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Related Concept Videos

Directing Effect of Substituents: meta-Directing Groups01:09

Directing Effect of Substituents: meta-Directing Groups

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Substituents on the benzene ring that direct an incoming electrophile to undergo substitution at the meta position are called meta directors. All meta directors either have a positive charge on the atom directly bonded to the ring or a partial positive charge. These groups function by withdrawing electrons from the ring through inductive and resonance effects. Consider the carbocation intermediates formed upon the addition of an electrophile on nitrobenzene at the...
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Plants present a rich source of nutrients for many organisms, making it a target for herbivores and infectious agents. Plants, though lacking a proper immune system, have developed an array of constitutive and inducible defenses to fend off these attacks.
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The human immune system is a complex network of cells, tissues, and organs that work together to defend the body against bacterial infections. It consists of various immune cells, each playing a specific role in the defense mechanism.
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Directing Effect of Substituents: ortho–para-Directing Groups01:14

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Ortho–para directors are substituent groups attached to the benzene ring and direct the addition of an electrophile to the positions ortho or para to the substituent. All electron-donating groups are considered ortho–para directors. They donate electrons to the ring and make the ring more electron-rich. The ring is therefore susceptible to the addition of electrophiles. Substituents such as amino, hydroxy, or alkoxy, containing lone pairs on the atom adjacent to the ring, donate...
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The cell membrane, or plasma membrane, is an ever-changing landscape. It is described as a fluid mosaic where various macromolecules are embedded in the phospholipid bilayer. Among the macromolecules are proteins. The protein content varies across cell types. For example, mitochondrial inner membranes contain ~76% protein content, while myelin contains ~18% protein content. Individual cells contain many types of membrane proteins—red blood cells contain over 50—and different cell...
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A key characteristic of life is the ability to separate the external environment from the internal space. To do this, cells have evolved semi-permeable membranes that regulate the passage of biological molecules. Additionally, the cell membrane defines a cell’s shape and interactions with the external environment. Eukaryotic cell membranes also serve to compartmentalize the internal space into organelles, including the endomembrane structures of the nucleus, endoplasmic reticulum and...
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Related Experiment Video

Updated: Feb 3, 2026

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Pathogen Identification Direct From Polymicrobial Specimens Using Membrane Glycolipids.

William E Fondrie1, Tao Liang2, Benjamin L Oyler3

  • 1Center for Vascular and Inflammatory Diseases, University of Maryland School of Medicine, Baltimore, MD, 21201, USA.

Scientific Reports
|October 28, 2018
PubMed
Summary
This summary is machine-generated.

Rapidly identify colistin-resistant bacteria like Acinetobacter baumannii and Klebsiella pneumoniae using machine learning analysis of lipid mass spectra. This approach aids in timely treatment of infections caused by these difficult-to-treat pathogens.

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Area of Science:

  • Microbiology
  • Analytical Chemistry
  • Computational Biology

Background:

  • Rising prevalence of multidrug-resistant Gram-negative bacteria necessitates effective antimicrobial stewardship.
  • Emergence of colistin resistance in key pathogens such as Acinetobacter baumannii and Klebsiella pneumoniae poses a significant clinical challenge.
  • Accurate and rapid pathogen identification is crucial for guiding appropriate patient treatment strategies.

Purpose of the Study:

  • To develop and validate a machine learning model for the identification of Acinetobacter baumannii and Klebsiella pneumoniae.
  • To detect colistin resistance in these identified bacterial species.
  • To establish a framework for translating lipid mass spectra data into actionable pathogen identification.

Main Methods:

  • Utilized a manually curated dataset of lipid mass spectra from 48 Gram-positive and Gram-negative organisms.
  • Developed machine learning classifiers trained on glycolipid mass spectrometry data.
  • Tested the classifiers on both isolated bacterial specimens and polymicrobial samples.

Main Results:

  • The machine learning models accurately identified Acinetobacter baumannii and Klebsiella pneumoniae.
  • The classifiers demonstrated capability in detecting colistin-resistant forms of these bacteria.
  • Successful identification was achieved in both pure cultures and complex polymicrobial specimens.

Conclusions:

  • Machine learning analysis of bacterial glycolipid mass spectra provides a robust method for identifying Acinetobacter baumannii and Klebsiella pneumoniae.
  • This approach can effectively detect colistin resistance in these critical pathogens.
  • The study establishes a promising framework for rapid pathogen identification and resistance detection in clinical settings.