meta-analysis of transcriptomic data from infertile (Asthenozoospermia, Teratozoospermia) and fertile sperm.
  • Screened a signature gene set to build the E-InfertilityTest prediction model.
  • Utilized an AI-based framework for classifying sperm and identifying dominant genes.

Main Results:

  • Identified a signature gene set associated with male infertility.
  • Developed the E-InfertilityTest model capable of classifying sperm based on genetic profiles.
  • The model provides dominant gene expression insights for patient-specific aberration analysis.

Conclusions:

  • The AI-based E-InfertilityTest serves as a proof-of-concept for predicting the genetic basis of male infertility.
  • This approach offers a deeper understanding of aberrant sperm populations beyond traditional analysis.
  • The tool is available on GitHub for further research and application.