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Unlike mitosis, meiosis aims for genetic diversity in its creation of haploid gametes. Dividing germ cells first begin this process in prophase I, where each chromosome—replicated in S phase—is now composed of two sister chromatids (identical copies) joined centrally.
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Crossing over is the exchange of genetic information between homologous chromosomes during prophase I of meiosis I. Genetic recombination gives rise to allelic diversity in the newly formed daughter cells. In humans, crossing over produces genetically distinct haploid egg and sperm cells that undergo fertilization to produce unique offspring. Before cell division starts, the germ cell’s chromosome(s) undergo duplication in the S phase of the cell cycle. As the cells enter prophase I,...
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Author Spotlight: Genetic Profiling for Fluorouracil Response in Gastric Cancer
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An integrated AI-enabled system using One Class Twin Cross Learning for early gastric cancer detection.

Xian-Xian Liu1,2, Yuanyuan Wei3,4, Yongze Guo5

  • 1Guangdong Institute of Intelligence Science and Technology, Zhuhai, China.

Frontiers in Oncology
|February 4, 2026
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Summary

This study introduces an AI-enabled imaging system for early gastric cancer detection, achieving 99.70% accuracy. The novel One Class Twin Cross Learning (OCT-X) algorithm enhances diagnostic speed and precision in point-of-care settings.

Keywords:
One Class Twin Cross Learning (OCT-X)artificial intelligence (AI)computer-aided detection (CAD)early gastric cancer (EGC)precision diagnostics

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Gastric cancer is a leading cause of cancer mortality globally.
  • Current diagnostic methods for gastric cancer have limitations, leading to frequent misdiagnoses.
  • There is a critical need for improved early detection technologies.

Purpose of the Study:

  • To develop and evaluate an integrated AI-enabled imaging system for enhanced gastric cancer detection.
  • To improve the accuracy and efficiency of early gastric cancer diagnosis.
  • To enable non-invasive early detection in point-of-care settings.

Main Methods:

  • Development of a novel One Class Twin Cross Learning (OCT-X) algorithm.
  • Utilization of a fast double-threshold grid search (FDT-GS) and a deep fully convolutional network.
  • Integration with an all-in-one point-of-care testing (POCT) device featuring high-resolution sensors and real-time processing.
  • Data acquisition and control using NI CompactDAQ and LabVIEW software.

Main Results:

  • The integrated system achieved a diagnostic accuracy of 99.70%.
  • Performance surpassed existing state-of-the-art models by up to 4.47%.
  • Demonstrated a 10% improvement in multirate adaptability for robust performance.

Conclusions:

  • The OCT-X algorithm and integrated platform show significant potential for early gastric cancer detection.
  • The system offers a more accurate, efficient, and non-invasive approach.
  • Successful application in point-of-care settings is highlighted.