Publications

Human mesenchymal stromal cells (hMSCs) seeded on calcium phosphate (CaP) bioceramics are extensively explored in bone tissue engineering and have recently shown effective clinical outcomes. In previous pre-clinical studies, hMSCs-CaP-mediated bone formation was preceded by osteoclastogenesis at the implantation site. The current study evaluates to what extent phase composition of CaPs affects the osteoclast response and ultimately influence bone formation. To this end, four different CaP bioceramics were used, hydroxyapatite (HA), beta-tricalcium phosphate (beta-TCP) and two biphasic composites of HA/beta- TCP ratios of 60/40 and 20/80 respectively, for in vitro osteoclast differentiation and correlation with in vivo osteoclastogenesis and bone formation. All ceramics allowed osteoclast formation in vitro from mouse and human precursors, except for pure HA, which significantly impaired their maturation. Ectopic implantation alongside hMSCs in subcutis sites of nude mice revealed new bone formation at 8 weeks in all conditions with relative amounts for beta-TCP > biphasic CaPs > HA. Surprisingly, while hMSCs were essential for osteoinduction, their survival did not correlate with bone formation. By contrast, the degree of early osteoclastogenesis (2 weeks) seemed to define the extent of subsequent bone formation. Together, our findings suggest that the osteoclastic response could be used as a predictive marker in hMSC-CaPbased bone regeneration and strengthens the need to understand the underlying mechanisms for future biomaterial development. Statement of significance The combination of mesenchymal stromal cells (MSCs) and calcium phosphate (CaP) materials has demonstrated its safety and efficacy for bone regeneration in clinical trials, despite our insufficient understanding of the underlying biological mechanisms. Osteoclasts were previously suggested as key mediators between the early inflammatory phase following biomaterial implantation and the subsequent bone formation. Here we compared the affinity of osteoclasts for various CaP materials with different ratios of hydroxyapatite to beta-tricalcium phosphate. We found that osteoclast formation, both in vitro and at early stages in vivo, correlates with bone formation when the materials were implanted alongside MSCs in mice. Surprisingly, MSC survival did not correlate with bone formation, suggesting that the number or phenotype of osteoclasts formed was more important. (c) 2024 The Author(s). Published by Elsevier Ltd on behalf of Acta Materialia Inc. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )

JTD Keywords: Acid phosphatase tartrate resistant isoenzyme, Animal, Animal cell, Animal experiment, Animal tissue, Animals, Article, Beta-tricalcium phosphate, Bioceramics, Biocompatible materials, Biomaterial, Bone, Bone development, Bone formation, Bone regeneration, Calcium phosphate, Calcium phosphate materials, Calcium phosphates, Cd14 antigen, Cell differentiation, Cell engineering, Cell maturation, Cell survival, Ceramics, Chemical composition, Controlled study, Correlation analysis, Correlation coefficient, Data correlation, Durapatite, Engraftment, Flowcharting, Human, Human cell, Human mesenchymal stromal cell, Human mesenchymal stromal cells, Humans, Hydroxyapatite, Hydroxyapatites, In vitro study, In vivo study, In-vitro, In-vivo, Mammals, Marrow stromal cells, Material composition, Material compositions, Mesenchymal stroma cell, Mesenchymal stromal cells, Mice, Mice, nude, Monocyte, Mouse, Nonhuman, Nude mouse, Ossification, Osteoclast, Osteoclastogenesis, Osteoclasts, Osteogenesis, Osteoinduction, Phase composition, Regeneration strategies, Resorption, Scaffolds, Stem-cells, Subcutaneous tissue, Tissue engineering, Transmission control protocol, Tri-calcium phosphates, Vimentin

Introduction: After a stroke, a wide range of deficits can occur with varying onset latencies. As a result, assessing impairment and recovery are enormous challenges in neurorehabilitation. Although several clinical scales are generally accepted, they are time-consuming, show high inter-rater variability, have low ecological validity, and are vulnerable to biases introduced by compensatory movements and action modifications. Alternative methods need to be developed for efficient and objective assessment. In this study, we explore the potential of computer-based body tracking systems and classification tools to estimate the motor impairment of the more affected arm in stroke patients. Methods: We present a method for estimating clinical scores from movement parameters that are extracted from kinematic data recorded during unsupervised computer-based rehabilitation sessions. We identify a number of kinematic descriptors that characterise the patients' hemiparesis (e.g., movement smoothness, work area), we implement a double-noise model and perform a multivariate regression using clinical data from 98 stroke patients who completed a total of 191 sessions with RGS. Results: Our results reveal a new digital biomarker of arm function, the Total Goal-Directed Movement (TGDM), which relates to the patients work area during the execution of goal-oriented reaching movements. The model's performance to estimate FM-UE scores reaches an accuracy of R-2: 0.38 with an error (sigma: 12.8). Next, we evaluate its reliability (r = 0.89 for test-retest), longitudinal external validity (95% true positive rate), sensitivity, and generalisation to other tasks that involve planar reaching movements (R-2: 0.39). The model achieves comparable accuracy also for the Chedoke Arm and Hand Activity Inventory (R-2: 0.40) and Barthel Index (R-2: 0.35). Conclusions: Our results highlight the clinical value of kinematic data collected during unsupervised goal-oriented motor training with the RGS combined with data science techniques, and provide new insight into factors underlying recovery and its biomarkers.

