Impact of Uniaxial Stretching on Both Gliding and Traction Areas of Tendon Explants in a Novel Bioreactor.

The effects of mechanical stress on cells and their extracellular matrix, especially in gliding sections of tendon, are still poorly understood. This study sought to compare the effects of uniaxial stretching on both gliding and traction areas in the same tendon. Flexor digitorum longus muscle tendons explanted from rats were subjected to stretching in a bioreactor for 6, 24, or 48 h, respectively, at 1 Hz and an amplitude of 2.5%. After stimulation, marker expression was quantified by histological and immunohistochemical staining in both gliding and traction areas. We observed a heightened intensity of scleraxis after 6 and 24 h of stimulation in both tendon types, though it had declined again 48 h after stimulation. We observed induced matrix metalloproteinase-1 and -13 protein expression in both tendon types. The bioreactor produced an increase in the mechanical structural strength of the tendon during the first half of the loading time and a decrease during the latter half. Uniaxial stretching of flexor tendon in our set-up can serve as an overloading model. A combination of mechanical and histological data allows us to improve the conditions for cultivating tendon tissues.

The role of hyperbaric oxygen treatment in the management of spondylodiscitis.

Introduction: This study analysed treatment outcomes in a patient cohort diagnosed with spondylodiscitis, who received adjunct hyperbaric oxygen treatment (HBOT) in addition to antibiotic therapy at our clinic. Important considerations included the timing of HBOT initiation on treatment success, and recurrence rates. Methods: We retrospectively reviewed the records of all patients diagnosed with spondylodiscitis who received HBOT at the Underwater and Hyperbaric Medicine Clinic in Gulhane Training and Research Hospital, between 1 November 2016 and 25 October 2022. The patients received HBOT at 243.2 kPa for a total of 120 minutes per session, once daily for five days a week for a total of 30 sessions. Results: Twenty-five patients with spondylodiscitis were evaluated before and after combination HBOT and targeted antibiotic treatment. After treatment, patients had lower median (range) visual analogue pain scores (8 [4-10] vs 3 [0-7], P < 0.001) and C-reactive protein (22.3 [4.3-79.9] mg·L⁻¹ vs 6.8 [0.1-96.0] mg·L⁻¹, P = 0.002) and lower mean (standard deviation) white blood cell counts (8.8 [3.5] x 109·L⁻¹ vs 6.1 [1.6] x 109·L⁻¹, P = 0.002). When patients were examined (median) 48 months (2-156 months) after the completion of treatment, there were no persistent cases of spondylodiscitis. Conclusions: Combination HBOT with targeted antibiotic therapy effectively managed our cohort of patients diagnosed with spondylodiscitis. Hyperbaric oxygen treatment was safe, with no complications experienced. Moreover, HBOT may have helped to eliminate persistence and recurrence of symptoms with long term follow-up. A randomised controlled study with a larger number of patients is needed for more definitive conclusions.

Development of convolutional neural networks for recognition of tenogenic differentiation based on cellular morphology.

BACKGROUND AND OBJECTIVE: The use of automated systems for image recognition is highly preferred for regenerative medicine applications to evaluate stem cell differentiation early in the culturing state with non-invasive methodologies instead of invasive counterparts. Bone marrow-derived mesenchymal stem cells (BMSCs) are able to differentiate into desired cell phenotypes, and thereby promise a proper cell source for tendon regeneration. The therapeutic success of stem cell therapy requires cellular characterization prior to the implantation of cells. The foremost problem is that traditional characterization techniques require cellular material which would be more useful for cell therapy, complex laboratory procedures, and human expertise. Convolutional neural networks (CNNs), a class of deep neural networks, have recently made great improvements in image-based classifications, recognition, and detection tasks. We, therefore, aim to develop a potential CNN model in order to recognize differentiated stem cells by learning features directly from image data of unlabelled cells. METHODS: The differentiation of bone marrow mesenchymal stem cells (BMSCs) into tenocytes was induced with the treatment of bone morphogenetic protein-12 (BMP-12). Following the treatment and incubation step, the phase-contrast images of cells were obtained and immunofluorescence staining has been applied to characterize the differentiated state of BMSCs. CNN models were developed and trained with the phase-contrast cell images. The comparison of CNN models was performed with respect to prediction performance and training time. Moreover, we have evaluated the effect of image enhancement method, data augmentation, and fine-tuning training strategy to increase classification accuracy of CNN models. The best model was integrated into a mobile application. RESULTS: All the CNN models can fit the biological data extracted from immunofluorescence characterization. CNN models enable the cell classification with satisfactory accuracies. The best result in terms of accuracy and training time is achieved by the model proposed based on Inception-ResNet V2 trained from scratch using image enhancement and data augmentation strategies (96.80%, 434.55 sec). CONCLUSION: Our study reveals that the CNN models show good performance by identifying stem cell differentiation. Importantly this technique provides a faster and real-time tool in comparison to traditional methods enabling the adjustment of culture conditions during cultivation to improve the yield of therapeutic stem cells.

Chondrocyte colonisation of a tissue-engineered cartilage substitute under a mechanical stimulus.

Cell-free collagen scaffolds as cartilage substitute for small focal defects show promising results in first clinical studies. However, chondrocyte migration between scaffolds and the colonisation process of a cell-free implant is yet to be fully understood. We here focus on mechanobiological interdependencies between cell migration and mechanical stimulus in a 3D environment. We develop an in vitro model composed of a human chondrocyte-seeded collagen base and adjacent cell-free collagen type I scaffolds of varying collagen concentrations. Constructs are either cultured statically or dynamically under the influence of a physiological compression (0.5Hz, 0.5% initial strain). After 20 days we identify vital chondrocytes inside all collagen implants, proving that chondrocytes migrated from the underlying scaffold into the implants. Chondrocytes have not colonised the entire sample and are predominantly found in the bottom of the implant. In static culture conditions, a nearly equal cell number is found inside of all collagen scaffolds. In dynamic culture, the total amount of cells is increased by 30% to 320%, with the highest population in a commercial implant. Differences in cell population between the materials in dynamic culturing can be referred to differences in mechanical properties of the scaffolds, such as strain-rate insensitivity fostering the colonisation process.

Sample-specific adaption of an improved electro-mechanical model of in vitro cardiac tissue.

We present an electromechanically coupled computational model for the investigation of a thin cardiac tissue construct consisting of human-induced pluripotent stem cell-derived atrial, ventricular and sinoatrial cardiomyocytes. The mechanical and electrophysiological parts of the finite element model, as well as their coupling are explained in detail. The model is implemented in the open source finite element code Code_Aster and is employed for the simulation of a thin circular membrane deflected by a monolayer of autonomously beating, circular, thin cardiac tissue. Two cardio-active drugs, S-Bay K8644 and veratridine, are applied in experiments and simulations and are investigated with respect to their chronotropic effects on the tissue. These results demonstrate the potential of coupled micro- and macroscopic electromechanical models of cardiac tissue to be adapted to experimental results at the cellular level. Further model improvements are discussed taking into account experimentally measurable quantities that can easily be extracted from the obtained experimental results. The goal is to estimate the potential to adapt the presented model to sample specific cell cultures.