Bioengineering, Volume 11, Issue 3 (March 2024) – 99 articles

Accurate estimation of hip joint center (HJC) position is crucial during gait analysis. HJC is obtained with predictive or functional methods. But in the functional method, there is no consensus on where to place the skin markers and which combination to use. The objective of this study was to analyze how different combinations of skin markers affect the estimation of HJC position relative to predictive methods. Forty-one healthy volunteers were included in this study; thirteen markers were placed on the pelvis and hip of each subject’s lower limbs. Various marker combinations were used to determine the HJC position based on ten calibration movement trials, captured by a motion capture system. The estimated HJC position for each combination was evaluated by focusing on the range and standard deviation of the mean norm values of HJC and the mean X, Y, Z coordinates of HJC for each limb. The combinations that produced the best estimates incorporated the markers on the pelvis and on proximal and easily identifiable muscles, with results close to predictive methods. The combination that excluded the markers on the pelvis was not robust in estimating the HJC position. Full article

Several studies have found a relationship between the rotational anatomy of the distal femur and the overall coronal lower limb alignment in knees with osteoarthritis (OA). Less is known about the rotation of the proximal tibia, especially in the context of total knee arthroplasty (TKA), where one of the goals of the surgery is to achieve the appropriate component-to-component rotation. The aim of this study was to investigate the relationship between the coronal alignment of the lower extremity and the relative proximal tibial rotation. A prospective cohort study of patients with an end-stage OA scheduled for TKA was conducted. All patients underwent a computed tomography (CT) scan and a standing X-ray of both lower limbs. A relative femorotibial rotation was measured separately for mechanical and kinematic alignment. A statistically significant correlation was found between the tibial varus and the external tibial rotation (p < 0.001). Out of 14 knees with high tibial varus (>5°), 13 (93%) and 7 (50%) knees had >10° of femorotibial rotation for the mechanical and kinematic alignment landmarks, respectively. In order to keep the component-to-component rotation within the 10° margin, more internal rotation of the tibial component is required in knees with higher tibial varus. Full article

Speech impairments often emerge as one of the primary indicators of Parkinson’s disease (PD), albeit not readily apparent in its early stages. While previous studies focused predominantly on binary PD detection, this research explored the use of deep learning models to automatically classify sustained vowel recordings into healthy controls, mild PD, or severe PD based on motor symptom severity scores. Popular convolutional neural network (CNN) architectures, VGG and ResNet, as well as vision transformers, Swin, were fine-tuned on log mel spectrogram image representations of the segmented voice data. Furthermore, the research investigated the effects of audio segment lengths and specific vowel sounds on the performance of these models. The findings indicated that implementing longer segments yielded better performance. The models showed strong capability in distinguishing PD from healthy subjects, achieving over 95% precision. However, reliably discriminating between mild and severe PD cases remained challenging. The VGG16 achieved the best overall classification performance with 91.8% accuracy and the largest area under the ROC curve. Furthermore, focusing analysis on the vowel /u/ could further improve accuracy to 96%. Applying visualization techniques like Grad-CAM also highlighted how CNN models focused on localized spectrogram regions while transformers attended to more widespread patterns. Overall, this work showed the potential of deep learning for non-invasive screening and monitoring of PD progression from voice recordings, but larger multi-class labeled datasets are needed to further improve severity classification. Full article

Segmenting and classifying nuclei in H&E histopathology images is often limited by the long-tailed distribution of nuclei types. However, the strong generalization ability of image segmentation foundation models like the Segment Anything Model (SAM) can help improve the detection quality of rare types of nuclei. In this work, we introduce category descriptors to perform nuclei segmentation and classification by prompting the SAM model. We close the domain gap between histopathology and natural scene images by aligning features in low-level space while preserving the high-level representations of SAM. We performed extensive experiments on the Lizard dataset, validating the ability of our model to perform automatic nuclei segmentation and classification, especially for rare nuclei types, where achieved a significant detection improvement in the F1 score of up to 12%. Our model also maintains compatibility with manual point prompts for interactive refinement during inference without requiring any additional training. Full article

The 12-lead electrocardiogram (ECG) is crucial in assessing patient decisions. However, portable ECG devices capable of acquiring a complete 12-lead ECG are scarce. For the first time, a deep learning-based method is proposed to reconstruct the 12-lead ECG from Frank leads (V_X, V_Y, and V_Z) or EASI leads (V_ES, V_AS, and V_AI). The innovative ECG reconstruction network called M2Eformer is composed of a 2D-ECGblock and a ProbDecoder module. The 2D-ECGblock module adaptively segments EASI leads into multi-periods based on frequency energy, transforming the 1D time series into a 2D tensor representing within-cycle and between-cycle variations. The ProbDecoder module aims to extract Probsparse self-attention and achieve one-step output for the target leads. Experimental results from comparing recorded and reconstructed 12-lead ECG using Frank leads indicate that M2Eformer outperforms traditional ECG reconstruction methods on a public database. In this study, a self-constructed database (10 healthy individuals + 15 patients) was utilized for the clinical diagnostic validation of ECG reconstructed from EASI leads. Subsequently, both the ECG reconstructed using EASI and the recorded 12-lead ECG were subjected to a double-blind diagnostic experiment conducted by three cardiologists. The overall diagnostic consensus among three cardiology experts, reaching a rate of 96%, indicates the significant utility of EASI-reconstructed 12-lead ECG in facilitating the diagnosis of cardiac conditions. Full article

