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Related Concept Videos

Primary Active Transport01:47

Primary Active Transport

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In contrast to passive transport, active transport involves a substance being moved through membranes in a direction against its concentration or electrochemical gradient. There are two types of active transport: primary active transport and secondary active transport. Primary active transport utilizes chemical energy from ATP to drive protein pumps that are embedded in the cell membrane. With energy from ATP, the pumps transport ions against their electrochemical gradients—a direction...
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Primary Active Transport01:29

Primary Active Transport

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In contrast to passive transport, active transport involves a substance being moved through membranes in a direction against its concentration or electrochemical gradient. There are two types of active transport: primary active transport and secondary active transport. Primary active transport utilizes chemical energy from ATP to drive protein pumps embedded in the cell membrane. With energy from ATP, the pumps transport ions against their electrochemical gradients—a direction they would...
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Primary and Secondary Growth in Roots and Shoots03:02

Primary and Secondary Growth in Roots and Shoots

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Vascular plants, which account for over 90% of the Earth’s vegetation, all undergo primary growth—which lengthens roots and shoots. Many land plants, notably woody plants, also undergo secondary growth—which thickens roots and shoots.
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Primary Production01:06

Primary Production

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The total amount of energy acquired by primary producers in an ecosystem is called gross primary production (GPP). However, of this energy, producers use some for metabolic processes, and some is lost as heat, decreasing the amount of energy available to the next trophic level. The remaining usable amount of energy is called the net primary productivity (NPP). In terrestrial ecosystems, NPP is driven by climate, while light penetration and nutrient availability drive NPP in aquatic ecosystems.
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Primary Distribution01:28

Primary Distribution

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Primary distribution systems deliver electrical power from substations to consumers through various voltage classes, with 15-kV class voltages being predominant among U.S. utilities. Older 2.5- and 5-kV classes are being replaced by 15-kV primaries, while higher 25- to 34.5-kV classes are used in high-density urban areas and rural regions with long feeders. Three-phase, four-wire multigrounded systems are widely employed for balanced power delivery, using the neutral wire as a grounding point.
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Nomenclature of Primary Amines01:17

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Primary, secondary, and tertiary amines are compounds consisting of one, two, and three alkyl groups connected to the amino group (–NH2), respectively. As depicted in Figure 1, the common name of the primary amines is obtained by adding the suffix -amine to the alkyl substituent attached to the amino group as the corresponding alkylamine.
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Prognostic models in primary biliary cholangitis.

Laura Cristoferi1, Alessandra Nardi2, Vincenzo Ronca1

  • 1Division of Gastroenterology, Department of Medicine and Surgery, University of Milan-Bicocca, Milan, Italy.

Journal of Autoimmunity
|November 14, 2018
PubMed
Summary
This summary is machine-generated.

Developing new risk prediction models for primary biliary cholangitis (PBC) is crucial. Integrating multi-omics data with artificial intelligence can personalize patient care and improve outcomes.

Keywords:
Alkaline phosphatasePersonalised medicinePrimary biliary cholangitisPrognostic modelsRisk prediction

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Area of Science:

  • Hepatology
  • Precision Medicine
  • Biomarker Discovery

Background:

  • Current primary biliary cholangitis (PBC) prognostic models rely on ursodeoxycholic acid treatment response.
  • Alkaline phosphatase levels correlate with treatment response and biliary injury markers.
  • There's a need for earlier risk stratification at diagnosis to predict treatment response.

Purpose of the Study:

  • To highlight the need for improved prognostic variables in PBC.
  • To advocate for an integrative approach combining genetic, environmental, clinical, and molecular data.
  • To emphasize the role of omics and AI in developing personalized risk prediction models.

Main Methods:

  • Leveraging large-scale omics platforms (genomics, proteomics, metabolomics, etc.) and whole-genome sequencing.
  • Applying artificial intelligence, including machine learning, to analyze vast omics datasets.
  • Integrating multi-layered information for comprehensive risk assessment.

Main Results:

  • Biomarker discovery is accelerating due to advanced omics technologies.
  • AI and machine learning provide the computational power for complex data analysis.
  • Personalized risk prediction models can be developed using integrated data.

Conclusions:

  • An integrative approach incorporating multi-omics data and AI is essential for PBC risk assessment.
  • This approach facilitates the development of personalized risk prediction models.
  • Integrating molecular diagnostics into clinical practice will advance personalized management of PBC patients.