Classification and analysis of retinal interneurons by computational structure under natural scenes.

Inhibitory neurons are diverse across the brain, but for the visual system we lack the ability to functionally classify these neurons under complex natural stimuli. Here we take the approach of classifying retinal amacrine cell responses to natural scenes using optical recording and an interpretable neural network model. We fit mouse amacrine cell responses to a two-layer convolutional neural network model of a class shown previously to accurately capture salamander ganglion cell responses to natural scenes. Using an approach from interpretable machine learning, we determined for each stimulus the model interneurons that generated each amacrine response, analogous to the set of bipolar cells that target the amacrine population. From this analysis we clustered amacrine cells not by their natural scene responses, but by the model presynaptic neurons that constructed those responses, conservatively finding approximately seven groups by this approach. By analyzing the set of model presynaptic input neurons for each amacrine cluster, we find that distributed rather than dedicated inputs generate natural scene responses for different amacrine cell types. Additional analyses revealed distinct transient and sustained modes exhibited by the network during the response to simple flashes. These results give insight into the computational structure of how the diverse amacrine cell population responds to natural scenes, and generate multiple quantitative hypotheses for how synaptic inputs generate those responses.

High-Loading Lithium-Sulfur Batteries with Solvent-Free Dry-Electrode Processing.

Lithium-sulfur (Li-S) batteries, with their high energy density, nontoxicity, and the natural abundance of sulfur, hold immense potential as the next-generation energy storage technology. To maximize the actual energy density of the Li-S batteries for practical applications, it is crucial to escalate the areal capacity of the sulfur cathode by fabricating an electrode with high sulfur loading. Herein, ultra-high sulfur loading (up to 12 mg cm-2 ) cathodes are fabricated through an industrially viable and sustainable solvent-free dry-processing method that utilizes a polytetrafluoroethylene binder fibrillation. Due to its low porosity cathode architecture formed by the binder fibrillation process, the dry-processed electrodes exhibit a relatively lower initial capacity compared to the slurry-processed electrode. However, its mechanical stability is well maintained throughout the cycling without the formation of electrode cracking, demonstrating significantly superior cycling stability. Additionally, through the optimization of the dry-processing, a single-layer pouch cell with a loading of 9 mg cm-2 and a novel multi-layer pouch cell that uses an aluminum mesh as its current collector with a total loading of 14 mg cm-2 are introduced. To address the reduced initial capacity of dry-processed electrodes, strategies such as incorporating electrocatalysts or employing prelithiated active materials are suggested.

Interphase Stabilization of LiNi0.5 Mn1.5 O4 Cathode for 5 V-Class All-Solid-State Batteries.

Employing high voltage cobalt-free spinel LiNi0.5 Mn1.5 O4 (LNMO) as a cathode is promising for high energy density and cost-effectiveness, but it has challenges in all-solid-state batteries (ASSBs). Here, it is revealed that the limitation of lithium argyrodite sulfide solid electrolyte (Li6 PS5 Cl) with the LNMO cathode is due to the intrinsic chemical incompatibility and poor oxidative stability. Through a careful analysis of the interphase of LNMO, it is elucidated that even the halide solid electrolyte (Li3 InCl6 ) with high oxidative stability can be decomposed to form resistive interphase layers with LNMO in ASSBs. Interestingly, with Fe-doping and a Li3 PO4 protective layer coating, LNMO with Li3 InCl6 displays stable cycle performance with a stabilized interphase at a high voltage (≈4.7 V) in ASSBs. The enhanced interfacial stability with the extended electrochemical stability window through doping and coating enables high electrochemical stability with LNMO in ASSBs. This work provides guidance for employing high-voltage cathodes in ASSBs and highlights the importance of stable interphases to enable stable cycling in ASSBs.

Machine learning-based quantification for disease uncertainty increases the statistical power of genetic association studies.

MOTIVATION: Allowance for increasingly large samples is a key to identify the association of genetic variants with Alzheimer's disease (AD) in genome-wide association studies (GWAS). Accordingly, we aimed to develop a method that incorporates patients with mild cognitive impairment and unknown cognitive status in GWAS using a machine learning-based AD prediction model. RESULTS: Simulation analyses showed that weighting imputed phenotypes method increased the statistical power compared to ordinary logistic regression using only AD cases and controls. Applied to real-world data, the penalized logistic method had the highest AUC (0.96) for AD prediction and weighting imputed phenotypes method performed well in terms of power. We identified an association (P<5.0×10-8) of AD with several variants in the APOE region and rs143625563 in LMX1A. Our method, which allows the inclusion of individuals with mild cognitive impairment, improves the statistical power of GWAS for AD. We discovered a novel association with LMX1A. AVAILABILITY AND IMPLEMENTATION: Simulation codes can be accessed at https://github.com/Junkkkk/wGEE_GWAS.

