Biomarker and pharmacodynamic activity of the transforming growth factor-beta (TGFß) inhibitor SAR439459 as monotherapy and in combination with cemiplimab in a phase I clinical study in patients with advanced solid tumors.

SAR439459, a 'second-generation' human anti-transforming growth factor-beta (TGFß) monoclonal antibody, inhibits all TGFß isoforms and improves the antitumor activity of anti-programmed cell death protein-1 therapeutics. This study reports the pharmacodynamics (PD) and biomarker results from phase I/Ib first-in-human study of SAR439459 ± cemiplimab in patients with advanced solid tumors (NCT03192345). In dose-escalation phase (Part 1), SAR439459 was administered intravenously at increasing doses either every 2 weeks (Q2W) or every 3 weeks (Q3W) with cemiplimab IV at 3 mg/kg Q2W or 350 mg Q3W, respectively, in patients with advanced solid tumors. In dose-expansion phase (Part 2), patients with melanoma received SAR439459 IV Q3W at preliminary recommended phase II dose (pRP2D) of 22.5/7.5 mg/kg or at 22.5 mg/kg with cemiplimab 350 mg IV Q3W. Tumor biopsy and peripheral blood samples were collected for exploratory biomarker analyses to assess target engagement and PD, and results were correlated with patients' clinical parameters. SAR439459 ± cemiplimab showed decreased plasma and tissue TGFß, downregulation of TGFß-pathway activation signature, modulation of peripheral natural killer (NK) and T cell expansion, proliferation, and increased secretion of CXCL10. Conversion of tumor tissue samples from 'immune-excluded' to 'immune-infiltrated' phenotype in a representative patient with melanoma SAR439459 22.5 mg/kg with cemiplimab was observed. In paired tumor and plasma, active and total TGFß1 was more consistently elevated followed by TGFß2, whereas TGFß3 was only measurable (lower limit of quantitation ≥2.68 pg/mg) in tumors. SAR439459 ± cemiplimab showed expected peripheral PD effects and TGFß alteration. However, further studies are needed to identify biomarkers of response.

To overcome the limitations of fixed life patterns, plants can generate meristems throughout life.

The fixed life pattern of plants is the most threatening factor that hinders the survival and reproduction rate of plants. Maximization of reproduction is determined by the survival rate of the organism. If part of a shoot apical meristem or root apical meristem is cut and planted in soil with appropriate nutrients and survival conditions, a cloned plant known as an ramet, may be developed. Therefore, the ability of plants to constantly produce meristems is essential for survival. In addition, meristem stem cells have enabled plants to evolve a wide variety of asexual reproductive systems. When a tree is pruned, at least one or more new meristems are formed in the surrounding area, and those meristems develop into new branches. In other cases, stem cells normally derived from meristems alone exhibit the potential for asexual reproduction through their seed-like roles. Alternatively, some plants can form somatic cells, which are important in various types of asexual reproduction. There are 125 species of plants in the genus of Kalanchoe, which are succulent plants, and most of these species are well known to reproduce asexually through somatic cells. When we cut the stem of a plant, a callus is formed at the end of the cut side. Plant callus is mainly used to develop new plant varieties in tissue culture research. Alternatively, the plant callus is also used as a material for asexual reproduction. Callus can also form if the plant is infected with bacteria such as Agrobacterium tumefaciens. Differentiated cells of a plant can reproduce asexually by acquiring the ability to function as stems through transdifferentiation. These characteristics play important roles in adapting to environmental changes and extending the lifespan of woody plants.

Dimensional accuracy, mechanical property, and optical stability of zirconia orthodontic bracket according to yttria proportions.

This in vitro study evaluated comprehensively the performances of zirconia brackets with varying yttria proportions in manufacturing advanced orthodontic brackets. Three experimental groups of zirconia brackets were fabricated using yttria-stabilized zirconia (YSZ) materials with different yttria proportions-3 mol% yttria (3Y-YSZ), 4 mol% yttria (4Y-YSZ), and 5 mol% yttria (5Y-YSZ) (Tosoh Ceramic, Japan). A polycrystalline alumina ceramic bracket (3M™ Clarity™ Advanced, MBT 0.022-in. slot) was employed as the control group. Morphological properties, including slot surface structure and dimensions, were examined using scanning electron microscopy and surface profiler analysis. Manufacturing accuracy was assessed with root mean square calculations of trueness and precision. Mechanical properties were tested, encompassing static and kinetic frictional resistance (FR) and fracture strength. Optical stability was evaluated through 20,000 cycles of thermocycling and a 7-day immersion in various coloring agents. Within the limitations of this study, zirconia brackets containing 3 to 5 mol% YSZ presented enhanced reliability in terms of dimensional accuracy and demonstrated favorable optical stability. Notably, owing to its advantageous mechanical properties, the 3Y-YSZ variant showed remarkable potential as an advanced material for fabricating orthodontic brackets.

