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Articular cartilage degeneration poses a significant public health challenge; techniques such as 3D bioprinting are being explored for its regeneration in vitro. Gelatin-based hydrogels represent one of the most promising biopolymers used in cartilage tissue engineering, especially for its collagen composition and tunable mechanical properties. However, there are no standard protocols that define process parameters such as the crosslinking method to apply. To this aim, a reproducible study was conducted for exploring the influence of different crosslinking methods on 3D bioprinted gelatin structures. This study assessed mechanical properties and cell viability in relation to various crosslinking techniques, revealing promising results particularly for dual (photo + ionic) crosslinking methods, which achieved high cell viability and tunable stiffness. These findings offer new insights into the effects of crosslinking methods on 3D bioprinted gelatin for cartilage applications. For example, ionic and photo-crosslinking methods provide softer materials, with photo-crosslinking supporting cell stretching and diffusion, while ionic crosslinking preserves a spherical stem cell morphology. On the other hand, dual crosslinking provides a stiffer, optimized solution for creating stable cartilage-like constructs. The results of this study offer a new perspective on the standardization of gelatin for cartilage bioprinting, bridging the gap between research and clinical applications.

Scientific conferences increasingly suffer from the need for short presentations in which speakers like to dwell on the details of their work. A mitigating factor is to encourage discussion and planning of collaborations by organizing small meetings in a hotel large enough to host all attendees. This extends discussions' opportunities during morning breakfasts, lunches, dinners and long evenings together. Even if the vast majority of participants will not stay for the entire duration of the Conference, the possibilities for specialists to interact with specialists who are even very distant in terms of knowledge increase enormously. In any case, the results in terms of new job opportunities for young participants outweigh the costs for the organizers. Thirty years of Padova Muscle Days offer many examples, but the authors of this report on the state of the art of Mobility Medicine testify that this also happened in the 2024 Five Days of Muscle and Mobility Medicine (2024Pdm3) hosted at the Hotel Petrarca, Thermae of Euganea Hills and Padua, Italy which is in fact a valid countermeasure to the inevitable tendencies towards hyperspecialization that the explosive increase in scientific progress brings with it.

At the end of the 2023 Padua Days of Muscle and Mobility Medicine the next year's meeting was scheduled from 27 February to 2 March 2024 (2024Pdm3). During the summer and autumn the program was confirmed with Scientific Sessions that will take place over five days, starting in the afternoon of February 27, 2024 at the Conference Room of the Hotel Petrarca, Thermae of Euganean Hills (Padua), Italy. As usual, the next day will be spent in Padua, in this occasion at the San Luca Hall of the Santa Giustina monastery in Prato della Valle, Padua, Italy. Collected during Autumn 2023, many more titles and abstracts than expected were submitted, forcing the organization of parallel sessions both on March 1 and March 2 2024 confirming attractiveness of the 2024 Pdm3. The five days will include oral presentations of scientists and clinicians from Argentina, Austria, Belgium, Brazil, Canada, Denmark, Egypt, France, Germany, Iceland, Ireland, Italy, Romania, Russia, Slovenia, Switzerland, UK and USA. Together with the preliminary Program at December 1, 2023, the early submitted Abstracts is e-published in this Issue 33 (4) 2023 of the European Journal of Translational Myology (EJTM). You are invited to join, submitting your Last Minute Abstracts to ugo.carraro@unipd.it by February 1, 2024. Furthermore, with the more generous deadline of May 20, 2024, submit please “Communications” to the European Journal of Translational Myology (Clarivate’s ESCI Impact factor 2.2; SCOPUS Cite Score: 3.2). See you soon at the Hotel Petrarca in Montegrotto Terme, Padua, on February 27, 2024, but the complete program can be followed from home via zoom connection.

Introduction: The aging population poses significant challenges to healthcare systems globally, necessitating a comprehensive understanding of age-related changes affecting physical function. Age-related functional decline highlights the urgency of understanding how tissue composition changes impact mobility, independence, and quality of life in older adults. Previous research has emphasized the influence of muscle quality, but the role of tissue composition asymmetry across various tissue types remains understudied. This work develops asymmetry indicators based on muscle, connective and fat tissue extracted from cross-sectional CT scans, and shows their interplay with BMI and lower extremity function among community-dwelling older adults.Methods: We used data from 3157 older adults from 71 to 98 years of age (mean: 80.06). Tissue composition asymmetry was defined by the differences between the right and left sides using CT scans and the non-Linear Trimodal Regression Analysis (NTRA) parameters. Functional mobility was measured through a 6-meter gait (Normal-GAIT and Fast-GAIT) and the Timed Up and Go (TUG) performance test. Statistical analysis included paired t-tests, polynomial fitting curves, and regression analysis to uncover relationships between tissue asymmetry, age, and functional mobility.Results: Findings revealed an increase in tissue composition asymmetry with age. Notably, muscle and connective tissue width asymmetry showed significant variation across age groups. BMI classifications and gait tasks also influenced tissue asymmetry. The Fast-GAIT task demonstrated a substantial separation in tissue asymmetry between normal and slow groups, whereas the Normal-GAIT and the TUG task did not exhibit such distinction. Muscle quality, as reflected by asymmetry indicators, appears crucial in understanding age-related changes in muscle function, while fat and connective tissue play roles in body composition and mobility.Discussion: Our study emphasizes the importance of tissue asymmetry indicators in understanding how muscle function changes with age in older individuals, demonstrating their role as risk factor and their potential employment in clinical assessment. We also identified the influence of fat and connective tissue on body composition and functional mobility. Incorporating the NTRA technology into clinical evaluations could enable personalized interventions for older adults, promoting healthier aging and maintaining physical function.

IntroductionThere is accumulating evidence that many pathological conditions affecting human balance are consequence of postural control (PC) failure or overstimulation such as in motion sickness. Our research shows the potential of using the response to a complex postural control task to assess patients with early-stage Parkinson's Disease (PD).MethodsWe developed a unique measurement model, where the PC task is triggered by a moving platform in a virtual reality environment while simultaneously recording EEG, EMG and CoP signals. This novel paradigm of assessment is called BioVRSea. We studied the interplay between biosignals and their differences in healthy subjects and with early-stage PD.ResultsDespite the limited number of subjects (29 healthy and nine PD) the results of our work show significant differences in several biosignals features, demonstrating that the combined output of posturography, muscle activation and cortical response is capable of distinguishing healthy from pathological.DiscussionThe differences measured following the end of the platform movement are remarkable, as the induced sway is different between the two groups and triggers statistically relevant cortical activities in α and θ bands. This is a first important step to develop a multi-metric signature able to quantify PC and distinguish healthy from pathological response.