JTD Keywords: interactive feedback, motion classification, motion sensing, multivariate regression, posture monitoring, rehabilitation, stroke, Adult, Aged, Analytic method, Arm movement, Article, Barthel index, Brain hemorrhage, Cerebrovascular accident, Chedoke arm and hand activity inventory, Clinical protocol, Cognitive defect, Computer analysis, Controlled study, Convergent validity, Correlation coefficient, Disease severity, External validity, Female, Fugl meyer assessment for the upper extremity, Functional assessment, Functional status assessment, General health status assessment, Hemiparesis, Human, Interactive feedback, Ischemic stroke, Kinematics, Major clinical study, Male, Mini mental state examination, Motion classification, Motion sensing, Motor analog scale, Movement, Multivariate regression, Muscle function, Posture monitoring, Probability, Recovery, Rehabilitation, Reliability, Retrospective study, Stroke, Stroke patient, Test retest reliability, Therapy, Total goal directed movement, Upper extremities, Upper limb, Upper-limb, Wolf motor function test

Respiration rate can be assessed by analyzing respiratory changes of the electrocardiogram (ECG). Several methods can be applied to derive the respiratory signal from the ECG (EDR signal). In this study, four EDR estimation methods based on QRS features were analyzed. A database with 44 healthy subjects (16 females) in supine and sitting positions was analyzed. Respiratory flow and ECG recordings on leads I, II, III and a Chest lead was studied. A QR slope-based method, an RS slope-based method, an QRS angle-based method and an QRS area-based method were applied. Their performance was evaluated by the correlation coefficient with the reference respiratory volume signal. Significantly higher correlation coefficients in the range r = 0.77 – 0.86 were obtained with the Chest lead for all methods. The EDR estimation method based on the QRS angle provided the highest similarity with the volume signal for all recording leads and subject positions. We found no statistically significant differences according to gender or subject position.Clinical Relevance— This work analyzes the EDR signal from four electrocardiographic leads to obtain the respiratory signal and contributes to a simplified analysis of respiratory activity.

JTD Keywords: Electrocardiography, Lead, Estimation, Correlation coefficient, Databases, Heart, Correlation

The study of the mechanomyographic signal of the diaphragm muscle (MMGdi) is a promising technique in order to evaluate the respiratory muscles effort. The relationship between amplitude and frequency parameters of this signal with the respiratory effort performed during respiration is of great interest for researchers and physicians due to its diagnostic potentials. However, MMGdi signals are frequently contaminated by a cardiac vibration or mechanocardiographic (MCG) signal. An adaptive noise cancellation (ANC) can be used to reduce the MCG interference in the recorded MMGdi activity. In this paper, it is evaluated the proposed ANC scheme by means of a synthetic MMGdi signal with a controlled MCG interference. The Pearson's correlation coefficient (PCC) between both root mean square (RMS) and mean frequency (fm) of the synthetic MMGdi signal are considerably reduced with the presence of cardiac vibration noise (from 0.95 to 0.87, and from 0.97 to 0.76, respectively). With the ANC algorithm proposed the effect of the MCG noise on the amplitude and frequency of MMG parameters is reduced considerably (PCC of 0.93 and 0.97 for the RMS and fm, respectively). The ANC method proposed in this work is an interesting technique to attenuate the cardiac interference in respiratory MMG signals. Further investigation should be carried out to evaluate the performance of the ANC algorithm in real MMGdi signals.

JTD Keywords: Adaptive filters, Frequency modulation, Interference, Muscles, Noise cancellation, Vibrations, Cardiology, Medical signal processing, Muscle, Signal denoising, ANC algorithm, MCG interference, Pearson correlation coefficient, Adaptive noise cancellation, Cardiac vibration interference, Cardiac vibration noise, Diaphragm muscle, Mechanocardiographic signal, Mechanomyographic signals, Respiratory muscles effort