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| http://dx.doi.org/10.1002/mds.29186 | DOI Listing |
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Predicting the biological behavior and time to recurrence (TTR) of high-grade diffuse gliomas (HGG) after maximum safe neurosurgical resection and combined radiation and chemotherapy plays a pivotal role in planning clinical follow-up, selecting potentially necessary second-line treatment and improving the quality of life for patients diagnosed with a malignant brain tumor. The current standard-of-care (SoC) for HGG includes follow-up neuroradiological imaging to detect recurrence as early as possible and relies on several clinical, neuropathological, and radiological prognostic factors, which have limited accuracy in predicting TTR. In this study, using an in-silico analysis, we aim to improve predictive power for TTR by considering the role of (i) prognostically relevant information available through diagnostics used in the current SoC, (ii) advanced image-based information not currently part of the standard diagnostic workup, such as tumor-normal tissue interface (edge) features and quantitative data specific to biopsy positions within the tumor, and (iii) information on tumor-associated macrophages.
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Strategies to acquire high-efficiency luminogens that emit in the second near-infrared (NIR-II, 1000-1700 nm) range are still rare due to the impediment of the energy gap law. Herein, a feasible strategy is pioneered by installing large-volume encumbrances in a confined space to intensify the repulsive interactions arising from overlapping electron densities. The experimental results, including smaller coordinate displacement, reduced reorganization energy, and suppressed internal conversion, demonstrate that the repulsive interactions assist in the inhibition of radiationless deactivation.
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