Editor’s Choice Articles

Long-term home monitoring of people living with epilepsy cannot be achieved using the standard full-scalp electroencephalography (EEG) coupled with video. Wearable seizure detection devices, such as behind-the-ear EEG (bte-EEG), offer an unobtrusive method for ambulatory follow-up of this population. Combining bte-EEG with electrocardiography (ECG) can enhance automated seizure detection performance. However, such frameworks produce high false alarm rates, making visual review necessary. This study aimed to evaluate a semi-automated multimodal wearable seizure detection framework using bte-EEG and ECG. Using the SeizeIT1 dataset of 42 patients with focal epilepsy, an automated multimodal seizure detection algorithm was used to produce seizure alarms. Two reviewers evaluated the algorithm’s detections twice: (1) using only bte-EEG data and (2) using bte-EEG, ECG, and heart rate signals. The readers achieved a mean sensitivity of 59.1% in the bte-EEG visual experiment, with a false detection rate of 6.5 false detections per day. Adding ECG resulted in a higher mean sensitivity (62.2%) and a largely reduced false detection rate (mean of 2.4 false detections per day), as well as an increased inter-rater agreement. The multimodal framework allows for efficient review time, making it beneficial for both clinicians and patients. Full article

(1) Background: The difficulty of pelvic operation is greatly affected by anatomical constraints. Defining this difficulty and assessing it based on conventional methods has some limitations. Artificial intelligence (AI) has enabled rapid advances in surgery, but its role in assessing the difficulty of laparoscopic rectal surgery is unclear. This study aimed to establish a difficulty grading system to assess the difficulty of laparoscopic rectal surgery, as well as utilize this system to evaluate the reliability of pelvis-induced difficulties described by MRI-based AI. (2) Methods: Patients who underwent laparoscopic rectal surgery from March 2019 to October 2022 were included, and were divided into a non-difficult group and difficult group. This study was divided into two stages. In the first stage, a difficulty grading system was developed and proposed to assess the surgical difficulty caused by the pelvis. In the second stage, AI was used to build a model, and the ability of the model to stratify the difficulty of surgery was evaluated at this stage, based on the results of the first stage; (3) Results: Among the 108 enrolled patients, 53 patients (49.1%) were in the difficult group. Compared to the non-difficult group, there were longer operation times, more blood loss, higher rates of anastomotic leaks, and poorer specimen quality in the difficult group. In the second stage, after training and testing, the average accuracy of the four-fold cross validation models on the test set was 0.830, and the accuracy of the merged AI model was 0.800, the precision was 0.786, the specificity was 0.750, the recall was 0.846, the F1-score was 0.815, the area under the receiver operating curve was 0.78 and the average precision was 0.69; (4) Conclusions: This study successfully proposed a feasible grading system for surgery difficulty and developed a predictive model with reasonable accuracy using AI, which can assist surgeons in determining surgical difficulty and in choosing the optimal surgical approach for rectal cancer patients with a structurally difficult pelvis. Full article

Electrical stimulation (ES) has been frequently used in different biomedical applications both in vitro and in vivo. Numerous studies have demonstrated positive effects of ES on cellular functions, including metabolism, proliferation, and differentiation. The application of ES to cartilage tissue for increasing extracellular matrix formation is of interest, as cartilage is not able to restore its lesions owing to its avascular nature and lack of cells. Various ES approaches have been used to stimulate chondrogenic differentiation in chondrocytes and stem cells; however, there is a huge gap in systematizing ES protocols used for chondrogenic differentiation of cells. This review focuses on the application of ES for chondrocyte and mesenchymal stem cell chondrogenesis for cartilage tissue regeneration. The effects of different types of ES on cellular functions and chondrogenic differentiation are reviewed, systematically providing ES protocols and their advantageous effects. Moreover, cartilage 3D modeling using cells in scaffolds/hydrogels under ES are observed, and recommendations on reporting about the use of ES in different studies are provided to ensure adequate consolidation of knowledge in the area of ES. This review brings novel insights into the further application of ES in in vitro studies, which are promising for further cartilage repair techniques. Full article

With the advent of human-induced pluripotent stem cells (hiPSCs) and differentiation protocols, methods to create in-vitro human-derived neuronal networks have been proposed. Although monolayer cultures represent a valid model, adding three-dimensionality (3D) would make them more representative of an in-vivo environment. Thus, human-derived 3D structures are becoming increasingly used for in-vitro disease modeling. Achieving control over the final cell composition and investigating the exhibited electrophysiological activity is still a challenge. Thence, methodologies to create 3D structures with controlled cellular density and composition and platforms capable of measuring and characterizing the functional aspects of these samples are needed. Here, we propose a method to rapidly generate neurospheroids of human origin with control over cell composition that can be used for functional investigations. We show a characterization of the electrophysiological activity exhibited by the neurospheroids by using micro-electrode arrays (MEAs) with different types (i.e., passive, C-MOS, and 3D) and number of electrodes. Neurospheroids grown in free culture and transferred on MEAs exhibited functional activity that can be chemically and electrically modulated. Our results indicate that this model holds great potential for an in-depth study of signal transmission to drug screening and disease modeling and offers a platform for in-vitro functional testing. Full article

Fibrous structures with anisotropic fillers as composites have found increasing interest in the field of biofabrication since they can mimic the extracellular matrix of anisotropic tissues such as skeletal muscle or nerve tissue. In the present work, the inclusion of anisotropic fillers in hydrogel-based filaments with an interpenetrating polymeric network (IPN) was evaluated and the dynamics of such fillers in the composite flow were analyzed using computational simulations. In the experimental part, microfabricated rods (200 and 400 μm length, 50 μm width) were used as anisotropic fillers in extrusion of composite filaments using two techniques of wet spinning and 3D printing. Hydrogels such as oxidized alginate (ADA) and methacrylated gelatin (GelMA) were used as matrices. In the computational simulation, a combination of computational fluid dynamics and coarse-grained molecular dynamics was used to study the dynamics of rod-like fillers in the flow field of a syringe. It showed that, during the extrusion process, microrods are far from being well aligned. Instead, many of them tumble on their way through the needle leading to a random orientation in the fiber which was confirmed experimentally. Full article

Background: Magnetic Resonance Imaging (MRI) data collected from multiple centres can be heterogeneous due to factors such as the scanner used and the site location. To reduce this heterogeneity, the data needs to be harmonised. In recent years, machine learning (ML) has been used to solve different types of problems related to MRI data, showing great promise. Objective: This study explores how well various ML algorithms perform in harmonising MRI data, both implicitly and explicitly, by summarising the findings in relevant peer-reviewed articles. Furthermore, it provides guidelines for the use of current methods and identifies potential future research directions. Method: This review covers articles published through PubMed, Web of Science, and IEEE databases through June 2022. Data from studies were analysed based on the criteria of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Quality assessment questions were derived to assess the quality of the included publications. Results: a total of 41 articles published between 2015 and 2022 were identified and analysed. In the review, MRI data has been found to be harmonised either in an implicit (n = 21) or an explicit (n = 20) way. Three MRI modalities were identified: structural MRI (n = 28), diffusion MRI (n = 7) and functional MRI (n = 6). Conclusion: Various ML techniques have been employed to harmonise different types of MRI data. There is currently a lack of consistent evaluation methods and metrics used across studies, and it is recommended that the issue be addressed in future studies. Harmonisation of MRI data using ML shows promises in improving performance for ML downstream tasks, while caution should be exercised when using ML-harmonised data for direct interpretation. Full article