Background: The purpose of this study was to determine whether cold therapy after the first exercise test influences the physical working capacity at the fatigue threshold (PWC_FT) during the second exercise test. We hypothesized that cold therapy would delay the onset of PWC_FT for the second exercise test relative to the control visit (i.e., no cold therapy). Methods: Eight healthy college-aged men volunteered for the present study. For each of the two visits, subjects performed incremental, single-leg, knee-extensor ergometer, followed by either resting for 30 min (control visit) or having a cold pack applied for 15 min and then resting for 15 min (experimental visit). Then, the same exercise test was performed. The order of visits (control vs. experimental) was randomized for each subject. The exercise indices and PWC_FT were determined for each of the two visits and statistically analyzed using two-way repeated measures analysis of variance. Results: The results indicate no significant (p > 0.05) mean differences for maximal power output, heart rate at end-exercise, and PWC_FT between the control and cold therapy visits. Moreover, there were no significant (p > 0.05) mean differences between the first and second exercise workbout within each visit. Conclusions: The findings of this study suggest that cold therapy did not influence neuromuscular fatigue. Full article

This paper describes the analysis of electrodermal activity (EDA) in the context of students’ scholastic activity. Taking a multidisciplinary, citizen science and maker-centric approach, low-cost, bespoken wearables, such as a mini weather station and biometric wristband, were built. To investigate both physical health as well as stress, the instruments were first validated against research grade devices. Following this, a research experiment was created and conducted in the context of students’ scholastic activity. Data from this experiment were used to train machine learning models, which were then applied to interpret the relationships between the environment, health, and stress. It is hoped that analyses of EDA data will further strengthen the emerging model describing the intersections between local microclimate and physiological and neurological stress. The results suggest that temperature and air quality play an important role in students’ physiological well-being, thus demonstrating the feasibility of understanding the extent of the effects of various microclimatic factors. This highlights the importance of thermal comfort and air ventilation in real-life applications to improve students’ well-being. We envision our work making a significant impact by showcasing the effectiveness and feasibility of inexpensive, self-designed wearable devices for tracking microclimate and electrodermal activity (EDA). The affordability of these wearables holds promising implications for scalability and encourages crowd-sourced citizen science in the relatively unexplored domain of microclimate’s influence on well-being. Embracing citizen science can then democratize learning and expedite rapid research advancements. Full article

Researchers commonly use the ‘free-fall’ paradigm to investigate motor control during landing impacts, particularly in drop landings and depth jumps (DJ). While recent studies have focused on the impact of vision on landing motor control, previous research fully removed continuous visual input, limiting ecological validity. The aim of this investigation was to evaluate the effects of stroboscopic vision on depth jump (DJ) motor control. Ground reaction forces (GRF) and lower-extremity surface electromyography (EMG) were collected for 20 young adults (11 male; 9 female) performing six depth jumps (0.51 m drop height) in each of two visual conditions (full vision vs. 3 Hz stroboscopic vision). Muscle activation magnitude was estimated from EMG signals using root-mean-square amplitudes (RMS) over specific time intervals (150 ms pre-impact; 30–60 ms, 60–85 ms, and 85–120 ms post-impact). The main effects of and interactions between vision and trial number were assessed using two-way within-subjects repeated measures analyses of variance. Peak GRF was 6.4% greater, on average, for DJs performed with stroboscopic vision compared to full vision (p = 0.042). Tibialis anterior RMS EMG during the 60–85 ms post-impact time interval was 14.1% lower for DJs performed with stroboscopic vision (p = 0.020). Vastus lateralis RMS EMG during the 85–120 ms post-impact time interval was 11.8% lower for DJs performed with stroboscopic vision (p = 0.017). Stroboscopic vision altered DJ landing mechanics and lower-extremity muscle activation. The observed increase in peak GRF and reduction in RMS EMG of the tibialis anterior and vastus lateralis post-landing may signify a higher magnitude of lower-extremity musculotendinous stiffness developed pre-landing. The results indicate measurable sensorimotor disruption for DJs performed with stroboscopic vision, warranting further research and supporting the potential use of stroboscopic vision as a sensorimotor training aid in exercise and rehabilitation. Stroboscopic vision could induce beneficial adaptations in multisensory integration, applicable to restoring sensorimotor function after injury and preventing injuries in populations experiencing landing impacts at night (e.g., military personnel). Full article

