Photoactive chlorin e6 is a multifunctional modulator of amyloid-β aggregation and toxicity via specific interactions with its histidine residues

Abstract

Photoactive chlorin e6 selectively damage the histidine residues of amyloid-β and reduce its aggregation and toxicity even in the presence of Cu ions.

Manganese‐doped green tea‐derived carbon quantum dots as a targeted dual imaging and photodynamic therapy platform

“…Photodynamic therapy (PDT) is a cancer therapeutic modality in which a combination of light, oxygen, and a photosensitizer is exploited to generate reactive oxygen species (ROS) that can oxidize and degrade biopolymers such as DNA and proteins (Girma, Dehvari, Ling, & Chang, ; Leshem et al, ). Chlorin e6 (Ce6) is widely used as a PDT and FL agent in the near‐infrared (NIR) region.…”

“…Structurally, chlorins are characterized by 20 π electrons at their cores that are composed of three pyrroles and one pyrroline bridged by four sp 2 hybridized carbon atoms without the presence of a metal. It exhibits high stability and minimal dark toxicity with a high quantum yield of singlet oxygen ( 1 O 2 ) production (Leshem et al, ; Mojzisova, Bonneau, Maillard, Berg, & Brault, ). Recently, the integration of Ce6 with imaging, targeting, and treatment modalities has adopted as a multifunctional platform for simultaneous diagnosis and therapy (Chen et al, ; Choi, Nam, Cho, Jung, & Park, ; Dehvari, Lin, & Chang, ; Yu et al, ).…”

Photo-excited Toluidine Blue disaggregates the Repeat Tau and modulates cytoskeletal structure in neuronal cells

“…Phthalocyanine dye has been reported to be effective against pathological prion protein (PrPC) (Kostelanska et al 2019). Moreover photo-active chlorin 6 dye was also found to reduce the aggregation by modulating the histidine residues (Leshem et al 2019). Methylene blue derived Leuco-methylthioninium Bis (Hydromehanesulphonate) (LMTM) entered phase-3 clinical trial for treatment of mild Alzheimer's disease (Wilcock et al 2018), however LMTM in further studies was found to be ineffective (Sun et al 2018).…”

Machine learning for the solution of the Schrödinger equation

“…It is therefore important to recognize similarities and differences in the requirements that can be addressed via ML among different applications.In this Perspective, we survey recent uses of ML techniques to solve the Schrödinger equation, including the vibrational Schrödinger equation and the electronic problem: the electronic Schrödinger equation and the related problems of constructing exchange-correlation and kinetic energy functionals for Kohn-Sham (KS-) and orbital-free (OF-) density functional theory (DFT) as well as use of ML for semi-empirical approximations used in density functional based approaches such as DFTB (density functional tight binding) and dispersion-corrected DFT (DFT-D). We only consider the use of ML for the solution of the vibrational and electronic SE or the KS equation or the Hohenberg-Kohn (HK) equation and not methods that aim to avoid such solutions (such as those directly mapping the molecular structure to the spectrum or energy or properties without solving for the density or wavefunction [29][30][31][32][33][34][35][36]); we also do not consider uses of ML for other types of quantum mechanical modelling or other types of differential or integral equations [37][38][39][40][41][42].We do not aim to present a comprehensive review but rather a survey allowing similarities and differences of ML uses in all these applications, as well as promising directions for future research, to transpire. This is not a review of ML methods; the key machine learning techniques that found use in quantum chemistry are well reviewed elsewhere [3-5, 43, 44], their description will not be repeated there; the reader is advised to consult the literature for their introduction, such as [45,46] for neural networks, [47,48] for Gaussian process regression (GPR), [49] for kernel ridge regression (KRR; note the similarity in the form of the function representation between GPR and KRR), [50] for genetic algorithms (GA) and [51] for particle swarm optimization.…”

“…In this Perspective, we survey recent uses of ML techniques to solve the Schrödinger equation, including the vibrational Schrödinger equation and the electronic problem: the electronic Schrödinger equation and the related problems of constructing exchange-correlation and kinetic energy functionals for Kohn-Sham (KS-) and orbital-free (OF-) density functional theory (DFT) as well as use of ML for semi-empirical approximations used in density functional based approaches such as DFTB (density functional tight binding) and dispersion-corrected DFT (DFT-D). We only consider the use of ML for the solution of the vibrational and electronic SE or the KS equation or the Hohenberg-Kohn (HK) equation and not methods that aim to avoid such solutions (such as those directly mapping the molecular structure to the spectrum or energy or properties without solving for the density or wavefunction [29][30][31][32][33][34][35][36]); we also do not consider uses of ML for other types of quantum mechanical modelling or other types of differential or integral equations [37][38][39][40][41][42].…”