| 1. |
- Anwer, Danish M., et al.
(författare)
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A comparison of machine learning approaches for the quantification of microglial cells in the brain of mice, rats and non-human primates
- 2023
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Ingår i: PLoS ONE. - 1932-6203. ; 18:5 MAY
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Tidskriftsartikel (refereegranskat)abstract
- Microglial cells are brain-specific macrophages that swiftly react to disruptive events in the brain. Microglial activation leads to specific modifications, including proliferation, morphological changes, migration to the site of insult, and changes in gene expression profiles. A change in inflammatory status has been linked to many neurodegenerative diseases such as Parkinson's disease and Alzheimer's disease. For this reason, the investigation and quantification of microglial cells is essential for better understanding their role in disease progression as well as for evaluating the cytocompatibility of novel therapeutic approaches for such conditions. In the following study we implemented a machine learning-based approach for the fast and automatized quantification of microglial cells; this tool was compared with manual quantification (ground truth), and with alternative free-ware such as the threshold-based ImageJ and the machine learning-based Ilastik. We first trained the algorithms on brain tissue obtained from rats and non-human primate immunohistochemically labelled for microglia. Subsequently we validated the accuracy of the trained algorithms in a preclinical rodent model of Parkinson's disease and demonstrated the robustness of the algorithms on tissue obtained from mice, as well as from images provided by three collaborating laboratories. Our results indicate that machine learning algorithms can detect and quantify microglial cells in all the three mammalian species in a precise manner, equipotent to the one observed following manual counting. Using this tool, we were able to detect and quantify small changes between the hemispheres, suggesting the power and reliability of the algorithm. Such a tool will be very useful for investigation of microglial response in disease development, as well as in the investigation of compatible novel therapeutics targeting the brain. As all network weights and labelled training data are made available, together with our step-by-step user guide, we anticipate that many laboratories will implement machine learning-based quantification of microglial cells in their research.
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| 2. |
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| 3. |
- Petrik, Igor D., et al.
(författare)
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An Engineered Glutamate in Biosynthetic Models of Heme-Copper Oxidases Drives Complete Product Selectivity by Tuning the Hydrogen-Bonding Network
- 2021
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Ingår i: Biochemistry. - : American Chemical Society (ACS). - 0006-2960 .- 1520-4995. ; 60:4, s. 346-355
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Tidskriftsartikel (refereegranskat)abstract
- Efficiently carrying out the oxygen reduction reaction (ORR) is critical for many applications in biology and chemistry, such as bioenergetics and fuel cells, respectively. In biology, this reaction is carried out by large, transmembrane oxidases such as heme-copper oxidases (HCOs) and cytochrome bd oxidases. Common to these oxidases is the presence of a glutamate residue next to the active site, but its precise role in regulating the oxidase activity remains unclear. To gain insight into its role, we herein report that incorporation of glutamate next to a designed heme-copper center in two biosynthetic models of HCOs improves O2 binding affinity, facilitates protonation of reaction intermediates, and eliminates release of reactive oxygen species. High-resolution crystal structures of the models revealed extended, water-mediated hydrogen-bonding networks involving the glutamate. Electron paramagnetic resonance of the cryoreduced oxy-ferrous centers at cryogenic temperature followed by thermal annealing allowed observation of the key hydroperoxo intermediate that can be attributed to the hydrogen-bonding network. By demonstrating these important roles of glutamate in oxygen reduction biochemistry, this work offers deeper insights into its role in native oxidases, which may guide the design of more efficient artificial ORR enzymes or catalysts for applications such as fuel cells.
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| 4. |
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| 5. |
- Niemi, MEK, et al.
(författare)
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- 2021
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swepub:Mat__t
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| 6. |
- Kanai, M, et al.
(författare)
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- 2023
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swepub:Mat__t
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