The field of regenerative medicine has recently witnessed groundbreaking advancements that hold immense promise for treating a wide range of diseases and injuries. At the forefront of this revolutionary progress are stem cells. Stem cells typically reside in specialized environments in vivo, known as microenvironments or niches, which play critical roles in regulating stem cell behavior and determining their fate. Therefore, understanding the complex microenvironments that surround stem cells is crucial for advancing treatment options in regenerative medicine and tissue engineering applications. Several research articles have made significant contributions to this field by exploring the interactions between stem cells and their surrounding niches, investigating the influence of biomechanical and biochemical cues, and developing innovative strategies for tissue regeneration. This review highlights the key findings and contributions of these studies, shedding light on the diverse applications that may arise from the understanding of stem cell microenvironments, thus harnessing the power of these microenvironments to transform the landscape of medicine and offer new avenues for regenerative therapies. Full article

The central nervous system (CNS) controls movements and regulates joint stiffness with muscle co-activation, but until now, few studies have examined muscle pairs during running. This study aims to investigate differences in lower limb muscle coactivation during gait at different speeds, from walking to running. Nineteen healthy runners walked and ran at speeds ranging from 0.8 km/h to 9.3 km/h. Twelve lower limb muscles’ co-activation was calculated using the time-varying multi-muscle co-activation function (TMCf) with global, flexor–extension, and rostro–caudal approaches. Spatiotemporal and kinematic parameters were also measured. We found that TMCf, spatiotemporal, and kinematic parameters were significantly affected by gait speed for all approaches. Significant differences were observed in the main parameters of each co-activation approach and in the spatiotemporal and kinematic parameters at the transition between walking and running. In particular, significant differences were observed in the global co-activation (CI_glob, main effect F_(1,17) = 641.04, p < 0.001; at the transition p < 0.001), the stride length (main effect F_(1,17) = 253.03, p < 0.001; at the transition p < 0.001), the stride frequency (main effect F_(1,17) = 714.22, p < 0.001; at the transition p < 0.001) and the Center of Mass displacement in the vertical (CoM_y, main effect F_(1,17) = 426.2, p < 0.001; at the transition p < 0.001) and medial–lateral (CoM_z, main effect F_(1,17) = 120.29 p < 0.001; at the transition p < 0.001) directions. Regarding the correlation analysis, the CoM_y was positively correlated with a higher CI_glob (r = 0.88, p < 0.001) and negatively correlated with Full Width at Half Maximum (FWHM_glob, r = −0.83, p < 0.001), whereas the CoM_z was positively correlated with the global Center of Activity (CoA_glob, r = 0.97, p < 0.001). Positive and negative strong correlations were found between global co-activation parameters and center of mass displacements, as well as some spatiotemporal parameters, regardless of gait speed. Our findings suggest that walking and running have different co-activation patterns and kinematic characteristics, with the whole-limb stiffness exerted more synchronously and stably during running. The co-activation indexes and kinematic parameters could be the result of global co-activation, which is a sensory-control integration process used by the CNS to deal with more demanding and potentially unstable tasks like running. Full article

Critical-size bone defects up to 25 cm can be treated successfully using the induced membrane technique established by Masquelet. To shorten this procedure, human acellular dermis (HAD) has had success in replacing this membrane in rat models. The aim of this study was to compare bone healing for smaller and larger defects using an induced membrane and HAD in a rat model. Using our established femoral defect model in rats, the animals were placed into four groups and defects of 5 mm or 10 mm size were set, either filling them with autologous spongiosa and surrounding the defect with HAD or waiting for the induced membrane to form around a cement spacer and filling this cavity in a second operation with a cancellous bone graft. Healing was assessed eight weeks after the operation using µ-CT, histological staining, and an assessment of the progress of bone formation using an established bone healing score. The α-smooth muscle actin used as a signal of blood vessel formation was stained and counted. The 5 mm defects showed significantly better bone union and a higher bone healing score than the 10 mm defects. HAD being used for the smaller defects resulted in a significantly higher bone healing score even than for the induced membrane and significantly higher blood vessel formation, corroborating the good results achieved by using HAD in previous studies. In comparison, same-sized groups showed significant differences in bone healing as well as blood vessel formation, suggesting that 5 mm defects are large enough to show different results in healing depending on treatment; therefore, 5 mm is a viable size for further studies on bone healing. Full article