Phase slips arise from state transitions of the coordinated activity of cortical neurons which can be extracted from the EEG data. The phase slip rates (PSRs) were studied from the high-density (256 channel) EEG data, sampled at 16.384 kHz, of five adult subjects during covert visual object naming tasks. Artifact-free data from 29 trials were averaged for each subject. The analysis was performed to look for phase slips in the theta (4–7 Hz), alpha (7–12 Hz), beta (12–30 Hz), and low gamma (30–49 Hz) bands. The phase was calculated with the Hilbert transform, then unwrapped and detrended to look for phase slip rates in a 1.0 ms wide stepping window with a step size of 0.06 ms. The spatiotemporal plots of the PSRs were made by using a montage layout of 256 equidistant electrode positions. The spatiotemporal profiles of EEG and PSRs during the stimulus and the first second of the post-stimulus period were examined in detail to study the visual evoked potentials and different stages of visual object recognition in the visual, language, and memory areas. It was found that the activity areas of PSRs were different as compared with EEG activity areas during the stimulus and post-stimulus periods. Different stages of the insight moments during the covert object naming tasks were examined from PSRs and it was found to be about 512 ± 21 ms for the ‘Eureka’ moment. Overall, these results indicate that information about the cortical phase transitions can be derived from the measured EEG data and can be used in a complementary fashion to study the cognitive behavior of the brain.

Articular cartilage morphology and composition are essential factors in joint biomechanics, and their alteration is a crucial aspect of osteoarthritis (OA), a prevalent disease that causes pain and functional loss. This research focuses on developing patient-specific synthetic cartilage using innovative Digital Anatomy polymers. The objectives include investigating the morphology, characterizing the mechanical properties, and replicating the architecture of natural cartilage. This approach offers potential alternatives to traditional manufacturing methods and reduces the need for expensive in vivo experiments. Finite Element Analysis (FEA) validates a novel patient-specific measurement setup. It provides insights into the role of morphology in the distribution of stress and strain within cartilage. CAD design is also utilized to create standardized fiber-reinforced samples that mimic the layered micro-architecture of natural cartilage, allowing for the study of their contribution to the overall mechanical properties. The results demonstrate that 3D-printed polymers can effectively replicate the elastic properties of cartilage. The proposed patient-specific simulator produces reliable results, which have been validated through FEM analysis. While the recreated microstructure closely resembles biological cartilage samples, the elastic properties are slightly underestimated. In conclusion, designing an in silico knee joint is a feasible approach that offers numerous advantages for further development. The Young’s modulus values of our synthetic cartilage modules range from 2.43 MPa to 7.24 MPa, within the range reported in the literature. Moreover, Young´s modulus at the micro level shows the differences between surface 1.74 MPa and internal substrate 1.83 MPa depending on the fiber orientation. Finally, our model proves to be mechanically and morphologically accurate at both the macro and micro levels.

Abstract Current diagnosis of concussion relies on self-reported symptoms and medical records rather than objective biomarkers. This work uses a novel measurement setup called BioVRSea to quantify concussion status. The paradigm is based on brain and muscle signals (EEG, EMG), heart rate and center of pressure (CoP) measurements during a postural control task triggered by a moving platform and a virtual reality environment. Measurements were performed on 54 professional athletes who self-reported their history of concussion or non-concussion. Both groups completed a concussion symptom scale (SCAT5) before the measurement. We analyzed biosignals and CoP parameters before and after the platform movements, to compare the net response of individual postural control. The results showed that BioVRSea discriminated between the concussion and non-concussion groups. Particularly, EEG power spectral density in delta and theta bands showed significant changes in the concussion group and right soleus median frequency from the EMG signal differentiated concussed individuals with balance problems from the other groups. Anterior–posterior CoP frequency-based parameters discriminated concussed individuals with balance problems. Finally, we used machine learning to classify concussion and non-concussion, demonstrating that combining SCAT5 and BioVRSea parameters gives an accuracy up to 95.5%. This study is a step towards quantitative assessment of concussion.

At the end of the 2022 Padua Days of Muscle and Mobility Medicine (Pdm3) the next year's meeting was scheduled from 29 March to 1 April 2023. Despite the worsening evolution of the crisis in Eastern Europe, the program was confirmed in autumn 2022 with Scientific Sessions that will take place over three full days in the Aula Guariento of the Galileian Academy of Arts, Letters and Sciences of Padua (March 29, 2023) and then at the Conference Room of the Hotel Petrarca, Thermae of Euganean Hills (Padua), Italy. Collected during autumn and early winter, many titles and abstracts where submitted (about 100 Oral presentations are listed in the preliminary Program by January 31, 2023) confirming attractiveness of the 2023 Pdm3. The four days will include oral presentations of scientists and clinicians from Austria, Bulgaria, Canada, Denmark, France, Georgia, Germany, Iceland, Ireland, Italy, Mongolia, Norway, Russia, Slovakia, Slovenia, Spain, Switzerland, The Netherlands and USA. Together with the preliminary Program at January 31, 2023, the Collection of Abstracts is e-published in this Issue 33 (1) 2023 of the European Journal of Translational Myology (EJTM). You are invited to join, submitting your Last Minute Abstracts to ugo.carraro@unipd.it by March 15, 2023. Furthermore, with the more generous deadline of May 20, 2023, submit please “Communications” to the European Journal of Translational Myology (SCOPUS Cite Score Tracker 2023: 3.2 by January 5, 2023) and/or to the 2023 Special Issue: “Pdm3” of the Journal Diagnostics, MDPI, Basel (I.F. near to 4.0) with deadline September 30, 2023. Both journals will provide discounts to the first accepted typescripts. See you soon at the Hotel Petrarca of Montegrotto Terme, Padua, Italy. For a promo of the 2023 Pdm3 link to: https://www.youtube.com/watch?v=zC02D4uPWRg

Manual material handling and load lifting are activities that can cause work-related musculoskeletal disorders. For this reason, the National Institute for Occupational Safety and Health proposed an equation depending on the following parameters: intensity, duration, frequency, and geometric characteristics associated with the load lifting. In this paper, we explore the feasibility of several Machine Learning (ML) algorithms, fed with frequency-domain features extracted from electromyographic (EMG) signals of back muscles, to discriminate biomechanical risk classes defined by the Revised NIOSH Lifting Equation. The EMG signals of the multifidus and erector spinae muscles were acquired by means of a wearable device for surface EMG and then segmented to extract several frequency-domain features relating to the Total Power Spectrum of the EMG signal. These features were fed to several ML algorithms to assess their prediction power. The ML algorithms produced interesting results in the classification task, with the Support Vector Machine algorithm outperforming the others with accuracy and Area under the Receiver Operating Characteristic Curve values of up to 0.985. Moreover, a correlation between muscular fatigue and risky lifting activities was found. These results showed the feasibility of the proposed methodology—based on wearable sensors and artificial intelligence—to predict the biomechanical risk associated with load lifting. A future investigation on an enriched study population and additional lifting scenarios could confirm the potential of the proposed methodology and its applicability in the field of occupational ergonomics.

