Mesoporous silica thin film as effective coating for enhancing osteogenesis through selective protein adsorption and blood clotting.

The role of blood clots in tissue repair has been identified for a long time; however, its participation in the integration between implants and host tissues has attracted attention only in recent years. In this work, a mesoporous silica thin film (MSTF) with either vertical or parallel orientation was deposited on titania nanotubes surface, resulting in superhydrophilic nanoporous surfaces. A proteomic analysis of blood plasma adsorption revealed that the MSTF coating could significantly increase the abundance of acidic proteins and the adsorption of coagulation factors (XII and XI), with the help of cations (Na+, Ca2+) binding. As a result, both the activation of platelets and the formation of blood clots were significantly enhanced on the MSTF surface with more condensed fibrin networks. The two classical growth factors of platelets-derived growth factors-AB and transformed growth factors-ßwere enriched in blood clots from the MSTF surface, which accounted for robust osteogenesis bothin vitroandin vivo. This study demonstrates that MSTF may be a promising coating to enhance osteogenesis by modulating blood clot formation.

Deep learning enhancing guide RNA design for CRISPR/Cas12a-based diagnostics.

Rapid and accurate diagnostic tests are fundamental for improving patient outcomes and combating infectious diseases. The Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) Cas12a-based detection system has emerged as a promising solution for on-site nucleic acid testing. Nonetheless, the effective design of CRISPR RNA (crRNA) for Cas12a-based detection remains challenging and time-consuming. In this study, we propose an enhanced crRNA design system with deep learning for Cas12a-mediated diagnostics, referred to as EasyDesign. This system employs an optimized convolutional neural network (CNN) prediction model, trained on a comprehensive data set comprising 11,496 experimentally validated Cas12a-based detection cases, encompassing a wide spectrum of prevalent pathogens, achieving Spearman's ρ = 0.812. We further assessed the model performance in crRNA design for four pathogens not included in the training data: Monkeypox Virus, Enterovirus 71, Coxsackievirus A16, and Listeria monocytogenes. The results demonstrated superior prediction performance compared to the traditional experiment screening. Furthermore, we have developed an interactive web server (https://crispr.zhejianglab.com/) that integrates EasyDesign with recombinase polymerase amplification (RPA) primer design, enhancing user accessibility. Through this web-based platform, we successfully designed optimal Cas12a crRNAs for six human papillomavirus (HPV) subtypes. Remarkably, all the top five predicted crRNAs for each HPV subtype exhibited robust fluorescent signals in CRISPR assays, thereby suggesting that the platform could effectively facilitate clinical sample testing. In conclusion, EasyDesign offers a rapid and reliable solution for crRNA design in Cas12a-based detection, which could serve as a valuable tool for clinical diagnostics and research applications.

Reservoir Computing-Based Design of ZnO Memristor-Type Digital Identification Circuits.

Reservoir Computing (RC) is a network architecture inspired by biological neural systems that maps time-dimensional input features to a high-dimensional space for computation. The key to hardware implementation of the RC system is whether sufficient reservoir states can be generated. In this paper, a laboratory-prepared zinc oxide (ZnO) memristor is reported and modeled. The device is found to have nonlinear dynamic responses and characteristics of simulating neurosynaptic long-term potentiation (LTP) and long-term depression (LTD). Based on this, a novel two-level RC structure based on the ZnO memristor is proposed. Novel synaptic encoding is used to maintain stress activity based on the characteristics of after-discharge and proneness to fatigue during synaptic transmission. This greatly alleviates the limitations of the self-attenuating characteristic reservoir of the duration and interval of the input signal. This makes the reservoir, in combination with a fully connected neural network, an ideal system for time series classification. The experimental results show that the recognition rate for the complete MNIST dataset is 95.08% when 35 neurons are present as hidden layers while achieving low training consumption.