Direct pulp capping (DPC) aims to preserve the vitality of the dental pulp by placing a protective biocompatible material over the exposed pulp tissue to facilitate healing. There are several calcium-silicate materials that have been designed to promote mineralization and the regulation of inflammation. These have strong potential for the repair and regeneration of dental pulp. Among them, Biodentine (BD) and EndoSequence RRM Putty (ES) have been found to promote in vitro and in vivo mineralization while minimizing some of the limitations of the first-generation calcium-silicate-based materials. Theracal-LC (TLC), a light-cured, resin-modified calcium-silicate material, is a newer product with potential to improve the clinical outcomes of DPC, but existing studies have reported conflicting findings regarding its biocompatibility and ability to support pulpal healing in direct contact with the pulp. A comprehensive assessment of the biocompatibility and pulpal protection provided by these three capping materials has not yet been performed.

Asymmetries are common during squats following anterior cruciate ligament reconstruction (ACLR). This study examined interlimb loading differences between squat phases at 6 months post-ACLR. Thirty-five participants performed bodyweight squats at self-selected speed and were analyzed using 3D motion capture. Vertical ground reaction force impulse (vGRFi), external knee flexion moment impulse (KFMi) and hip-to-knee flexion moment impulse ratio (HKRi) were calculated, along with interlimb ratios (ILR). Squat phase durations were also recorded. Paired t-tests and ANCOVA (controlling for time) were used to compare biomechanical variables across squat phases. Greater asymmetry was observed during ascent for vGRFi ILR ( = 0.045), KFMi ILR ( < 0.001) and HKRi ILR ( = 0.006). The ascent phase was faster than descent ( = 0.036). After adjusting for time, phase-related differences in ILRs were no longer significant. These findings suggest that greater limb and knee-specific loading asymmetries occur during the ascent phase of squats but may be influenced by movement speed. Importantly, significant knee-specific loading asymmetries persisted regardless of squat phase. At 6 months post-ACLR, addressing neuromuscular control and movement speed during rehabilitation may help reduce biomechanical imbalances during closed kinetic chain exercises.

Although upper limb impairment is one of the most common deficits post-stroke and contributes substantially to diminished functional independence, many survivors receive low dosages of upper limb task training in the inpatient setting. This study evaluates the feasibility and user experience of a progressive-challenge cued activity program, delivered via wearable technology, to promote upper limb activity in an inpatient rehabilitation facility (IRF) post-stroke. Participants ( = 30) wore our wearable system , which provided vibrotactile cues to prompt activity in the more-involved arm during idle time. Compliance with the program was high (94% in the IRF), and the system successfully prompted increased activity, as evidenced by significantly higher post-cue response rates compared to pre-cue activity rates (mean difference = 35.1%, (28) = 9.398, < 0.001). User experience was positive, with participants reporting high usability, satisfaction, and motivation. Follow-up data collected in unstructured home settings ( = 23) demonstrated continued high compliance (96%) and favorable user experience. These findings suggest that and its cued activity program can effectively convert idle time into therapeutic activity while minimizing caregiver burden. Future research should focus on enhancing user engagement and evaluating the clinical efficacy of this approach in improving functional outcomes post-stroke.

This study investigates the impact of subglottic stenosis (SGS) on voice production using a subject-specific laryngeal and airway model. Direct numerical simulations of fluid-structure-acoustic interaction were employed to analyze glottal flow dynamics, vocal fold vibration, and acoustics under realistic conditions. The model accurately captured key physiological parameters, including the glottal flow rate, vocal fold vibration patterns, and the first four formant frequencies. Simulations of varying SGS severity revealed that up to 75% stenosis, vocal function remains largely unaffected. However, at 90% severity, significant changes in glottal flow and acoustics were observed, with vocal fold vibration remaining stable. At 96%, severe reductions in glottal flow and acoustics, along with marked changes in vocal fold dynamics, were detected. Flow resistance, the ratio of glottal to stenosis area, and pressure drop across the vocal folds were identified as critical factors influencing these changes. The use of anatomically realistic airway and vocal fold geometries revealed that while anatomical variations minimally affect voice production at lower stenosis grades, they become critical at severe stenosis levels (>90%), particularly in capturing distinct anterior-posterior opening patterns and focused jet effects that alter glottal dynamics. These findings suggest that while simplified models suffice for analyzing mild to moderate stenosis, patient-specific geometric details are essential for accurate prediction of vocal fold dynamics in severe cases.

