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Related Concept Videos

Changes in Skin Color: Clinical Perspectives01:14

Changes in Skin Color: Clinical Perspectives

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The first thing a clinician sees is the skin, so the examination of the skin should be part of any thorough physical examination. Most skin disorders are relatively benign, but a few, including melanomas, can be fatal if untreated. A couple of the more noticeable disorders, albinism and vitiligo, affect the appearance of the skin and its accessory organs.
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Translation01:31

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Lesson: Translation
Translation is the process of synthesizing proteins from the genetic information carried by messenger RNA (mRNA). Following transcription, it constitutes the final step in the expression of genes. This process is carried out by ribosomes, complexes of protein and specialized RNA molecules. Ribosomes, transfer RNA (tRNA), and other proteins produce a chain of amino acids—the polypeptide—as the end product of translation.
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The term "intelligence" is complex because it refers to both behavior and individuals, and its interpretation varies across cultures. European Americans tend to link intelligence with reasoning and cognitive skills, while in Kenya, it is tied to responsible participation in family and social life. In Uganda, intelligence is seen as the ability to know the right actions and carry them out effectively, while the Iatmul people of Papua New Guinea associate it with the capacity to remember...
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Initiation of Translation02:33

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Initiating translation is complex because it involves multiple molecules. Initiator tRNA, ribosomal subunits, and eukaryotic initiation factors (eIFs) are all required to assemble on the initiation codon of mRNA. This process consists of several steps that are mediated by different eIFs.
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The large ribosomal subunit has several important structures essential to translation. These include the peptidyl transferase center (PTC) - which is the site where the peptide bond is formed - and a large, internal, water-filled tube through which the nascent polypeptide moves. This latter structure is called the Peptide Exit Tunnel, and it begins at the PTC and spans the body of the large ribosomal subunit. During translation, as the nascent polypeptide chain is synthesized, it passes through...
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Artificial intelligence and machine learning in clinical development: a translational perspective.

Pratik Shah1, Francis Kendall1,2, Sean Khozin3

  • 11Massachusetts Institute of Technology, Media Laboratory, Cambridge, MA USA.

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The future of clinical development is transforming with AI and machine learning. These technologies analyze digital data to improve patient care and modernize drug development pathways.

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Area of Science:

  • Computational biology and bioinformatics
  • Clinical trial methodology
  • Regulatory science

Background:

  • Clinical development is undergoing a significant transformation driven by the convergence of digital data, advanced computing power, and regulatory adaptation.
  • Artificial intelligence (AI) and machine learning (ML) algorithms are key to identifying meaningful patterns in large datasets.
  • Collaboration between academia, industry, regulators, and technology sectors is crucial for this evolution.

Purpose of the Study:

  • To summarize insights, developments, and recommendations for integrating computational evidence into clinical development and healthcare.
  • To outline strategies for modernizing clinical development using AI/ML and secure computing.
  • To discuss the impact of digital algorithmic evidence on improving patient medical care.

Main Methods:

  • Analysis of publicly available biomedical and clinical trial datasets.
  • Utilizing real-world evidence from sensors and health records.
  • Application of machine learning architectures for data analysis and pattern identification.

Main Results:

  • Identification of key trends and challenges in adopting computational evidence.
  • Development of strategies for integrating AI/ML into clinical development workflows.
  • Outlining of new regulatory pathways for digital health technologies.

Conclusions:

  • The integration of AI and ML offers a powerful approach to enhance clinical development and healthcare outcomes.
  • Actionable computational evidence is essential for future medical advancements.
  • Regulatory bodies are increasingly supportive of innovative digital methods in drug development and patient care.