Machine learning to predict mesenchymal stem cell efficacy for cartilage repair.

Inconsistent therapeutic efficacy of mesenchymal stem cells (MSCs) in regenerative medicine has been documented in many clinical trials. Precise prediction on the therapeutic outcome of a MSC therapy based on the patient's conditions would provide valuable references for clinicians to decide the treatment strategies. In this article, we performed a meta-analysis on MSC therapies for cartilage repair using machine learning. A small database was generated from published in vivo and clinical studies. The unique features of our neural network model in handling missing data and calculating prediction uncertainty enabled precise prediction of post-treatment cartilage repair scores with coefficient of determination of 0.637 ± 0.005. From this model, we identified defect area percentage, defect depth percentage, implantation cell number, body weight, tissue source, and the type of cartilage damage as critical properties that significant impact cartilage repair. A dosage of 17 - 25 million MSCs was found to achieve optimal cartilage repair. Further, critical thresholds at 6% and 64% of cartilage damage in area, and 22% and 56% in depth were predicted to significantly compromise on the efficacy of MSC therapy. This study, for the first time, demonstrated machine learning of patient-specific cartilage repair post MSC therapy. This approach can be applied to identify and investigate more critical properties involved in MSC-induced cartilage repair, and adapted for other clinical indications.

Exchange Bias in Magnetic Topological Insulator Superlattices.

Magnetic doping and proximity coupling can open a band gap in a topological insulator (TI) and give rise to dissipationless quantum conduction phenomena. Here, by combining these two approaches, we demonstrate a novel TI superlattice structure that is alternately doped with transition and rare earth elements. An unexpected exchange bias effect is unambiguously confirmed in the superlattice with a large exchange bias field using magneto-transport and magneto-optical techniques. Further, the Curie temperature of the Cr-doped layers in the superlattice is found to increase by 60 K compared to a Cr-doped single-layer film. This result is supported by density-functional-theory calculations, which indicate the presence of antiferromagnetic ordering in Dy:Bi2Te3 induced by proximity coupling to Cr:Sb2Te3 at the interface. This work provides a new pathway to realizing the quantum anomalous Hall effect at elevated temperatures and axion insulator state at zero magnetic field by interface engineering in TI heterostructures.

Coherent Spin and Quasiparticle Dynamics in Solution-Processed Layered 2D Lead Halide Perovskites.

Layered 2D halide perovskites with their alternating organic and inorganic atomic layers that form a self-assembled quantum well system are analogues of the purely inorganic 2D transition metal dichalcogenides. Within their periodic structures lie a hotbed of photophysical phenomena such as dielectric confinement effect, optical Stark effect, strong exciton-photon coupling, etc. Detailed understanding into the strong light-matter interactions in these hybrid organic-inorganic semiconductor systems remains modest. Herein, the intricate coherent interplay of exciton, spin, and phonon dynamics in (C6H5C2H4NH3)2PbI4 thin films using transient optical spectroscopy is explicated. New insights into the hotly debated origins of transient spectral features, relaxation pathways, ultrafast spin relaxation via exchange interaction, and strong coherent exciton-phonon coupling are revealed from the detailed phenomenological modeling. Importantly, this work unravels the complex interplay of spin-quasiparticle interactions in these layered 2D halide perovskites with large spin-orbit coupling.