Endotracheal tube forces exerted on the larynx and a novel support device to reduce it.

Objective: Endotracheal tubes (ETTs) are commonly associated with laryngeal injury that may be short lasting and temporary or more severe and life altering. Injury is believed to result from forces that these ETTs exert on the larynx. Here we quantify the forces of ETTs of various sizes on the laryngotracheal complex to gain a more quantitative understanding of these potential damaging forces. Here we also perform preclinical testing of a novel support device to offload these forces. Methods: Endotracheal intubation was performed on a fresh human cadaver using various ETT sizes. A strain-sensitive graphene nanosheet sensor and a commercially available force sensing resistor were secured behind the larynx, anterior to the prevertebral fascia. The forces exerted on the larynx were measured for each of the commonly used ETTs. A novel support device, ETT clip (Endo Clip), was attached to the ETTs and changes in these forces were observed. Results: Forces exerted on the laryngotracheal complex by various ETTs were observed to increase with increasing tube size. This pressure can be significantly reduced with a novel ETT clip. Conclusion: Here we demonstrate the first quantitative measurement of forces that ETTs exert on the larynx. We demonstrate a novel device that can easily clip onto an ETT reducing pressure on the laryngotracheal complex. This preclinical test paves the way for a human clinical trial. Level of evidence: 5.

Muscle Engagement Monitoring Using Self-Adhesive Elastic Nanocomposite Fabrics.

Insight into, and measurements of, muscle contraction during movement may help improve the assessment of muscle function, quantification of athletic performance, and understanding of muscle behavior, prior to and during rehabilitation following neuromusculoskeletal injury. A self-adhesive, elastic fabric, nanocomposite, skin-strain sensor was developed and validated for human movement monitoring. We hypothesized that skin-strain measurements from these wearables would reveal different degrees of muscle engagement during functional movements. To test this hypothesis, the strain sensing properties of the elastic fabric sensors, especially their linearity, stability, repeatability, and sensitivity, were first verified using load frame tests. Human subject tests conducted in parallel with optical motion capture confirmed that they can reliably measure tensile and compressive skin-strains across the calf and tibialis anterior. Then, a pilot study was conducted to assess the correlation of skin-strain measurements with surface electromyography (sEMG) signals. Subjects did biceps curls with different weights, and the responses of the elastic fabric sensors worn over the biceps brachii and flexor carpi radialis (i.e., forearm) were well-correlated with sEMG muscle engagement measures. These nanocomposite fabric sensors were validated for monitoring muscle engagement during functional activities and did not suffer from the motion artifacts typically observed when using sEMGs in free-living community settings.

A Feature-Encoded Physics-Informed Parameter Identification Neural Network for Musculoskeletal Systems.

Identification of muscle-tendon force generation properties and muscle activities from physiological measurements, e.g., motion data and raw surface electromyography (sEMG), offers opportunities to construct a subject-specific musculoskeletal (MSK) digital twin system for health condition assessment and motion prediction. While machine learning approaches with capabilities in extracting complex features and patterns from a large amount of data have been applied to motion prediction given sEMG signals, the learned data-driven mapping is black-box and may not satisfy the underlying physics and has reduced generality. In this work, we propose a feature-encoded physics-informed parameter identification neural network (FEPI-PINN) for simultaneous prediction of motion and parameter identification of human MSK systems. In this approach, features of high-dimensional noisy sEMG signals are projected onto a low-dimensional noise-filtered embedding space for the enhancement of forwarding dynamics prediction. This FEPI-PINN model can be trained to relate sEMG signals to joint motion and simultaneously identify key MSK parameters. The numerical examples demonstrate that the proposed framework can effectively identify subject-specific muscle parameters and the trained physics-informed forward-dynamics surrogate yields accurate motion predictions of elbow flexion-extension motion that are in good agreement with the measured joint motion data.

Distributed Strain Monitoring Using Nanocomposite Paint Sensing Meshes.

