IAS Gyan

Daily News Analysis


24th April, 2024 Health


Source: LiveScience

Disclaimer: Copyright infringement not intended.


  • A new AI-powered test could one day be used to accurately and quickly detect three major types of cancer using only a single spot of dried blood.


  • The new tool uses machine learning, a type of artificial intelligence (AI), to analyze by-products of metabolism, or metabolites, in blood samples.
  • These metabolites — which are found in the liquid portion of blood known as serum — act as "biomarkers" that can potentially flag cancer's presence in the body

Key Findings

  • Test Accuracy: The preliminary experiments indicate that the test can distinguish between patients with pancreatic, gastric, or colorectal cancer and those without cancer with high accuracy, around 82% to 100% of the time.
  • Speed and Efficiency: The analysis using this new tool takes only minutes, providing rapid results compared to traditional diagnostic methods.
  • Minimal Blood Requirement: The test requires less than 0.05 milliliters of dried blood, making it minimally invasive and potentially easier to administer than traditional blood tests.

Potential Impact

  • Early Detection: Screening for blood biomarkers offers the potential for earlier cancer diagnosis, improving survival rates, especially for cancers like pancreatic, colorectal, and gastric, which currently lack standalone blood tests.
  • Global Access: The use of dried blood samples could democratize cancer screening by improving access to testing, particularly in remote or resource-limited areas.
  • Reducing Undiagnosed Cases: The test could significantly reduce the proportion of undiagnosed cases of these cancers if implemented in large-scale screening programs.

About Biomarkers

  • Biomarkers are measurable indicators of biological processes, conditions, or responses to interventions.
  • They can be molecular, biochemical, physiological, or imaging-based characteristics that can be objectively measured and evaluated.

Types of Biomarkers:

  • Genetic Biomarkers: These biomarkers involve variations in an individual's DNA sequence and can indicate susceptibility to certain diseases or predict treatment response.
  • Protein Biomarkers: Proteins such as enzymes, hormones, or antibodies can serve as biomarkers, reflecting various physiological states or disease conditions.
  • Metabolic Biomarkers: These biomarkers represent the products of metabolic processes and can indicate the presence or progression of diseases like diabetes or metabolic syndrome.
  • Imaging Biomarkers: Imaging techniques like MRI, CT scans, or PET scans can reveal structural or functional changes in tissues or organs, serving as biomarkers for disease diagnosis or monitoring.
  • Epigenetic Biomarkers: Changes in gene expression patterns due to modifications in DNA or histone proteins can serve as epigenetic biomarkers, offering insights into disease mechanisms or environmental exposures.

Significance of Biomarkers:

  • Disease Diagnosis: Biomarkers facilitate early detection and diagnosis of diseases, enabling timely interventions and improved patient outcomes.
  • Prognosis: Certain biomarkers can predict disease progression, treatment response, and patient survival rates, aiding clinicians in tailoring personalized treatment plans.
  • Monitoring Treatment Efficacy: Biomarkers help monitor the effectiveness of therapeutic interventions, allowing adjustments in treatment strategies as needed.
  • Drug Development: Biomarkers play a crucial role in drug discovery and development by identifying potential therapeutic targets, predicting drug responses, and stratifying patient populations for clinical trials.
  • Precision Medicine: Biomarker-based approaches support the concept of precision medicine by enabling targeted therapies based on individual genetic, molecular, or phenotypic characteristics.

Applications of Biomarkers:

  • Cancer Diagnosis and Management: Biomarkers such as tumor-specific antigens, genetic mutations, or circulating tumor cells are used for cancer detection, staging, and monitoring treatment response.
  • Cardiovascular Disease Risk Assessment: Biomarkers like cholesterol levels, C-reactive protein, or cardiac troponins help assess cardiovascular risk and guide preventive strategies.
  • Neurological Disorders: Biomarkers such as amyloid-beta and tau proteins are used in the diagnosis and monitoring of neurodegenerative diseases like Alzheimer's.
  • Infectious Diseases: Biomarkers such as viral RNA or specific antibodies aid in the diagnosis and management of infectious diseases like HIV/AIDS or hepatitis.
  • Personalized Medicine: Biomarker-based approaches enable personalized treatment regimens tailored to an individual's genetic makeup, disease subtype, or treatment response.


  • Biomarker discovery requires rigorous validation to ensure reliability and reproducibility across different populations and clinical settings.
  • Biological variability and disease heterogeneity pose challenges in identifying universal biomarkers applicable to diverse patient populations.
  • Ethical issues related to patient privacy, informed consent, and data sharing must be addressed, along with regulatory requirements for biomarker development and clinical use.
  • Advancements in technology are needed to enhance the sensitivity, specificity, and cost-effectiveness of biomarker detection methods.
  • Integrating data from genomics, proteomics, metabolomics, and other omics disciplines requires sophisticated bioinformatics tools and interdisciplinary collaboration.

Must read articles:





Q.  Discuss the significance of biomarkers in modern healthcare, highlighting their role in disease diagnosis, prognosis, and personalized medicine. (250 words)