Understanding how different areas of the human brain communicate with each other is a crucial issue in neuroscience. The concepts of structural, functional and effective connectivity have been widely exploited to describe the human connectome, consisting of brain networks, their structural connections and functional interactions. Despite high-spatial-resolution imaging techniques such as functional magnetic resonance imaging (fMRI) being widely used to map this complex network of multiple interactions, electroencephalographic (EEG) recordings claim high temporal resolution and are thus perfectly suitable to describe either spatially distributed and temporally dynamic patterns of neural activation and connectivity. In this work, we provide a technical account and a categorization of the most-used data-driven approaches to assess brain-functional connectivity, intended as the study of the statistical dependencies between the recorded EEG signals. Different pairwise and multivariate, as well as directed and non-directed connectivity metrics are discussed with a pros–cons approach, in the time, frequency, and information-theoretic domains. The establishment of conceptual and mathematical relationships between metrics from these three frameworks, and the discussion of novel methodological approaches, will allow the reader to go deep into the problem of inferring functional connectivity in complex networks. Furthermore, emerging trends for the description of extended forms of connectivity (e.g., high-order interactions) are also discussed, along with graph-theory tools exploring the topological properties of the network of connections provided by the proposed metrics. Applications to EEG data are reviewed. In addition, the importance of source localization, and the impacts of signal acquisition and pre-processing techniques (e.g., filtering, source localization, and artifact rejection) on the connectivity estimates are recognized and discussed. By going through this review, the reader could delve deeply into the entire process of EEG pre-processing and analysis for the study of brain functional connectivity and learning, thereby exploiting novel methodologies and approaches to the problem of inferring connectivity within complex networks. Full article

In recent years, the development of adaptive models to tailor instructional content to learners by measuring their cognitive load has become a topic of active research. Brain fog, also known as confusion, is a common cause of poor performance, and real-time detection of confusion is a challenging and important task for applications in online education and driver fatigue detection. In this study, we propose a deep learning method for cognitive load recognition based on electroencephalography (EEG) signals using a long short-term memory network (LSTM) with an attention mechanism. We obtained EEG signal data from a database of brainwave information and associated data on mental load. We evaluated the performance of the proposed LSTM technique in comparison with random forest, Adaptive Boosting (AdaBoost), support vector machine, eXtreme Gradient Boosting (XGBoost), and artificial neural network models. The experimental results demonstrated that the proposed approach had the highest accuracy of 87.1% compared to those of other algorithms, including random forest (64%), AdaBoost (64.31%), support vector machine (60.9%), XGBoost (67.3%), and artificial neural network models (71.4%). The results of this study support the development of a personalized adaptive learning system designed to measure and actively respond to learners’ cognitive load in real time using wireless portable EEG systems. Full article

Interpreting blood gas analysis results can be challenging for the clinician, especially in stressful situations under time pressure. To foster fast and correct interpretation of blood gas results, we developed Visual Blood. This computer-based, multicentre, noninferiority study compared Visual Blood and conventional arterial blood gas (ABG) printouts. We presented six scenarios to anaesthesiologists, once with Visual Blood and once with the conventional ABG printout. The primary outcome was ABG parameter perception. The secondary outcomes included correct clinical diagnoses, perceived diagnostic confidence, and perceived workload. To analyse the results, we used mixed models and matched odds ratios. Analysing 300 within-subject cases, we showed noninferiority of Visual Blood compared to ABG printouts concerning the rate of correctly perceived ABG parameters (rate ratio, 0.96; 95% CI, 0.92–1.00; p = 0.06). Additionally, the study revealed two times higher odds of making the correct clinical diagnosis using Visual Blood (OR, 2.16; 95% CI, 1.42–3.29; p < 0.001) than using ABG printouts. There was no or, respectively, weak evidence for a difference in diagnostic confidence (OR, 0.84; 95% CI, 0.58–1.21; p = 0.34) and perceived workload (Coefficient, 2.44; 95% CI, −0.09–4.98; p = 0.06). This study showed that participants did not perceive the ABG parameters better, but using Visual Blood resulted in more correct clinical diagnoses than using conventional ABG printouts. This suggests that Visual Blood allows for a higher level of situation awareness beyond individual parameters’ perception. However, the study also highlighted the limitations of today’s virtual reality headsets and Visual Blood. Full article

Motor imagery-based brain–computer interfaces (BCI) have been widely recognized as beneficial tools for rehabilitation applications. Moreover, visually guided motor imagery was introduced to improve the rehabilitation impact. However, the reported results to support these techniques remain unsatisfactory. Electroencephalography (EEG) signals can be represented by a sequence of a limited number of topographies (microstates). To explore the dynamic brain activation patterns, we conducted EEG microstate and microstate-specific functional connectivity analyses on EEG data under motor imagery (MI), motor execution (ME), and guided MI (GMI) conditions. By comparing sixteen microstate parameters, the brain activation patterns induced by GMI show more similarities to ME than MI from a microstate perspective. The mean duration and duration of microstate four are proposed as biomarkers to evaluate motor condition. A support vector machine (SVM) classifier trained with microstate parameters achieved average accuracies of 80.27% and 66.30% for ME versus MI and GMI classification, respectively. Further, functional connectivity patterns showed a strong relationship with microstates. Key node analysis shows clear switching of key node distribution between brain areas among different microstates. The neural mechanism of the switching pattern is discussed. While microstate analysis indicates similar brain dynamics between GMI and ME, graph theory-based microstate-specific functional connectivity analysis implies that visual guidance may reduce the functional integration of the brain network during MI. Thus, we proposed that combined MI and GMI for BCI can improve neurorehabilitation effects. The present findings provide insights for understanding the neural mechanism of microstates, the role of visual guidance in MI tasks, and the experimental basis for developing new BCI-aided rehabilitation systems. Full article

This study presents a multilayer in vitro human skin platform to quantitatively relate predicted spatial time–temperature history with measured tissue injury response. This information is needed to elucidate high-temperature, short-duration burn injury kinetics and enables determination of relevant input parameters for computational models to facilitate treatment planning. Multilayer in vitro skin platforms were constructed using human dermal keratinocytes and fibroblasts embedded in collagen I hydrogels. After three seconds of contact with a 50–100 °C burn tip, ablation, cell death, apoptosis, and HSP70 expression were spatially measured using immunofluorescence confocal microscopy. Finite element modeling was performed using the measured thermal characteristics of skin platforms to determine the temperature distribution within platforms over time. The process coefficients for the Arrhenius thermal injury model describing tissue ablation and cell death were determined such that the predictions calculated from the time–temperature histories fit the experimental burn results. The activation energy for thermal collagen ablation and cell death was found to be significantly lower for short-duration, high-temperature burns than those found for long-duration, low-temperature burns. Analysis of results suggests that different injury mechanisms dominate at higher temperatures, necessitating burn research in the temperature ranges of interest and demonstrating the practicality of the proposed skin platform for this purpose. Full article

