Inhibitory regulation of FoxO1 in PPARδ expression drives mitochondria dysfunction and insulin resistance.

Forkhead box protein O1 (FoxO1) regulates muscle growth, but the metabolic role of FoxO1 in skeletal muscle and its mechanisms remain unclear. To explore the metabolic role of FoxO1 in skeletal muscle, we generated skeletal muscle-specific FoxO1 inducible knockout (mFoxO1 iKO) mice and fed them a high-fat diet to induce obesity. We measured insulin sensitivity, fatty acid oxidation, mitochondrial function, and exercise capacity in obese mFoxO1 iKO mice, and assessed the correlation between FoxO1 and mitochondrial-related protein in the skeletal muscle of diabetic patients. Obese mFoxO1 iKO mice exhibited improved mitochondrial respiratory capacity, which was followed by attenuated insulin resistance, enhanced fatty acid oxidation, and improved skeletal muscle exercise capacity. Transcriptional inhibition of FoxO1 in peroxisome proliferator-activated receptor δ (PPARδ) expression was confirmed in skeletal muscle and deletion of PPARδ abolished the beneficial effects of FoxO1 deficiency. FoxO1 protein levels were higher in the skeletal muscle of diabetic patients and negatively correlated with PPARδ and electron transport chain protein levels. These findings highlight FoxO1 as a new repressor in PPARδ gene expression in skeletal muscle and suggest that FoxO1 links insulin resistance and mitochondrial dysfunction in skeletal muscle via PPARδ.

Impact of typhoons on anthropogenic nitrogen sources in Lake Sihwa, South Korea.

This study investigated the nitrate dual isotopic compositions (δ15NNO3 and δ18ONO3) of water samples to trace nitrate sources in Lake Sihwa, which encompasses various land-use types (e.g., urban, industry, wetland, and agriculture). The biogeochemical interactions of anthropogenic nitrogen sources (e.g., soil, road dust, and septic water) were also evaluated through multiple pathways from terrestrial boundaries to the water column. Based on increased concentrations of dissolved total nitrogen (DTN; 3.1 ± 1.6 mg/L) after typhoon, the variation of element stoichiometry (N:P:Si) in this system shifted to the relatively N-rich conditions (DIN/DIP; 14.1 ± 8.1, DIN/DSi; 1.4 ± 1.8), potentially triggering the occurrence of harmful algal blooms. Furthermore, discriminative isotopic compositions (δ15NNO3; 4.0 ± 2.1 ‰, δ18ONO3; 6.1 ± 4.3 ‰) after the typhoon suggested the increased DTN input of anthropogenic origins within Lake Sihwa would be mainly transported from urban sources (76 ± 9 %). Consequently, the isotopic-based approach may be useful for effective water quality management under increased anthropogenic activities near aquatic systems.

Atmospheric mercury uptake and accumulation in forests dependent on climatic factors.

The environmental and climatic factors dictating atmospheric mercury (Hg) uptake by foliage and accumulation within the forest floor are evaluated across six mountain sites, South Korea, using Hg concentration and Hg stable isotope analyses. The isotope ratios of total gaseous Hg (TGM) at six mountains are explained by local anthropogenic Hg emission influence and partly by mountain elevation and wind speed. The extent to which TGM is taken up by foliage is not dependent on the site-specific TGM concentration, but by the local wind speed, which facilitates TGM passage through dense deciduous canopies in the Korean forests. This is depicted by the significant positive relationship between wind speed and foliage Hg concentration (r2 = 0.92, p < 0.05) and the magnitude of δ202Hg shift from TGM to foliage (r2 = 0.37, p > 0.05), associated with TGM uptake and oxidation by foliar tissues. The litter and topsoil Hg concentrations and isotope ratios reveal relationships with a wide range of factors, revealing lower Hg level and greater isotopic fractionation at sites with low elevation, high wind speed, and high mean warmest temperature. We attribute this phenomenon to active TGM re-emission from the forest floor at sites with high wind speed and high temperature, caused by turnover of labile organic matter and decomposition. In contrast to prior studies, we observe no significant effect of precipitation on forest Hg accumulation but precipitation appears to reduce foliage-level Hg uptake by scavenging atmospheric Hg species available for stomata uptake. The results of this study would enable better prediction of future atmospheric and forest Hg influence under climate change.