Strain measurements are vital for monitoring the load-bearing capacity and safety of structures. A common approach is to affix strain gages onto structural surfaces. On the other hand, most aerospace, automotive, civil, and mechanical structures are painted and coated, often with many layers, prior to their deployment. There is an opportunity to design smart and multifunctional paints that can be directly pre-applied onto structural surfaces to serve as a sensing layer among their other layers of functional paints. Therefore, the objective of this study was to design a strain-sensitive paint that can be used for structural monitoring. Carbon nanotubes (CNT) were dispersed in paint by high-speed shear mixing, while paint thinner was employed for adjusting the formulation's viscosity and nanomaterial concentration. The study started with the design and fabrication of the CNT-based paint. Then, the nanocomposite paint's electromechanical properties and its sensitivity to applied strains were characterized. Third, the nanocomposite paint was spray-coated onto patterned substrates to form "Sensing Meshes" for distributed strain monitoring. An electrical resistance tomography (ERT) measurement strategy and algorithm were utilized for reconstructing the conductivity distribution of the Sensing Meshes, where the magnitude of conductivity (or resistivity) corresponded to the magnitude of strain, while strain directionality was determined based on the strut direction in the mesh.

Biofabrication of Gingival Fibroblast Cell-Laden Collagen/Strontium-Doped Calcium Silicate 3D-Printed Bi-Layered Scaffold for Osteoporotic Periodontal Regeneration.

Periodontal disease is a chronic disease that can lead to lose teeth and even tooth loss if left untreated. Osteoporosis and periodontal disease share similar characteristics and associated factors. Current regenerative techniques for periodontal diseases are ineffective in restoring complete function and structural integrity of periodontium due to unwanted migration of cells. In this study, we applied the concept of guided tissue regeneration (GTR) and 3D fabricated gingival fibroblast cell-laden collagen/strontium-doped calcium silicate (SrCS) bi-layer scaffold for periodontal regeneration. The results revealed that the bioactive SrCS had a hydroxyapatite formation on its surface after 14 days of immersion and that SrCS could release Sr and Si ions even after 6 months of immersion. In addition, in vitro results showed that the bi-layer scaffold enhanced secretion of FGF-2, BMP-2, and VEGF from human gingival fibroblasts and increased secretion of osteogenic-related proteins ALP, BSP, and OC from WJMSCs. In vivo studies using animal osteoporotic models showed that the 3D-printed cell-laden collagen/SrCS bi-layer scaffold was able to enhance osteoporotic bone regeneration, as seen from the increased Tb.Th and BV/TV ratio and the histological stains. In conclusion, it can be seen that the bi-layer scaffolds enhanced osteogenesis and further showed that guided periodontal regeneration could be achieved using collagen/SrCS scaffolds, thus making it a potential candidate for future clinical applications.

Effect of CYP2C19 Gene Polymorphisms on Proton Pump Inhibitor, Amoxicillin, and Levofloxacin Triple Therapy for Eradication of Helicobacter Pylori.

BACKGROUND The effects of PPI are variable owing to the CYP2C19 polymorphisms. However, whether the polymorphisms could affect the Hp eradication efficacy of triple therapy is still not clear. The present study aimed to assess the effects of CYP2C19 gene polymorphisms on proton pump inhibitor (PPI), amoxicillin, and levofloxacin triple therapy for Helicobacter pylori (Hp) eradication. MATERIAL AND METHODS We randomly assigned 160 Hp-positive patients with chronic gastritis to 2 groups to receive either 20 mg bid omeprazole (OAL group, n=80) or 10 mg bid rabeprazole (RAL group, n=80), combined with 1000 mg bid amoxicillin and 500 mg qd levofloxacin. The 2 groups were treated for 10 days. The CYP2C19 genotypes included wild-type, M1 mutant gene (*2, the mutation of exon 5), and M2 mutant gene (*3, the mutation of exon 4) identified by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFIP). According to CYP2C19 genotype combinations, the patients were divided into extensive metabolizer (EM), intermediate metabolizer (IM), and poor metabolizer (PM) subgroups. The eradication efficacy of Hp was evaluated by 14C-UBT at 28 days after treatment. RESULTS The trial was completed by 155 patients. Hp eradication rates in OAL and RAL groups were 78.2% and 88.3%, respectively, on per-protocol (PP) analysis, indicating no significant difference (P>0.05). Regarding CYP2C19 genotypes, eradication rates of 60.7%, 84.2%, and 100% were obtained for EM, IM, and PM subgroups, respectively, of the OAL group. EM group eradication rates were significantly lower than IM and PM group values (P<0.05). In the RAL group, no such difference was observed (P>0.05). Hp eradication rates were significantly lower in the EM subgroup of the OAL group compared with that of the RAL group. CONCLUSIONS Hp eradication rates were higher in the RAL group than in OAL-treated patients. Interestingly, omeprazole-based therapy was significantly affected by the CYP2C19 genotype, unlike the rabeprazole-based therapy.