Information Geometry of the Retinal Representation Manifold.

The ability for the brain to discriminate among visual stimuli is constrained by their retinal representations. Previous studies of visual discriminability have been limited to either low-dimensional artificial stimuli or pure theoretical considerations without a realistic encoding model. Here we propose a novel framework for understanding stimulus discriminability achieved by retinal representations of naturalistic stimuli with the method of information geometry. To model the joint probability distribution of neural responses conditioned on the stimulus, we created a stochastic encoding model of a population of salamander retinal ganglion cells based on a three-layer convolutional neural network model. This model not only accurately captured the mean response to natural scenes but also a variety of second-order statistics. With the model and the proposed theory, we computed the Fisher information metric over stimuli to study the most discriminable stimulus directions. We found that the most discriminable stimulus varied substantially across stimuli, allowing an examination of the relationship between the most discriminable stimulus and the current stimulus. By examining responses generated by the most discriminable stimuli we further found that the most discriminative response mode is often aligned with the most stochastic mode. This finding carries the important implication that under natural scenes, retinal noise correlations are information-limiting rather than increasing information transmission as has been previously speculated. We additionally observed that sensitivity saturates less in the population than for single cells and that as a function of firing rate, Fisher information varies less than sensitivity. We conclude that under natural scenes, population coding benefits from complementary coding and helps to equalize the information carried by different firing rates, which may facilitate decoding of the stimulus under principles of information maximization.

Stable Cycling with Intimate Contacts Enabled by Crystallinity-Controlled PTFE-Based Solvent-Free Cathodes in All-Solid-State Batteries.

All-solid-state batteries (ASSBs) employing Li-metal anodes and inorganic solid electrolytes are attracting great attention due to high safety and energy density for next-generation energy storage devices. However, the volume change of cathode active materials can cause contact loss, resulting in charge carrier isolation, heterogeneous current distribution, and poor electrochemical properties in ASSBs. Here, a simple, yet effective, solvent-free electrode engineering approach with polytetrafluoroethylene (PTFE) as a binder for ASSBs is reported, enabling intimate contact and stable interfaces with the cathode. It is substantiated that the crystallinity of PTFE can be controlled depending on the heat history, and highly crystalline PTFE displays robust mechanical properties. High-nickel LiNi0 . 8 Mn0.1 Co0.1 O2 cathode prepared with crystalline PTFE show improved cycle and rate performances in ASSBs. In addition, it is revealed that the intimate contact between cathode particles with a stable cathode electrolyte layer is maintained during cycling by postmortem studies. This simple engineering method can be applied to prepare cathodes with a variety of active materials and solid electrolytes in ASSBs.

A Case-Control Clinical Trial on a Deep Learning-Based Classification System for Diagnosis of Amyloid-Positive Alzheimer's Disease.

OBJECTIVE: A deep learning-based classification system (DLCS) which uses structural brain magnetic resonance imaging (MRI) to diagnose Alzheimer's disease (AD) was developed in a previous recent study. Here, we evaluate its performance by conducting a single-center, case-control clinical trial. METHODS: We retrospectively collected T1-weighted brain MRI scans of subjects who had an accompanying measure of amyloid-beta (Aß) positivity based on a 18F-florbetaben positron emission tomography scan. The dataset included 188 Aß-positive patients with mild cognitive impairment or dementia due to AD, and 162 Aß-negative controls with normal cognition. We calculated the sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve (AUC) of the DLCS in the classification of Aß-positive AD patients from Aß-negative controls. RESULTS: The DLCS showed excellent performance, with sensitivity, specificity, positive predictive value, negative predictive value, and AUC of 85.6% (95% confidence interval [CI], 79.8-90.0), 90.1% (95% CI, 84.5-94.2), 91.0% (95% CI, 86.3-94.1), 84.4% (95% CI, 79.2-88.5), and 0.937 (95% CI, 0.911-0.963), respectively. CONCLUSION: The DLCS shows promise in clinical settings where it could be routinely applied to MRI scans regardless of original scan purpose to improve the early detection of AD.

Deep learning-based diagnosis of Alzheimer's disease using brain magnetic resonance images: an empirical study.