Applications of single-cell RNA sequencing in drug discovery and development.

Single-cell technologies, particularly single-cell RNA sequencing (scRNA-seq) methods, together with associated computational tools and the growing availability of public data resources, are transforming drug discovery and development. New opportunities are emerging in target identification owing to improved disease understanding through cell subtyping, and highly multiplexed functional genomics screens incorporating scRNA-seq are enhancing target credentialling and prioritization. ScRNA-seq is also aiding the selection of relevant preclinical disease models and providing new insights into drug mechanisms of action. In clinical development, scRNA-seq can inform decision-making via improved biomarker identification for patient stratification and more precise monitoring of drug response and disease progression. Here, we illustrate how scRNA-seq methods are being applied in key steps in drug discovery and development, and discuss ongoing challenges for their implementation in the pharmaceutical industry.

AMEERA-1 phase 1/2 study of amcenestrant, SAR439859, in postmenopausal women with ER-positive/HER2-negative advanced breast cancer.

AMEERA-1 is a Phase 1/2 open-label single-arm study evaluating once-daily (QD) amcenestrant, an orally bioavailable selective estrogen receptor (ER) degrader, in postmenopausal women with ER+/HER2- advanced breast cancer (NCT03284957), who were mostly heavily pretreated (including targeted therapies and fulvestrant). In the dose escalation phase (Part A: n = 16), patients received amcenestrant 20-600 mg QD. Based on absence of dose-limiting toxicities, paired functional 18F-fluoroestradiol positron emission tomography, and pharmacokinetics, 400 mg QD was selected as recommended Phase 2 dose (RP2D) for the dose expansion phase (Part B: n = 49). No Grade ≥3 treatment-related adverse events or clinically significant cardiac/eye toxicities were reported. The Part B primary endpoint, confirmed objective response rate (ORR) was 3/45 at the interim analysis and 5/46 (10.9%) at the final analysis. The overall clinical benefit rate (CBR) was 13/46 (28.3%). CBRs among patients with baseline wild-type and mutated ESR1 were 9/26 (34.6%) and 4/19 (21.1%), respectively. Paired tumor biopsy and cell-free DNA analyses revealed ER inhibition and degradation, and a reduction in detectable ESR1 mutations, including Y537S. In conclusion, amcenestrant at RP2D of 400 mg QD for monotherapy is well-tolerated with no dose-limiting toxicities, and demonstrates preliminary antitumor activity irrespective of baseline ESR1 mutation status.

Computational Fractional Flow Reserve From Coronary Computed Tomography Angiography-Optical Coherence Tomography Fusion Images in Assessing Functionally Significant Coronary Stenosis.

Background: Coronary computed tomography angiography (CTA) and optical coherence tomography (OCT) provide additional functional information beyond the anatomy by applying computational fluid dynamics (CFD). This study sought to evaluate a novel approach for estimating computational fractional flow reserve (FFR) from coronary CTA-OCT fusion images. Methods: Among patients who underwent coronary CTA, 148 patients who underwent both pressure wire-based FFR measurement and OCT during angiography to evaluate intermediate stenosis in the left anterior descending artery were included from the prospective registry. Coronary CTA-OCT fusion images were created, and CFD was applied to estimate computational FFR. Based on pressure wire-based FFR as a reference, the diagnostic performance of Fusion-FFR was compared with that of CT-FFR and OCT-FFR. Results: Fusion-FFR was strongly correlated with FFR (r = 0.836, P < 0.001). Correlation between FFR and Fusion-FFR was stronger than that between FFR and CT-FFR (r = 0.682, P < 0.001; z statistic, 5.42, P < 0.001) and between FFR and OCT-FFR (r = 0.705, P < 0.001; z statistic, 4.38, P < 0.001). Area under the receiver operating characteristics curve to assess functionally significant stenosis was higher for Fusion-FFR than for CT-FFR (0.90 vs. 0.83, P = 0.024) and OCT-FFR (0.90 vs. 0.83, P = 0.043). Fusion-FFR exhibited 84.5% accuracy, 84.6% sensitivity, 84.3% specificity, 80.9% positive predictive value, and 87.5% negative predictive value. Especially accuracy, specificity, and positive predictive value were superior for Fusion-FFR than for CT-FFR (73.0%, P = 0.007; 61.4%, P < 0.001; 64.0%, P < 0.001) and OCT-FFR (75.7%, P = 0.021; 73.5%, P = 0.020; 69.9%, P = 0.012). Conclusion: CFD-based computational FFR from coronary CTA-OCT fusion images provided more accurate functional information than coronary CTA or OCT alone. Clinical Trial Registration: [www.ClinicalTrials.gov], identifier [NCT03298282].