Degenerative cervical myelopathy (DCM) is characterized by progressive neurological dysfunction, yet the contribution of intramedullary stress and strain during neck motion remains unclear. This study used patient-specific finite element models (FEMs) of the cervical spine and spinal cord to examine the relationship between spinal cord biomechanics and neurological dysfunction. Twenty DCM patients (mean age 62.7 ± 11.6 years; thirteen females) underwent pre-surgical MRI-based modeling to quantify von Mises stress and maximum principal strains at the level of maximum spinal cord compression during simulated neck flexion and extension. Pre-surgical functional assessments included hand sensation, dexterity, and balance. During flexion, the mean intramedullary stress and strain at the level of maximum compression were 7.6 ± 3.7 kPa and 4.3 ± 2.0%, respectively. Increased intramedullary strain during flexion correlated with decreased right-hand sensation (r = -0.58, = 0.014), impaired right-hand dexterity (r = -0.50, = 0.048), and prolonged dexterity time (r = 0.52, = 0.039). Similar correlations were observed with intramedullary stress. Patients with severe DCM exhibited significantly greater stress during flexion than those with mild/moderate disease ( = 0.03). These findings underscore the impact of dynamic spinal cord biomechanics on neurological dysfunction and support their potential utility in improving DCM diagnosis and management.

Hematoma expansion (HE) is an independent predictor of poor outcomes and a modifiable treatment target in intracerebral hemorrhage (ICH). Evaluating HE in large datasets requires segmentation of hematomas on admission and follow-up CT scans, a process that is time-consuming and labor-intensive in large-scale studies. Automated segmentation of hematomas can expedite this process; however, cumulative errors from segmentation on admission and follow-up scans can hamper accurate HE classification. In this study, we combined a tandem deep-learning classification model with automated segmentation to generate probability measures for false HE classifications. With this strategy, we can limit expert review of automated hematoma segmentations to a subset of the dataset, tailored to the research team's preferred sensitivity or specificity thresholds and their tolerance for false-positive versus false-negative results. We utilized three separate multicentric cohorts for cross-validation/training, internal testing, and external validation ( = 2261) to develop and test a pipeline for automated hematoma segmentation and to generate ground truth binary HE annotations (≥3, ≥6, ≥9, and ≥12.5 mL). Applying a 95% sensitivity threshold for HE classification showed a practical and efficient strategy for HE annotation in large ICH datasets. This threshold excluded 47-88% of test-negative predictions from expert review of automated segmentations for different HE definitions, with less than 2% false-negative misclassification in both internal and external validation cohorts. Our pipeline offers a time-efficient and optimizable method for generating ground truth HE classifications in large ICH datasets, reducing the burden of expert review of automated hematoma segmentations while minimizing misclassification rate.

The simulation of gas flow in the bronchial tree using computational fluid dynamics (CFD) has become a useful tool for the analysis of gas flow mechanics, structural deformation, ventilation, and particle deposition for drug delivery during spontaneous and assisted breathing. CFD allows for new hypotheses to be tested , and detailed results generated without performing expensive experimental procedures that could be potentially harmful to patients. Such computational techniques are also useful for analyzing structure-function relationships in healthy and diseased lungs, assessing regional ventilation at various time points over the course of clinical treatment, or elucidating the changes in airflow patterns over the life span. CFD has also allowed for the development and use of image-based (i.e., patient-specific) models of three-dimensional (3D) airway trees with realistic boundary conditions to achieve more meaningful and personalized data that may be useful for planning effective treatment protocols. This focused review will present a summary of the techniques used in generating realistic 3D airway tree models, the limitations of such models, and the methodologies used for CFD airflow simulation. We will discuss mathematical and image-based geometric models, as well as the various boundary conditions that may be imposed on these geometric models. The results from simulations utilizing mathematical and image-based geometric models of the airway tree will also be discussed in terms of similarities to actual gas flow in the human lung.