The limited accessibility of medical specialists for Alzheimer's disease (AD) can make obtaining an accurate diagnosis in a timely manner challenging and may influence prognosis. We investigated whether VUNO Med-DeepBrain AD (DBAD) using a deep learning algorithm can be employed as a decision support service for the diagnosis of AD. This study included 98 elderly participants aged 60 years or older who visited the Seoul Asan Medical Center and the Korea Veterans Health Service. We administered a standard diagnostic assessment for diagnosing AD. DBAD and three panels of medical experts (ME) diagnosed participants with normal cognition (NC) or AD using T1-weighted magnetic resonance imaging. The accuracy (87.1% for DBAD and 84.3% for ME), sensitivity (93.3% for DBAD and 80.0% for ME), and specificity (85.5% for DBAD and 85.5% for ME) of both DBAD and ME for diagnosing AD were comparable; however, DBAD showed a higher trend in every analysis than ME diagnosis. DBAD may support the clinical decisions of physicians who are not specialized in AD; this may enhance the accessibility of AD diagnosis and treatment.

Diagnostic performance of deep learning-based automatic white matter hyperintensity segmentation for classification of the Fazekas scale and differentiation of subcortical vascular dementia.

PURPOSE: To validate the diagnostic performance of commercially available, deep learning-based automatic white matter hyperintensity (WMH) segmentation algorithm for classifying the grades of the Fazekas scale and differentiating subcortical vascular dementia. METHODS: This retrospective, observational, single-institution study investigated the diagnostic performance of a deep learning-based automatic WMH volume segmentation to classify the grades of the Fazekas scale and differentiate subcortical vascular dementia. The VUNO Med-DeepBrain was used for the WMH segmentation system. The system for segmentation of WMH was designed with convolutional neural networks, in which the input image was comprised of a pre-processed axial FLAIR image, and the output was a segmented WMH mask and its volume. Patients presented with memory complaint between March 2017 and June 2018 were included and were split into training (March 2017-March 2018, n = 596) and internal validation test set (April 2018-June 2018, n = 204). RESULTS: Optimal cut-off values to categorize WMH volume as normal vs. mild/moderate/severe, normal/mild vs. moderate/severe, and normal/mild/moderate vs. severe were 3.4 mL, 9.6 mL, and 17.1 mL, respectively, and the AUC were 0.921, 0.956 and 0.960, respectively. When differentiating normal/mild vs. moderate/severe using WMH volume in the test set, sensitivity, specificity, and accuracy were 96.4%, 89.9%, and 91.7%, respectively. For distinguishing subcortical vascular dementia from others using WMH volume, sensitivity, specificity, and accuracy were 83.3%, 84.3%, and 84.3%, respectively. CONCLUSION: Deep learning-based automatic WMH segmentation may be an accurate and promising method for classifying the grades of the Fazekas scale and differentiating subcortical vascular dementia.

Modulating SEI formation via tuning the solvation sheath for lithium metal batteries.

The solvation sheath of Li+-glyme was modulated to enhance Li+-TFSI- association by adopting a highly polar solvent, especially water molecules, which affects the solid electrolyte interface (SEI) layer composition. By the Li+-TFSI- association, a TFSI- anion-derived SEI layer is formed on the Li metal anode, resulting in higher Li metal anode efficiency.

A missense variant in SHARPIN mediates Alzheimer's disease-specific brain damages.

Established genetic risk factors for Alzheimer's disease (AD) account for only a portion of AD heritability. The aim of this study was to identify novel associations between genetic variants and AD-specific brain atrophy. We conducted genome-wide association studies for brain magnetic resonance imaging measures of hippocampal volume and entorhinal cortical thickness in 2643 Koreans meeting the clinical criteria for AD (n = 209), mild cognitive impairment (n = 1449) or normal cognition (n = 985). A missense variant, rs77359862 (R274W), in the SHANK-associated RH Domain Interactor (SHARPIN) gene was associated with entorhinal cortical thickness (p = 5.0 × 10-9) and hippocampal volume (p = 5.1 × 10-12). It revealed an increased risk of developing AD in the mediation analyses. This variant was also associated with amyloid-ß accumulation (p = 0.03) and measures of memory (p = 1.0 × 10-4) and executive function (p = 0.04). We also found significant association of other SHARPIN variants with hippocampal volume in the Alzheimer's Disease Neuroimaging Initiative (rs3417062, p = 4.1 × 10-6) and AddNeuroMed (rs138412600, p = 5.9 × 10-5) cohorts. Further, molecular dynamics simulations and co-immunoprecipitation indicated that the variant significantly reduced the binding of linear ubiquitination assembly complex proteins, SHPARIN and HOIL-1 Interacting Protein (HOIP), altering the downstream NF-κB signaling pathway. These findings suggest that SHARPIN plays an important role in the pathogenesis of AD.