Type 1 diabetes mellitus (T1DM) is a complex metabolic disease characterized by a massive loss of insulin-producing cells due to an autoimmune reaction. Currently, daily subcutaneous administration of exogenous insulin is the only effective treatment. Therefore, in recent years considerable interest has been given to stem cell therapy and in particular to the use of three-dimensional (3D) cell cultures to better reproduce in vivo conditions. The goal of this study is to provide a reliable cellular model that could be investigated for regenerative medicine applications for the replacement of insulin-producing cells in T1DM. To pursue this aim we create a co-culture spheroid of amniotic epithelial cells (AECs) and Wharton’s jelly mesenchymal stromal cells (WJ-MSCs) in a one-to-one ratio. The resulting co-culture spheroids were analyzed for viability, extracellular matrix production, and hypoxic state in both early- and long-term cultures. Our results suggest that co-culture spheroids are stable in long-term culture and are still viable with a consistent extracellular matrix production evaluated with immunofluorescence staining. These findings suggest that this co-culture may potentially be differentiated into endo-pancreatic cells for regenerative medicine applications in T1DM. Full article

Polysomnography (PSG) remains the gold standard for sleep monitoring but is obtrusive in nature. Advances in camera sensor technology and data analysis techniques enable contactless monitoring of heart rate variability (HRV). In turn, this may allow remote assessment of sleep stages, as different HRV metrics indirectly reflect the expression of sleep stages. We evaluated a camera-based remote photoplethysmography (PPG) setup to perform automated classification of sleep stages in near darkness. Based on the contactless measurement of pulse rate variability, we use a previously developed HRV-based algorithm for 3 and 4-class sleep stage classification. Performance was evaluated on data of 46 healthy participants obtained from simultaneous overnight recording of PSG and camera-based remote PPG. To validate the results and for benchmarking purposes, the same algorithm was used to classify sleep stages based on the corresponding ECG data. Compared to manually scored PSG, the remote PPG-based algorithm achieved moderate agreement on both 3 class (Wake–N1/N2/N3–REM) and 4 class (Wake–N1/N2–N3–REM) classification, with average $\kappa$ of 0.58 and 0.49 and accuracy of 81% and 68%, respectively. This is in range with other performance metrics reported on sensing technologies for wearable sleep staging, showing the potential of video-based non-contact sleep staging. Full article

Tooth decay, or dental caries, is a widespread and costly disease that is reversible when detected early in its formation. Current dental caries diagnostic methods including X-ray imaging and intraoral examination lack the sensitivity and specificity required to routinely detect caries early in its formation. Thermophotonic imaging presents itself as a highly sensitive and non-ionizing solution, making it suitable for the frequent monitoring of caries progression. Here, we utilized a treatment protocol to produce bacteria-induced caries lesions. The lesions were imaged using two related three-dimensional photothermal imaging modalities: truncated correlation photothermal coherence tomography (TC-PCT) and its enhanced modification eTC-PCT. In addition, micro-computed tomography (μ-CT) and visual inspection by a clinical dentist were used to validate and quantify the severities of the lesions. The observational findings demonstrate the high sensitivity and depth profiling capabilities of the thermophotonic modalities, showcasing their potential use as a non-ionizing clinical tool for the early detection of dental caries. Full article

To fulfil the growing interest in investigating microbial interactions in co-cultures, a novel two-compartment bioreactor system was developed, characterised, and implemented. The system allowed for the exchange of amino acids and peptides via a polyethersulfone membrane that retained biomass. Further system characterisation revealed a Bodenstein number of 18, which hints at backmixing. Together with other physical settings, the existence of unwanted inner-compartment substrate gradients could be ruled out. Furthermore, the study of Damkoehler numbers indicated that a proper metabolite supply between compartments was enabled. Implementing the two-compartment system (2cs) for growing Streptococcus thermophilus and Lactobacillus delbrueckii subs. bulgaricus, which are microorganisms commonly used in yogurt starter cultures, revealed only a small variance between the one-compartment and two-compartment approaches. The 2cs enabled the quantification of the strain-specific production and consumption rates of amino acids in an interacting S. thermophilus–L. bulgaricus co-culture. Therefore, comparisons between mono- and co-culture performance could be achieved. Both species produce and release amino acids. Only alanine was produced de novo from glucose through potential transaminase activity by L. bulgaricus and consumed by S. thermophilus. Arginine availability in peptides was limited to S. thermophilus’ growth, indicating active biosynthesis and dependency on the proteolytic activity of L. bulgaricus. The application of the 2cs not only opens the door for the quantification of exchange fluxes between microbes but also enables continuous production modes, for example, for targeted evolution studies. Full article

The timely delivery of drugs to specific locations in the body is imperative to ensure the efficacy of treatment. This study introduces a portable facial device that can deliver drugs efficiently using iontophoresis. Two types of power supplies—direct current and pulse ionization supplies—were manufactured by injection molding. Electrical stimulation elements, which contained Ag metal wires, were woven into facial mask packs. The diffusion phenomenon in the skin and iontophoresis were numerically modeled. Injection molding was simulated before the device was manufactured. Analysis using rhodamine B demonstrated a remarkable increase in the moisture content of the skin and effective absorption of the drug under an applied electric field upon the application of iontophoresis. The proposed concept and design constitute a new method of achieving effective drug absorption with wearable devices. Full article

The underlying mechanisms of spinal cord injury (SCI)-induced chronic pain involve dysfunctional GABAergic signaling and enhanced NMDA signaling. Our previous studies showed that SCI hypersensitivity in rats can be attenuated by recombinant rat GABAergic cells releasing NMDA blocker serine-histogranin (SHG) and by intensive locomotor training (ILT). The current study combines these approaches and evaluates their analgesic effects on a model of SCI pain in rats. Cells were grafted into the spinal cord at 4 weeks post-SCI to target the chronic pain, and ILT was initiated 5 weeks post-SCI. The hypersensitivity was evaluated weekly, which was followed by histological and biochemical assays. Prolonged effects of the treatment were evaluated in subgroups of animals after we discontinued ILT. The results show attenuation of tactile, heat and cold hypersensitivity in all of the treated animals and reduced levels of proinflammatory cytokines IL1β and TNFα in the spinal tissue and CSF. Animals with recombinant grafts and ILT showed the preservation of analgesic effects even during sedentary periods when the ILT was discontinued. Retraining helped to re-establish the effect of long-term training in all of the groups, with the greatest impact being in animals with recombinant grafts. These findings suggest that intermittent training in combination with cell therapy might be an efficient approach to manage chronic pain in SCI patients. Full article