Focal vibratory stimulation (FVS) and neuromuscular electrical stimulation (NMES) are promising technologies for sensory rehabilitation after stroke. However, the differences between these techniques in immediate neuromodulatory effects on the poststroke cortex are not yet fully understood. In this research, cortical responses in persons with chronic stroke (n = 15) and unimpaired controls (n = 15) were measured by whole-brain electroencephalography (EEG) when FVS and NMES at different intensities were applied transcutaneously to the forearm muscles. Both FVS and sensory-level NMES induced alpha and beta oscillations in the sensorimotor cortex after stroke, significantly exceeding baseline levels (p < 0.05). These oscillations exhibited bilateral sensory deficiency, early adaptation, and contralesional compensation compared to the control group. FVS resulted in a significantly faster P300 response (p < 0.05) and higher theta oscillation (p < 0.05) compared to NMES. The beta desynchronization over the contralesional frontal–parietal area remained during NMES (p > 0.05), but it was significantly weakened during FVS (p < 0.05) after stroke. The results indicated that both FVS and NMES effectively activated the sensorimotor cortex after stroke. However, FVS was particularly effective in eliciting transient involuntary attention, while NMES primarily fostered the cortical responses of the targeted muscles in the contralesional motor cortex. Full article

The reusability of by-products in the food industry is consistent with sustainable and greener production; therefore, the aim of this paper was to evaluate the applicability of multiple linear regression (MLR), piecewise linear regression (PLR) and artificial neural network models (ANN) to the prediction of grape-skin compost’s physicochemical properties (moisture, dry matter, organic matter, ash content, carbon content, nitrogen content, C/N ratio, total colour change of compost samples, pH, conductivity, total dissolved solids and total colour change of compost extract samples) during in-vessel composting based on the initial composting conditions (air-flow rate, moisture content and day of sampling). Based on the coefficient of determination for prediction, the adjusted coefficient of determination for calibration, the root-mean-square error of prediction (RMSEP), the standard error of prediction (SEP), the ratio of prediction to deviation (RPD) and the ratio of the error range (RER), it can be concluded that all developed MLR and PLR models are acceptable for process screening. Furthermore, the ANN model developed for predicting moisture and dry-matter content can be used for quality control (RER >11). The obtained results show the great potential of multivariate modelling for analysis of the physicochemical properties of compost during composting, confirming the high applicability of modelling in greener production processes. Full article

Following the idea of a circular bioeconomy, the use of side streams as substitutes for cultivation media (components) in bioprocesses would mean an enormous economic and ecological advantage. Costly compounds in conventional media for the production of the triterpene squalene in thraustochytrids are the main carbon source and complex nitrogen sources. Among other side streams examined, extracts from the spent mycelium of the basidiomycete Pleurotus ostreatus were best-suited to acting as alternative nitrogen sources in cultivation media for thraustochytrids. The total nitrogen (3.76 ± 0.01 and 4.24 ± 0.04%, respectively) and protein (16.47 ± 0.06 and 18.57 ± 0.18%, respectively) contents of the fruiting body and mycelium were determined. The fungal cells were hydrolyzed and extracted to generate accessible nitrogen sources. Under preferred conditions, the extracts from the fruiting body and mycelium contained 73.63 ± 1.19 and 89.93 ± 7.54 mM of free amino groups, respectively. Cultivations of Schizochytrium sp. S31 on a medium using a mycelium extract as a complex nitrogen source showed decelerated growth but a similar squalene yield (123.79 ± 14.11 mg/L after 216 h) compared to a conventional medium (111.29 ± 19.96 mg/L, although improvable by additional complex nitrogen source). Full article

Here, we present an effective application of adaptive cooperative networks, namely assisting disables in navigating in a crowd in a pandemic or emergency situation. To achieve this, we model crowd movement and introduce a cooperative learning approach to enable cooperation and self-organization of the crowd members with impaired health or on wheelchairs to ensure their safe movement in the crowd. Here, it is assumed that the movement path and the varying locations of the other crowd members can be estimated by each agent. Therefore, the network nodes (agents) should continuously reorganize themselves by varying their speeds and distances from each other, from the surrounding walls, and from obstacles within a predefined limit. It is also demonstrated how the available wireless trackers such as AirTags can be used for this purpose. The model effectiveness is examined with respect to the real-time changes in environmental parameters and its efficacy is verified. Full article

Hydrogen creates water during combustion. Therefore, it is expected to be the most promising environmentally friendly energy alternative in the coming years. This study used extract liquid obtained from the waste nigella sativa generated by the black cumin oil industry. The performance of biological hydrogen manufacturing via dark fermentation was investigated in the fluidized bed reactor (FBR) and completely stirred tank reactor (CSTR) under the operation conditions of pH 5.0, 4.0, and 6.0 and a hydraulic retention time (HRT) of 36 and 24 h. The performance of hydrogen manufacturing was determined to be good under an organic loading ratio (OLR) of 6.66 g.nigella sativa extract/L and pH 4.0. According to these conditions, the maximum amount of hydrogen in CSTR and FBR was found to be 20.8 and 7.6 mL H₂/day, respectively. The operating process of the reactors displayed that a reduction in HRT augmented biohydrogen manufacturing. The work that used mixed culture found that the dominant microbial population at pH 4.0 involved Hydrogenimonas thermophila, Sulfurospirillum carboxydovorans, Sulfurospirillum cavolei, Sulfurospirillum alkalitolerans, and Thiofractor thiocaminus. No research on waste black cumin extract was found in biohydrogen studies, and it was determined that this substrate source is applicable for biological hydrogen manufacturing. Full article