The Effect of Short-Term Wingate-Based High Intensity Interval Training on Anaerobic Power and Isokinetic Muscle Function in Adolescent Badminton Players.

Badminton requires both aerobic fitness and anaerobic ability for high performance. High intensity interval training (HIIT) is a traditional training method for improving fitness. In this study, we investigated whether short-term Wingate-based HIIT is effective for improving anaerobic activity in youth badminton players. Participants included 32 total badminton players in middle school and high school. They were divided into two groups (HIIT and moderate continuous training (MCT)). Training occurred for 4 weeks in total, three times a week, for 30 min each session. A body composition test, isokinetic knee muscle function test (60°/s, 240°/s), Wingate anaerobic power test (30 s × 5 sets), and analysis of heart rate changes were undertaken before and after training. After 4 weeks, body fat decreased in the HIIT group (p = 0.019); they also showed superior anaerobic ability compared to the MCT group. Differences were statistically significant in 3-4 sets (three sets, p = 0.019; four sets, p = 0.021). Regarding fatigue, the HIIT group showed superior fatigue improvement after training and better fatigue recovery ability in 3~5 sets (three sets, p = 0.032; four sets, p = 0.017; five sets, p = 0.003) than the MCT group. Neither group exhibited changes in heart rate during the anaerobic power test after training. Both groups improved in terms of isokinetic knee muscle function at 60°/s with no differences. However, at 240°/s, the HIIT group showed a statistically significant improvement (p = 0.035). Therefore, HIIT for 4 weeks improved the athletes' performance and physical strength.

Epitaxial Growth of Nanostructured Li2 Se on Lithium Metal for All Solid-State Batteries.

Lithium is considered to be the ultimate anode material for high energy-density rechargeable batteries. Recent emerging technologies of all solid-state batteries based on sulfide-based electrolytes raise hope for the practical use of lithium, as it is likely to suppress lithium dendrite growth. However, such devices suffer from undesirable side reactions and a degradation of electrochemical performance. In this work, nanostructured Li2 Se epitaxially grown on Li metal by chemical vapor deposition are investigated as a protective layer. By adjusting reaction time and cooling rate, a morphology of as-prepared Li2 Se is controlled, resulting in nanoparticles, nanorods, or nanowalls with a dominant (220) plane parallel to the (110) plane of the Li metal substrate. Uniaxial pressing the layers under a pressure of 50 MPa for a cell preparation transforms more compact and denser. Dual compatibility of the Li2 Se layers with strong chemical bonds to Li metal and uniform physical contact to a Li6 PS5 Csulfide electrolyte prevents undesirable side reactions and enables a homogeneous charge transfer at the interface upon cycling. As a result, a full cell coupled with a LiCoO2 -based cathode shows significantly enhanced electrochemical performance and demonstrates the practical use of Li anodes with Li2 Se layers for all solid-state battery applications.

Animal Evidence for Synergistic Induction of Hepatic Injury by Dietary Fat and Alcohol Consumption and Its Potential Mechanisms.

In contrast to nonalcoholic fatty liver disease (NAFLD), metabolic-associated fatty liver disease (MAFLD) as an innovative definition can coexist with significant alcohol consumption. Massive clinical observations have indicated that high-fat/-calorie diet induced metabolic dysfunction along with alcohol intake deteriorates steatotic liver injury. To explore the potential mechanisms of fatty diet together with alcohol-induced steatohepatitis, we adopted a rat model by comparing a half-dose combination of fat diet (20%) and alcohol (10%) with their corresponding double dose of 40% fat diet and 20% alcohol for 8 weeks. The notable alterations in histopathology, acceleration in the oxidation parameters (ROS, NO and lipid peroxidation) and serum transaminase levels were shown in the concomitant group. Concomitant use of a high-fat diet and alcohol provoked hepatic endoplasmic reticulum stress, but did not activate mitochondria-mediated apoptosis parameters compared to F. In contrast, the notable activation of caspase-12 and nuclear translocation of CCAAT/enhancer-binding protein (C/EBP) homologous protein (CHOP) were observed only in the combined treatment group. The concomitant dietary fat intake and alcohol consumption lead to liver injury initially and later to steatohepatitis by the overdose of fat or alcohol, and in which the CHOP and caspase-12 might be involved in synergistic acceleration of steatohepatitis through a mitochondria-independent manner.