Objective: To monitor fetal health and growth, fetal heart rate is a critical indicator. The non-invasive fetal electrocardiogram is a widely employed measurement for fetal heart rate estimation, which is extracted from the electrodes placed on the surface of the maternal abdomen. The qualities of the fetal ECG recordings, however, are frequently affected by the noises from various interference sources. In general, the fetal heart rate estimates are unreliable when low-quality fetal ECG signals are used for fetal heart rate estimation, which makes accurate fetal heart rate estimation a challenging task. So, the signal quality assessment for the fetal ECG records is an essential step before fetal heart rate estimation. In other words, some low-quality fetal ECG signal segments are supposed to be detected and removed by utilizing signal quality assessment, so as to improve the accuracy of fetal heart rate estimation. A few supervised learning-based fetal ECG signal quality assessment approaches have been introduced and shown to accurately classify high- and low-quality fetal ECG signal segments, but large fetal ECG datasets with quality annotation are required in these methods. Yet, the labeled fetal ECG datasets are limited. Proposed methods: An unsupervised learning-based multi-level fetal ECG signal quality assessment approach is proposed in this paper for identifying three levels of fetal ECG signal quality. We extracted some features associated with signal quality, including entropy-based features, statistical features, and ECG signal quality indices. Additionally, an autoencoder-based feature is calculated, which is related to the reconstruction error of the spectrograms generated from fetal ECG signal segments. The high-, medium-, and low-quality fetal ECG signal segments are classified by inputting these features into a self-organizing map. Main results: The experimental results showed that our proposal achieved a weighted average F1-score of 90% in three-level fetal ECG signal quality classification. Moreover, with the acceptable removal of detected low-quality signal segments, the errors of fetal heart rate estimation were reduced to a certain extent. Full article

In the existing research on prosthetic footplates, rehabilitation insoles, and robot feet, the cushioning parts are basically based on simple mechanisms and elastic pads. Most of them are unable to provide adequate impact resistance especially during contact with the ground. This paper developed a bioinspired heel pad by optimizing the inner structures inspired from human heel pad which has great cushioning performance. The distinct structures of the human heel pad were determined through magnetic resonance imaging (MRI) technology and related literatures. Five-layer pads with and without inner structures by using two materials (soft rubber and resin) were obtained, resulting in four bionic heel pads. Three finite element simulations (static, impact, and walking) were conducted to compare the cushioning effects in terms of deformations, ground reactions, and principal stress. The optimal pad with bionic structures and soft rubber material reduced 28.0% peak vertical ground reaction force (GRF) during walking compared with the unstructured resin pad. Human walking tests by a healthy subject wearing the 3D printed bionic pads also showed similar findings, with an almost 20% decrease in peak vertical GRF at normal speed. The soft rubber heel pad with bionic structures has the best cushioning performance, while the unstructured resin pad depicts the poorest. This study proves that with proper design of the inner structures and materials, the bionic pads will demonstrate distinct cushioning properties, which could be applied to the engineering fields, including lower limb prosthesis, robotics, and rehabilitations. Full article

In multiple sclerosis (MS), the transition from relapsing-remitting to the secondary-progressive phase is characterized by a progression independent of relapse activity (PIRA), resulting in physical disability accumulation and invisible symptoms, i.e., fatigue and cognitive impairment (CI). These symptoms are related to neurodegenerative processes and have been correlated with MRI measures of brain atrophy only at a group level; however, the application in clinical practice of atrophy-based measurements for single-patient evaluation is yet to be fully investigated. In the present study, we aimed to evaluate the association between brain atrophy, measured with easy-to-use automatic software, and the “invisible” MS symptoms of cognition and fatigue. A total of 69 MS patients were included in the study; cognitive impairment and fatigue (FSS) (in addition to neurological disability, EDSS) were assessed and correlated with brain volumes calculated using the automated software QyScore^® which is validated for single-patient use in the clinical setting. Results showed that the cognitive status was accurately reflected by measures of atrophy, with a sensitivity of up to 90%. CI patients showed a lower volume compared to cognitively normal patients in the whole brain (p = 0.017), gray matter (p = 0.042), insula (p = 0.035), cerebellum (p = 0.008), and limbic lobe (p = 0.049). FSS was associated with temporal lobe (r = −0.37, p = 0.013) and insular (r = −0.36, p = 0.019) volumes. The volumes of the same regions were also associated with EDSS. The global/regional atrophy results, assessed with automatic and easy-to-use software, correlated with cognitive and fatigue symptoms, thus supporting the clinical application in routine patient management. Full article

Hand gestures represent a natural way to express concepts and emotions which are peculiar to each culture. Several studies exploit biometric traits, such as fingerprint, iris or face for subject identification purposes. Within this paper, a novel ultrasound system for person identification that exploits hand gestures is presented. The system works as a sonar, measuring the ultrasonic pressure waves scattered by the subject’s hand, and analysing its Doppler information. Further, several transformations for obtaining time/frequency representations of the acquired signal are computed and a deep learning detector is implemented. The proposed system is cheap, reliable, contactless and can be easily integrated with other personal identification approaches allowing different security levels. The performances are evaluated via experimental tests carried out on a group of 25 volunteers. Results are encouraging, showing the promising potential of the system. Full article

Background: The long-term prognosis of current treatments for anal sphincter incontinence (ASI) is poor. Here, we explored the efficacy of tissue adipose stromal vascular fraction SVF (tSVF) on ASI and compared it to that of cellular SVF (cSVF). We then investigated possible mechanisms. Methods: Rat cSVF and tSVF were isolated and labeled with DIL. One day after modeling, three groups received phosphate-buffered saline (PBS), cSVF, tSVF, respectively. The control group received nil modeling nor any treatments. The effect was assessed by function test for anal pressure and electromyography, and staining for fiber content, proliferation and differentiation at day 5 and day 10. Results: cSVF injection resulted in faster healing than tSVF. The cSVF group showed significant improvement on anal pressure on day 10. For the electromyography test, cSVF showed significant improvement for the frequencies on day 10, and for the peak values on both time points, while tSVF showed significant improvement for the peak values on day 10. The two SVF both alleviated fibrosis. Immunofluorescence tracing identified differentiation of some injected cells towards myosatellite cells and smooth muscle cells in both SVF groups. For all the tests, the tSVF group tends to have similar or lower effects than the cSVF group with no significant difference. Conclusion: cSVF and tSVF are both safe and effective in treating ASI, while the effect of cSVF is slighter higher than tSVF. Full article

Hypertension is a chronic condition that is one of the prominent reasons behind cardiovascular disease, brain stroke, and organ failure. Left unnoticed and untreated, the deterioration in a health condition could even result in mortality. If it can be detected early, with proper treatment, undesirable outcomes can be avoided. Until now, the gold standard is the invasive way of measuring blood pressure (BP) using a catheter. Additionally, the cuff-based and noninvasive methods are too cumbersome or inconvenient for frequent measurement of BP. With the advancement of sensor technology, signal processing techniques, and machine learning algorithms, researchers are trying to find the perfect relationships between biomedical signals and changes in BP. This paper is a literature review of the studies conducted on the cuffless noninvasive measurement of BP using biomedical signals. Relevant articles were selected using specific criteria, then traditional techniques for BP measurement were discussed along with a motivation for cuffless measurement use of biomedical signals and machine learning algorithms. The review focused on the progression of different noninvasive cuffless techniques rather than comparing performance among different studies. The literature survey concluded that the use of deep learning proved to be the most accurate among all the cuffless measurement techniques. On the other side, this accuracy has several disadvantages, such as lack of interpretability, computationally extensive, standard validation protocol, and lack of collaboration with health professionals. Additionally, the continuing work by researchers is progressing with a potential solution for these challenges. Finally, future research directions have been provided to encounter the challenges. Full article