Advancements in biological wastewater treatment with sustainable and circularity approaches have a wide scope of application. Biological wastewater treatment is widely used to remove/recover organic pollutants and nutrients from a diverse wastewater spectrum. However, conventional biological processes face challenges, such as low efficiency, high energy consumption, and the generation of excess sludge. To overcome these limitations, integrated strategies that combine biological treatment with other physical, chemical, or biological methods have been developed and applied in recent years. This review emphasizes the recent advances in integrated strategies for biological wastewater treatment, focusing on their mechanisms, benefits, challenges, and prospects. The review also discusses the potential applications of integrated strategies for diverse wastewater treatment towards green energy and resource recovery, along with low-carbon fuel production. Biological treatment methods, viz., bioremediation, electro-coagulation, electro-flocculation, electro-Fenton, advanced oxidation, electro-oxidation, bioelectrochemical systems, and photo-remediation, are summarized with respect to non-genetically modified metabolic reactions. Different conducting materials (CMs) play a significant role in mass/charge transfer metabolic processes and aid in enhancing fermentation rates. Carbon, metal, and nano-based CMs hybridization in different processes provide favorable conditions to the fermentative biocatalyst and trigger their activity towards overcoming the limitations of the conventional process. The emerging field of nanotechnology provides novel additional opportunities to surmount the constraints of conventional process for enhanced waste remediation and resource valorization. Holistically, integrated strategies are promising alternatives for improving the efficiency and effectiveness of biological wastewater treatment while also contributing to the circular economy and environmental protection. Full article

Cerebral palsy poses challenges in walking, necessitating ankle foot orthoses (AFOs) for stability. Gait analysis, particularly on slopes, is crucial for effective AFO assessment. The study aimed to compare the performance of commercially available AFOs with a new sports-specific AFO in children with hemiplegic cerebral palsy and to assess the effects of varying slopes on gait. Eighteen participants, aged 6–11, with hemiplegia, underwent gait analysis using GRAIL technology. Two AFO types were tested on slopes (uphill +10 deg, downhill −5 deg, level-ground). Kinematic, kinetic, and spatiotemporal parameters were analyzed. The new AFO contributed to significant changes in ankle dorsi-plantar-flexion, foot progression, and trunk and hip rotation during downhill walking. Additionally, the new AFO had varied effects on spatiotemporal gait parameters, with an increased stride length during downhill walking. Slope variations significantly influenced the kinematics and kinetics. This study provides valuable insights into AFO effectiveness and the impact of slopes on gait in hemiplegic cerebral palsy. The findings underscore the need for personalized interventions, considering environmental factors, and enhancing clinical and research approaches for improving mobility in cerebral palsy. Full article

Background: The lack of resources limits the treatment of craniomaxillofacial fractures (CMF) in low-income countries (LIC). Therefore, Barton bandages and/or interdental wiring are considered in these regions. Fracture reduction is maintained by permanent occlusion for 6 weeks, which often leads to limited compliance and dissatisfying results. The aim of this cadaver-based study is to evaluate the feasibility of the use of an external face fixator (EFF) for the treatment of CMF, its biomechanical values and to define the optimal pin insertion points and angles. Materials and Methods: An AO hand fixator was used. CMF of types Le Fort 1–3 with split fractures of the hard palate were treated with EFF on 13 anatomical specimens. Fractures were created using a chisel, and pins were placed in specific anatomical regions. The maximal pull-out force [N] of pins was analysed by a tensile force gauge, and Fmax of the mandibular pins was evaluated. Computer tomography scans were performed on the healthy, fractured and EFF-treated skulls. Results: The pull-out forces for the single pins were mandibular pins (n = 15, median 488.0 N), supraorbital pins (n = 15, median 455.0 N), zygomatic pins (n = 14, median 269.1 N), medial hard palate pins (n = 12, median 208.4 N) and lateral hard palate pins (n = 8, median 49.6 N). Conclusions: The results indicate that the operation technique is feasible, and the stability of the EFF is sufficient for maintaining the reduction. The required pins can safely be inserted into the described areas with good reduction results. Using EFF offers a feasible alternative to the non-surgical treatment of CMF in LIC. Full article