Characteristics of HONO and its impact on O3 formation in the Seoul Metropolitan Area during the Korea-US Air Quality Study.

Photolysis of nitrous acid (HONO) is recognized as an early-morning source of OH radicals in the urban air. During the Korea-US air quality (KORUS-AQ) campaign, HONO was measured using quantum cascade - tunable infrared laser differential absorption spectrometer (QC-TILDAS) at Olympic Park in Seoul from 17 May, 2016 to 14 June, 2016. The HONO concentration was in the range of 0.07-3.46 ppbv, with an average of 0.93 ppbv. Moreover, it remained high from 00:00-05:00 LST. During this time, the mean concentration was higher during the high-O3 episodes (1.82 ppbv) than the non-episodes (1.20 ppbv). In the morning, the OH radicals that were produced from HONO photolysis were 50% higher (0.95 pptv) during the high-O3 episodes than the non-episodes. Diurnal variations in HOx and O3 concentrations were simulated by the F0AM model, which revealed a difference of ~20 ppbv in the daily maximum O3 concentrations between the high-O3 episodes and non-episodes. Furthermore, the HONO concentration increased with an increase in relative humidity (RH) up to 80%; the highest HONO was associated with the top 10% NO2 in each RH group, confirming that NO2 is one of the main precursors of HONO. At night, the conversion ratio of NO2 to HONO was estimated to be 0.88×10-2 h-1; this ratio was found to increase with an increase in RH. The Aitken mode particles (30-120 nm), which act as catalyst surfaces, exhibited a similar tendency with a conversion ratio that increased along with RH, indicating the coupling of surfaces with HONO conversion. Using an artificial neural network (ANN) model, HONO concentrations were successfully simulated with measured variables (r2 = 0.66 as an average of five models). Among these variables, NOx, aerosol surface area, and RH were found to be the main factors affecting the ambient HONO concentrations. The results reveal that RH facilitates the conversion of NO2 to HONO by constraining the availability of aerosol surfaces. This study demonstrates the coupling of HONO with the HOx-O3 cycle in the Seoul Metropolitan Area (SMA) and provides practical evidence of the heterogeneous formation of HONO by employing the ANN model.

Chitosan-Alginate-Pectin-coated Suspended-Liquid-Encapsulating (CAPSuLE) marbles for therapeutic agent storage and delivery.

Developing a cutting-edge system capable of ensuring long-lasting functionality of therapeutic agents and implementing diverse delivery modes is challenging. A quasi-spherical triple-layered capsule containing suspended liquid droplets and allowing multi-modal delivery of therapeutic agents in the aqueous phase was developed, primarily by adopting the core principles for creating liquid marbles. A naturally derived wettable polysaccharide-pectin-was utilized as a liquid-air interfacial barrier to keep the liquid droplets in the core zone. To tailor the pectin-coated droplet as a therapeutic agent carrier, anionic alginate and cationic chitosan layers were sequentially formed via additional interactions: physically stacking substances with structural chirality (pectin-alginate) and inducing electrostatic association to create the reversible complex coacervates (alginate-chitosan). The resulting system, which is called a Chitosan-Alginate-Pectin-coated Suspended-Liquid-Encapsulating (CAPSuLE) marble, had sufficient mechanical strength to resist external harsh environments and exhibited unique features: ecofriendly sustainability, responsiveness to external stimuli, coacervate-driven coalescence for linking adjacent marbles, and a self-repairing ability. The proposed CAPSuLE system can facilitate the adoption of the liquid-marble concept to biomedical fields, extending its applicability in the fields of biology and applied engineering.

Total Antioxidant Capacity in HBV Carriers, a Promising Biomarker for Evaluating Hepatic Fibrosis: A Pilot Study.