Optimization of FFR prediction algorithm for gray zone by hemodynamic features with synthetic model and biometric data.

BACKGROUND: Recent attempts on adopting artificial intelligence algorithm on coronary diagnosis had limitations on data quantity and quality. While most of previous studies only used vessel image as input data, flow features and biometric features should be also considered. Moreover, the accuracy should be optimized within gray zone as the purpose is to decide stent insertion with estimated fractional flow reserve. OBJECTIVES: The main purpose of this study is to develop an artificial intelligence-based coronary vascular diagnosis system focused on performance in the gray zone, from CT image extraction to FFR estimation. Three main issues should be considered for an algorithm to be used for pre-screening: algorithm optimization in the gray zone, minimization of labor during image processing, and consideration of flow and biometric features. This paper introduces a full FFR pre-screening system from automatic image extraction to an algorithm for estimating the FFR value. METHOD: The main techniques used in this study are an automatic image extraction algorithm, lattice Boltzmann method based computational fluid dynamics analysis of a synthetic model and patient data, and an AI algorithm optimization. For feature extraction, this study focused on an automatic process to reduce manual labor. The algorithm consisted of two steps: the first algorithm calculates flow features from geometrical features, and the second algorithm estimates the FFR value from flow features and patient biometric features. Algorithm selection, outlier elimination, and k-fold selection were included to optimize the algorithm. CONCLUSION: Eight types of algorithms including two neural network models and six machine learning models were optimized and tested. The random forest model shows the highest performance before optimization, whereas the multilayer perceptron regressor shows the highest gray zone accuracy after optimization.

A metallic anti-biofouling surface with a hierarchical topography containing nanostructures on curved micro-riblets.

Metallic surface finishes have been used in the anti-biofouling, but it is very difficult to produce surfaces with hierarchically ordered structures. In the present study, anti-biofouling metallic surfaces with nanostructures superimposed on curved micro-riblets were produced via top-down fabrication. According to the attachment theory, these surfaces feature few attachment points for organisms, the nanostructures prevent the attachment of bacteria and algal zoospores, while the micro-riblets prohibit the settlement of macrofoulers. Anodic oxidation was performed to induce superhydrophilicity. It forms a hydration layer on the surface, which physically blocks foulant adsorption along with the anti-biofouling topography. We characterized the surfaces via scanning electron and atomic force microscopy, contact-angle measurement, and wear-resistance testing. The contact angle of the hierarchical structures was less than 1°. Laboratory settlement assays verified that bacterial attachment was dramatically reduced by the nanostructures and/or the hydration layer, attributable to superhydrophilicity. The micro-riblets prohibited the settlement of macrofoulers. Over 77 days of static immersion in the sea during summer, the metallic surface showed significantly less biofouling compared to a surface painted with an anticorrosive coating.

Diagnosis of obstructive sleep apnea with prediction of flow characteristics according to airway morphology automatically extracted from medical images: Computational fluid dynamics and artificial intelligence approach.