In this paper, we propose a dense multi-scale adaptive graph convolutional network (DMA-GCN) method for automatic segmentation of the knee joint cartilage from MR images. Under the multi-atlas setting, the suggested approach exhibits several novelties, as described in the following. First, our models integrate both local-level and global-level learning simultaneously. The local learning task aggregates spatial contextual information from aligned spatial neighborhoods of nodes, at multiple scales, while global learning explores pairwise affinities between nodes, located globally at different positions in the image. We propose two different structures of building models, whereby the local and global convolutional units are combined by following an alternating or a sequential manner. Secondly, based on the previous models, we develop the DMA-GCN network, by utilizing a densely connected architecture with residual skip connections. This is a deeper GCN structure, expanded over different block layers, thus being capable of providing more expressive node feature representations. Third, all units pertaining to the overall network are equipped with their individual adaptive graph learning mechanism, which allows the graph structures to be automatically learned during training. The proposed cartilage segmentation method is evaluated on the entire publicly available Osteoarthritis Initiative (OAI) cohort. To this end, we have devised a thorough experimental setup, with the goal of investigating the effect of several factors of our approach on the classification rates. Furthermore, we present exhaustive comparative results, considering traditional existing methods, six deep learning segmentation methods, and seven graph-based convolution methods, including the currently most representative models from this field. The obtained results demonstrate that the DMA-GCN outperforms all competing methods across all evaluation measures, providing $DSC=95.71\%$ and $DSC=94.02\%$ for the segmentation of femoral and tibial cartilage, respectively. Full article

Background: People with multiple sclerosis (PwMS) frequently present both cognitive and motor impairments, so it is reasonable to assume they may have difficulties in executing dual-tasks (DT). The aim of the present study is to identify novel technology-based parameters to assess cognitive-motor interference (CMI) in PwMS. In particular, we focused on the definition of dual-task cost (DTC) measures using wearable and portable tools such as insoles and mobile apps. Methods: All participants underwent a verbal fluency task (cognitive single-task, ST), a motor ST of walking, and a combination of these tasks (DT). Number of words uttered in the cognitive ST and steps recorded by insoles were used to calculate the motor and cognitive DTC. Results: The number of steps strongly correlated with the walked meters for both single- (r = 0.88, p < 0.05) and dual- (r = 0.91, p < 0.05) tasks. Motor but not cognitive performances significantly worsened during DT. Over the cognitive ST and DT, the number of pronounced words progressively decreased, probably due to the activation of different cognitive processes. Cognitive efforts could be the cause of cognitive task prioritization. Conclusions: Our findings promote the use of low-cost devices to assess CMI easily in the clinical context and to detect ecologically valid DT impairments. Full article

EEG, which can provide brain alteration information via recording the electrical activity of neurons in the cerebral cortex, has been widely used in neurophysiology. However, conventional wet electrodes in EEG monitoring typically suffer from inherent limitations, including the requirement of skin pretreatment, the risk of superficial skin infections, and signal performance deterioration that may occur over time due to the air drying of the conductive gel. Although the emergence of dry electrodes has overcome these shortcomings, their electrode–skin contact impedance is significantly high and unstable, especially in hair-covered areas. To address the above problems, an active claw-shaped dry electrode is designed, moving from electrode morphological design, slurry preparation, and coating to active electrode circuit design. The active claw-shaped dry electrode, which consists of a claw-shaped electrode and active electrode circuit, is dedicated to offering a flexible solution for elevating electrode fittings on the scalp in hair-covered areas, reducing electrode–skin contact impedance and thus improving the quality of the acquired EEG signal. The performance of the proposed electrodes was verified by impedance, active electrode circuit, eyes open-closed, steady-state visually evoked potential (SSVEP), and anti-interference tests, based on EEG signal acquisition. Experimental results show that the proposed claw-shaped electrodes (without active circuit) can offer a better fit between the scalp and electrodes, with a low electrode–skin contact impedance (18.62 KΩ@1 Hz in the hairless region and 122.15 KΩ@1 Hz in the hair-covered region). In addition, with the active circuit, the signal-to-noise ratio (SNR) of the acquiring EEG signal was improved and power frequency interference was restrained, therefore, the proposed electrodes can yield an EEG signal quality comparable to wet electrodes. Full article

The gait recognition of exoskeletons includes motion recognition and gait phase recognition under various road conditions. The recognition of gait phase is a prerequisite for predicting exoskeleton assistance time. The estimation of real-time assistance time is crucial for the safety and accurate control of lower-limb exoskeletons. To solve the problem of predicting exoskeleton assistance time, this paper proposes a gait recognition model based on inertial measurement units that combines the real-time motion state recognition of support vector machines and phase recognition of long short-term memory networks. A recognition validation experiment was conducted on 30 subjects to determine the reliability of the gait recognition model. The results showed that the accuracy of motion state and gait phase were 99.98% and 98.26%, respectively. Based on the proposed SVM-LSTM gait model, exoskeleton assistance time was predicted. A test was conducted on 10 subjects, and the results showed that using assistive therapy based on exercise status and gait stage can significantly improve gait movement and reduce metabolic costs by an average of more than 10%. Full article