Electrospun polymeric matrices have long been investigated as constructs for use in regenerative medicine, yet relatively few have been commercialized for human clinical use. In 2017, a novel electrospun matrix, composed of two synthetic biocompatible polymers, polyglactin 910 (PLGA 10:90) and polydioxanone (PDO) of varying pore and fiber sizes (i.e., hybrid-scale) was developed and cleared by the FDA for human clinical use. The present review aims to explain the mechanism of action and review the preclinical and clinical results to summarize the efficacy of the matrix across multiple use cases within the wound care setting, including an assessment of over 150 wounds of varying etiologies treated with the synthetic matrix. Clinical data demonstrated effective use of the synthetic hybrid-scale fiber matrix across a variety of wound etiologies, including diabetic foot and venous leg ulcers, pressure ulcers, burns, and surgical wounds. This review represents a comprehensive clinical demonstration of a synthetic, electrospun, hybrid-scale matrix and illustrates its value and versatility across multiple wound etiologies. Full article

Kidney–ureter–bladder (KUB) imaging is a radiological examination with a low cost, low radiation, and convenience. Although emergency room clinicians can arrange KUB images easily as a first-line examination for patients with suspicious urolithiasis, interpreting the KUB images correctly is difficult for inexperienced clinicians. Obtaining a formal radiology report immediately after a KUB imaging examination can also be challenging. Recently, artificial-intelligence-based computer-aided diagnosis (CAD) systems have been developed to help clinicians who are not experts make correct diagnoses for further treatment more effectively. Therefore, in this study, we proposed a CAD system for KUB imaging based on a deep learning model designed to help first-line emergency room clinicians diagnose urolithiasis accurately. A total of 355 KUB images were retrospectively collected from 104 patients who were diagnosed with urolithiasis at Kaohsiung Chang Gung Memorial Hospital. Then, we trained a deep learning model with a ResNet architecture to classify KUB images in terms of the presence or absence of kidney stones with this dataset of pre-processed images. Finally, we tuned the parameters and tested the model experimentally. The results show that the accuracy, sensitivity, specificity, and F1-measure of the model were 0.977, 0.953, 1, and 0.976 on the validation set and 0.982, 0.964, 1, and 0.982 on the testing set, respectively. Moreover, the results demonstrate that the proposed model performed well compared to the existing CNN-based methods and was able to detect urolithiasis in KUB images successfully. We expect the proposed approach to help emergency room clinicians make accurate diagnoses and reduce unnecessary radiation exposure from computed tomography (CT) scans, along with the associated medical costs. Full article

Cell and gene therapies (CGT) have reached new therapeutic targets but have noticeably high prices. Solutions to reduce production costs might be found in CGT storage and transportation since they typically involve cryopreservation, which is a heavily burdened process. Encapsulation at hypothermic temperatures (e.g., 2–8 °C) could be a feasible alternative. Adipose tissue-derived mesenchymal stromal cells (MSC(AT)) expanded using fetal bovine serum (FBS)- (MSC-FBS) or human platelet lysate (HPL)-supplemented mediums (MSC-HPL) were encapsulated in alginate beads for 30 min, 5 days, and 12 days. After bead release, cell recovery and viability were determined to assess encapsulation performance. MSC identity was verified by flow cytometry, and a set of assays was performed to evaluate functionality. MSC(AT) were able to survive encapsulated for a standard transportation period of 5 days, with recovery values of 56 ± 5% for MSC-FBS and 77 ± 6% for MSC-HPL (which is a negligible drop compared to earlier timepoints). Importantly, MSC function did not suffer from encapsulation, with recovered cells showing robust differentiation potential, expression of immunomodulatory molecules, and hematopoietic support capacity. MSC(AT) encapsulation was proven possible for a remarkable 12 day period. There is currently no solution to completely replace cryopreservation in CGT logistics and supply chain, although encapsulation has shown potential to act as a serious competitor. Full article

A 3D tumor spheroid has been increasingly applied in pharmaceutical development for its simulation of the tumor structure and microenvironment. The embedded-culture of a tumor spheroid within a hydrogel microenvironment could help to improve the mimicking of in vivo cell growth and the development of 3D models for tumor invasiveness evaluation, which could enhance its drug efficiency prediction together with cell viability detection. NCI-H23 spheroids and CT-26 spheroids, from a non–small cell lung cancer and colorectal cancer cell line, respectively, together with extracellular matrix were generated for evaluating their sensitivity to AMG510 (a KRAS^G12C inhibitor) under normoxia and hypoxia conditions, which were created by an on-stage environmental chamber. Results demonstrated that NCI-H23, the KRAS^G12C moderate expression cell line, only mildly responded to AMG510 treatment in normal 2D and 3D cultures and could be clearly evaluated by our system in hypoxia conditions, while the negative control CT-26 (G12D-mutant) spheroid exhibited no significant response to AMG510 treatment. In summary, our system, together with a controlled microenvironment and imaging methodology, provided an easily assessable and effective methodology for 3D in vitro drug efficiency testing and screenings. Full article

Successful surgery on drug-resistant epilepsy patients (DRE) needs precise localization of the seizure onset zone (SOZ). Previous studies analyzing this issue still face limitations, such as inadequate analysis of features, low sensitivity and limited generality. Our study proposed an innovative and effective SOZ localization method based on multiple epileptogenic biomarkers (spike and HFOs), and analysis of single-contact (MEBM-SC) to address the above problems. We extracted contacts epileptic features from signal distributions and signal energy based on machine learning and end-to-end deep learning. Among them, a normalized pathological ripple rate was designed to reduce the disturbance of physiological ripple and enhance the performance of SOZ localization. Then, a feature selection algorithm based on Shapley value and hypothetical testing (ShapHT+) was used to limit interference from irrelevant features. Moreover, an attention mechanism and a focal loss algorithm were used on the classifier to learn significant features and overcome the unbalance of SOZ/nSOZ contacts. Finally, we provided an SOZ prediction and visualization on magnetic resonance imaging (MRI). Ten patients with DRE were selected to verify our method. The experiment performed cross-validation and revealed that MEBM-SC obtains higher sensitivity. Additionally, the spike has better sensitivity while HFOs have better specificity, and the combination of these biomarkers can achieve the best performance. The study confirmed that MEBM-SC can increase the sensitivity and accuracy of SOZ localization and help clinicians to perform a precise and reliable preoperative evaluation based on interictal SEEG. Full article

