Publications by authors named "L Heliot"

Article Synopsis
  • The paper discusses the use of plasmonic photothermal therapy for targeting uropathogenic bacteria using reduced graphene oxide and gold nanorods/rGO composites.
  • An expression of concern has been published, addressing potential issues related to the methodology or data integrity in the research.
  • Researchers are encouraged to critically review the findings and verify the results to ensure scientific accuracy and reliability.
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Article Synopsis
  • The article discusses the development of gold nanorods with a silica core-shell structure, which are enhanced with verteporfin, a photosensitizer used in photodynamic therapy.
  • It emphasizes the high effectiveness of these nanostructures in the photodynamic inactivation of targeted cells or pathogens.
  • The authors express concern regarding potential issues or limitations identified in the study, which may impact the reproducibility or applicability of the findings in clinical settings.
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Expression of concern for 'Particle-based photodynamic therapy based on indocyanine green modified plasmonic nanostructures for inactivation of a Crohn's disease-associated strain' by Roxana Jijie , , 2016, , 2598-2605, https://doi.org/10.1039/C5TB02697K.

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Gene expression orchestration is a key question in fundamental and applied research. Different models for transcription regulation were proposed, yet the dynamic regulation of RNA polymerase II (RNAP II) activity remains a matter of debate. To improve our knowledge of this topic, we investigated RNAP II motility in eukaryotic cells by combining single particle tracking (SPT) and fluorescence correlation spectroscopy (FCS) techniques, to take advantage of their different sensitivities in order to analyze together slow and fast molecular movements.

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Fluorescence lifetime imaging microscopy (FLIM) is a powerful technique to probe the molecular environment of fluorophores. The analysis of FLIM images is usually performed with time consuming fitting methods. For accelerating this analysis, sophisticated deep learning architectures based on convolutional neural networks have been developed for restrained lifetime ranges but they require long training time.

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