Mandibular fractures are very common in maxillofacial trauma surgery. While previous studies have focused on possible risk factors related to post-operative complications, none have tried to identify pre-existing conditions that may increase the risk of mandibular fractures. We hypothesized, through clinical observation, that anatomical conditions involving poor dental contacts, such as malocclusions, may increase the risk of mandibular fractures. This work was subdivided into two parts. In the first part, Digital Imaging and Communications in Medicine (DICOM) data of four healthy patients characterized by different dentoskeletal occlusions (class I, class II, class III, and anterior open bite) have been used to develop four finite element models (FEMs) that accurately reproduce human bone structure. A vertical and lateral impact have been simulated at increasing speed on each model, analyzing the force distribution within the mandibular bone. Both vertical and lateral impact showed higher level of stress at the impact point and in the condylar area in models characterized by malocclusion. Specifically, the class III and the open bite models, at the same speed of impact, had higher values for a longer period, reaching critical stress levels that are correlated with mandibular fracture, while normal occlusion seems to be a protective condition. In the second part of this study, the engineering results were validated through the comparison with a sample of patients previously treated for mandibular fracture. Data from 223 mandibular fractures, due to low-energy injuries, were retrospectively collected to evaluate a possible correlation between pre-existing malocclusion and fracture patterns, considering grade of displacement, numbers of foci, and associated CFI score. Patients were classified, according to their occlusion, into Class I, Class II, Class III, and anterior open bite or poor occlusal contact (POC). Class I patients showed lower frequencies of fracture than class II, III, and open bite or POC patients. Class I was associated with displaced fractures in 16.1% of cases, class II in 47.1%, class III in 48.8% and open bite/POC in 65.2% of cases (p-value < 0.0001). In class I patients we observed a single non-displaced fracture in 51.6% of cases, compared to 12.9% of Class II, 19.5% of Class III and 22.7% of the open bite/POC group. Our analysis shows that class I appears to better dissipate forces applied on the mandible in low-energy injuries. A higher number of dental contacts showed a lower rate of multifocal and displaced fractures, mitigating the effect of direct forces onto the bone. The correlation between clinical data and virtual simulation on FEM models seems to point out that virtual simulation successfully predicts fracture patterns and risk of association with different type of occlusion. Better knowledge of biomechanics and force dissipation on the human body may lead to the development of more effective safety devices, and help select patients to plan medical, orthodontic/dental, and/or surgical intervention to prevent injuries. Full article

Ophthalmological services face global inadequacies, especially in low- and middle-income countries, which are marked by a shortage of practitioners and equipment. This study employed a portable slit lamp microscope with video capabilities and cloud storage for more equitable global diagnostic resource distribution. To enhance accessibility and quality of care, this study targets corneal opacity, which is a global cause of blindness. This study has two purposes. The first is to detect corneal opacity from videos in which the anterior segment of the eye is captured. The other is to develop an AI pipeline to detect corneal opacities. First, we extracted image frames from videos and processed them using a convolutional neural network (CNN) model. Second, we manually annotated the images to extract only the corneal margins, adjusted the contrast with CLAHE, and processed them using the CNN model. Finally, we performed semantic segmentation of the cornea using annotated data. The results showed an accuracy of 0.8 for image frames and 0.96 for corneal margins. Dice and IoU achieved a score of 0.94 for semantic segmentation of the corneal margins. Although corneal opacity detection from video frames seemed challenging in the early stages of this study, manual annotation, corneal extraction, and CLAHE contrast adjustment significantly improved accuracy. The incorporation of manual annotation into the AI pipeline, through semantic segmentation, facilitated high accuracy in detecting corneal opacity. Full article

Narrow-diameter implants (NDI) serve as a solution for treating limited bone volume in the anterior mandible. This study aimed to evaluate the one-year clinical outcomes of various NDIs in the mandibular incisor area after immediate loading in partially edentulous patients. This single-center, prospective, single-blinded, randomized controlled trial study included 21 patients, with 7 patients in each of the following groups: control (BLT NC SLActive^®; Straumann), experimental group 1 (CMI IS-III Active^® S-Narrow; Neobiotech), and experimental group 2 (CMI IS-III Active^® Narrow; Neobiotech). Using full digital flow, two fixtures were placed in each patient and immediately provisionalized on the day of surgery. Evaluations encompassed periapical radiographs, implant stability quotient (ISQ), implant stability test (IST) readings, per-implant soft tissue health, patient satisfaction surveys, and esthetic score assessments. Definitive prostheses were delivered twelve weeks post-surgery (CRiS, number: KCT0007300). Following exclusions due to low stability values (n = 2), fixture failure (n = 5), and voluntary withdrawal (n = 1), the implant success rate for patients completing all clinical protocols stood at 100%. The resulting patient failure rates in the control, experimental group 1, and experimental group 2 were 50.0%, 42.9%, and 14.3%, respectively. There were no significant differences between the groups in terms of marginal bone loss, soft tissue health, patient satisfaction, and esthetic scores. Narrow implants showed superior clinical outcomes, followed by S-Narrow and Straumann implants. Calculated one-year survival rates at the implant level were 66.7% for the control group, 85.7% for experimental group 1, and 100% for experimental group 2. All three types of NDIs showed acceptable clinical and radiographic results during the year-long observation period. Full article

