No abstract

Cell-laden hydrogels are widely used in tissue engineering and regenerative medicine. However, many of these hydrogels are not optimized for use in the oral environment, where they are exposed to blood and saliva. To address these challenges, we engineered an alginate-based adhesive, photocrosslinkable, and osteoconductive hydrogel biomaterial (AdhHG) with tunable mechanical properties. The engineered hydrogel was used as an injectable mesenchymal stem cell (MSC) delivery vehicle for craniofacial bone tissue engineering applications. Subcutaneous implantation in mice confirmed the biodegradability, biocompatibility, and osteoconductivity of the hydrogel. In a well-established rat peri-implantitis model, application of the adhesive hydrogel encapsulating gingival mesenchymal stem cells (GMSCs) resulted in complete bone regeneration around ailing dental implants with peri-implant bone loss. Together, we have developed a distinct bioinspired adhesive hydrogel with tunable mechanical properties and biodegradability that effectively delivers patient-derived dental-derived MSCs. The hydrogel is photocrosslinkable and, due to the presence of MSC aggregates and hydroxyapatite microparticles, promotes bone regeneration for craniofacial tissue engineering applications.

Small-diameter synthetic vascular grafts have high failure rate and tissue-engineered blood vessels are limited by the scalability. Here we engineered bioactive materials for in situ vascular tissue engineering, which recruits two types of endogenous progenitor cells for the regeneration of blood vessels. Heparin was conjugated to microfibrous vascular grafts to suppress thrombogenic responses, and stromal cell-derived factor-1α (SDF-1α) was immobilized onto heparin to recruit endogenous progenitor cells. Heparin-bound SDF-1α was more stable than adsorbed SDF-1α under both static and flow conditions. Microfibrous grafts were implanted in rats by anastomosis to test the functional performance. Heparin coating improved the short-term patency, and immobilized SDF-1α further improved the long-term patency. SDF-1α effectively recruited endothelial progenitor cells (EPCs) to the luminal surface of the grafts, which differentiated into endothelial cells (ECs) and accelerated endothelialization. More interestingly, SDF-1α increased the recruitment of smooth muscle progenitor cells (SMPCs) to the grafts, and SMPCs differentiated into smooth muscle cells (SMCs) in vivo and in vitro. Consistently, SDF-1α-immobilized grafts had significantly higher elastic modulus. This work demonstrates the feasibility of simultaneously recruiting progenitor cells of ECs and SMCs for in situ blood vessel regeneration. This in situ tissue engineering approach will have broad applications in regenerative medicine.

Magnetoelastic effect characterizes the change of materials’ magnetic properties under mechanical deformation, which is conventionally observed in some rigid metals or metal alloys. Here we show magnetoelastic effect can also exist in 1D soft fibers with stronger magnetomechanical coupling than that in traditional rigid counterparts. This effect is explained by a wavy chain model based on the magnetic dipole-dipole interaction and demagnetizing factor. To facilitate practical applications, we further invented a textile magnetoelastic generator (MEG), weaving the 1D soft fibers with conductive yarns to couple the observed magnetoelastic effect with magnetic induction, which paves a new way for biomechanical-to-electrical energy conversion with short-circuit current density of 0.63 mA cm−2, internal impedance of 180 Ω, and intrinsic waterproofness. Textile MEG was demonstrated to convert the arterial pulse into electrical signals with a low detection limit of 0.05 kPa,  even with heavy perspiration or in underwater situations without encapsulations.

Background Cardiogenic shock (CS) is a heterogeneous syndrome with varied presentations and outcomes. We used a machine learning approach to test the hypothesis that patients with CS have distinct phenotypes at presentation, which are associated with unique clinical profiles and in‐hospital mortality. Methods and Results We analyzed data from 1959 patients with CS from 2 international cohorts: CSWG (Cardiogenic Shock Working Group Registry) (myocardial infarction [CSWG‐MI; n=410] and acute‐on‐chronic heart failure [CSWG‐HF; n=480]) and the DRR (Danish Retroshock MI Registry) (n=1069). Clusters of patients with CS were identified in CSWG‐MI using the consensus k means algorithm and subsequently validated in CSWG‐HF and DRR. Patients in each phenotype were further categorized by their Society of Cardiovascular Angiography and Interventions staging. The machine learning algorithms revealed 3 distinct clusters in CS: "non‐congested (I)", "cardiorenal (II)," and "cardiometabolic (III)" shock. Among the 3 cohorts (CSWG‐MI versus DDR versus CSWG‐HF), in‐hospital mortality was 21% versus 28% versus 10%, 45% versus 40% versus 32%, and 55% versus 56% versus 52% for clusters I, II, and III, respectively. The "cardiometabolic shock" cluster had the highest risk of developing stage D or E shock as well as in‐hospital mortality among the phenotypes, regardless of cause. Despite baseline differences, each cluster showed reproducible demographic, metabolic, and hemodynamic profiles across the 3 cohorts. Conclusions Using machine learning, we identified and validated 3 distinct CS phenotypes, with specific and reproducible associations with mortality. These phenotypes may allow for targeted patient enrollment in clinical trials and foster development of tailored treatment strategies in subsets of patients with CS.