BACKGROUND: Obstructive sleep apnea syndrome (OSAS) is being observed in an increasing number of cases. It can be diagnosed using several methods such as polysomnography. OBJECTIVES: To overcome the challenges of time and cost faced by conventional diagnostic methods, this paper proposes computational fluid dynamics (CFD) and machine-learning approaches that are derived from the upper-airway morphology with automatic segmentation using deep learning. METHOD: We adopted a 3D UNet deep-learning model to perform medical image segmentation. 3D UNet prevents the feature-extraction loss that may occur by concatenating layers and extracts the anteroposterior coordination and width of the airway morphology. To create flow characteristics of the upper airway training data, we analyzed the changes in flow characteristics according to the upper-airway morphology using CFD. A multivariate Gaussian process regression (MVGPR) model was used to train the flow characteristic values. The trained MVGPR enables the prompt prediction of the aerodynamic features of the upper airway without simulation. Unlike conventional regression methods, MVGPR can be trained by considering the correlation between the flow characteristics. As a diagnostic step, a support vector machine (SVM) with predicted aerodynamic and biometric features was used in this study to classify patients as healthy or suffering from moderate OSAS. SVM is beneficial as it is easy to learn even with a small dataset, and it can diagnose various flow characteristics as factors while enhancing the feature via the kernel function. As the patient dataset is small, the Monte Carlo cross-validation was used to validate the trained model. Furthermore, to overcome the imbalanced data problem, the oversampling method was applied. RESULT: The segmented upper-airway results of the high-resolution and low-resolution models present overall average dice coefficients of 0.76±0.041 and 0.74±0.052, respectively. Furthermore, the classification accuracy, sensitivity, specificity, and F1-score of the diagnosis algorithm were 81.5%, 89.3%, 86.2%, and 87.6%, respectively. CONCLUSION: The convenience and accuracy of sleep apnea diagnosis are improved using deep learning and machine learning. Further, the proposed method can aid clinicians in making appropriate decisions to evaluate the possible applications of OSAS.

Binding characteristics of staphylococcal protein A and streptococcal protein G for fragment crystallizable portion of human immunoglobulin G.

In the wide array of physiological processes, protein-protein interactions and their binding are the most basal activities for achieving adequate biological metabolism. Among the studies on binding proteins, the examination of interactions between immunoglobulin G (IgG) and natural immunoglobulin-binding ligands, such as staphylococcal protein A (spA) and streptococcal protein G (spG), is essential in the development of pharmaceutical science, biotechnology, and affinity chromatography. The widespread utilization of IgG-spA/spG binding characteristics has allowed researchers to investigate these molecular interactions. However, the detailed binding strength of each ligand and the corresponding binding mechanisms have yet to be fully investigated. In this study, the authors analyzed the binding strengths of IgG-spA and IgG-spG complexes and identified the mechanisms enabling these bindings using molecular dynamics simulation, steered molecular dynamics, and advanced Poisson-Boltzmann Solver simulations. Based on the presented data, the binding strength of the spA ligand was found to significantly exceed that of the spG ligand. To find out which non-covalent interactions or amino acid sites have a dominant role in the tight binding of these ligands, further detailed analyses of electrostatic interactions, hydrophobic bonding, and binding free energies have been performed. In investigating their binding affinity, a relatively independent and different unbinding mechanism was found in each ligand. These distinctly different mechanisms were observed to be highly correlated to the protein secondary and tertiary structures of spA and spG ligands, as explicated from the perspective of hydrogen bonding.

An experimental model of anti-PD-1 resistance exhibits activation of TGFß and Notch pathways and is sensitive to local mRNA immunotherapy.

Immune checkpoint blockade elicits durable anti-cancer responses in the clinic, however a large proportion of patients do not benefit from treatment. Several mechanisms of innate and acquired resistance to checkpoint blockade have been defined and include mutations of MHC I and IFNγ signaling pathways. However, such mutations occur in a low frequency of patients and additional mechanisms have yet to be elucidated. In an effort to better understand acquired resistance to checkpoint blockade, we generated a mouse tumor model exhibiting in vivo resistance to anti-PD-1 antibody treatment. MC38 tumors acquired resistance to PD-1 blockade following serial in vivo passaging. Lack of sensitivity to PD-1 blockade was not attributed to dysregulation of PD-L1 or ß2M expression, as both were expressed at similar levels in parental and resistant cells. Similarly, IFNγ signaling and antigen processing and presentation pathways were functional in both parental and resistant cell lines. Unbiased gene expression analysis was used to further characterize potential resistance mechanisms. RNA-sequencing revealed substantial differences in global gene expression, with tumors resistant to anti-PD-1 displaying a marked reduction in expression of immune-related genes relative to parental MC38 tumors. Indeed, resistant tumors exhibited reduced immune infiltration across multiple cell types, including T and NK cells. Pathway analysis revealed activation of TGFß and Notch signaling in anti-PD-1 resistant tumors, and activation of these pathways was associated with poorer survival in human cancer patients. While pharmacological inhibition of TGFß and Notch in combination with PD-1 blockade decelerated tumor growth, a local mRNA-based immunotherapy potently induced regression of resistant tumors, resulting in complete tumor remission, and resensitized tumors to treatment with anti-PD-1. Overall, this study describes a novel anti-PD-1 resistant mouse tumor model and underscores the role of two well-defined signaling pathways in response to immune checkpoint blockade. Furthermore, our data highlights the potential of intratumoral mRNA therapy in overcoming acquired resistance to PD-1 blockade.