Postural imbalance is a leading cause of injury in older adults. Our study investigated the effectiveness of virtual reality (VR)-based interventions on balance ability in this population. Here, we examined 21 older, healthy adults (75.8 ± 5.2 years old). Participants performed 6 weeks of balance training, twice per week for 30 min; the experimental group donned an Oculus VR headset during the training while control participants did not. To assess balance ability, a force platform measured displacement of the center of pressure (COP) during quiet standing in double-leg, tandem, and single-leg stances with eyes closed pre- and post-assessment. COP measurements included mediolateral (ML) and anterior-posterior (AP) directions for root mean square (RMS), peak-to-peak displacement (MAXD), total excursion (TE), and 95% confidence area ellipse (AE) parameters. Post-training assessments showed improvements (significant decreases) in the COP parameters. Control group COP parameters improved in various stances ranging from a 3% to 40% decrease on average. The VR group improved MAXD, TE, and 95% AE ranging from a 5% to 47% decrease, on average, across various stances post-compared to pre-training. VR-based exercise training programs may encourage older adults to engage in mobility exercises, leading to a reduced risk of falls or injuries.

Recurrent bleeding in the synovial joint, such as the knee, can give rise to chronic synovitis and degenerative arthritis, which are major causes of morbidity. Whereas chronic arthropathy affects one-fifth of hemophiliacs, conditions such as rheumatoid arthritis (RA), periarticular and articular fractures, osteochondral autograft transplantation surgery, and anterior cruciate ligament (ACL) injury are also associated with joint bleeding. Synovial joint trauma is associated with inflammation, acute pain, bloody joint effusion, and knee instability. Clinically, some physicians have advocated for blood aspiration from the joint post-injury to mitigate the harmful effects of bleeding. Despite the significant potential clinical impact of joint bleeding, the mechanism(s) by which joint bleeding, acute or microbleeds, leads to deleterious changes to the synovial joint remains understudied. This review will address the impact of blood on synovial joint tissues observed from in vitro and in vivo studies. While the deleterious effects of blood on cartilage and synovium are well-described, there are much fewer reports describing the negative effects of blood on the meniscus, cruciate ligaments, and subchondral bone. Based on our studies of blood in co-culture with chondrocytes/cartilage, we raise the possibility that ferroptosis, an iron-dependent, nonapoptotic form of regulated cell death, plays a contributing role in mediating hemophilic arthropathy (HA) and may represent a therapeutic target in reducing the negative impact of joint bleeds.

This paper describes the principles behind the radio-frequency (RF) sensing of bacterial biofilms in pipes and heat exchangers in a dairy processing plant using an electromagnetic simulation. Biofilm formation in dairy processing plants is a common issue where the absence of timely detection and subsequent cleaning can cause serious illness. Biofilms are known for causing health issues and cleaning requires a large volume of water and harsh chemicals. In this work, milk transportation pipes are considered circular waveguides, and pasteurizers/heat exchangers are considered resonant cavities. Simulations were carried out using the CST studio suite high-frequency solver to determine the effectiveness of the real-time RF sensing. The respective dielectric constants and loss tangents were applied to milk and biofilm. In our simulation, it was observed that a 1 μm thick layer of biofilm in a milk-filled pipe shifted the reflection coefficient of a 10.16 cm diameter stainless steel circular waveguide from 0.229 GHz to 0.19 GHz. Further sensitivity analysis revealed a shift in frequency from 0.8 GHz to 1.2 GHz for a film thickness of 5 μm to 10 μm with the highest wave reflection (S11) peak of ≈-120 dB for a 6 μm thick biofilm. A dielectric patch antenna to launch the waves into the waveguide through a dielectric window was also designed and simulated. Simulation using the antenna demonstrated a similar S11 response, where a shift in reflection coefficient from 0.229 GHz to 0.19 GHz was observed for a 1 μm thick biofilm. For the case of the resonant cavity, the same antenna approach was used to excite the modes in a 0.751 m × 0.321 m × 170 m rectangular cavity with heat exchange fins and filled with milk and biofilm. The simulated resonance frequency shifted from 1.52 GHz to 1.54 GHz, for a film thickness varying from 1 μm to 10 μm. This result demonstrated the sensitivity of the microwave detection method. Overall, these results suggest that microwave sensing has promise in the rapid, non-invasive, and real-time detection of biofilm formation in dairy processing plants.

