Facile and rapid 3D fabrication of strong, bioactive materials can address challenges that impede repair of large-to-massive rotator cuff tears including personalized grafts, limited mechanical support, and inadequate tissue regeneration. Herein, we developed a facile and rapid methodology that generates visible light-crosslinkable polythiourethane (PHT) pre-polymer resin (∼30 min at room temperature), yielding 3D-printable scaffolds with tendon-like mechanical attributes capable of delivering tenogenic bioactive factors. Ex vivo characterization confirmed successful fabrication, robust human supraspinatus tendon (SST)-like tensile properties (strength: 23 MPa, modulus: 459 MPa, at least 10,000 physiological loading cycles without failure), excellent suture retention (8.62-fold lower than acellular dermal matrix (ADM)-based clinical graft), slow degradation, and controlled release of fibroblast growth factor-2 (FGF-2) and transforming growth factor-β3 (TGF-β3). In vitro studies showed cytocompatibility and growth factor-mediated tenogenic-like differentiation of mesenchymal stem cells. In vivo studies demonstrated biocompatibility (3-week mouse subcutaneous implantation) and ability of growth factor-containing scaffolds to notably regenerate at least 1-cm of tendon with native-like biomechanical attributes as uninjured shoulder (8-week, large-to-massive 1-cm gap rabbit rotator cuff injury). This study demonstrates use of a 3D-printable, strong, and bioactive material to provide mechanical support and pro-regenerative cues for challenging injuries such as large-to-massive rotator cuff tears.

Repair of functionally graded biological interfaces requires joining dissimilar materials such as hard bone to soft tendon/ligament, with re-injuries/re-tears expected to be minimized by incorporating biomimicking, stress-reducing features within grafts. At bone-tendon interfaces (entheses), stress can be reduced via angled insertion, geometric flaring, mechanical gradation, and interdigitation of tissues. Here, we incorporated enthesis attributes into 3D in silico and physical models of a unique suture anchor-tendon hybrid graft (SATHG) and investigated their effects on stress reduction via finite element analyses (FEA) studies. Over 20 different simulations altering SATHG angulation, flaring, mechanical gradation, and interdigitation identified an optimal design, which included 90° angulation, 25° flaring, and a compliant (ascending then descending) mechanical gradient in SATHG's bone-to-tendon-like transitional region. This design reduced peak stress concentration factor (SCF) by 43.6 % relative to an ascending-only mechanical gradient typically used in hard-to-soft tissue engineering. To verify FEA results, SATHG models were fabricated using a photocrosslinkable bone-tendon-like polyurethane (QHM polymer) for ex vivo tensile assessment. Tensile testing showed that ultimate load (132.9 N), displacement-at-failure (1.78 mm), stiffness (135.4 N/mm), and total work-to-failure (422.1 × 10-3 J) were highest in the optimized design. Furthermore, to assess envisioned usage, SATHG pull-out testing and 6-week in vivo implantation into large, 0.5-cm segmental supraspinatus tendon defects was performed. SATHG pull-out testing showed secure bone attachment while histological assessment such as hematoxylin and eosin (H&E) together with Safranin-O staining showed biocompatibility including enthesis regeneration. This work demonstrates that engineering biomaterials with FEA-optimized, enthesis-like attributes shows potential for enhancing hard-to-soft tissue repair. STATEMENT OF SIGNIFICANCE: Successful repair of hard-to-soft tissue injuries is challenging due to high stress concentrations within bone-tendon/ligament grafts that mechanically compromise repair strength. While stress-reducing gradient biomaterials have been reported, little-to-no attention has focused on other bone-tendon/ligament interface (enthesis) features. To this end, a unique bone-tendon graft (SATHG) was developed by combining two common orthopaedic devices along with biomimetic incorporation of four enthesis-like features to reduce stress and encourage widespread clinician adoption. Notably, utilizing designs based on natural stress dissipation principles such as anchor insertion angle, geometric flaring, and mechanical gradation reduced stress by 43.6 % in silico, which was confirmed ex vivo, while in vivo studies showed SATHG's ability to support native enthesis regeneration. Thus, SATHG shows promise for hard-to-soft tissue repairs.

