Aqueous Nanocoating Approach to Strong Natural Microfibers with Tunable Electrical Conductivity for Wearable Electronic Textiles

Electronically conductive and mechanically strong natural fibers with desirable durability, flexibility, and environmental compatibility are in great need for manufacturing multifunctional textiles. Here we reveal a facile yet effective nanocoating approach to immobilization of few-layer graphene oxide (GO) nanosheets at ramie fiber by hydrogen-bond-driven self-assembly process under all-aqueous and additive-free condition. The surface morphology and chemistry of GO-functionalized ramie fiber (GOFR) were significantly altered with the homogeneous decoration of trace amount of GO nanosheets, readying remarkable property improvements and functionality realization. The tensile strength of GOFR (553 MPa) witnessed an increase of over 25% compared to pristine ramie, as accompanied by moderate promotion of extensibility with the assistance of robust GO nanosheets. It is worth noting that exceptionally high electrical conductivity was achieved for thermally reduced GOFR (rGOFR), with values as high as 83.2 S/cm after reduction at 250 °C for only 30 min. By tuning the reduction time and temperature, the conductivity of rGOFR was well controlled in an extremely wide range of 0.1–83.2 S/cm. This effort provides useful insights into the fabrication of highly strong and conductive natural fibers in the promising field of smart fabrics integrating high strength, flexibility, intelligent functionality, and environmental compatibility

SS-GNN: A Simple-Structured Graph Neural Network for Affinity Prediction

Efficient and effective drug-target binding affinity (DTBA) prediction is a challenging task due to the limited computational resources in practical applications and is a crucial basis for drug screening. Inspired by the good representation ability of graph neural networks (GNNs), we propose a simple-structured GNN model named SS-GNN to accurately predict DTBA. By constructing a single undirected graph based on a distance threshold to represent protein–ligand interactions, the scale of the graph data is greatly reduced. Moreover, ignoring covalent bonds in the protein further reduces the computational cost of the model. The graph neural network-multilayer perceptron (GNN-MLP) module takes the latent feature extraction of atoms and edges in the graph as two mutually independent processes. We also develop an edge-based atom-pair feature aggregation method to represent complex interactions and a graph pooling-based method to predict the binding affinity of the complex. We achieve state-of-the-art prediction performance using a simple model (with only 0.6 M parameters) without introducing complicated geometric feature descriptions. SS-GNN achieves Pearson’s Rp = 0.853 on the PDBbind v2016 core set, outperforming state-of-the-art GNN-based methods by 5.2%. Moreover, the simplified model structure and concise data processing procedure improve the prediction efficiency of the model. For a typical protein–ligand complex, affinity prediction takes only 0.2 ms. All codes are freely accessible at https://github.com/xianyuco/SS-GNN

SS-GNN: A Simple-Structured Graph Neural Network for Affinity Prediction

Efficient and effective drug-target binding affinity (DTBA) prediction is a challenging task due to the limited computational resources in practical applications and is a crucial basis for drug screening. Inspired by the good representation ability of graph neural networks (GNNs), we propose a simple-structured GNN model named SS-GNN to accurately predict DTBA. By constructing a single undirected graph based on a distance threshold to represent protein–ligand interactions, the scale of the graph data is greatly reduced. Moreover, ignoring covalent bonds in the protein further reduces the computational cost of the model. The graph neural network-multilayer perceptron (GNN-MLP) module takes the latent feature extraction of atoms and edges in the graph as two mutually independent processes. We also develop an edge-based atom-pair feature aggregation method to represent complex interactions and a graph pooling-based method to predict the binding affinity of the complex. We achieve state-of-the-art prediction performance using a simple model (with only 0.6 M parameters) without introducing complicated geometric feature descriptions. SS-GNN achieves Pearson’s Rp = 0.853 on the PDBbind v2016 core set, outperforming state-of-the-art GNN-based methods by 5.2%. Moreover, the simplified model structure and concise data processing procedure improve the prediction efficiency of the model. For a typical protein–ligand complex, affinity prediction takes only 0.2 ms. All codes are freely accessible at https://github.com/xianyuco/SS-GNN