Understanding metabolic cost through biomechanical data, including ground reaction forces (GRFs) and joint moments, is vital for health, sports, and rehabilitation. The long stabilization time (2-5 min) of indirect calorimetry poses challenges in prolonged tests. This study investigated using artificial neural networks (ANNs) to predict metabolic costs from the GRF and joint moment time series. Data from 20 participants collected over 270 walking trials, including the GRF and joint moments, formed a detailed dataset. Two ANN models were crafted, net for the GRF and net for joint moments, and both underwent training, validation, and testing to validate their predictive accuracy for metabolic cost. Net (six hidden layers, two input delays) showed significant correlations: 0.963 (training), 0.927 (validation), 0.883 (testing), < 0.001. Net (three hidden layers, one input delay) had correlations of 0.920 (training), 0.956 (validation), 0.874 (testing), < 0.001. The models' low mean squared errors reflect their precision. Using Partial Dependence Plots, we demonstrated how gait cycle phases affect metabolic cost predictions, pinpointing key phases. Our findings show that the GRF and joint moments data can accurately predict metabolic costs via ANN models, with net being notably consistent. This emphasizes ANNs' role in biomechanics as a crucial method for estimating metabolic costs, impacting sports science, rehabilitation, assistive technology development, and fostering personalized advancements.

Each year, over 25% of adults aged sixty-five years old or older suffer a fall, and three million are treated for fall-related injuries due to lack of balance. Here, we aimed to investigate how virtual reality (VR)-based training affects balance performance and confidence in older adults. To accomplish this goal, we studied 21 healthy, older individuals between 60 and 85 years old, both pre- and post-training (6 weeks of training, twice per week (or 12 sessions)). The VR group donned an Oculus VR headset and consisted of nine participants (aged 75.9 ± 3.7 years old), while the control group (aged 75.1 ± 6.7 years old) performed training without a headset and consisted of eight participants that completed our study. To assess balance ability, we utilized the Balance Error Scoring System (BESS) and the Timed Up and Go (TUG) test. To assess balance confidence, we implemented the Activities-Specific Balance Confidence (ABC) Scale and, to assess fear of falling, the Tinetti Falls Efficacy Scale (FES). Further, we assessed depression (via the Geriatric Depression Scale (GDS)) and cognitive ability (via the Mini-Mental State Examination (MMSE)). The post-training results showed improvements in balance ability for both the VR and control groups, as well as changes in the relationship between balance confidence and balance ability for the VR group only. Further, improvements in cognitive ability were seen in the control group. This study is an indication that older individuals' balance ability may benefit from several weeks of targeted training.

The disruption of the blood-brain barrier (BBB) in Alzheimer's Disease (AD) is largely influenced by amyloid beta (Aβ). In this study, we developed a high-throughput microfluidic BBB model devoid of a physical membrane, featuring endothelial cells interacting with an extracellular matrix (ECM). This paper focuses on the impact of varying concentrations of Aβ oligomers on BBB dysfunction by treating them in the luminal. Our findings reveal a pronounced accumulation of Aβ oligomers at the BBB, resulting in the disruption of tight junctions and subsequent leakage evidenced by a barrier integrity assay. Additionally, cytotoxicity assessments indicate a concentration-dependent increase in cell death in response to Aβ oligomers (LC50 ~ 1 μM). This study underscores the utility of our membrane-free vascular chip in elucidating the dysfunction induced by Aβ with respect to the BBB.