Neuromorphic van der Waals crystals for substantial energy generation.

Controlling ion transport in nanofluidics is fundamental to water purification, bio-sensing, energy storage, energy conversion, and numerous other applications. For any of these, it is essential to design nanofluidic channels that are stable in the liquid phase and enable specific ions to pass. A human neuron is one such system, where electrical signals are transmitted by cation transport for high-speed communication related to neuromorphic computing. Here, we present a concept of neuro-inspired energy harvesting that uses confined van der Waals crystal and demonstrate a method to maximise the ion diffusion flux to generate an electromotive force. The confined nanochannel is robust in liquids as in neuron cells, enabling steady-state ion diffusion for hundred of hours and exhibiting ion selectivity of 95.8%, energy conversion efficiency of 41.4%, and power density of 5.26 W/m2. This fundamental understanding and rational design strategy can enable previously unrealisable applications of passive-type large-scale power generation.

SAR439859, a Novel Selective Estrogen Receptor Degrader (SERD), Demonstrates Effective and Broad Antitumor Activity in Wild-Type and Mutant ER-Positive Breast Cancer Models.

Primary treatment for estrogen receptor-positive (ER+) breast cancer is endocrine therapy. However, substantial evidence indicates a continued role for ER signaling in tumor progression. Selective estrogen receptor degraders (SERD), such as fulvestrant, induce effective ER signaling inhibition, although clinical studies with fulvestrant report insufficient blockade of ER signaling, possibly due to suboptimal pharmaceutical properties. Furthermore, activating mutations in the ER have emerged as a resistance mechanism to current endocrine therapies. New oral SERDs with improved drug properties are under clinical investigation, but the biological profile that could translate to improved therapeutic benefit remains unclear. Here, we describe the discovery of SAR439859, a novel, orally bioavailable SERD with potent antagonist and degradation activities against both wild-type and mutant Y537S ER. Driven by its fluoropropyl pyrrolidinyl side chain, SAR439859 has demonstrated broader and superior ER antagonist and degrader activities across a large panel of ER+ cells, compared with other SERDs characterized by a cinnamic acid side chain, including improved inhibition of ER signaling and tumor cell growth. Similarly, in vivo treatment with SAR439859 demonstrated significant tumor regression in ER+ breast cancer models, including MCF7-ESR1 wild-type and mutant-Y537S mouse tumors, and HCI013, a patient-derived tamoxifen-resistant xenograft tumor. These findings indicate that SAR439859 may provide therapeutic benefit to patients with ER+ breast cancer, including those who have resistance to endocrine therapy with both wild-type and mutant ER.

Interfacial behavior of surfactant-covered double emulsion in extensional flow.

We analyze the interface-interface interactions of a surfactant-covered double emulsion using the lattice Boltzmann method and study the interaction of the inner and outer interfaces and the local surfactant distribution under a uniaxial extensional flow. First, the capillary effects are analyzed. Upon surfactant application, the outer droplet deformation increases and the inner droplet deformation decreases. The concentrated surfactants on the outer interface increase deformation, and the inner droplet is affected by the inner flow. At a fixed Péclet number (Pe), the surfactant concentration at the outer interface increases with an increase in capillary number (Ca); however, such a tendency is difficult to identify at the inner interface. Next, the Pe effects are analyzed. With an increase in Pe, the deformation of the inner droplet decreases significantly. The local distribution of the surfactant considerably affects the double emulsion stabilization, which is analyzed in terms of internal flow. The interfacial tension gradient induced by the surfactant generates vortices internally, which is verified by applying the surfactant to each interface independently. The radius ratio affects droplet deformation and surfactant transport. The compression of the inner flow region increases the viscous force and decreases the interface velocity. Therefore, with an increase in radius ratio, the deformation increases, and the surfactant transport becomes slow.