Tissue-engineered bone tissue grafts are a promising alternative to the more conventional use of natural donor bone grafts. However, choosing an appropriate biomaterial/scaffold to sustain cell survival, proliferation, and differentiation in a 3D environment remains one of the most critical issues in this domain. Recently, chitosan/gelatin/genipin (CGG) hybrid scaffolds have been proven as a more suitable environment to induce osteogenic commitment in undifferentiated cells when doped with graphene oxide (GO). Some concern is, however, raised towards the use of graphene and graphene-related material in medical applications. The purpose of this work was thus to check if the osteogenic potential of CGG scaffolds without added GO could be increased by improving the medium diffusion in a 3D culture of differentiating cells. To this aim, the level of extracellular matrix (ECM) mineralization was evaluated in human bone-marrow-derived stem cell (hBMSC)-seeded 3D CGG scaffolds upon culture under a perfusion flow in a dedicated custom-made bioreactor system. One week after initiating dynamic culture, histological/histochemical evaluations of CGG scaffolds were carried out to analyze the early osteogenic commitment of the culture. The analyses show the enhanced ECM mineralization of the 3D perfused culture compared to the static counterpart. The results of this investigation reveal a new perspective on more efficient clinical applications of CGG scaffolds without added GO.

Postural control is a complex feedback system that relies on vast array of sensory inputs in order to maintain a stable upright stance. The brain cortex plays a crucial role in the processing of this information and in the elaboration of a successful adaptive strategy to external stimulation preventing loss of balance and falls. In the present work, the participants postural control system was challenged by disrupting the upright stance via a mechanical skeletal muscle vibration applied to the calves. The EEG source connectivity method was used to investigate the cortical response to the external stimulation and highlight the brain network primarily involved in high-level coordination of the postural control system. The cortical network reconfiguration was assessed during two experimental conditions of eyes open and eyes closed and the network flexibility (i.e. its dynamic reconfiguration over time) was correlated with the sample entropy of the stabilogram sway. The results highlight two different cortical strategies in the alpha band: the predominance of frontal lobe connections during open eyes and the strengthening of temporal-parietal network connections in the absence of visual cues. Furthermore, a high correlation emerges between the flexibility in the regions surrounding the right temporo-parietal junction and the sample entropy of the CoP sway, suggesting their centrality in the postural control system. These results open the possibility to employ network-based flexibility metrics as markers of a healthy postural control system, with implications in the diagnosis and treatment of postural impairing diseases.

Introduction: Postural control is a sensorimotor mechanism that can reveal neurophysiological disorder. The present work studies the quantitative response to a complex postural control task.Methods: We measure electroencephalography (EEG), electromyography (EMG), and center of pressure (CoP) signals during a virtual reality (VR) experience called BioVRSea with the aim of classifying different postural control responses. The BioVRSea paradigm is based on six different phases where motion and visual stimulation are modulated throughout the experiment, inducing subjects to a different adaptive postural control strategy. The goal of the study is to assess the predictability of those responses. During the experiment, brain activity was recorded from a 64-channel EEG, muscle activity was determined with six wireless EMG sensors placed on lower leg muscles, and individual movement measured by the CoP. One-hundred and seventy-two healthy individuals underwent the BioVRSea paradigm and 318 features were extracted from each phase of the experiment. Machine learning techniques were employed to: (1) classify the phases of the experiment; (2) assess the most notable features; and (3) identify a quantitative pattern for healthy responses.Results: The results show that the EEG features are not sufficient to predict the distinct phases of the experiment, but they can distinguish visual and motion onset stimulation. EMG features and CoP features, when used jointly, can predict five out of six phases with a mean accuracy of 74.4% (±8%) and an AUC of 0.92. The most important feature to identify the different adaptive strategies is the Squared Root Mean Distance of points on Medio-Lateral axis (RDIST_ML).Discussion: This work shows the importance and the feasibility of a quantitative evaluation in a complex postural control task and demonstrates the potential of EEG, CoP, and EMG for assessing pathological conditions. These predictive systems pave the way for developing an objective assessment of pathological behavior PC responses. This will be a first step in identifying individual disorders and treatment options.

Postural instability and loss of vestibular and somatosensory acuity can be part of the signs encountered in Parkinson’s Disease (PD). Visual dependency is described in PD. These modifications of sensory input hierarchy are predictors of motion sickness (MS). The aim of this study was to assess MS susceptibility and effects of real induced MS in posture. 63 PD patients, whose medication levels (levodopa) reflected the pathology were evaluated, and 27 healthy controls, filled a MS questionnaire; 9 PD patients and 43 healthy controls were assessed by posturography using virtual reality. Drug amount predicted visual MS (p=0.01), but not real induced MS susceptibility. PD patients did not experience postural instability in virtual reality, contrary to healthy controls. Since PD patients do not seem to feel vestibular stimulated MS, they may not rely on vestibular and somatosensory inputs during the stimulation. However, they feel visually induced MS more with increased levodopa drug effect. Levodopa amount can increase visual dependency. The strongest MS predictors must be studied in PD to better understand the effect of visual stimulation and its absence in vestibular stimulation.

Abstract Although previous studies have highlighted the association between physical activity and lower extremity function (LEF) in elderly individuals, the mechanisms underlying this relationship remain debated. Our recent work has recognized the utility of nonlinear trimodal regression analysis (NTRA) parameters in characterizing changes in soft tissue radiodensity as a quantitative construct for sarcopenia in the longitudinal, population-based cohort of the AGES-Reykjavík study. For the present work, we assembled a series of prospective multivariate regression models to interrogate whether NTRA parameters mediate the 5-year longitudinal relationship between physical activity and LEF in AGES-Reykjavík participants. Healthy elderly volunteers from the AGES-Reykjavík cohort underwent mid-thigh X-ray CT scans along with a four-part battery of LEF tasks: normal gait speed, fastest-comfortable gait speed, isometric leg strength, and timed up-and-go. These data were recorded at two study timepoints which were separated by approximately 5 years: AGES-I (n = 3157) and AGES-II (n = 3098). Participants in AGES-I were likewise administered a survey to approximate their weekly frequency of engaging in moderate-to-vigorous physical activity (PAAGES-I). Using a multivariate mediation analysis framework, linear regression models were assembled to test whether NTRA parameters mediated the longitudinal relationship between PAAGES-I and LEFAGES-II; all models were covariate-adjusted for age, sex, BMI, and baseline LEF, and results were corrected for multiple statistical comparisons. Our first series of models confirmed that all four LEF tasks were significantly related to PAAGES-I; next, modelling the relationship between PAAGES-I and NTRAAGES-II identified muscle amplitude (N m ) and location (μ m ) as potential mediators of LEF to test. Finally, adding these two parameters into our PAAGES-I → LEFAGES-II models attenuated the prior effect of PAAGES-I; bootstrapping confirmed N m and μ m as significant partial mediators of the PAAGES-I → LEFAGES-II relationship, with the strongest effect found in isometric leg strength. This work describes a novel approach toward clarifying the mechanisms that underly the relationship between physical activity and LEF in aging individuals. Identifying N m and μ m as significant partial mediators of this relationship provides strong evidence that physical activity protects aging mobility through the preservation of both lean tissue quantity and quality.