Cardiomyocyte hypertrophy, characterized by an increase in cell size, is associated with various cardiovascular diseases driven by factors including hypertension, myocardial infarction, and valve dysfunction. In vitro primary cardiomyocyte culture models have yielded numerous insights into the intrinsic and extrinsic mechanisms driving hypertrophic growth. However, due to limitations in current approaches, the dynamics of cardiomyocyte hypertrophic responses remain poorly characterized. In this study, we evaluate the application of the Holomonitor M4 digital holographic imaging microscope to track dynamic changes in cardiomyocyte surface area and volume in response to norepinephrine treatment, a model hypertrophic stimulus. The Holomonitor M4 permits non-invasive, label-free imaging of three-dimensional changes in cell morphology with minimal phototoxicity, thus enabling long-term imaging studies. Untreated and norepinephrine-stimulated primary neonatal rat cardiomyocytes were live-imaged on the Holomonitor M4, which was followed by image segmentation and single-cell tracking using the HOLOMONITOR App Suite software version 4.0.1.546. The 24 h treatment of cultured cardiomyocytes with norepinephrine increased cardiomyocyte spreading and optical volume as expected, validating the reliability of the approach. Single-cell tracking of both cardiomyocyte surface area and three-dimensional optical volume revealed dynamic increases in these parameters throughout the 24 h imaging period, demonstrating the potential of this technology to explore cardiomyocyte hypertrophic responses with greater temporal resolution; however, technological limitations were also observed and should be considered in the experimental design and interpretation of results. Overall, leveraging the unique advantages of the Holomonitor M4 digital holographic imaging system has the potential to empower future work towards understanding the molecular and cellular mechanisms underlying cardiomyocyte hypertrophy with enhanced temporal clarity.

Bacterial infections are the second-leading cause of death, globally. The prevalence of antibacterial resistance has kept the demand strong for the development of new and potent drug candidates. It has been demonstrated that Src protein tyrosine kinases (TKs) play an important role in the regulation of inflammatory responses to tissue injury, which can trigger the onset of several severe diseases. We carried out a search for novel Src protein TK inhibitors, commencing from reported highly potent anti-bacterial compounds obtained using the Mannich reaction, using a combination of e-pharmacophore modeling, virtual screening, ensemble docking, and core hopping. The top-scoring compounds from ligand-based virtual screening were modified using protein structure-based design approaches and their binding to the Src homology-2 domain of p56 TK was predicted using ensemble molecular docking. We have prepared a database of 202 small molecules and have identified 6 novel top hits that can be subjected to further investigation. We have also performed ADMET property prediction for the hit compounds. This combined computer-aided drug design approach can serve as a starting point for identifying novel TK inhibitors that could be further subjected to studies and validation of antimicrobial activity.

Radiomics involves the extraction of information from medical images that are not visible to the human eye. There is evidence that these features can be used for treatment stratification and outcome prediction. However, there is much discussion about the reproducibility of results between different studies. This paper studies the reproducibility of CT texture features used in radiomics, comparing two feature extraction implementations, namely the MATLAB toolkit and Pyradiomics, when applied to independent datasets of CT scans of patients: (i) the open access RIDER dataset containing a set of repeat CT scans taken 15 min apart for 31 patients (RIDER Scan 1 and Scan 2, respectively) treated for lung cancer; and (ii) the open access HN1 dataset containing 137 patients treated for head and neck cancer. Gross tumor volume (GTV), manually outlined by an experienced observer available on both datasets, was used. The 43 common radiomics features available in MATLAB and Pyradiomics were calculated using two intensity-level quantization methods with and without an intensity threshold. Cases were ranked for each feature for all combinations of quantization parameters, and the Spearman's rank coefficient, , calculated. Reproducibility was defined when a highly correlated feature in the RIDER dataset also correlated highly in the HN1 dataset, and vice versa. A total of 29 out of the 43 reported stable features were found to be highly reproducible between MATLAB and Pyradiomics implementations, having a consistently high correlation in rank ordering for RIDER Scan 1 and RIDER Scan 2 ( > 0.8). 18/43 reported features were common in the RIDER and HN1 datasets, suggesting they may be agnostic to disease site. Useful radiomics features should be selected based on reproducibility. This study identified a set of features that meet this requirement and validated the methodology for evaluating reproducibility between datasets.