Identifying the external N and Hg inputs to the estuary ecosystem based on the triple isotopic information (δ15NNO3, Δ17ONO3 and δ18ONO3).

The supply and sources of N and Hg in the Geum estuary of the western coast of Korea were evaluated. Triple isotope proxies (δ15NNO3, Δ17ONO3 and δ18ONO3) of NO3- combined with conservative mixing between river and ocean waters were used to improve isotope finger-printing methods. The N pool in the Geum estuary was primarily influenced by the Yellow Sea water, followed by riverine discharge (821 × 106 mol yr-1) and atmospheric deposition (51 × 106 mol yr-1). The influence of the river was found to be greater for Hg than that of the atmosphere. The triple isotope proxies revealed that the riverine and atmospheric inputs of N have been affected by septic wastes and fossil fuel burning, respectively. From the inner estuary towards offshore region, the influence of the river diminishes, thus increasing the relative impact of the atmosphere. Moreover, the isotope proxies showed a significant influence of N assimilation in February and nitrification in May.

Quantitative source tracking for organic foulants in ultrafiltration membrane using stable isotope probing approach.

Quantitatively identifying the primary sources of organic membrane fouling is essential for the effective implementation of membrane technology and optimal water resource management prior to the treatment. This study leveraged carbon stable isotope tracers to estimate the quantitative contributions of various organic sources to membrane fouling in an ultrafiltration system. Effluent organic matter (EfOM) and aquatic natural organic matter (NOM), two common sources, were combined in five different proportions to evaluate their mixed effects on flux decline and the consequent fouling behaviors. Generally, biopolymer (BP) and low molecular weight neutral (LMWN) size fractions - abundantly present in EfOM - were identified as significant contributors to reversible and irreversible fouling, respectively. Fluorescence spectroscopy disclosed that a protein-like component notably influenced overall membrane fouling, whereas humic-like components were predominantly responsible for irreversible fouling rather than reversible fouling. Fluorescence index (FI) and biological index (BIX), common fluorescence source tracers, showed promise in determining the source contribution for reversible foulants. However, these optical indices were insufficient in accurately determining individual source contributions to irreversible fouling, resulting in inconsistencies with the observed hydraulic analysis. Conversely, applying a carbon stable isotope-based mixing model yielded reasonable estimates for all membrane fouling. The contribution of EfOM surpassed 60 % for reversible fouling and increased with its content in DOM source mixtures. In contrast, aquatic NOM dominated irreversible fouling, contributing over 85 %, regardless of the source mixing ratios. This study emphasizes the potential of stable isotope techniques in accurately estimating the contributions of different organic matter sources to both reversible and irreversible membrane fouling.

Development and Validation of a Convolutional Neural Network Model to Predict a Pathologic Fracture in the Proximal Femur Using Abdomen and Pelvis CT Images of Patients With Advanced Cancer.