The attention on orthopedic biomaterials has shifted from their direct osteogenic properties to their osteoimmunomodulation, especially the modulation of macrophage polarization. Presently, advanced technologies endow polyetheretherketone (PEEK) with good osteoimmunomodulation by modifying PEEK surface characteristics or incorporating bioactive substances with regulating macrophage polarization. Recent studies have demonstrated that the fabrication of a hydrophilic surface and the incorporation of bioactive substances into PEEK (e.g., zinc, calcium, and phosphate) are good strategies to promote osteogenesis by enhancing the polarization of M2 macrophages. Furthermore, the modification by other osteoimmunomodulatory composites (e.g., lncRNA-MM2P, IL-4, IL-10, and chitosan) and their controlled and desired release may make PEEK an optimal bio-activated implant for regulating and balancing the osteogenic system and immune system. The purpose of this review is to comprehensively evaluate the potential of bio-activated PEEK in polarizing macrophages into M2 phenotype to improve osteogenesis. For this objective, we retrieved and discussed different kinds of bio-activated PEEK regarding improving osteogenesis through modulating macrophage polarization. Meanwhile, the relevant challenges and outlook were presented. We hope that this review can shed light on the development of bio-activated PEEK with more favorable osteoimmunomodulation. Full article

A recently introduced model-based deep learning (MoDL) technique successfully incorporates convolutional neural network (CNN)-based regularizers into physics-based parallel imaging reconstruction using a small number of network parameters. Wave-controlled aliasing in parallel imaging (CAIPI) is an emerging parallel imaging method that accelerates imaging acquisition by employing sinusoidal gradients in the phase- and slice/partition-encoding directions during the readout to take better advantage of 3D coil sensitivity profiles. We propose wave-encoded MoDL (wave-MoDL) combining the wave-encoding strategy with unrolled network constraints for highly accelerated 3D imaging while enforcing data consistency. We extend wave-MoDL to reconstruct multicontrast data with CAIPI sampling patterns to leverage similarity between multiple images to improve the reconstruction quality. We further exploit this to enable rapid quantitative imaging using an interleaved look-locker acquisition sequence with T₂ preparation pulse (3D-QALAS). Wave-MoDL enables a 40 s MPRAGE acquisition at 1 mm resolution at 16-fold acceleration. For quantitative imaging, wave-MoDL permits a 1:50 min acquisition for T₁, T₂, and proton density mapping at 1 mm resolution at 12-fold acceleration, from which contrast-weighted images can be synthesized as well. In conclusion, wave-MoDL allows rapid MR acquisition and high-fidelity image reconstruction and may facilitate clinical and neuroscientific applications by incorporating unrolled neural networks into wave-CAIPI reconstruction. Full article

With advances in portable and wearable devices, it should be possible to analyze and interpret the collected biosignals from those devices to tailor a psychological intervention to help patients. This study focuses on detecting anxiety by using a portable device that collects electrocardiogram (ECG) and respiration (RSP) signals. The feature extraction focused on heart-rate variability (HRV) and breathing-rate variability (BRV). We show that a significant change in these signals occurred between the non-anxiety-induced and anxiety-induced states. The HRV biomarkers were the mean heart rate (MHR; $\overline{p}$ = 0.04), the standard deviation of the heart rate (SD; $\overline{p}$ = 0.01), and the standard deviation of NN intervals (SDNN; $\overline{p}$ = 0.03) for ECG signals, and the mean breath rate (MBR; $\overline{p}$ = 0.002), the standard deviation of the breath rate (SD; $\overline{p}$ < 0.0001), the root mean square of successive differences (RMSSD; $\overline{p}$ < 0.0001) and SDNN ($\overline{p}$ < 0.0001) for RSP signals. This work extends the existing literature on the relationship between stress and HRV/BRV by being the first to introduce a transitional phase. It contributes to systematically processing mental and emotional impulse data in humans measured via ECG and RSP signals. On the basis of these identified biomarkers, artificial-intelligence or machine-learning algorithms, and rule-based classification, the automated biosignal-based psychological assessment of patients could be within reach. This creates a broad basis for detecting and evaluating psychological abnormalities in individuals upon which future psychological treatment methods could be built using portable and wearable devices. Full article

Robotic patients show great potential for helping to improve medical palpation training, as they can provide feedback that cannot be obtained in a real patient. They provide information about internal organ deformation that can significantly enhance palpation training by giving medical trainees visual insight based on the pressure they apply for palpation. This can be achieved by using computational models of abdomen mechanics. However, such models are computationally expensive, and thus unable to provide real-time predictions. In this work, we proposed an innovative surrogate model of abdomen mechanics by using machine learning (ML) and finite element (FE) modelling to virtually render internal tissue deformation in real time. We first developed a new high-fidelity FE model of the abdomen mechanics from computerized tomography (CT) images. We performed palpation simulations to produce a large database of stress distribution on the liver edge, an area of interest in most examinations. We then used artificial neural networks (ANNs) to develop the surrogate model and demonstrated its application in an experimental palpation platform. Our FE simulations took 1.5 h to predict stress distribution for each palpation while this only took a fraction of a second for the surrogate model. Our results show that our artificial neural network (ANN) surrogate has an accuracy of 92.6%. We also showed that the surrogate model is able to use the experimental input of palpation location and force to provide real-time projections onto the robotics platform. This enhanced robotics platform has the potential to be used as a training simulator for trainees to hone their palpation skills. Full article

The management and definitive treatment of segmental bone defects in the setting of acute trauma, fracture non-union, revision joint arthroplasty, and tumor surgery are challenging clinical problems with no consistently satisfactory solution. Orthopaedic surgeons are developing novel strategies to treat these problems, including three-dimensional (3D) printing combined with growth factors and/or cells. This article reviews the current strategies for management of segmental bone loss in orthopaedic surgery, including graft selection, bone graft substitutes, and operative techniques. Furthermore, we highlight 3D printing as a technology that may serve a major role in the management of segmental defects. The optimization of a 3D-printed scaffold design through printing technique, material selection, and scaffold geometry, as well as biologic additives to enhance bone regeneration and incorporation could change the treatment paradigm for these difficult bone repair problems. Full article

Point-of-care (POC) tests for the diagnosis of diseases are critical to the improvement of the standard of living, especially for resource-limited areas or countries. In recent years, nanobiosensors based on noble metal nanoparticles (NM NPs) have emerged as a class of effective and versatile POC testing technology. The unique features of NM NPs ensure great performance of associated POC nanobiosensors. In particular, NM NPs offer various signal transduction principles, such as plasmonics, catalysis, photothermal effect, and so on. Significantly, the detectable signal from NM NPs can be tuned and optimized by controlling the physicochemical parameters (e.g., size, shape, and elemental composition) of NPs. In this article, we introduce the inherent merits of NM NPs that make them attractive for POC testing, discuss recent advancement of NM NPs-based POC tests, highlight their social impacts, and provide perspectives on challenges and opportunities in the field. We hope the review and insights provided in this article can inspire new fundamental and applied research in this emerging field. Full article