Knee Osteoarthritis (OA) is a highly prevalent condition affecting knee joint that causes loss of physical function and pain. Clinical treatments are mainly focused on pain relief and limitation of disabilities; therefore, it is crucial to find new paradigms assessing cartilage conditions for detecting and monitoring the progression of OA. The goal of this paper is to highlight the predictive power of several features, such as cartilage density, volume and surface. These features were extracted from the 3D reconstruction of knee joint of forty-seven different patients, subdivided into two categories: degenerative and non-degenerative. The most influent parameters for the degeneration of the knee cartilage were determined using two machine learning classification algorithms (logistic regression and support vector machine); later, box plots, which depicted differences between the classes by gender, were presented to analyze several of the key features’ trend. This work is part of a strategy that aims to find a new solution to assess cartilage condition based on new-investigated features.

Objective: Propionibacterium acnes has been implicated in the pathogenesis of prostate disease as acute and chronic prostatic inflammation, benign prostatic hyperplasia and prostate cancer although it should still be clarified if Propionibacterium acnes (P. acnes) is a commensal or accidental prostate pathogen. Aiming to evaluate the pathogenic potential for genitourinary tract of Propionibacterium acnes, we investigated the frequency of P. acnes genome in urine or semen samples from men with recurrent symptoms of urinary infection and negative testing for the most common urinary tract pathogens and sexually transmitted infections (STI) agents as Chlamydia trachomatis, Mycoplasma genitalium, Mycoplasma hominis, Ureaplasma parvum and Ureaplasma urealyticum. Materials and methods: The DNA extracted from urine and semen samples was analyzed for evaluating the P. acnes genome presence by real-time polymerase chain reaction (PCR). Infections were treated with vancomycin and cephalosporins antibiotics and then the search for the P.acnes genome by realtime PCR was repeated. Results: The P. acnes qualitative real-time PCR revealed the genome in 73 out of 159 samples examined (108 urine and 51 semen). After antibiotic therapy, P. acnes was never detected. Conclusions: These results suggested that P. acnes genome determination should be performed in cases of chronic inflammation in the urinary tract to identify an unknown potential pathogen of genitourinary tract.

Aging well is directly associated to a healthy lifestyle. The focus of this paper is to relate individual wellness with medical image features. Non-linear trimodal regression analysis (NTRA) is a novel method that models the radiodensitometric distributions of x-ray computed tomography (CT) cross-sections. It generates 11 patient-specific parameters that describe the quality and quantity of muscle, fat, and connective tissues. In this research, the relationship of these 11 NTRA parameters with age, physical activity, and lifestyle is investigated in the 3,157 elderly volunteers AGES-I dataset. First, univariate statistical analyses were performed, and subjects were grouped by age and self-reported past (youth–midlife) and present (within 12 months of the survey) physical activity to ascertain which parameters were the most influential. Then, machine learning (ML) analyses were conducted to classify patients using NTRA parameters as input features for three ML algorithms. ML is also used to classify a Lifestyle index using the age groups. This classification analysis yielded robust results with the lifestyle index underlying the relevant differences of the soft tissues between age groups, especially in fat and connective tissue. Univariate statistical models suggested that NTRA parameters may be susceptible to age and differences between past and present physical activity levels. Moreover, for both age and physical activity, lean muscle parameters expressed more significant variation than fat and connective tissues.

Regenerative medicine is the branch of medicine that effectively uses stem cell therapy and tissue engineering strategies to guide the healing or replacement of damaged tissues or organs. A crucial element is undoubtedly the biomaterial that guides biological events to restore tissue continuity. The polymers, natural or synthetic, find wide application thanks to their great adaptability. In fact, they can be used as principal components, coatings or vehicles to functionalize several biomaterials. There are many leading centers for the research and development of biomaterials in Italy. The aim of this review is to provide an overview of the current state of the art on polymer research for regenerative medicine purposes. The last five years of scientific production of the main Italian research centers has been screened to analyze the current advancement in tissue engineering in order to highlight inputs for the development of novel biomaterials and strategies.

Motion sickness (MS) and postural control (PC) conditions are common complaints among those who passively travel. Many theories explaining a probable cause for MS have been proposed but the most prominent is the sensory conflict theory, stating that a mismatch between vestibular and visual signals causes MS. Few measurements have been made to understand and quantify the interplay between muscle activation, brain activity, and heart behavior during this condition. We introduce here a novel multimetric system called BioVRSea based on virtual reality (VR), a mechanical platform and several biomedical sensors to study the physiology associated with MS and seasickness. This study reports the results from 28 individuals: the subjects stand on the platform wearing VR goggles, a 64-channel EEG dry-electrode cap, two EMG sensors on the gastrocnemius muscles, and a sensor on the chest that captures the heart rate (HR). The virtual environment shows a boat surrounded by waves whose frequency and amplitude are synchronized with the platform movement. Three measurement protocols are performed by each subject, after each of which they answer the Motion Sickness Susceptibility Questionnaire. Nineteen parameters are extracted from the biomedical sensors (5 from EEG, 12 from EMG and, 2 from HR) and 13 from the questionnaire. Eight binary indexes are computed to quantify the symptoms combining all of them in the Motion Sickness Index (IMS). These parameters create the MS database composed of 83 measurements. All indexes undergo univariate statistical analysis, with EMG parameters being most significant, in contrast to EEG parameters. Machine learning (ML) gives good results in the classification of the binary indexes, finding random forest to be the best algorithm (accuracy of 74.7 for IMS). The feature importance analysis showed that muscle parameters are the most relevant, and for EEG analysis, beta wave results were the most important. The present work serves as the first step in identifying the key physiological factors that differentiate those who suffer from MS from those who do not using the novel BioVRSea system. Coupled with ML, BioVRSea is of value in the evaluation of PC disruptions, which are among the most disturbing and costly health conditions affecting humans.

The purpose of this study is to use Health Technology Assessment (HTA) through the Six Sigma (SS) and DMAIC (Define, Measure, Analyse, Improve, Control) problem-solving strategies for comparing cemented and uncemented prostheses in terms of the costs incurred for Total hip arthroplasty (THA) and the length of hospital stay (LOS). Multinomial logistic regression analysis for modelling the data was also performed. Quantitative parameters extracted from gait analysis, electromyography and computed tomography images were used to compare the approaches, but the analysis did not show statistical significance. The variables regarding costs were studied with the Mann-Whitney and Kruskal-Wallis tests. No statistically significant difference between cemented and uncemented prosthesis for the total cost of LOS was found, but the cost of the surgeon had an influence on the overall expenses, affecting the cemented prosthetic approach. The material costs of surgery for the uncemented prosthesis and the cost of theatre of surgery for the cemented prosthesis were the most influential. Multinomial logistic regression identified the Vastus Lateralis variable as statistically significant. The overall accuracy of the model is 93.0%. The use of SS and DMAIC cycle as tools of HTA proved that the cemented and uncemented approaches for THA have similar costs and LOSy.