A computational neuromuscular control system that generates lung pressure and three intrinsic laryngeal muscle activations (cricothyroid, thyroarytenoid, and lateral cricoarytenoid) to control the vocal source was developed. In the current study, , a biophysical computational model of the vocal system was used as the physical plant. In the , a three-mass vocal fold model was used to simulate self-sustained vocal fold oscillation. A constant/ǝ/vowel was used for the vocal tract shape. The trachea was modeled after MRI measurements. The neuromuscular control system generates control parameters to achieve four acoustic targets (fundamental frequency, sound pressure level, normalized spectral centroid, and signal-to-noise ratio) and four somatosensory targets (vocal fold length, and longitudinal fiber stress in the three vocal fold layers). The deep-learning-based control system comprises one acoustic feedforward controller and two feedback (acoustic and somatosensory) controllers. Fifty thousand steady speech signals were generated using the for training the control system. The results demonstrated that the control system was able to generate the lung pressure and the three muscle activations such that the four acoustic and four somatosensory targets were reached with high accuracy. After training, the motor command corrections from the feedback controllers were minimal compared to the feedforward controller except for thyroarytenoid muscle activation.

Gracilioether M () and 11,12-dihydrogracilioether M (), two polyketides with a [2(5H)-furanylidene]ethanoate moiety, along with known plakortone G () and its new naturally occurring derivative 9,10-dihydroplakortone G (), were isolated from the Caribbean marine sponge . The structures and absolute configuration of , , and were characterized by analysis of HRESIMS and NMR spectroscopic data, chemical derivatization, and side-by-side comparisons with published NMR data of related analogs. Compounds and and a mixture of and were evaluated for cytotoxicity against MCF-7 human breast cancer cells. In addition, the in vitro antiplasmodial activity against of these compounds was scrutinized using a drug luminescence assay.

This study pursued two objectives: (1) to determine the potential association between listener ( = 51) judgments of 20 male tracheoesophageal speaker samples for two auditory-perceptual dimensions of voice, overall severity (OS) and listener comfort (LC); and (2) to assess the temporal and spectral acoustic correlates for these auditory-perceptual dimensions.

Stress is widely recognized as a major contributor to a variety of health issues. Stress prediction using biosignal data recorded by wearables is a key area of study in mobile sensing research because real-time stress prediction can enable digital interventions to immediately react at the onset of stress, helping to avoid many psychological and physiological symptoms such as heart rhythm irregularities. Electrodermal activity (EDA) is often used to measure stress. However, major challenges with the prediction of stress using machine learning include the subjectivity and sparseness of the labels, a large feature space, relatively few labels, and a complex nonlinear and subjective relationship between the features and outcomes. To tackle these issues, we examined the use of model personalization: training a separate stress prediction model for each user. To allow the neural network to learn the temporal dynamics of each individual's baseline biosignal patterns, thus enabling personalization with very few labels, we pre-trained a one-dimensional convolutional neural network (1D CNN) using self-supervised learning (SSL). We evaluated our method using the Wearable Stress and Affect Detection(WESAD) dataset. We fine-tuned the pre-trained networks to the stress-prediction task and compared against equivalent models without any self-supervised pre-training. We discovered that embeddings learned using our pre-training method outperformed the supervised baselines with significantly fewer labeled data points: the models trained with SSL required less than 30% of the labels to reach equivalent performance without personalized SSL. This personalized learning method can enable precision health systems that are tailored to each subject and require few annotations by the end user, thus allowing for the mobile sensing of increasingly complex, heterogeneous, and subjective outcomes such as stress.

In robotic bin-picking applications, autonomous robot action is guided by a perception system integrated with the robot. Unfortunately, many perception systems output data contaminated by spurious points that have no correspondence to the real physical objects. Such spurious points in 3D data are the outliers that may spoil obstacle avoidance planning executed by the robot controller and impede the segmentation of individual parts in the bin. Thus, they need to be removed. Many outlier removal procedures have been proposed that work very well on unorganized 3D point clouds acquired for different, mostly outdoor, scenarios, but these usually do not transfer well to the manufacturing domain. This paper presents a new filtering technique specifically designed to deal with the organized 3D point cloud acquired from a cluttered scene, which is typical for a bin-picking task. The new procedure was tested on six different datasets (bins filled with different parts) and its performance was compared with the generic statistical outlier removal procedure. The new method outperforms the general procedure in terms of filtering efficacy, especially on datasets heavily contaminated by numerous outliers.