BACKGROUND: Improvement in survival in patients with advanced cancer is accompanied by an increased probability of bone metastasis and related pathologic fractures (especially in the proximal femur). The few systems proposed and used to diagnose impending fractures owing to metastasis and to ultimately prevent future fractures have practical limitations; thus, novel screening tools are essential. A CT scan of the abdomen and pelvis is a standard modality for staging and follow-up in patients with cancer, and radiologic assessments of the proximal femur are possible with CT-based digitally reconstructed radiographs. Deep-learning models, such as convolutional neural networks (CNNs), may be able to predict pathologic fractures from digitally reconstructed radiographs, but to our knowledge, they have not been tested for this application. QUESTIONS/PURPOSES: (1) How accurate is a CNN model for predicting a pathologic fracture in a proximal femur with metastasis using digitally reconstructed radiographs of the abdomen and pelvis CT images in patients with advanced cancer? (2) Do CNN models perform better than clinicians with varying backgrounds and experience levels in predicting a pathologic fracture on abdomen and pelvis CT images without any knowledge of the patients' histories, except for metastasis in the proximal femur? METHODS: A total of 392 patients received radiation treatment of the proximal femur at three hospitals from January 2011 to December 2021. The patients had 2945 CT scans of the abdomen and pelvis for systemic evaluation and follow-up in relation to their primary cancer. In 33% of the CT scans (974), it was impossible to identify whether a pathologic fracture developed within 3 months after each CT image was acquired, and these were excluded. Finally, 1971 cases with a mean age of 59 ± 12 years were included in this study. Pathologic fractures developed within 3 months after CT in 3% (60 of 1971) of cases. A total of 47% (936 of 1971) were women. Sixty cases had an established pathologic fracture within 3 months after each CT scan, and another group of 1911 cases had no established pathologic fracture within 3 months after CT scan. The mean age of the cases in the former and latter groups was 64 ± 11 years and 59 ± 12 years, respectively, and 32% (19 of 60) and 53% (1016 of 1911) of cases, respectively, were female. Digitally reconstructed radiographs were generated with perspective projections of three-dimensional CT volumes onto two-dimensional planes. Then, 1557 images from one hospital were used for a training set. To verify that the deep-learning models could consistently operate even in hospitals with a different medical environment, 414 images from other hospitals were used for external validation. The number of images in the groups with and without a pathologic fracture within 3 months after each CT scan increased from 1911 to 22,932 and from 60 to 720, respectively, using data augmentation methods that are known to be an effective way to boost the performance of deep-learning models. Three CNNs (VGG16, ResNet50, and DenseNet121) were fine-tuned using digitally reconstructed radiographs. For performance measures, the area under the receiver operating characteristic curve, accuracy, sensitivity, specificity, precision, and F1 score were determined. The area under the receiver operating characteristic curve was used to evaluate three CNN models mainly, and the optimal accuracy, sensitivity, and specificity were calculated using the Youden J statistic. Accuracy refers to the proportion of fractures in the groups with and without a pathologic fracture within 3 months after each CT scan that were accurately predicted by the CNN model. Sensitivity and specificity represent the proportion of accurately predicted fractures among those with and without a pathologic fracture within 3 months after each CT scan, respectively. Precision is a measure of how few false-positives the model produces. The F1 score is a harmonic mean of sensitivity and precision, which have a tradeoff relationship. Gradient-weighted class activation mapping images were created to check whether the CNN model correctly focused on potential pathologic fracture regions. The CNN model with the best performance was compared with the performance of clinicians. RESULTS: DenseNet121 showed the best performance in identifying pathologic fractures; the area under the receiver operating characteristic curve for DenseNet121 was larger than those for VGG16 (0.77 ± 0.07 [95% CI 0.75 to 0.79] versus 0.71 ± 0.08 [95% CI 0.69 to 0.73]; p = 0.001) and ResNet50 (0.77 ± 0.07 [95% CI 0.75 to 0.79] versus 0.72 ± 0.09 [95% CI 0.69 to 0.74]; p = 0.001). Specifically, DenseNet121 scored the highest in sensitivity (0.22 ± 0.07 [95% CI 0.20 to 0.24]), precision (0.72 ± 0.19 [95% CI 0.67 to 0.77]), and F1 score (0.34 ± 0.10 [95% CI 0.31 to 0.37]), and it focused accurately on the region with the expected pathologic fracture. Further, DenseNet121 was less likely than clinicians to mispredict cases in which there was no pathologic fracture than cases in which there was a fracture; the performance of DenseNet121 was better than clinician performance in terms of specificity (0.98 ± 0.01 [95% CI 0.98 to 0.99] versus 0.86 ± 0.09 [95% CI 0.81 to 0.91]; p = 0.01), precision (0.72 ± 0.19 [95% CI 0.67 to 0.77] versus 0.11 ± 0.10 [95% CI 0.05 to 0.17]; p = 0.0001), and F1 score (0.34 ± 0.10 [95% CI 0.31 to 0.37] versus 0.17 ± 0.15 [95% CI 0.08 to 0.26]; p = 0.0001). CONCLUSION: CNN models may be able to accurately predict impending pathologic fractures from digitally reconstructed radiographs of the abdomen and pelvis CT images that clinicians may not anticipate; this can assist medical, radiation, and orthopaedic oncologists clinically. To achieve better performance, ensemble-learning models using knowledge of the patients' histories should be developed and validated. The code for our model is publicly available online at https://github.com/taehoonko/CNN_path_fx_prediction . LEVEL OF EVIDENCE: Level III, diagnostic study.