Design, Synthesis, and Biological Characterization of a Caspase 3/7 Selective Isatin Labeled with 2-[¹⁸F]fluoroethylazide

Imaging of programmed cell death (apoptosis) is important in the assessment of therapeutic response in oncology and for diagnosis in cardiac and neurodegenerative disorders. The executioner caspases 3 and 7 ultimately effect cellular death, thus providing selective molecular targets for in vivo quantification of apoptosis. To realize this potential, we aimed to develop 18F-labeled isatin sulfonamides with high metabolic stability and moderate lipophilicity while retaining selectivity and affinity for caspase 3/7. A small library of isatins modified with fluorinated aromatic groups and heterocycles was synthesized. A lead compound incorporating 2′-fluoroethyl-1,2,3-triazole was identified with subnanomolar affinity for caspase 3. “Click labeling” provided the 18F-labeled tracer in 65 ± 6% decay-corrected radiochemical yield from 2-[18F]fluoroethylazide. The compound showed high stability in vivo with rapid uptake and elimination in healthy tissues and tumor. The novel 18F-labeled isatin is a candidate radiotracer for further preclinical evaluation for imaging of apoptosis

Natural Fiber-Anchored Few-Layer Graphene Oxide Nanosheets for Ultrastrong Interfaces in Poly(lactic acid)

The development of high-performance graphene oxide (GO) nanocomposites is challenged by the lack of a feasible and effective route to disperse and exfoliate GO nanosheets, as well as technical gaps in providing precision control over the interfacial interactions. Here we disclose an all-aqueous processing method to immobilize few-layer extended GO nanosheets at ramie fiber driven by hydrogen bonding. The reinforcing efficacy of GO-functionalized ramie (GOFR) was examined in a poly­(lactic acid) (PLA) matrix, wherein the GOFR provided large active surfaces to induce chain ordering and lamellar organization. It permitted the preferable formation of well-organized PLA transcrystallinity at GOFR, in contrast to calabash-like and less-ordered transcrystallinity induced by pristine ramie due to the inferior nucleation activity. The transcrystallization kinetics and lamellar orientation degree were significantly facilitated by the addition of chain mobility accelerator in PLA, permitting the formation of prevailing transcrystallinity with large sizes at GOFR. The profound control of interphase morphology conferred remarkable improvements in interfacial properties with weak relation to crystallization temperature, as indicated by an over 3-fold increase in interfacial shear strength between GOFR and PLA matrix compared to the counterparts incorporated with pristine ramie. The effort reveals the appealing application of natural fibers as an ideal template to extend GO nanosheets, potentially motivating further efforts toward revolutionary advancements occurring in many fields of materials science and nanotechnology

Nat Commun-De novo adipocyte differentiation from Pdgfrβ+ preadipocytes protects against pathologic visceral adipose expansion in obesity.pdf

Pathologic expansion of white adipose tissue (WAT) in obesity is characterized by adipocytehypertrophy, inflammation, and fibrosis; however, factors triggering this maladaptive remodelingare largely unknown. Here, we test the hypothesis that the potential to recruit newadipocytes from Pdgfrβ+ preadipocytes determines visceral WAT health in obesity. Wemanipulate levels of Pparg, the master regulator of adipogenesis, in Pdgfrβ+ precursors ofadult mice. Increasing the adipogenic capacity of Pdgfrβ+ precursors through Pparg overexpressionresults in healthy visceral WAT expansion in obesity and adiponectin-dependentimprovements in glucose homeostasis. Loss of mural cell Pparg triggers pathologic visceralWAT expansion upon high-fat diet feeding. Moreover, the ability of the TZD class of antidiabeticdrugs to promote healthy visceral WAT remodeling is dependent on mural cell Pparg.These data highlight the protective effects of de novo visceral adipocyte differentiation inthese settings, and suggest Pdgfrβ+ adipocyte precursors as targets for therapeutic interventionin diabetes.</div

Additional file 1 of PDGFRβ + cell HIF2α is dispensable for white adipose tissue metabolic remodeling and hepatic lipid accumulation in obese mice

Supplementary material 1