Magnetically driven formation of 3D freestanding soft bioscaffolds.

3D soft bioscaffolds have great promise in tissue engineering, biohybrid robotics, and organ-on-a-chip engineering applications. Though emerging three-dimensional (3D) printing techniques offer versatility for assembling soft biomaterials, challenges persist in overcoming the deformation or collapse of delicate 3D structures during fabrication, especially for overhanging or thin features. This study introduces a magnet-assisted fabrication strategy that uses a magnetic field to trigger shape morphing and provide remote temporary support, enabling the straightforward creation of soft bioscaffolds with overhangs and thin-walled structures in 3D. We demonstrate the versatility and effectiveness of our strategy through the fabrication of bioscaffolds that replicate the complex 3D topology of branching vascular systems. Furthermore, we engineered hydrogel-based bioscaffolds to support biohybrid soft actuators capable of walking motion triggered by cardiomyocytes. This approach opens new possibilities for shaping hydrogel materials into complex 3D morphologies, which will further empower a broad range of biomedical applications.

Enhancing Cytochrome C Recognition and Adsorption through Epitope-Imprinted Mesoporous Silica with a Tailored Pore Size.

We have reported the synthesis of epitope-imprinted mesoporous silica (EIMS) with an average pore size of 6.2 nm, which is similar to the geometrical size of the target protein, cytochrome C (Cyt c, 2.6 × 3.2 × 3.3 nm3), showing great recognition and large-scale adsorption performance. The characteristic fragment of Cyt c was used as a template and docked onto the surface of C16MIMCl micelles via multiple interactions. Nitrogen adsorption-desorption and transmission electron microscopy confirmed the successful preparation of EIMS. Due to the ordered pore structure, larger pore size, and high specific surface area, the prepared EIMS show superior specificity (IF = 3.8), excellent selectivity toward Cyt c, high adsorption capacity (249.6 mg g-1), and fast adsorption equilibrium (10 min). This study demonstrates the potential application of EIMS with a controllable pore size for high-effective and large-scale separation of Cyt c, providing a new approach for effective biomacromolecular recognition.

Undescribed matrine-type alkaloids from Sophora alopecuroides with anti-inflammatory activity.

A phytochemical investigation on the alkaloid fractions of Sophora alopecuroides L. led to the production of 11 undescribed matrine-type alkaloids, sophaloseedlines I-S (1-11), 12 known analogs (12-23), and an unexpected artificial matrine-derived Al(III) complex (24). The corresponding structures were elucidated by the interpretation of spectroscopic analyses, quantum chemical calculation, and six instances (1-4, 18, and 24), verified by X-ray crystallography. The biological activities screening demonstrated that none of the isolates exhibited cytotoxicity against four human cancer cell lines (HepG2, A549, THP-1, and MCF-7) and respiratory syncytial virus (RSV) at 50 µM, while moderate anti-inflammatory activity with IC50 value from 15.6 to 47.8 µM was observed. The key structure-activity relationships of those matrine-type alkaloids for anti-inflammatory effects have been summarized. In addition, the most potent 7-epi-sophoramine (19) and aluminum sophaloseedline T (24) could effectively inhibit the release of pro-inflammatory factors (TNF-α, IL-6, and IL-1ß), as well as the expression of iNOS and COX-2 proteins.

ECG signal classification in wearable devices based on compressed domain.

Wearable devices are often used to diagnose arrhythmia, but the electrocardiogram (ECG) monitoring process generates a large amount of data, which will affect the detection speed and accuracy. In order to solve this problem, many studies have applied deep compressed sensing (DCS) technology to ECG monitoring, which can under-sampling and reconstruct ECG signals, greatly optimizing the diagnosis process, but the reconstruction process is complex and expensive. In this paper, we propose an improved classification scheme for deep compressed sensing models. The framework is comprised of four modules: pre-processing; compression; and classification. Firstly, the normalized ECG signals are compressed adaptively in the three convolutional layers, and then the compressed data is directly put into the classification network to obtain the results of four kinds of ECG signals. We conducted our experiments on the MIT-BIH Arrhythmia Database and Ali Cloud Tianchi ECG signal Database to validate the robustness of our model, adopting Accuracy, Precision, Sensitivity and F1-score as the evaluation metrics. When the compression ratio (CR) is 0.2, our model has 98.16% accuracy, 98.28% average accuracy, 98.09% Sensitivity and 98.06% F1-score, all of which are better than other models.

A novel label-free fluorescence assay for dipeptidyl peptidase 4 activity detection based on supramolecular self-assembly.

This article presents a new label-free fluorescence assay based on supramolecular self-assembly of cucurbit[7]uril and specific peptide Gly-Pro-Phe-Gly for monitoring DPP4 activity in clinical samples. It also displays a good potential application in high-throughput screening of DPP4 inhibitors.