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MALDI-TOF MS has transformed clinical microbiology by offering a rapid and reliable method for pathogen identification. The traditional approach to microbial identification typically involves time-consuming culture techniques and biochemical tests, which can delay the initiation of appropriate antimicrobial therapy. MALDI-TOF MS avoids these delays by using characteristic ribosomal protein mass patterns of microbial cells, enabling accurate species-level identification within minutes.Principle...

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MIPD: Molecules, Imagings, and Clinical Phenotype Integrated Database.

Jiaojiao Zhao1, Min Wu2, Meihua Wan3

  • 1Department of Clinical Research Center, Sichuan Clinical Research Center for Medical Imaging, Dazhou Key Laboratory for Precision Cancer Therapy, Dazhou Key Laboratory for Artificial Intelligence and Medical Imaging, Dazhou Central Hospital, 56 Nan Yue Miao Street, Dazhou 635000, China.

Database : the Journal of Biological Databases and Curation
|April 21, 2025
PubMed
Summary

This study introduces a comprehensive database integrating molecular, imaging, and clinical data for colorectal cancer. It aids in understanding tumor heterogeneity and developing personalized precision medicine strategies.

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Area of Science:

  • Oncology
  • Bioinformatics
  • Medical Imaging

Background:

  • Tumor heterogeneity poses a challenge to current colorectal cancer treatments.
  • Integrated analysis of multi-modal data can characterize tumor heterogeneity for personalized medicine.
  • Cross-modal databases are underexplored for colorectal cancer research.

Purpose of the Study:

  • To establish a comprehensive, user-friendly database integrating molecular, imaging, and clinical data for colorectal cancer.
  • To facilitate the interpretability of cross-modal data for advancing precision medicine.
  • To identify distinct molecules and imaging features associated with clinical phenotypes and survival.

Main Methods:

  • Developed a database incorporating genes, proteins, metabolites, pathways, imaging features, and clinical factors from colorectal cancer patients.
  • Performed integrated analysis to identify significant associations between molecular, imaging, and clinical data.
  • Implemented functionalities for data query, browse, search, visualization, and download.

Main Results:

  • The database contains 9965 genes, 5449 proteins, 1121 metabolites, 283 pathways, 854 imaging features, and 73 clinical factors.
  • Identified distinct molecules and imaging features associated with clinical phenotypes and patient survival.
  • Established correlations between genetic molecules, clinical factors, and imaging features, including predictive models for gene expression.

Conclusions:

  • The Molecules, Imaging, and Clinical Phenotype Correlation Database (MIPD) is a valuable resource for colorectal cancer research.
  • MIPD enables interpretability of cross-modal data, supporting the development of precision medicine approaches.
  • The platform facilitates understanding tumor heterogeneity and improving treatment strategies for colorectal cancer patients.