Schizophrenia is a complex disorder about which much is still unknown. Potential treatments, such as transcranial magnetic stimulation (TMS), have not been exploited, in part because of the variability in behavioral response. This can be overcome with the use of response biomarkers. It has been however shown that repetitive transcranial magnetic stimulation (rTMS) can the relieve positive and negative symptoms of schizophrenia, particularly auditory verbal hallucinations (AVH). This exploratory work aims to establish a quantitative methodological tool, based on high-density electroencephalogram (HD-EEG) data analysis, to assess the effect of rTMS on patients with schizophrenia and AVH. Ten schizophrenia patients with drug-resistant AVH were divided into two groups: the treatment group (TG) received 1 Hz rTMS treatment during 10 daily sessions (900 pulses/session) over the left T3-P3 International 10-20 location. The control group (CG) received rTMS treatment over the Cz (vertex) EEG location. We used the P300 oddball auditory paradigm, known for its reduced amplitude in schizophrenia with AVH, and recorded high-density electroencephalography (HD-EEG, 256 channels), twice for each patient: pre-rTMS and 1 week post-rTMS treatment. The use of HD-EEG enabled the analysis of the data in the time domain, but also in the frequency and source-space connectivity domains. The HD-EEG data were linked with the clinical outcome derived from the auditory hallucinations subscale (AHS) of the Psychotic Symptom Rating Scale (PSYRATS), the Quality of Life Scale (QoLS), and the Depression, Anxiety and Stress Scale (DASS). The general results show a variability between subjects, independent of the group they belong to. The time domain showed a higher N1-P3 amplitude post-rTMS, the frequency domain a higher power spectral density (PSD) in the alpha and beta bands, and the connectivity analysis revealed a higher brain network integration (quantified using the participation coefficient) in the beta band. Despite the small number of subjects and the high variability of the results, this work shows a robust data analysis and an interplay between morphology, spectral, and connectivity data. The identification of a trend post-rTMS for each domain in our results is a first step toward the definition of quantitative neurophysiological parameters to assess rTMS treatment.

The nonlinear trimodal regression analysis (NTRA) method based on radiodensitometric CT images distributions was developed for the quantitative characterization of soft tissue changes according to the lower extremity function of elderly subjects. In this regard, the NTRA method defines 11 subject-specific soft tissue parameters and has illustrated high sensitivity to changes in skeletal muscle form and function. The present work further explores the use of these 11 NTRA parameters in the construction of a machine learning (ML) system to predict body mass indexand isometric leg strength using tree-based regression algorithms. Results obtained from thesemodels demonstrate that when using an ML approach, these soft tissue features have a significant predictive value for these physiological parameters. These results further support the use of NTRA-based ML predictive assessment and support the future investigation of other physiological parametersand comorbidities.

We present preliminary results from the ongoing study entitled “Icelandic AVH-TMS” which aim is to study the effectiveness of repetitive transcranial magnetic stimulation (rTMS) treatment for patients with schizophrenia and with persistent auditory verbal hallucinations (AVH) using symptoms and psychometric scales and high-density EEG system (256 channels). The aim of the present work was to describe cortical topography of the auditory evoked responses like P50 and N100-P300 complex in healthy participants and patients with schizophrenia and to define a robust methodology of signal quantification using dense-array EEG. Preliminary data is shown for three healthy participants and three patients in baseline conditions and for two patients we show the results recorded before and after 10 days rTMS treatment. Our results show differences in sensory gating (P50 suppresion) and a stronger N100-P300 response to rare audio stimulus after the treatment. Moreover we show the value of assessing brain electrical activity from high-density EEG (256 channels) analyzing the results in different regions of interest. However, it is premature and hazardous to assume that rTMS treatment effectiveness in patients with AVH can be assessed using P50 suppression ratio.

The successful clinical application of bone tissue engineering requires customized implants based on the receiver’s bone anatomy and defect characteristics. Three-dimensional (3D) printing in small animal orthopedics has recently emerged as a valuable approach in fabricating individualized implants for receiver-specific needs. In veterinary medicine, because of the wide range of dimensions and anatomical variances, receiver-specific diagnosis and therapy are even more critical. The ability to generate 3D anatomical models and customize orthopedic instruments, implants, and scaffolds are advantages of 3D printing in small animal orthopedics. Furthermore, this technology provides veterinary medicine with a powerful tool that improves performance, precision, and cost-effectiveness. Nonetheless, the individualized 3D-printed implants have benefited several complex orthopedic procedures in small animals, including joint replacement surgeries, critical size bone defects, tibial tuberosity advancement, patellar groove replacement, limb-sparing surgeries, and other complex orthopedic procedures. The main purpose of this review is to discuss the application of 3D printing in small animal orthopedics based on already published papers as well as the techniques and materials used to fabricate 3D-printed objects. Finally, the advantages, current limitations, and future directions of 3D printing in small animal orthopedics have been addressed.

For the observation of human joint cartilage, X-ray, computed tomography (CT) or magnetic resonance imaging (MRI) are the main diagnostic tools to evaluate pathologies or traumas. The current work introduces a set of novel measurements and 3D features based on MRI and CT data of the knee joint, used to reconstruct bone and cartilages and to assess cartilage condition from a new perspective. Forty-seven subjects presenting a degenerative disease, a traumatic injury or no symptoms or trauma were recruited in this study and scanned using CT and MRI. Using medical imaging software, the bone and cartilage of the knee joint were segmented and 3D reconstructed. Several features such as cartilage density, volume and surface were extracted. Moreover, an investigation was carried out on the distribution of cartilage thickness and curvature analysis to identify new markers of cartilage condition. All the extracted features were used with advanced statistics tools and machine learning to test the ability of our model to predict cartilage conditions. This work is a first step towards the development of a new gold standard of cartilage assessment based on 3D measurements.

There are two surgical approaches to performing total hip arthroplasty (THA): a cemented or uncemented type of prosthesis. The choice is usually based on the experience of the orthopaedic surgeon and on parameters such as the age and gender of the patient. Using machine learning (ML) techniques on quantitative biomechanical and bone quality data extracted from computed tomography, electromyography and gait analysis, the aim of this paper was, firstly, to help clinicians use patient-specific biomarkers from diagnostic exams in the prosthetic decision-making process. The second aim was to evaluate patient long-term outcomes by predicting the bone mineral density (BMD) of the proximal and distal parts of the femur using advanced image processing analysis techniques and ML. The ML analyses were performed on diagnostic patient data extracted from a national database of 51 THA patients using the Knime analytics platform. The classification analysis achieved 93% accuracy in choosing the type of prosthesis; the regression analysis on the BMD data showed a coefficient of determination of about 0.6. The start and stop of the electromyographic signals were identified as the best predictors. This study shows a patient-specific approach could be helpful in the decision-making process and provide clinicians with information regarding the follow up of patients.

