Optimal diameter reduction ratio of acinar airways in human lungs

In the airway network of a human lung, the airway diameter gradually decreases through multiple branching. The diameter reduction ratio of the conducting airways that transport gases without gas exchange is 0.79, but this reduction ratio changes to 0.94 in acinar airways beyond transitional bronchioles. While the reduction in the conducting airways was previously rationalized on the basis of Murray's law, our understanding of the design principle behind the acinar airways has been far from clear. Here we elucidate that the change in gas transfer mode is responsible for the transition in the diameter reduction ratio. The oxygen transfer rate per unit surface area is maximized at the observed geometry of acinar airways, which suggests the minimum cost for the construction and maintenance of the acinar airways. The results revitalize and extend the framework of Murray's law over an entire human lung

Electricity production by the application of a low voltage DC-DC boost converter to a continuously operating flat-plate microbial fuel cell

An ultra-low voltage customized DC-DC booster circuit was developed using a LTC3108 converter, and used continuously on a flat-plate microbial fuel cell (FPM) system. The boost converter successfully stepped up the microbial fuel cell (MFC) voltage from ~0.5 V to 3.3 and 5.0 V of outputs. The designed circuit and system displayed the dynamic variations of the source FPM as well as the output voltage through the designed three connection points within the booster circuit. The source MFC voltage was interrelated with the booster circuit and its performance, and it adapted to the set points of the booster dynamically. The maximum output power density of the MFC with the DC-DC booster circuit was 8.16 W/m3 compared to the maximum source FPM input power of 14.27 W/m3 at 100 Ω, showing a conversion efficiency of 26–57%, but with a 10-fold higher output than that of the source voltage. The combined LTC3108 with FPM supplied power for electronic devices using synthetic and real domestic wastewater. This report presents a promising strategy for utilizing the electrical energy produced from MFCs, and expands the applicability of bioelectrochemical systems with an improved energy efficiency of the present wastewater treatment system

A novel sphingosylphosphorylcholine and sphingosine-1-phosphate receptor 1 antagonist, KRO-105714, for alleviating atopic dermatitis

Background Atopic dermatitis (eczema) is a type of inflammation of the skin, which presents with itchy, red, swollen, and cracked skin. The high global incidence of atopic dermatitis makes it one of the major skin diseases threatening public health. Sphingosylphosphorylcholine (SPC) and sphingosine-1-phosphate (S1P) act as pro-inflammatory mediators, as an angiogenesis factor and a mitogen in skin fibroblasts, respectively, both of which are important biological responses to atopic dermatitis. The SPC level is known to be elevated in atopic dermatitis, resulting from abnormal expression of sphingomyelin (SM) deacylase, accompanied by a deficiency in ceramide. Also, S1P and its receptor, sphingosine-1-phosphate receptor 1 (S1P1) are important targets in treating atopic dermatitis. Results In this study, we found a novel antagonist of SPC and S1P1, KRO-105714, by screening 10,000 compounds. To screen the compounds, we used an SPC-induced cell proliferation assay based on a high-throughput screening (HTS) system and a human S1P1 protein-based [35S]-GTPγS binding assay. In addition, we confirmed the inhibitory effects of KRO-105714 on atopic dermatitis through related cell-based assays, including a tube formation assay, a cell migration assay, and an ELISA assay on inflammatory cytokines. Finally, we confirmed that KRO-105714 alleviates atopic dermatitis symptoms in a series of mouse models. Conclusions Taken together, our data suggest that SPC and S1P1 antagonist KRO-105714 has the potential to alleviate atopic dermatitis.This work was supported by a grant from the Korea Research Council for Industrial Science and Technology (KK-1933-20) to HC, under the industrial infrastructure program for fundamental technologies and Korea Institute for Advancement of Technology through the Inter-ER Cooperation Projects (R0002017) which are funded by the Ministry of Trade, Industry & Energy, Korea to YDG