Adenovirus (Ad) is a widely studied viral vector for cancer therapy as it can be engineered to cause selective lysis of cancer cells. However, Ad delivery is limited in treating cancers that do not have coxsackievirus and adenovirus receptors (CAR). To overcome this challenge, Ad-encapsulated liposomes were developed that enhance the delivery of Ads and increase therapeutic efficacy. Cationic empty liposomes were manufactured first, to which an anionic Ad were added, which resulted in encapsulated Ad liposomes through charge interaction. Optimization of the liposome formula was carried out with series of formulation variables experiments using an extrusion process, which is ideal for laboratory-scale small batches. Later, the optimized formulation was manufactured with a homogenization technique—A high shear rotor-stator blending, that is ideal for large-scale manufacturing and is in compliance with Good Manufacturing Practices (GMP). Comparative in vitro transduction, physicochemical characterization, long-term storage stability at different temperature conditions, and in vivo animal studies were performed. Ad encapsulated liposomes transduced CAR deficient cells 100-fold more efficiently than the unencapsulated Ad (p ≤ 0.0001) in vitro, and 4-fold higher in tumors injected in nude mice in vivo. Both extrusion and homogenization performed similarly–with equivalent in vitro and in vivo transduction efficiencies, physicochemical characterization, and long-term storage stability. Thus, two Ad encapsulated liposomes preparation methods used herein, i.e., extrusion vs. homogenization were equivalent in terms of enhanced Ad performance and long-term storage stability; this will, hopefully, facilitate translation to the clinic. Full article

Cervical cancer, a common chronic disease, is one of the most prevalent and curable cancers among women. Pap smear images are a popular technique for screening cervical cancer. This study proposes a computer-aided diagnosis for cervical cancer utilizing the novel Cervical Net deep learning (DL) structures and feature fusion with Shuffle Net structural features. Image acquisition and enhancement, feature extraction and selection, as well as classification are the main steps in our cervical cancer screening system. Automated features are extracted using pre-trained convolutional neural networks (CNN) fused with a novel Cervical Net structure in which 544 resultant features are obtained. To minimize dimensionality and select the most important features, principal component analysis (PCA) is used as well as canonical correlation analysis (CCA) to obtain the best discriminant features for five classes of Pap smear images. Here, five different machine learning (ML) algorithms are fed into these features. The proposed strategy achieved the best accuracy ever obtained using a support vector machine (SVM), in which fused features between Cervical Net and Shuffle Net is 99.1% for all classes. Full article

Despite a significant advance in the pathophysiological understanding of peripheral nerve damage, the successful treatment of large nerve defects remains an unmet medical need. In this article, axon growth guidance for peripheral nerve regeneration was systematically reviewed and discussed mainly from the engineering perspective. In addition, the common approaches to surgery, bioengineering approaches to emerging technologies such as optogenetic stimulation and magnetic stimulation for functional recovery were discussed, along with their pros and cons. Additionally, clear future perspectives of axon guidance and nerve regeneration were addressed. Full article

Gliomas are the most common type of primary brain tumors and one of the highest causes of mortality worldwide. Accurate grading of gliomas is of immense importance to administer proper treatment plans. In this paper, we develop a comprehensive non-invasive multimodal magnetic resonance (MR)-based computer-aided diagnostic (CAD) system to precisely differentiate between different grades of gliomas (Grades: I, II, III, and IV). A total of 99 patients with gliomas (M = 49, F = 50, age range = 1–79 years) were included after providing their informed consent to participate in this study. The proposed imaging-based glioma grading (GG-CAD) system utilizes three different MR imaging modalities, namely; contrast-enhanced T1-MR, T2-MR known as fluid-attenuated inversion-recovery (FLAIR), and diffusion-weighted (DW-MR) to extract the following imaging features: (i) morphological features based on constructing the histogram of oriented gradients (HOG) and estimating the glioma volume, (ii) first and second orders textural features by constructing histogram, gray-level run length matrix (GLRLM), and gray-level co-occurrence matrix (GLCM), (iii) functional features by estimating voxel-wise apparent diffusion coefficients (ADC) and contrast-enhancement slope. These features are then integrated together and processed using a Gini impurity-based selection approach to find the optimal set of significant features. The reduced significant features are then fed to a multi-layer perceptron artificial neural networks (MLP-ANN) classification model to obtain the final diagnosis of a glioma tumor as Grade I, II, III, or IV. The GG-CAD system was evaluated on the enrolled 99 gliomas (Grade I = 13, Grade II = 22, Grade III = 22, and Grade IV = 42) using a leave-one-subject-out (LOSO) and k-fold stratified (with k = 5 and 10) cross-validation approach. The GG-CAD achieved 0.96 ± 0.02 quadratic-weighted Cohen’s kappa and 95.8% ± 1.9% overall diagnostic accuracy at LOSO and an outstanding diagnostic performance at k = 10 and 5. Alternative classifiers, including RFs and SVM${}_{lin}$ produced inferior results compared to the proposed MLP-ANN GG-CAD system. These findings demonstrate the feasibility of the proposed CAD system as a novel tool to objectively characterize gliomas using the comprehensive extracted and selected imaging features. The developed GG-CAD system holds promise to be used as a non-invasive diagnostic tool for Precise Grading of Glioma. Full article

The aim of this study was to assess the condylar volume in adult patients with different skeletal classes and vertical patterns using cone-beam computed tomography (CBCT). CBCT scans of 146 condyles from 73 patients (mean age 30 ± 12 years old; 49 female, 24 male) were selected from the archive of the Department of Dentistry and Maxillofacial Surgery of Fondazione IRCCS Ca’ Granda, Milan, Italy, and retrospectively analyzed. The following inclusion criteria were used: adult patients; CBCT performed with the same protocol (0.4 mm slice thickness, 16 × 22 cm field of view, 20 s scan time); no systemic diseases; and no previous orthodontic treatments. Three-dimensional cephalometric tracings were performed for each patient, the mandibular condyles were segmented and the relevant volumes calculated using Mimics Materialize 20.0^® software (Materialise, Leuven, Belgium). Right and left variables were analyzed together using random-intercept linear regression models. No significant association between condylar volumes and skeletal class was found. On the other hand, in relation to vertical patterns, the mean values of the mandibular condyle volumes in hyperdivergent subjects (688 mm³) with a post-rotation growth pattern (625 mm³) were smaller than in hypodivergent patients (812 mm³) with a horizontal growth pattern (900 mm³). Patients with an increased divergence angle had smaller condylar volumes than subjects with normal or decreased mandibular plane divergence. This relationship may help the clinician when planning orthodontic treatment. Full article

We carry out a critical assessment of machine learning and deep learning models for the classification of skin tumors. Machine learning (ML) algorithms tested in this work include logistic regression, linear discriminant analysis, k-nearest neighbors classifier, decision tree classifier and Gaussian naive Bayes, while deep learning (DL) models employed are either based on a custom Convolutional Neural Network model, or leverage transfer learning via the use of pre-trained models (VGG16, Xception and ResNet50). We find that DL models, with accuracies up to 0.88, all outperform ML models. ML models exhibit accuracies below 0.72, which can be increased to up to 0.75 with ensemble learning. To further assess the performance of DL models, we test them on a larger and more imbalanced dataset. Metrics, such as the F-score and accuracy, indicate that, after fine-tuning, pre-trained models perform extremely well for skin tumor classification. This is most notably the case for VGG16, which exhibits an F-score of 0.88 and an accuracy of 0.88 on the smaller database, and metrics of 0.70 and 0.88, respectively, on the larger database. Full article