Systematic tracing of nitrate sources in a complex river catchment: An integrated approach using stable isotopes and hydrological models.

Quantitative estimation for tracking the transport of various nitrate sources is required to effectively manage nitrate loading in complex river systems. In this study, we validated an integrated framework using field isotopic data (δ15NNO3 and δ18ONO3) of nitrates and hydrological modeling (hydrological simulation program FORTRAN; HSPF) to determine anthropogenic nitrate flux among different land-use types within a watershed. Nitrate isotopic compositions showed different ranges among four land-use types (4.9 to 15.5‰ for δ15NNO3, -4.9 to 12.1‰ for δ18ONO3), reflecting the different nitrate sources (sewage, synthetic fertilizer, effluent and soil) within watersheds. Based on the integration of HSPF modeling, we also found that total nitrate loads might be partially controlled by hydrological conditions such as water discharge (12,040.3-22,793.2 L/s) from upstream to downstream. Among the nitrate sources, the sewage transport showed unique enhancement near urban boundaries, along with an increase in total nitrate load (>193.5 NO3-N g/s km2) in downstream areas. In addition, the isotopic- and model-based nitrate fluxes showed good correlation for urban sources (R2=0.73, p < 0.05) but poor correlations for agriculture-dominated land use (R2=0.13, p > 0.05), reflecting the potential influence of surface runoff and ground infiltration into the watershed. Consequently, this research provided useful information to establish nitrogen management policy controlling point and non-point nitrate source loads in various land-use types for the restoration of water quality and aquatic ecosystem in the complex river system. Considering the recent increase in human activities near aquatic environments, this framework would be effective for individually estimating the quantitative contributions of anthropogenic nitrate sources transported along river-coastal systems.

Innovative approach to reveal source contribution of dissolved organic matter in a complex river watershed using end-member mixing analysis based on spectroscopic proxies and multi-isotopes.

Dissolved organic matter (DOM) in river watersheds dynamically changes based on its source during a monsoon period with storm event. However, the variations in DOM in urban and rural river watersheds that are dominated by point and non-point sources have not been adequately explored to date. We developed an innovative approach to reveal DOM sources in complex river watershed systems during pre-monsoon, monsoon, and post-monsoon periods using end-member mixing analysis (EMMA) by combining multi-isotope values (δ13C-DOC, δ15N-NO3 and δ18O-NO3) and spectroscopic indices (fluorescence index [FI], biological index [BIX], humification index [HIX], and specific UV absorbance [SUVA]). Several potential end-members of DOM sources were collected from watersheds, including top-soils, groundwater, plant group (fallen leaves, riparian plants, suspended algae), and different effluents (cattle and pig livestock, agricultural land, urban, industry facility, swine treatment facility and wastewater treatment facility). Concentrations of dissolved organic carbon, dissolved organic nitrogen, NO3-N, and NH4-N increased during the monsoon period with an increase in the input of anthropogenic DOM, which have higher HIX values owing to the flushing effect. The results of EMMA indicate that soil and agricultural effluents accounted for a substantial contribution of anthropogenic DOM at varying rates based on seasons. We also found that results of EMMA based on combining spectroscopic indices and δ13C-DOC isotope values were more accurate in tracing DOM sources with respect to land-use characteristics compared to applying only spectroscopic indices. The positive relationship between FI, BIX and δ15N-NO3 were revealed that nitrate would be decomposed from DOM affected by intensive agricultural activities. In addition, consistent with the EMMA results, the molecular composition of the DOM was clearly evidenced by a large number of CHON formulas, accounting for over 50% of the total characterized compounds, including pesticides and pharmaceuticals used in agriculture farmland and livestock. Our results clearly demonstrated that EMMA based on combing multi-stable isotopes and spectroscopic indices could be trace the DOM source, which is important for understanding changes in the DOM quality, and application of nitrate isotopes and molecular analysis supports in-depth interpretation. This study provides easy and intuitive techniques for the estimation of the relative impacts of DOM sources in complex river watersheds, which can be verified in various ways rather than relying on a single technique approach.

The Gut Bacterial Community of Chlaenius pallipes (Coleoptera: Carabidae) Associates with Their Habitat and Morphology.

