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Updated: Dec 10, 2025

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
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Through Predictive Personalized Medicine.

Giuseppe Giglia1,2, Giuditta Gambino1, Pierangelo Sardo1

  • 1Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), Section of Human Physiology, University of Palermo, 90134 Palermo, Italy.

Brain Sciences
|September 3, 2020
PubMed
Summary
This summary is machine-generated.

Artificial intelligence models predict neuroblastoma (NBM) treatment responses. This approach aims for more effective immunotherapy by analyzing signaling pathways for targeted therapies against this pediatric cancer.

Keywords:
PD-L1computational modellingimmunotherapyneuroblastoma

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Area of Science:

  • Oncology
  • Computational Biology
  • Immunotherapy

Background:

  • Neuroblastoma (NBM) is a prevalent pediatric solid tumor with challenging treatments for aggressive forms.
  • Current therapeutic strategies include surgery, chemotherapy, and radiotherapy, with a need for improved and safer options.
  • Immunotherapy is emerging as a promising, potentially safer, complementary treatment for NBM.

Discussion:

  • In-silico predictive models analyze data to forecast NBM phenotypes for enhanced immunotherapy.
  • These models leverage knowledge of intracellular signaling pathways to predict responses to PD-L1/PD-1 blockade.

Key Insights:

  • Computational models can predict how NBM phenotypes respond to anti-PD-1/PD-L1 immunotherapy.
  • This predictive capability is crucial for developing more targeted therapeutic strategies.

Outlook:

  • Artificial intelligence and machine learning offer novel tools for personalized cancer treatment.
  • Future applications may lead to more reliable and targeted immunotherapy for neuroblastoma.