Sarcopenic muscular degeneration has been consistently identified as an independent risk factor for mortality in aging populations. Recent investigations have realized the quantitative potential of computed tomography (CT) image analysis to describe skeletal muscle volume and composition; however, the optimum approach to assessing these data remains debated. Current literature reports average Hounsfield unit (HU) values and/or segmented soft tissue cross-sectional areas to investigate muscle quality. However, standardized methods for CT analyses and their utility as a comorbidity index remain undefined, and no existing studies compare these methods to the assessment of entire radiodensitometric distributions. The primary aim of this study was to present a comparison of nonlinear trimodal regression analysis (NTRA) parameters of entire radiodensitometric muscle distributions against extant CT metrics and their correlation with lower extremity function (LEF) biometrics (normal/fast gait speed, timed up-and-go, and isometric leg strength) and biochemical and nutritional parameters, such as total solubilized cholesterol (SCHOL) and body mass index (BMI). Data were obtained from 3,162 subjects, aged 66-96 years, from the population-based AGES-Reykjavik Study. 1-D k-means clustering was employed to discretize each biometric and comorbidity dataset into twelve subpopulations, in accordance with Sturges' Formula for Class Selection. Dataset linear regressions were performed against eleven NTRA distribution parameters and standard CT analyses (fat/muscle cross-sectional area and average HU value). Parameters from NTRA and CT standards were analogously assembled by age and sex. Analysis of specific NTRA parameters with standard CT results showed linear correlation coefficients greater than 0.85, but multiple regression analysis of correlative NTRA parameters yielded a correlation coefficient of 0.99 (P

There is something in our genome that dictates life expectancy and there is nothing that can be done to avoid this; indeed, there is not yet any record of a person who has cheated death. Our physical prowess can vacillate substantially in our lifetime according to our activity levels and nutritional status and we may fight aging, but we will inevitably lose. We have presented strong evidence that the atrophy which accompanies aging is to some extent caused by loss of innervation. We compared muscle biopsies of sedentary seniors to those of life long active seniors, and show that these groups indeed have a different distribution of muscle fiber diameter and fiber type. The senior sportsmen have many more slow fiber-type groupings than the sedentary people which provides strong evidence of denervation-reinnervation events in muscle fibers. It appears that activity maintains the motoneurons and the muscle fibers. Premature or accelerated aging of muscle may occur as the result of many chronic diseases. One extreme case is provided by irreversible damage of the Conus and Cauda Equina, a spinal cord injury (SCI) sequela in which the human leg muscles may be completely and permanently disconnected from the nervous system with the almost complete disappearance of muscle fibers within 3-5 years from SCI. In cases of this extreme example of muscle degeneration, we have used 2D Muscle Color CT to gather data supporting the idea that electrical stimulation of denervated muscles can retain and even regain muscle. We show here that, if people are compliant, atrophy can be reversed. A further example of activity-related muscle adaptation is provided by the fact that mitochondrial distribution and density are significantly changed by functional electrical stimulation in horse muscle biopsies relative to those not receiving treatment. All together, the data indicate that FES is a good way to modify behaviors of muscle fibers by increasing the contraction load per day. Indeed, it should be possible to defer the muscle decline that occurs in aging people and in those who have become unable to participate in physical activities. Thus, FES should be considered for use in rehabilitation centers, nursing facilities and in critical care units when patients are completely inactive even for short periods of time.

Improving EEG signal interpretation, specificity, and sensitivity is a primary focus of many current investigations, and the successful application of EEG signal processing methods requires a detailed knowledge of both the topography and frequency spectra of low-amplitude, high-frequency craniofacial EMG. This information remains limited in clinical research, and as such, there is no known reliable technique for the removal of these artifacts from EEG data. The results presented herein outline a preliminary investigation of craniofacial EMG high-frequency spectra and 3D MRI segmentation that offers insight into the development of an anatomically-realistic model for characterizing these effects. The data presented highlights the potential for confounding signal contribution from around 60 to 200 Hz, when observed in frequency space, from both low and high-amplitude EMG signals. This range directly overlaps that of both low γ (30-50 Hz) and high γ (50-80 Hz) waves, as defined traditionally in standatrd EEG measurements, and mainly with waves presented in dense-array EEG recordings. Likewise, average EMG amplitude comparisons from each condition highlights the similarities in signal contribution of low-activity muscular movements and resting, control conditions. In addition to the FFT analysis performed, 3D segmentation and reconstruction of the craniofacial muscles whose EMG signals were measured was successful. This recapitulation of the relevant EMG morphology is a crucial first step in developing an anatomical model for the isolation and removal of confounding low-amplitude craniofacial EMG signals from EEG data. Such a model may be eventually applied in a clinical setting to ultimately help to extend the use of EEG in various clinical roles.

Despite the ravages of long term denervation there is structural and ultrastructural evidence for survival of muscle fibers in mammals, with some fibers surviving at least ten months in rodents and 3-6 years in humans. Further, in rodents there is evidence that muscle fibers may regenerate even after repeated damage in the absence of the nerve, and that this potential is maintained for several months after denervation. While in animal models permanently denervated muscle sooner or later loses the ability to contract, the muscles may maintain their size and ability to function if electrically stimulated soon after denervation. Whether in mammals, humans included, this is a result of persistent de novo formation of muscle fibers is an open issue we would like to explore in this review. During the past decade, we have studied muscle biopsies from the quadriceps muscle of Spinal Cord Injury (SCI) patients suffering with Conus and Cauda Equina syndrome, a condition that fully and irreversibly disconnects skeletal muscle fibers from their damaged innervating motor neurons. We have demonstrated that human denervated muscle fibers survive years of denervation and can be rescued from severe atrophy by home-based Functional Electrical Stimulation (h-bFES). Using immunohistochemistry with both non-stimulated and the h-bFES stimulated human muscle biopsies, we have observed the persistent presence of muscle fibers which are positive to labeling by an antibody which specifically recognizes the embryonic myosin heavy chain (MHCemb). Relative to the total number of fibers present, only a small percentage of these MHCemb positive fibers are detected, suggesting that they are regenerating muscle fibers and not pre-existing myofibers re-expressing embryonic isoforms. Although embryonic isoforms of acetylcholine receptors are known to be re-expressed and to spread from the end-plate to the sarcolemma of muscle fibers in early phases of muscle denervation, we suggest that the MHCemb positive muscle fibers we observe result from the activation, proliferation and fusion of satellite cells, the myogenic precursors present under the basal lamina of the muscle fibers. Using morphological features and molecular biomarkers, we show that severely atrophic muscle fibers, with a peculiar cluster reorganization of myonuclei, are present in rodent muscle seven-months after neurectomy and in human muscles 30-months after complete Conus-Cauda Equina Syndrome and that these are structurally distinct from early myotubes. Beyond reviewing evidence from rodent and human studies, we add some ultrastructural evidence of muscle fiber regeneration in long-term denervated human muscles and discuss the options to substantially increase the regenerative potential of severely denervated human muscles not having been treated with h-bFES. Some of the mandatory procedures, are ready to be translated from animal experiments to clinical studies to meet the needs of persons with longterm irreversible muscle denervation. An European Project, the trial Rise4EU (Rise for You, a personalized treatment for recovery of function of denervated muscle in long-term stable SCI) will hopefully follow.