We investigated whether the gut bacterial community of Chlaenius pallipes could represent the health conditions of individuals or populations based on where these beetles inhabit. Considering the ecological traits of the species, the gut bacterial communities of carabid populations inhabiting stable or unstable habitats were compared. Food resource quality (δ15N) and morphological shape, especially body and wing size, may be significant factors that directly or indirectly affect the gut bacterial community of carabid beetles. Firmicutes (51.7%) and Proteobacteria (36.3%) were the predominant phyla in the gut bacterial community of C. pallipes. A significant difference in the gut bacterial community structure was observed between organisms inhabiting unstable and stable habitats in this study. Wing size, as estimated by centroid size, was correlated with differences in the gut bacterial community composition of the species. Even if a factor is not strong enough to determine the survival of carabid beetles, the composition of the gut bacterial community can change. We found that although each individual has a large variation in the gut bacterial community composition, the gut bacterial community can be used to evaluate the condition of each habitat through consistent investigation. Habitat assessment based on changes in the number of carabid beetle species and their composition requires relatively long-term research; however, the gut bacterial community of carabid beetles can help identify short-term environmental changes.

Ecological Traits of Fish for Mercury Biomonitoring: Insights from Compound-Specific Nitrogen and Stable Mercury Isotopes.

We coupled compound-specific isotopic analyses of nitrogen (N) in amino acids (δ15NGlu, δ15NPhe) and mercury stable isotopes (δ202Hg, Δ199Hg) to quantify ecological traits governing the concentration, variability, and source of Hg in largemouth bass (LB) and pike gudgeon (PG) across four rivers, South Korea. PG displayed uniform Hg concentration (56-137 ng/g), trophic position (TPcorrected; 2.6-3.0, n = 9), and N isotopes in the source amino acid (δ15NPhe; 7-13‰), consistent with their specialist feeding on benthic insects. LB showed wide ranges in Hg concentration (45-693 ng/g), TPcorrected (2.8-3.8, n = 14), and δ15NPhe (1.3-16‰), reflecting their opportunistic feeding behavior. Hg sources assessed using Hg isotopes reveal low and uniform Δ199Hg in PG (0.20-0.49‰), similar to Δ199Hg reported in sediments. LB displayed site-specific δ202Hg (-0.61 to -0.04‰) and Δ199Hg (0.53-1.09‰). At the Yeongsan River, LB displayed elevated Δ199Hg and low δ15NPhe, consistent with Hg and N sourced from the atmosphere. LB at the Geum River displayed low Δ199Hg and high δ15NPhe, both similar to the isotope values of anthropogenic sources. Our results suggest that a specialist fish (PG) with consistent ecological traits and Hg concentration is an effective bioindicator species for Hg. When accounting for Hg sources, however, LB better captures site-specific Hg sources.

Optimal Intervention for Initial Treatment of Anastomotic Biliary Complications After Right Lobe Living Donor Liver Transplantation.

Background: This study evaluated endoscopic retrograde cholangiopancreatography (ERCP) and percutaneous transhepatic biliary drainage (PTBD) as interventions for patients with anastomotic biliary complications (ABC) after living donor liver transplantation (LDLT). Methods: Prospectively collected data of patients who were diagnosed with ABC after LDLT between January 2013 and June 2017 were retrospectively reviewed. Results: There were 57 patients who underwent LDLT with a right liver graft using duct-to-duct biliary reconstruction and experienced ABC. Among the patients with RAD involvement, there were no significant differences in the intervention success (p = 0.271) and patency rates (p = 0.267) between ERCP and PTBD. Similarly, among the patients with RPD involvement, there were no significant differences in the intervention success (p = 0.148) and patency rates (p = 0.754) between the two procedures. Graft bile duct variation (p = 0.013) and a large angle between the recipient and graft bile duct (R-G angle) (p = 0.012) significantly increased the likelihood of failure of ERCP in the RAD. When the R-G angle was greater than 47.5°, the likelihood of ERCP failure increased. Conclusion: We recommend PTBD when graft bile duct variation is presented in patients with RAD involvement and/or when the R-G angle is greater than 47.5°.