Fuchs endothelial corneal dystrophy (FECD) is a complex genetic disorder characterized by the slow and progressive degeneration of corneal endothelial cells. Thus, it may result in corneal endothelial decompensation and irreversible corneal edema. Moreover, FECD is associated with alterations in all corneal layers, such as thickening of the Descemet membrane, stromal scarring, subepithelial fibrosis, and the formation of epithelial bullae. Hence, anterior segment imaging devices that enable precise measurement of functional and anatomical changes in the cornea are essential for the management of FECD. In this review, the authors will introduce studies on the application of various imaging modalities, such as anterior segment optical coherence tomography, Scheimpflug corneal tomography, specular microscopy, in vitro confocal microscopy, and retroillumination photography, in the diagnosis and monitoring of FECD and discuss the results of these studies. The application of novel technologies, including image processing technology and artificial intelligence, that are expected to further enhance the accuracy, precision, and speed of the imaging technologies will also be discussed. Full article

Medical imaging serves as a crucial tool in current cancer diagnosis. However, the quality of medical images is often compromised to minimize the potential risks associated with patient image acquisition. Computer-aided diagnosis systems have made significant advancements in recent years. These systems utilize computer algorithms to identify abnormal features in medical images, assisting radiologists in improving diagnostic accuracy and achieving consistency in image and disease interpretation. Importantly, the quality of medical images, as the target data, determines the achievable level of performance by artificial intelligence algorithms. However, the pixel value range of medical images differs from that of the digital images typically processed via artificial intelligence algorithms, and blindly incorporating such data for training can result in suboptimal algorithm performance. In this study, we propose a medical image-enhancement scheme that integrates generic digital image processing and medical image processing modules. This scheme aims to enhance medical image data by endowing them with high-contrast and smooth characteristics. We conducted experimental testing to demonstrate the effectiveness of this scheme in improving the performance of a medical image segmentation algorithm. Full article

Computational fluid dynamics is intensively used to deepen our understanding of aneurysm growth and rupture in an attempt to support physicians during therapy planning. Numerous studies assumed fully rigid vessel walls in their simulations, whose sole haemodynamics may fail to provide a satisfactory criterion for rupture risk assessment. Moreover, direct in vivo observations of intracranial aneurysm pulsation were recently reported, encouraging the development of fluid–structure interaction for their modelling and for new assessments. In this work, we describe a new fluid–structure interaction functional setting for the careful evaluation of different aneurysm shapes. The configurations consist of three real aneurysm domes positioned on a toroidal channel. All geometric features, employed meshes, flow quantities, comparisons with the rigid wall model and corresponding plots are provided for the sake of reproducibility. The results emphasise the alteration of flow patterns and haemodynamic descriptors when wall deformations were taken into account compared with a standard rigid wall approach, thereby underlining the impact of fluid–structure interaction modelling. Full article

Microcarrier-based cell culture is a commonly used method to facilitate the growth of anchorage-dependent cells like MA 104 for antigen manufacturing. However, conventionally, static cell culture is employed for cell propagation before seeding the production bioreactor with microcarriers (MCs). This study demonstrates the effective replacement of the conventional method by serial subculturing on MCs with in situ cell detachment under optimal conditions in closed culture units. This study proves that MA 104 can be subcultured at least five times on Cytodex 1 MC without the need for separating cells and MC after cell harvest. Process parameters impacting cell growth were studied post in situ cell detachment in a scaled-down model. Optimization, using augmented Design of Experiments (DoE) combined with hybrid modeling, facilitated rapid screening of the design space for critical process parameters (CPPs). Optimized conditions included an inoculation density of >16 cells/bead, 3.5–4.5 g/L of Cytodex 1, and a controlled agitation speed, starting at N_js (minimum agitation speed) for the first day with a maximum increase of 25% thereafter. With these design spaces for CPPs, a cell density of 2.6 ± 0.5 × 10⁶ cells/mL was achieved after five days. This refined bioprocess methodology offers a reliable and efficient approach for seed training in stirred tank reactors, which is particularly beneficial for viral vaccine production. Full article