This report outlines the use of a customized false-color 3D computed tomography (CT) protocol for the imaging of the rectus femoris of spinal cord injury (SCI) patients suffering from complete and permanent denervation, as characterized by complete Conus and Cauda Equina syndrome. This muscle imaging method elicits the progression of the syndrome from initial atrophy to eventual degeneration, as well as the extent to which patients' quadriceps could be recovered during four years of home-based functional electrical stimulation (h-b FES). Patients were pre-selected from several European hospitals and functionally tested by, and enrolled in the EU Commission Shared Cost Project RISE (Contract n. QLG5-CT-2001-02191) at the Department of Physical Medicine, Wilhelminenspital, Vienna, Austria. Denervated muscles were electrically stimulated using a custom-designed stimulator, large surface electrodes, and customized progressive stimulation settings. Spiral CT images and specialized computational tools were used to isolate the rectus femoris muscle and produce 3D and 2D reconstructions of the denervated muscles. The cross sections of the muscles were determined by 2D Color CT, while muscle volumes were reconstructed by 3D Color CT. Shape, volume, and density changes were measured over the entirety of each rectus femoris muscle. Changes in tissue composition within the muscle were visualized by associating different colors to specified Hounsfield unit (HU) values for fat, (yellow: [-200; -10]), loose connective tissue or atrophic muscle, (cyan: [-9; 40]), and normal muscle, fascia and tendons included, (red: [41; 200]). The results from this analysis are presented as the average HU values within the rectus femoris muscle reconstruction, as well as the percentage of these tissues with respect to the total muscle volume. Results from this study demonstrate that h-b FES induces a compliance-dependent recovery of muscle volume and size of muscle fibers, as evidenced by the gain and loss in muscle mass. These results highlight the particular utility of this modality in the quantitative longitudinal assessment of the responses of skeletal muscle to long-term denervation and h-b FES recovery.

The fields of tissue engineering and regenerative medicine utilize implantable biomaterials and engineered tissues to regenerate damaged cells or replace lost tissues. There are distinct challenges in all facets of this research, but functional assessments and monitoring of such complex environments as muscle tissues present the current strategic priority. Many extant methods for addressing these questions result in the destruction or alteration of tissues or cell populations under investigation. Modern advances in non-invasive imaging modalities present opportunities to rethink some of the anachronistic methods, however, their standard employment may not be optimal when considering advancements in myology. New image analysis protocols and/or combinations of established modalities need to be addressed. This review focuses on efficacies and limitations of available imaging modalities to the functional assessment of implantable myogenic biomaterials and engineered muscle tissues.

Total Hip Arthroplasty (THA) remains the gold standard of treatment for patients who suffer with a variety of hip-related pathological degeneration or trauma. These patients often exhibit significantly less post-operative pain and an increase in the range of motion of the joint, but there are still relatively common instances of debilitating periprosthetic complications that call into question the method for pre-surgical implant choice. Currently, there are two principal options for THA prostheses: cemented or non cemented. Utilizing the cemented procedure ensures a faster acquisition of adequate implant stability than with the non cemented procedure, but can eventually lead to an increased periprosthetic fracture risk. Non cemented prosthetic stems are more frequently revised within the first few years following THA due to periprosthetic fracture, but non cemented revision surgeries generally result in fewer complications than those of cemented implants. Surgeons typically rely on experience or simple patient metrics such as age and sex to prescribe which implant procedure is optimal, and while this may work for most patients, there is a clear need to analyze more rigoriously patient conditions that correlate to optimal post-THA outcomes. The results from the investigation reported herein indicate that an understanding of how the percent composition and quality of a patient's quadriceps muscle in both healthy and operated legs may be a better indicator for prosthetic choice. Additionally, these data emphasize that the traditional metrics of age and sex inadequately predict changes in quadriceps composition and quality and thereby have comparatively minor utility in determining the patient-appropriate prosthetic type.

Total hip arthroplasty (THA) is performed with or without the use of bone cement. Facing the lack of reliable clinical guidelines on decision making whether a patient should receive THA with or without bone cement, a joint clinical and engineering approach is proposed here with the objective to assess patient recovery developing monitoring techniques based on gait analysis, measurements of bone mineral density and structural and functional changes of quadriceps muscles. A clinical trial was conducted with 36 volunteer patients that were undergoing THA surgery for the first time: 18 receiving cemented implant and 18 receiving non-cemented implant. The patients are scanned with Computer Tomographic (CT) modality prior-, immediately- and 12 months post-surgery. The CT data are further processed to segment muscles and bones for calculating bone mineral density (BMD). Quadriceps muscle density Hounsfield (HU) based value is calculated from the segmented file on healthy and operated leg before and after THA surgery. Furthermore clinical assessment is performed using gait analysis technologies such as a sensing carpet, wireless electrodes and video. Patients undergo these measurements prior-, 6 weeks post - and 52 weeks post-surgery. The preliminary results indicate computational tools and methods that are able to quantitatively analyze patient’s condition pre and post-surgery: The spatial parameters such as step length and stride length increase 6 weeks post op in the patient group receiving cemented implant while the angle in the toe in/out parameter decrease in both patient groups.

Medical imaging is of particular interest in the field of translational myology, as extant literature describes the utilization of a wide variety of techniques to non-invasively recapitulate and quantity various internal and external tissue morphologies. In the clinical context, medical imaging remains a vital tool for diagnostics and investigative assessment. This review outlines the results from several investigations on the use of computed tomography (CT) and image analysis techniques to assess muscle conditions and degenerative process due to aging or pathological conditions. Herein, we detail the acquisition of spiral CT images and the use of advanced image analysis tools to characterize muscles in 2D and 3D. Results from these studies recapitulate changes in tissue composition within muscles, as visualized by the association of tissue types to specified Hounsfield Unit (HU) values for fat, loose connective tissue or atrophic muscle, and normal muscle, including fascia and tendon. We show how results from these analyses can be presented as both average HU values and compositions with respect to total muscle volumes, demonstrating the reliability of these tools to monitor, assess and characterize muscle degeneration.

Total hip arthroplasty (THA) is performed with or without the use of bone cement. The lack of reliable clinical guidelines for deciding which one to implement has encouraged this approach of joint clinical and engineering with the following objectives: 1. Validate quadriceps muscles and femur bone atrophy by extracting the mineral density from Computer Tomographic (CT) images. 2. Validate computational processes based on 3-D modeling and Finite Element Methods (FEM). A clinical trial was started, where 36 volunteer patients underwent THA surgery for the first time: 18 receiving cemented implant and 18 receiving uncemented implant. The patients were CT scanned prior-, immediately after and 12 months post-surgery. The CT data are further processed to segment muscles and bones and to create 3D-models for the simulation and for calculating bone mineral density (BMD). Furthermore quadriceps muscle density Hounsfield (HU) based value is calculated from the segmented file on healthy and operated leg. These preliminary results indicate computational tools and methods that are able to quantitatively analyse patient’s condition pre and post-surgery. The BMD and muscle density measurement in correlation with the fracture risk analysis display a potential method for eligibility to receive non-cemented implant; the preliminary results show that also elderly that according with current clinical evaluation receives a cemented implant are suitable for the non-cemented type. The risk for structural failure during THA surgery is estimated by calculating femoral bone fracture risk index (FRI) as a ratio between compressive stress during surgery and estimated failure stress on bone. The correlations with the BMD observations during the clinical trial will assess and validate this potential predictor tool.

