Chondroitin sulfate/silk fibroin hydrogel incorporating graphene oxide quantum dots with photothermal-effect promotes type H vessel-related wound healing.

Chronic wounds with bacterial infection present formidable clinical challenges. In this study, a versatile hydrogel dressing with antibacterial and angiogenic activity composite of silk fibroin (SF), chondroitin sulfate (CS), and graphene oxide quantum dots (GOQDs) is fabricated. GOQDs@SF/CS (GSC) hydrogel is rapidly formed through the enzyme catalytic action of horseradish peroxidase. With the incorporation of GOQDs both gelation speed and mechanical properties have been enhanced, and the photothermal characteristics of GOQDs in GSC hydrogel enabled bacterial killing through photothermal treatment (PTT) at ∼51 °C. In vitro studies show that the GSC hydrogels demonstrate excellent antibacterial performance and induce type H vessel differentiation of endothelial cells via the activated ERK1/2 signaling pathway and upregulated SLIT3 expression. In vivo results show that the hydrogel significantly promotes type H vessels formation, which is related to the collagen deposition, epithelialization and, ultimately, accelerates the regeneration of infected skin defects. Collectively, this multifunctional GSC hydrogel, with dual action of antibacterial efficacy and angiogenesis promotion, emerges as an innovative skin dressing with the potential for advancing in infected wound healing.

Synthesis of Diverse γ-Lactams via Rh-Catalyzed C(sp2)-H Addition to Aliphatic Nitriles.

We herein report an unprecedented pathway to access γ-lactams using acetonitrile analogues as coupling partners without oxidants, ligands, and Lewis acids. The reaction undergoes Rh-catalyzed C(sp2)-H addition to carbon-bound nitriles with the aid of an amide traceless auxiliary followed by an annulation sequence, featuring a broad substrate scope, good functional group tolerance, and excellent chemo/stereoselectivity. Scale-up reactions and late-stage derivatizations highlight the potential synthetic utility of this methodology. A plausible mechanism is proposed based on mechanistic investigations.

Extracting deep features from short ECG signals for early atrial fibrillation detection.

Atrial Fibrillation (AF) at an early stage has a short duration and is sometimes asymptomatic, making it difficult to detect. Although the use of mobile sensing devices has provided the possibility of real-time cardiac detection, it is highly susceptible to the noise signals generated by body movement. Therefore, it is of great importance to study early AF detection for mobile terminals with noise immunity. Extracting effective features is critical to AF detection, but most existing studies used shallow time, frequency or time-frequency energy (TFE) features with weak representation that need to rely on long ECG signals to capture the variation in information and cannot sensitively capture the subtle variation caused by early AF. In addition, most studies only considered the discrimination of AF from normal sinus rhythm (SR) signals, ignoring the interference of noise and other signals. This study proposes three new deep features that can accurately capture the subtle variation in short ECG segments caused by early AF, examines the interference of noise and other signals generated by the mobile terminal and proposes a new feature set for early AF detection. We use six popular classifiers to evaluate the relative effectiveness of the deep features we developed against the features extracted by two conventional time-frequency methods, and the performance of the proposed feature set for detecting early AF. Our study shows that the best results for classifying AF and SR are obtained by Random Forest (RF), with 0.96 F1 score. The best results for classifying four types of signal are obtained by Extreme Gradient Boosting (XGBoost), with overall F1 score 0.88 and the individual F1 score for classifying SR, AF, Other and Noisy with 0.91, 0.90, 0.73, and 0.96, respectively.