Quaternary Ammonium Assisted Synthesis of Melanin-like Poly(l-DOPA) Nanoparticles with a Boosted Photothermal Effect.

Poly(levodopa) nanoparticles (P(l-DOPA) NPs) are another kind of melanin mimetic besides well-established polydopamine nanoparticles (PDA NPs). Due to the presence of carboxyl groups, the oxidative polymerization of l-DOPA to obtain particles was not as efficient as that of dopamine. Several established methods toward P(l-DOPA) NP fabrication do not combine convenience, morphological regularity, size controllability, low cost, and adaptability to metal-free application scenarios. In this work, P(l-DOPA) NPs were successfully prepared in hot water with the assistant of organic quaternary ammonium, due to the extra physical cross-linking mediated by cations. The employed physical interactions could also be affected by quaternary ammonium structure (i.e., number of cation heads, length of alkyl chain) to achieve different polymerization acceleration effects. The obtained P(l-DOPA) NPs retained superior photothermal properties and outperformed PDA-based melanin materials. Furthermore, P(l-DOPA) NPs were used in photothermal tumor therapy and showed better efficacy. This study offers new insights into the synthesis of melanin-like materials, as well as new understanding of the interaction between quaternary ammonium and bioinspired polyphenolic materials.

20-esterification of 5-spiro CPT and their anticancer activity in vitro.

20-ester of 5-spirocycle campthothecin derivatives were successfully constructed and synthesised by Steglich esterification in a moderate yield. These derivatives showed a better solubility. Compared to parent compound, most of these 20-ester-5-spirocycle campthothecin derivatives (besides 3g) showed a better inhibition activity against HepG2 cell line.

TEMPO/PhI(OAc)2 promotes the α-aminophosphinoylation of alcohols with amines and H-phosphine oxides in aqueous medium.

A novel method for synthesizing α-aminoalkyl phosphine oxides in aqueous medium, using Ar2P(O)-H reagents, alcohols and amines, is described. This method: (i) allows for the smooth aminophosphinoylation of alcohols with amines and H-phosphine oxides under mild conditions; (ii) provides an efficient and alternative approach to access various α-aminoalkylphosphine oxides. Although various amines exhibited remarkable versatility and tolerance for functional groups in this reaction, alcohols and H-phosphine oxides demonstrated limited applicability as reactants. Hence, further investigation using a wider range of substrates is crucial. The postulated mechanism indicated that the three-component reaction followed the imine pathway due to the in situ oxidation of alcohol to aldehyde.

Eumelanin-like Poly(levodopa) Nanoscavengers for Inflammation Disease Therapy.

In the current years, polydopamine nanoparticles (PDA NPs) have been extensively investigated as an eumelanin mimic. However, unlike natural eumelanin, PDA NPs contain no 5,6-dihydroxyindole-2-carboxylic acid (DHICA)-derived units and may be limited in certain intrinsic properties; superior eumelanin-like nanomaterials are still actively being sought. Levodopa (l-DOPA) is a natural eumelanin precursor and expected to convert into DHICA and further remain within the final product through covalent or physical interactions. Herein, poly(levodopa) nanoparticles [P(l-DOPA) NPs] were synthesized with the assistance of zinc oxide as a supplement to synthetic eumelanin. This study found that P(l-DOPA) NPs had ∼90% DHICA-derived subunits on their surface and exhibited superior antioxidant activity compared to PDA NPs due to their looser polymeric microstructure. Benefitting from a stronger ROS scavenging ability, P(l-DOPA) NPs outperformed PDA NPs in treating cellular oxidative stress and acute inflammation. This research opens up new possibilities for the development and application of novel melanin-like materials.

UV absorption enhanced polydopamine coating.

Mussel-inspired polydopamine (PDA) coatings have gained significant attention in various fields, including biomedicine, energy, detection, and UV protection, owing to their versatile and promising properties. Among these properties, UV shielding stands out as a key feature of PDA coatings. Nevertheless, the current methods for tuning the UV-shielding properties of PDA coatings are quite limited, and only rely on thickness adjustment, which might involve additional issues like color and visible light transmittance to the coating layer. In this study, we propose a facile and modular approach to enhance the UV absorption of PDA coatings by incorporating thiol-heterocycle (TH) derivatives. Both pre- and post-modification strategies can effectively impede the formation of conjugated structures within PDA, leading to enhanced UV absorption within the PDA layers. More importantly, these strategies can improve the UV absorption of PDA coatings while reducing the visible light absorption. Furthermore, this method enabled efficient regulation of the UV absorption of PDA coatings by altering the ring type (benzene ring or pyridine ring) and substituent on the ring (methoxyl group or hydrogen atom). These PDA coatings with enhanced UV absorption demonstrate great promise for applications in UV protection, antibacterial activity, wound healing and dye degradation.

An in-depth survey on Deep Learning-based Motor Imagery Electroencephalogram (EEG) classification.

Electroencephalogram (EEG)-based Brain-Computer Interfaces (BCIs) build a communication path between human brain and external devices. Among EEG-based BCI paradigms, the most commonly used one is motor imagery (MI). As a hot research topic, MI EEG-based BCI has largely contributed to medical fields and smart home industry. However, because of the low signal-to-noise ratio (SNR) and the non-stationary characteristic of EEG data, it is difficult to correctly classify different types of MI-EEG signals. Recently, the advances in Deep Learning (DL) significantly facilitate the development of MI EEG-based BCIs. In this paper, we provide a systematic survey of DL-based MI-EEG classification methods. Specifically, we first comprehensively discuss several important aspects of DL-based MI-EEG classification, covering input formulations, network architectures, public datasets, etc. Then, we summarize problems in model performance comparison and give guidelines to future studies for fair performance comparison. Next, we fairly evaluate the representative DL-based models using source code released by the authors and meticulously analyse the evaluation results. By performing ablation study on the network architecture, we found that (1) effective feature fusion is indispensable for multi-stream CNN-based models. (2) LSTM should be combined with spatial feature extraction techniques to obtain good classification performance. (3) the use of dropout contributes little to improving the model performance, and that (4) adding fully connected layers to the models significantly increases their parameters but it might not improve their performance. Finally, we raise several open issues in MI-EEG classification and provide possible future research directions.