Computer simulation and modelling of the human body and its behaviour are very useful tools in situations where it is either too risky to perform an invasive procedure or too costly for in vivo experiments or simply impossible for ethical reasons. In this paper we describe a method to model the electrical behaviour of human brain from segmented MR images. The aim of the work is to use these models to predict the electrical activity of human brain under normal and pathological conditions. The image processing software package MIMICS is used for 3D volume segmentation of MR images. These models have detailed 3D representation of major tissue surfaces within the head, with over 12 different tissues segmented. In addition, computational tools in Matlab were developed for calculating normal vectors on the brain surface and for associating this information to the equivalent electrical dipole sources as an input into the model.

A novel clinical rehabilitation method for patients who have permanent and non recoverable muscle denervation in the legs was developed in the frame of the European Project RISE. The technique is based on FES and the project results shows, in these severely disabled patients, restoration of muscle tissue and function. This study propose novel methods based on image processing technique and medical modelling to monitor growth in denervated muscle treated with FES. Geometrical and structural changes in muscle and bone are studied and modelled. Secondary effects on the bone mineral density produced by the stimulation treatment and due the elicited muscle contraction are also investigated. The restoration process in DDM is an important object of discussion since there isn’t yet a complete understanding of the mechanisms regulating growth in denervated muscle. This study approaches the problem from a macroscopic point of view, developing 3-dimensional models of the whole stimulated muscles and following changes in volume, geometry and density very accurately. The method is based on the acquisition of high resolution Spiral CT scans from patients who have long-term flaccid paraplegia and the use of special image processing tools allowing tissue discriminations and muscle segmentation. Three patients were measured at different points of time during 4 years of electrical stimulation treatment. In this study is quantitatively demonstrated the influences of FES treatment on the different quadriceps bellies. The rectus femoris muscle is positioned in the middle of the quadriceps and responds (in general) better to stimulation. In a patient with abundant adipose tissue surrounding the quadriceps, rectus femoris almost doubled the volume during the FES treatment while in the other bellies the changes measured were minimal. The analysis of the density shows clearly a restoration of the muscular structure in the growing muscle. The remarkable increase of muscle mass and the improved quality of the muscle tissue was observed and measured on whole muscle volume. The 3-dimensional approach proposed in this work allows the geometrical changes in denervated muscle to be measured. Central lines in rectus femoris are calculated during the treatment and the relative curvature indexes are used to quantify differences between data sets. The result shows a correlation between degeneration status and changes in shape; the differences in curvature between control and denervated muscle diminish with the growth. Furthermore, bone mineral density is measured on the femur to monitor the structural changes induced by the current flow and to evaluate the effect on the patella bone density due to the quadriceps contraction. Though the evidences aren’t yet statistically relevant a kind of structural change in the femur is measured as secondary effect from the muscle stimulation. Beside, the force applied on the patella through the elicited contraction seems also to have a beneficial effect on bone density. Indeed the mineral loss in the patella is remarkably slowed down.

Denervation of a defined skeletal muscle is due to lower motor neuron (LMN) or peripheral nerve lesions that have major consequences on the muscle tissue. After early atrophy, the mid- and late-phases presents two very contrasting myofibers populations: beside those severely atrophic with internalized groups of myonuclei, large fast-type muscle fibers continue to be present 4 to 6 years after Spinal Cord Injury (SCI). Recent results of rat experiments provides the rational basis for understanding the residual functional characteristics of the long-term denervated muscle and the molecular explanation of its ability to respond to home-base functional electrical stimulation (h-b FES) using custom-designed electrodes and stimulators. Further outcomes of the Vienna-Padova ten-year collaboration are: 1. a world-unique Myo- Bank of muscle biopsies and 2. improved imaging procedures (Color Computer Tomography (CT) scan and Functional Echomyography), all demonstrating that h-b FES induces improvements in muscle contractility, tissue composition and mass, despite permanent LMN denervation. The benefits of h-b FES could be extended from patents suffering with complete Conus-Cauda Syndrome to the numerous patients with incomplete LMN denervation of skeletal muscles to determine whether h-b FES reduces secondary complications related to disuse and impaired blood perfusion (reduction in bone density, risk of bone fracture, decubitus ulcers, and pulmonary thromboembolism). We are confident that translation of the results of a clinical experiment, the EU Project RISE, to the larger cohort of incomplete LMN denervated muscles will provide the wanted results.

Bone loss and a decrease in bone mineral density is frequently seen in patients with motor neuron lesion due to lack of mechanical stimulation. This causes weakening of the bones and a greater risk of fracture. By using functional electrical stimulation it is possible to activate muscles in the body to produce the necessary muscle force to stimulate muscle growth and potentially decrease the rate of bone loss. A longitudinal study was carried out on a single patient undergoing electrical stimulation over a 6 year period. The patient underwent a CT scan each year and a full three dimensional finite element model for each year was created using Mimics (Materialise) and Abaqus (Simulia) to calculate the risk of fracture under physiologically relevant loading conditions. Using empirical formulas connecting the bone mineral density to the stiffness and ultimate tensile stress of the bone, each element was assigned a unique material property, based on its density. The risk of fracture was estimated by calculating the ratio between the predicted stress and the ultimate tensile stress, should it exceed unity, failure was assumed. The results showed that the number of elements that were predicted to be at risk of failure varied between years.

This paper reviews the novel use of CT and MRI data and image processing tools to segment and reconstruct tissue images in 3D to determine characteristics of muscle, bone and brain.This to study and simulate the structural changes occurring in healthy and pathological conditions as well as in response to clinical treatments. Here we report the application of this methodology to evaluate and quantify: 1. progression of atrophy in human muscle subsequent to permanent lower motor neuron (LMN) denervation, 2. muscle recovery as induced by functional electrical stimulation (FES), 3. bone quality in patients undergoing total hip replacement and 4. to model the electrical activity of the brain. Study 1: CT data and segmentation techniques were used to quantify changes in muscle density and composition by associating the Hounsfield unit values of muscle, adipose and fibrous connective tissue with different colors. This method was employed to monitor patients who have permanent muscle LMN denervation in the lower extremities under two different conditions: permanent LMN denervated not electrically stimulated and stimulated. Study 2: CT data and segmentation techniques were employed, however, in this work we assessed bone and muscle conditions in the pre-operative CT scans of patients scheduled to undergo total hip replacement. In this work, the overall anatomical structure, the bone mineral density (BMD) and compactness of quadriceps muscles and proximal femoral was computed to provide a more complete view for surgeons when deciding which implant technology to use. Further, a Finite element analysis provided a map of the strains around the proximal femur socket when solicited by typical stresses caused by an implant press fitting. Study 3 describes a method to model the electrical behavior of human brain using segmented MR images. The aim of the work is to use these models to predict the electrical activity of the human brain under normal and pathological conditions by developing detailed 3D representations of major tissue surfaces within the head, with over 12 different tissues segmented. In addition, computational tools in Matlab were developed for calculating normal vectors on the brain surface and for associating this information with the equivalent electrical dipole sources as an input into the model.