Favorable short-term oncologic outcomes following laparoscopic surgery for small T4 colon cancer: a multicenter comparative study.

BACKGROUND: Laparoscopic surgery for T4 colon cancer may be safe in selected patients. We hypothesized that small tumor size might preoperatively predict a good laparoscopic surgery outcome. Herein, we compared the clinicopathologic and oncologic outcomes of laparoscopic and open surgery in small T4 colon cancer. METHODS: In a retrospective multicenter study, we reviewed the data of 449 patients, including 117 patients with tumors ≤ 4.0 cm who underwent surgery for T4 colon cancer between January 2014 and December 2017. We compared the clinicopathologic and 3-year oncologic outcomes between the laparoscopic and open groups. Survival curves were estimated using the Kaplan-Meier method and compared using the log-rank test. Univariate and multivariate analyses were performed using the Cox proportional hazards model. A p < 0.05 was considered statistically significant. RESULTS: Blood loss, length of hospital stay, and postoperative morbidity were lower in the laparoscopic group than in the open group (median [range], 50 [0-700] vs. 100 [0-4000] mL, p < 0.001; 8 vs. 10 days, p < 0.001; and 18.0 vs. 29.5%, p = 0.005, respectively). There were no intergroup differences in 3-year overall survival or disease-free survival (86.6 vs. 83.2%, p = 0.180, and 71.7 vs. 75.1%, p = 0.720, respectively). Among patients with tumor size ≤ 4.0 cm, blood loss was significantly lower in the laparoscopic group than in the open group (median [range], 50 [0-530] vs. 50 [0-1000] mL, p = 0.003). Despite no statistical difference observed in the 3-year overall survival rate (83.3 vs. 78.7%, p = 0.538), the laparoscopic group had a significantly higher 3-year disease-free survival rate (79.2 vs. 53.2%, p = 0.012). CONCLUSIONS: Laparoscopic surgery showed similar outcomes to open surgery in T4 colon cancer patients and may have favorable short-term oncologic outcomes in patients with tumors ≤ 4.0 cm.

Cytoreductive Surgery and Hyperthermic Intraperitoneal Chemotherapy With Mitomycin C Used for Colorectal Peritoneal Carcinomatosis.

PURPOSE: This study aimed to assess the evaluation of clinical outcomes and consequences of complications after cytoreductive surgery (CRS) and hyperthermic intraperitoneal chemotherapy (HIPEC) for the peritoneal carcinomatosis (PC) from colorectal cancer. METHODS: A total 26 patients underwent CRS and HIPEC for PC from colorectal cancer between March 2009 and April 2018. All the patients underwent CRS with the purpose of complete or near-complete cytoreduction. Intraoperative HIPEC was performed simultaneously after the CRS. Mitomycin C was used as chemotherapeutic agent for HIPEC. RESULTS: Median disease-free survival was 27.8 months (range, 13.4-42.2 months). Median overall survival was 56.0 months (range, 28.6-83.5 months). The mean peritoneal cancer index (PCI) was 8.73 ± 5.54. The distributions thereof were as follows: PCI <10, 69.23%; PCI 10-19, 23.08%; and PCI ≥20, 7.69%. The completeness of cytoreduction was 96.2% of patients showed CC-0, with 3.8% achieved CC-1. The mean operation time was 8.5 hours, and the mean postoperative hospital stay was 21.6 days. The overall rate of early postoperative complications was 88.5%; the rate of late complications was 34.6%. In the early period, most complications were grades I-II complications (65.4%), compared to grades III-V (23.1%). All late complications, occurring in 7.7% of patients, were grades III-V. There was no treatment-related mortality. CONCLUSION: Although the complication rate was approximately 88%, but the rate of severe complication rate was low. In selective patients with peritoneal recurrence, more aggressive strategies for management, such as CRS with HIPEC, were able to be considered under the acceptable general condition and life-expectancy.

Effect of membrane insertion for tricuspid regurgitation using immersed-boundary lattice Boltzmann method.