Pyruvate Dehydrogenase Kinase Is a Metabolic Checkpoint for Polarization of Macrophages to the M1 Phenotype

Metabolic reprogramming during macrophage polarization supports the effector functions of these cells in health and disease. Here, we demonstrate that pyruvate dehydrogenase kinase (PDK), which inhibits the pyruvate dehydrogenase-mediated conversion of cytosolic pyruvate to mitochondrial acetyl-CoA, functions as a metabolic checkpoint in M1 macrophages. Polarization was not prevented by PDK2 or PDK4 deletion but was fully prevented by the combined deletion of PDK2 and PDK4; this lack of polarization was correlated with improved mitochondrial respiration and rewiring of metabolic breaks that are characterized by increased glycolytic intermediates and reduced metabolites in the TCA cycle. Genetic deletion or pharmacological inhibition of PDK2/4 prevents polarization of macrophages to the M1 phenotype in response to inflammatory stimuli (lipopolysaccharide plus IFN-γ). Transplantation of PDK2/4-deficient bone marrow into irradiated wild-type mice to produce mice with PDK2/4-deficient myeloid cells prevented M1 polarization, reduced obesity-associated insulin resistance, and ameliorated adipose tissue inflammation. A novel, pharmacological PDK inhibitor, KPLH1130, improved high-fat diet-induced insulin resistance; this was correlated with a reduction in the levels of pro-inflammatory markers and improved mitochondrial function. These studies identify PDK2/4 as a metabolic checkpoint for M1 phenotype polarization of macrophages, which could potentially be exploited as a novel therapeutic target for obesity-associated metabolic disorders and other inflammatory conditions

Interfacial Dipole Modulation Device With SiO_X Switching Species

In recent years, substantial research efforts have been devoted toward the development of non-volatile memory devices (NVM). In this regard, devices utilizing interfacial dipole modulation (IDM) have been considered. However, the narrow memory window of current IDM stack structures (HfO2-TiO2-SiO2) has been a major constraint. Herein, we propose a structure that exploits SiOx interfacial dipole layers, thereby overcoming the memory window limitation of conventional IDM stack structures. A memory window up to 8.05 V was achieved by using SiOx switching species together with HfO and TiO encapsulating layers owing to the bidirectional oxygen relocation process, resulting in numerous electric dipoles. Furthermore, we found that the memory window increases as the ratio of SiOX to Si2p and varies with the position of the SiOX layer, implying that the SiOx layer drives the switching mechanism. This IDM structure with an enhanced memory window can be used to develop practical NVM devices

Deep-Learning-Based Stroke Screening Using Skeleton Data from Neurological Examination Videos

According to the Korea Institute for Health and Social Affairs, in 2017, the elderly, aged 65 or older, had an average of 2.7 chronic diseases per person. The concern for the medical welfare of the elderly is increasing due to a low birth rate, an aging population, and the lack of medical personnel. The demand for services that take user age, cognitive capacity, and difficulty into account is rising. As a result, there is an increased demand for smart healthcare systems that can lower hospital admissions and offer patients individualized care. This has motivated us to develop an AI system that can easily screen and manage neurological diseases through videos. As neurological diseases can be diagnosed by visual analysis to some extent, in this study, we set out to estimate the possibility of a person having a neurological disease from videos. Among neurological diseases, we focus on stroke because it is a common condition in the elderly population and results in high mortality and morbidity worldwide. The proposed method consists of three steps: (1) transforming neurological examination videos into landmark data, (2) converting the landmark data into recurrence plots, and (3) estimating the possibility of a stroke using deep neural networks. Major features, such as the hand, face, pupil, and body movements of a person are extracted from test videos taken under several neurological examination protocols using deep-learning-based landmark extractors. Sequences of these landmark data are then converted into recurrence plots, which can be interpreted as images. These images can be fed into convolutional neural networks to classify stroke using feature-fusion techniques. A case study of the application of a disease screening test to assess the capability of the proposed method is presented