Despite a massive global preventative effort, heart failure remains the major cause of death globally. The number of patients requiring a heart transplant, the eventual last treatment option, far outnumbers the available donor hearts, leaving many to deteriorate or die on the transplant waiting list. Treating heart failure by transplanting a 3D bioprinted patient-specific cardiac patch to the infarcted region on the myocardium has been investigated as a potential future treatment. To date, several studies have created cardiac patches using 3D bioprinting; however, testing the concept is still at a pre-clinical stage. A handful of clinical studies have been conducted. However, moving from animal studies to human trials will require an increase in research in this area. This review covers key elements to the design of a patient-specific cardiac patch, divided into general areas of biological design and 3D modelling. It will make recommendations on incorporating anatomical considerations and high-definition motion data into the process of 3D-bioprinting a patient-specific cardiac patch. Full article

Allogeneic cell therapy products, such as therapeutic cells derived from pluripotent stem cells (PSCs), have amazing potential to treat a wide variety of diseases and vast numbers of patients globally. However, there are various challenges related to manufacturing PSCs in single-use bioreactors, particularly at larger volumetric scales. This manuscript addresses these challenges and presents potential solutions to alleviate the anticipated bottlenecks for commercial-scale manufacturing of high-quality therapeutic cells derived from PSCs. Full article

The ever-increasing use of plastics, their fossil origin, and especially their persistence in nature have started a wave of new innovations in materials that are renewable, offer the functionalities of plastics, and are biodegradable. One such class of biopolymers, polyhydroxyalkanoates (PHAs), are biosynthesized by numerous microorganisms through the conversion of carbon-rich renewable resources. PHA homo- and heteropolyesters are intracellular products of secondary microbial metabolism. When isolated from microbial biomass, PHA biopolymers mimic the functionalities of many of the top-selling plastics of petrochemical origin, but biodegrade in soil, freshwater, and marine environments, and are both industrial- and home-compostable. Only a handful of PHA biopolymers have been studied in-depth, and five of these reliably match the desired material properties of established fossil plastics. Realizing the positive attributes of PHA biopolymers, several established chemical companies and numerous start-ups, brand owners, and converters have begun to produce and use PHA in a variety of industrial and consumer applications, in what can be described as the emergence of the “PHA industry”. While this positive industrial and commercial relevance of PHA can hardly be described as the first wave in its commercial development, it is nonetheless a very serious one with over 25 companies and start-ups and 30+ brand owners announcing partnerships in PHA production and use. The combined product portfolio of the producing companies is restricted to five types of PHA, namely poly(3-hydroxybutyrate), poly(4-hydroxybutyrate), poly(3-hydroxybutyrate-co-3-hydroxyvalerate), poly(3-hydroxybutyrate-co-4-hydroxybutyrate), and poly(3-hydroxybutyrate-co-3-hydroxyhexanoate), even though PHAs as a class of polymers offer the potential to generate almost limitless combinations of polymers beneficial to humankind. To date, by varying the co-monomer type and content in these PHA biopolymers, their properties emulate those of the seven top-selling fossil plastics, representing 230 million t of annual plastics production. Capacity expansions of 1.5 million t over the next 5 years have been announced. Policymakers worldwide have taken notice and are encouraging industry to adopt biodegradable and compostable material solutions. This wave of commercialization of PHAs in single-use and in durable applications holds the potential to make the decisive quantum leap in reducing plastic pollution, the depletion of fossil resources, and the emission of greenhouse gases and thus fighting climate change. This review presents setbacks and success stories of the past 40 years and the current commercialization wave of PHA biopolymers, their properties, and their fields of application. Full article

Mesenchymal stem cells (MSCs) are primary candidates in tissue engineering and stem cell therapies due to their intriguing regenerative and immunomodulatory potential. Their ability to self-assemble into three-dimensional (3D) aggregates further improves some of their therapeutic properties, e.g., differentiation potential, secretion of cytokines, and homing capacity after administration. However, high hydrodynamic shear forces and the resulting mechanical stresses within commercially available dynamic cultivation systems can decrease their regenerative properties. Cells embedded within a polymer matrix, however, lack cell-to-cell interactions found in their physiological environment. Here, we present a “semi scaffold-free” approach to protect the cells from high shear forces by a physical barrier, but still allow formation of a 3D structure with in vivo-like cell-to-cell contacts. We highlight a relatively simple method to create core–shell capsules by inverse gelation. The capsules consist of an outer barrier made from sodium alginate, which allows for nutrient and waste diffusion and an inner compartment for direct cell-cell interactions. Next to capsule characterization, a harvesting procedure was established and viability and proliferation of human adipose-derived MSCs were investigated. In the future, this encapsulation and cultivation technique might be used for MSC-expansion in scalable dynamic bioreactor systems, facilitating downstream procedures, such as cell harvest and differentiation into mature tissue grafts. Full article

As medicine advances and physicians are able to provide patients with innovative solutions, including placement of temporary or permanent medical devices that drastically improve quality of life of the patient, there is the persistent, recurring problem of chronic bacterial infection, including osteomyelitis. Osteomyelitis can manifest as a result of traumatic or contaminated wounds or implant-associated infections. This bacterial infection can persist as a result of inadequate treatment regimens or the presence of biofilm on implanted medical devices. One strategy to mitigate these concerns is the use of implantable medical devices that simultaneously act as local drug delivery devices (DDDs). This classification of device has the potential to prevent or aid in clearing chronic bacterial infection by delivering effective doses of antibiotics to the area of interest and can be engineered to simultaneously aid in tissue regeneration. This review will provide a background on bacterial infection and current therapies as well as current and prospective implantable DDDs, with a particular emphasis on local DDDs to combat bacterial osteomyelitis. Full article

Lumbar disc herniation is one of the most common degenerative spinal conditions resulting in lower back pain and sciatica. Surgical treatment options include microdiscectomy, lumbar fusion, total disc replacement, and other minimally invasive approaches. At present, microdiscectomy procedures are the most used technique; however, the annulus fibrosus is left with a defect that without treatment may contribute to high reherniation rates and changes in the biomechanics of the lumbar spine. This paper aims to review current commercially available products that mechanically close the annulus including the AnchorKnot^® suture-passing device and the Barricaid^® annular closure device. Previous studies and reviews have focused mainly on a biomimetic biomaterials approach and have described some mechanical and biological requirements for an active annular repair/regeneration strategy but are still far away from clinical implementation. Therefore, in this paper we aim to create a design specification for a mechanical annular closure strategy by identifying the most important mechanical and biological design parameters, including consideration of material selection, preclinical testing requirements, and requirements for clinical implementation. Full article

Organ on chip (OOC) has emerged as a major technological breakthrough and distinct model system revolutionizing biomedical research and drug discovery by recapitulating the crucial structural and functional complexity of human organs in vitro. OOC are rapidly emerging as powerful tools for oncology research. Indeed, Cancer on chip (COC) can ideally reproduce certain key aspects of the tumor microenvironment (TME), such as biochemical gradients and niche factors, dynamic cell–cell and cell–matrix interactions, and complex tissue structures composed of tumor and stromal cells. Here, we review the state of the art in COC models with a focus on the microphysiological systems that host multicellular 3D tissue engineering models and can help elucidate the complex biology of TME and cancer growth and progression. Finally, some examples of microengineered tumor models integrated with multi-organ microdevices to study disease progression in different tissues will be presented. Full article