Biological and mechanical cues are both vital for biomaterial aided tendon repair and regeneration. Here, we fabricated mechanically tendon-like (0 s UV) QHM polyurethane scaffolds (Q: Quadrol, H: Hexamethylene diisocyanate; M: Methacrylic anhydride) and immobilized them with Growth and differentiation factor-7 (GDF-7) to produce mechanically strong and tenogenic scaffolds. In this study, we assessed QHM polymer cytocompatibility, amenability to fibrin-coating, immobilization and persistence of GDF-7, and capability to support GDF-7-mediated tendon differentiation in vitro as well as in vivo in mouse subcutaneous and acute rat rotator cuff tendon resection models. Cytocompatibility studies showed that QHM facilitated cell attachment, proliferation, and viability. Fibrin-coating and GDF-7 retention studies showed that mechanically tendon-like 0 s UV QHM polymer could be immobilized with GDF-7 and retained the growth factor (GF) for at least 1-week ex vivo. In vitro differentiation studies showed that GDF-7 mediated bone marrow-derived human mesenchymal stem cell (hMSC) tendon-like differentiation on 0 s UV QHM. Subcutaneous implantation of GDF-7-immobilized, fibrin-coated, QHM polymer in mice for 2 weeks demonstrated de novo formation of tendon-like tissue while implantation of GDF-7-immobilized, fibrin-coated, QHM polymer in a rat acute rotator cuff resection injury model indicated tendon-like tissue formation in situ and the absence of heterotopic ossification. Together, our work demonstrates a promising synthetic scaffold with human tendon-like biomechanical attributes as well as immobilized tenogenic GDF-7 for tendon repair and regeneration. STATEMENT OF SIGNIFICANCE: Biological activity and mechanical robustness are key features required for tendon-promoting biomaterials. While synthetic biomaterials can be mechanically robust, they often lack bioactivity. To biologically augment synthetic biomaterials, numerous drug and GF delivery strategies exist but the large tissue space within the shoulder is constantly flushed with saline during arthroscopic surgery, hindering efficacious controlled release of therapeutic molecules. Here, we coated QHM polymer (which exhibits human tendon-to-bone-like biomechanical attributes) with fibrin for GF binding. Unlike conventional drug delivery strategies, our approach utilizes immobilized GFs as opposed to released GFs for sustained, localized tissue regeneration. Our data demonstrated that GF immobilization can be broadly applied to synthetic biomaterials for enhancing bioactivity, and GDF-7-immobilized QHM exhibit high clinical translational potential for tendon repair.

Cell instance segmentation is important in biomedical research. For living cell analysis, microscopy images are captured under various conditions (e.g., the type of microscopy and type of cell). Deep-learning-based methods can be used to perform instance segmentation if sufficient annotations of individual cell boundaries are prepared as training data. Generally, annotations are required for each condition, which is very time-consuming and labor-intensive. To reduce the annotation cost, we propose a weakly supervised cell instance segmentation method that can segment individual cell regions under various conditions by only using rough cell centroid positions as training data. This method dramatically reduces the annotation cost compared with the standard annotation method of supervised segmentation. We demonstrated the efficacy of our method on various cell images; it outperformed several of the conventional weakly-supervised methods on average. In addition, we demonstrated that our method can perform instance cell segmentation without any manual annotation by using pairs of phase contrast and fluorescence images in which cell nuclei are stained as training data.

We propose a weakly-supervised cell tracking method that can train a convolutional neural network (CNN) by using only the annotation of "cell detection" (i.e., the coordinates of cell positions) without association information, in which cell positions can be easily obtained by nuclear staining. First, we train co-detection CNN that detects cells in successive frames by using weak-labels. Our key assumption is that co-detection CNN implicitly learns association in addition to detection. To obtain the association, we propose a backward-and-forward propagation method that analyzes the correspondence of cell positions in the outputs of co-detection CNN. Experiments demonstrated that the proposed method can associate cells by analyzing co-detection CNN. Even though the method uses only weak supervision, the performance of our method was almost the same as the state-of-the-art supervised method. Code is publicly available in https://github.com/naivete5656/WSCTBFP