BACKGROUND: Tricuspid regurgitation is treated by valve repair or replacement. However, these methods have limitations, and alternative treatment methods are therefore required. OBJECTIVES: In this study, a new method of tricuspid valve treatment using artificial membrane insertion is analyzed. We performed tricuspid valve simulations using an artificial membrane inserted into the right ventricle (RV) or right atrium (RA). METHODS: We use the lattice Boltzmann method with the immersed boundary condition to model the structural motion of the valve leaflet. The effect of membrane insertion is analyzed in terms of the stress, force, and impulse on the valve leaflet, along with the velocity, pressure, jet volume, and Reynolds stress in the flow field. RESULTS: While the use of either membrane (RA or RV) leads to improved valve closure relative to the use of no membrane, the RV membrane is more effective than the RA membrane in achieving improved valve closure. In addition, a larger membrane area with a shorter distance between the leaflet and membrane increases membrane efficacy. CONCLUSION: Our results suggest that membrane insertion can form an effective new method for the treatment of tricuspid regurgitation.

Coronary artery decision algorithm trained by two-step machine learning algorithm.

A two-step machine learning (ML) algorithm for estimating both fractional flow reserve (FFR) and decision (DEC) for the coronary artery is introduced in this study. The primary purpose of this model is to suggest the possibility of ML-based FFR to be more accurate than the FFR calculation technique based on a computational fluid dynamics (CFD) method. For this purpose, a two-step ML algorithm that considers the flow characteristics and biometric features as input features of the ML model is designed. The first step of the algorithm is based on the Gaussian progress regression model and is trained by a synthetic model using CFD analysis. The second step of the algorithm is based on a support vector machine with patient data, including flow characteristics and biometric features. Consequently, the accuracy of the FFR estimated from the first step of the algorithm was similar to that of the CFD-based method, while the accuracy of DEC in the second step was improved. This improvement in accuracy was analyzed using flow characteristics and biometric features.

Sparked Reduced Graphene Oxide for Low-Temperature Sodium-Beta Alumina Batteries.

Wetting Na metal on the solid electrolyte of a liquid Na battery determines the operating temperature and performance of the battery. At low temperatures below 200 °C, liquid Na wets poorly on a solid electrolyte near its melting temperature (Tm = 98 °C), limiting its suitability for use in low-temperature batteries used for large-scale energy-storage systems. Herein, we propose the use of sparked reduced graphene oxide (rGO) that can improve the Na wetting in sodium-beta alumina batteries (NBBs), allowing operation at lower temperatures. Experimental and computational studies indicated rGO layers with nanogaps exhibited complete liquid Na wetting regardless of the surface energy between the liquid Na and the graphene oxide, which originated from the capillary force in the gap. Employing sparked rGO significantly enhanced the cell performance at 175 °C; the cell retained almost 100% Coulombic efficiency after the initial cycle, which is a substantial improvement over cells without sparked rGO. These results suggest that coating sparked rGO is a promising but simple strategy for the development of low-temperature NBBs.

Computational analysis of airflow dynamics for predicting collapsible sites in the upper airways: machine learning approach.

Obstructive sleep apnea (OSA) is a common sleep breathing disorder. With the use of computational fluid dynamics (CFD), this study provides a quantitative standard for accurate diagnosis and effective surgery based on the investigation of the relationship between airway geometry and aerodynamic characteristics. Based on computed tomography data from patients having normal geometry, 4 major geometric parameters were selected and a total of 160 idealized cases were modeled and simulated. We created a predictive model using Gaussian process regression (GPR) through a data set obtained through numerical method. The results demonstrated that the mean accuracy of the overall GPR model was ~72% with respect to the CFD results for the realistic upper airway model. A support vector machine model was also used to identify the degree of OSA symptoms in patients as normal-mild and moderate and severe. We achieved an accuracy of 82.5% with the training data set and an accuracy of 80% with the test data set.NEW & NOTEWORTHY There have been many studies on the analysis of obstructive sleep apnea (OSA) through computational fluid dynamics and finite element analysis. However, these methods are not useful for practical medical applications because they have limited information for OSA symptom. This study employs the machine learning algorithm to predict flow characteristics quickly and to determine the symptoms of the patient's OSA. The overall Gaussian process regression model's mean accuracy was ~72%, and the accuracy for the classification of OSA was >80%.