Oxidative stress plays a pivotal role in the progression of chronic hepatitis B; however, it is unclear whether the status of blood oxidative stress and antioxidant components differs depending on the degree of hepatic fibrosis. To explore the relationship between oxidative stress/antioxidant capacity and the extent of hepatic fibrosis, fifty-four subjects with liver fibrosis (5.5 ≤ liver stiffness measurement (LSM) score ≤ 16.0 kPa) by chronic hepatitis B virus (HBV) were analyzed. From the analysis of eight kinds of serum oxidative stress/antioxidant profiles and liver fibrosis degrees, the level of total antioxidant capacity (TAC) reflected a negative correlation with the severity of hepatic fibrosis (Pearson correlation, r = -0.35, p = 0.01). Moreover, TAC showed higher sensitivity (73.91%) than the aspartate transaminase (AST) to platelet ratio index (APRI, 56.52%) in the receiver operating characteristic (ROC) curves. Interestingly, the TAC level finely reflected the fibrosis degree in inactive carriers (HBV DNA < 2000 IU/mL), while the APRI did in active carriers (HBV DNA > 2000 IU/mL). In conclusion, TAC is a promising biomarker for evaluating the progression of liver fibrosis in patients with HBV, and this finding may indicate the involvement of TAC-composing factors in the pathogenesis of hepatic fibrosis in chronic HBV carriers.

A mechanistically interpretable model of the retinal neural code for natural scenes with multiscale adaptive dynamics.

The visual system processes stimuli over a wide range of spatiotemporal scales, with individual neurons receiving input from tens of thousands of neurons whose dynamics range from milliseconds to tens of seconds. This poses a challenge to create models that both accurately capture visual computations and are mechanistically interpretable. Here we present a model of salamander retinal ganglion cell spiking responses recorded with a multielectrode array that captures natural scene responses and slow adaptive dynamics. The model consists of a three-layer convolutional neural network (CNN) modified to include local recurrent synaptic dynamics taken from a linear-nonlinear-kinetic (LNK) model [1]. We presented alternating natural scenes and uniform field white noise stimuli designed to engage slow contrast adaptation. To overcome difficulties fitting slow and fast dynamics together, we first optimized all fast spatiotemporal parameters, then separately optimized recurrent slow synaptic parameters. The resulting full model reproduces a wide range of retinal computations and is mechanistically interpretable, having internal units that correspond to retinal interneurons with biophysically modeled synapses. This model allows us to study the contribution of model units to any retinal computation, and examine how long-term adaptation changes the retinal neural code for natural scenes through selective adaptation of retinal pathways.

Factors Influencing Customer Decisions to Use Online Food Delivery Service during the COVID-19 Pandemic.

Despite the popularity of online food delivery systems in the foodservice industry, there have been few studies into customers' decision-making process to use online food delivery services during the Coronavirus disease (COVID-19) pandemic. This study applied the technology acceptance model (TAM) to examine the factors affecting customers' intention to use online food delivery services. Results showed (a) the perceived usefulness affects customer's online food delivery usage directly and indirectly through customer attitude; (b) enjoyment and trust are also key factors determining behavior intention toward customer attitude using online food delivery services; (c) positive relationship between social influence and customer attitude; and (d) a positive relationship between customer attitude and behavior intention in the online food delivery service context. These findings provide theoretical and managerial implications that contribute to the online food delivery service industry.

Anti-hepatofibrotic effects of CGX, a standardized herbal formula: A multicenter randomized clinical trial.

BACKGROUND: Chunggan extract (CGX) is an herbal formula used for the treatment of chronic liver disease in traditional Korean medicine. Many preclinical studies have suggested its therapeutic or preventive effects on liver fibrosis. To evaluate the efficacy and safety of CGX, we conducted a randomized controlled clinical trial of CGX in patients with liver fibrosis diagnosed by Fibroscan. METHODS: We enrolled 67 subjects at two hospitals with chronic liver disorders with a 5.5 ≤ liver stiffness measurement (LSM) score ≤ 16 kPa. Subjects were randomly assigned at a 1:1:1 ratio with stratification (with/without concomitant use of antivirals) and orally administered CGX (1 g or 2 g) or placebo twice daily for 24 weeks. The end point was the change in instantaneous elasticity of the liver assessed by Fibroscan before and after treatment. RESULTS: LSM scores were significantly decreased in both the CGX1 g (2.5 ± 1.7 kPa, p < 0.01) and CGX2 g (1.9 ± 2.0 kPa, p < 0.05) groups compared to the placebo (0.6 ± 1.6 kPa) group. The change was also significant in 35 subjects without concomitant use of antiviral agents in the CGX1 g group (placebo 0.1 ± 1.4 kPa vs. 2.7 ± 1.6 kPa, p < 0.01) but not in those with concomitant antiviral use (p > 0.05). No notable adverse events were present. CONCLUSION: CGX appeared to have a pharmacological effect against liver fibrosis. Further studies to confirm the results are needed in the future using a larger sample size.