Identifying pollution sources of sediment in Lake Jangseong, Republic of Korea, through an extensive survey: Internal disturbances of past aquaculture sedimentation.

Lake sediments are important sinks of various pollutants and preserve historical pollution records caused by anthropogenic activities. Recently, the sediments of Lake Jangseong, South Korea were first detected with high concentrations of organic matter (ignition loss [IL]; total organic carbon [TOC]), nutrients (total nitrogen [TN]; total phosphorus [TP]), and some heavy metals (Zn, Cu, Cd, and Hg). Here, we identified the origins of these concentrations accumulated in the sediments using extensive surveys and various assessments. Sediment pollution assessed by sediment quality guidelines, pollution load index, and potential ecological risk index was found to be of serious concern for IL, TN, TP, and Cd. Thus, we assessed pollution sources through spatial, grid, and vertical distributions and found that the high pollutant concentrations detected in 2020 were confirmed only at a certain location in the lake. Additionally, similar results were detected in the sedimentary layer below a sediment core at a depth of 15.0 cm. The high pollutant concentrations locally occurred around a "hotspot" site that was previously frequently used for aquaculture activities, indicating that the pollutants were accumulated in sediments owing to past cage fish farming rather than from influx of externally sourced pollution. Furthermore, chemical fractionation of phosphorus and heavy metals and assessment of stable isotopes (13C and 15N) of organic matter suggested that the pollutants in the sediments at the "hotspot" sites had different origins than those found at other sites. Accordingly, the by-products discharged after cage fish farming, such as residual feed, fish meal, and waste, accumulated in the sediments and were then exposed to natural internal disturbances caused by the effects of climate change-induced drought. This local distribution and the phosphorus and heavy metal chemical fraction results with low elution potential indicated that the pollutants in the sediments of Lake Jangseong had negligible impact on water quality.

Ultrasensitive and real-time optical detection of cellular oxidative stress using graphene-covered tunable plasmonic interfaces.

Reactive oxygen species (ROS) regulate various physiological and pathological conditions in cells by interacting with signaling molecules and inducing oxidative stress. Therefore, sensitive monitoring of ROS levels in living cells is important to track cellular state and study the complex role of ROS in the development of various pathologies. Herein, we present an optically tunable plasmonic interface covered with graphene to monitor cellular ROS levels with superior sensitivity and cellular comfortability. As a sensing principle, we employed plasmon resonance energy transfer (PRET)-based spectral quenching dips modulated by redox-active cytochrome c for real-time monitoring. By transferring graphene layers to plasmonic nanoparticles immobilized on a glass substrate, the scattering profiles of the nanoprobes were adjusted in terms of the position, width, and intensity of the peaks to determine the optimal conditions for measuring the PRET signal. Using the optimized graphene-covered plasmonic nanoprobe, we obtained calibration curves over a wide concentration range from femtomoles to millimoles for hydrogen peroxide based on the change in the PRET signal. Before monitoring cellular ROS, we confirmed that a high density of cells adhered well to the graphene-covered plasmonic interface by observing immunofluorescence images of the cytoskeleton of the immobilized cells. Finally, we monitored the real-time ROS generated by the cells under oxidative stress conditions by directly measuring the spectral changes of the probes around the cells. We believe that the proposed graphene-covered tunable plasmonic interface has versatile applicability for investigating cellular stress and disease progression by monitoring ROS levels under various cellular conditions.

Metalloendopeptidase ADAM-like Decysin 1 (ADAMDEC1) in Colonic Subepithelial PDGFRα+ Cells Is a New Marker for Inflammatory Bowel Disease.

Metalloendopeptidase ADAM-Like Decysin 1 (ADAMDEC1) is an anti-inflammatory peptidase that is almost exclusively expressed in the gastrointestinal (GI) tract. We have recently found abundant and selective expression of Adamdec1 in colonic mucosal PDGFRα+ cells. However, the cellular origin for this gene expression is controversial as it is also known to be expressed in intestinal macrophages. We found that Adamdec1 mRNAs were selectively expressed in colonic mucosal subepithelial PDGFRα+ cells. ADAMDEC1 protein was mainly released from PDGFRα+ cells and accumulated in the mucosal layer lamina propria space near the epithelial basement membrane. PDGFRα+ cells significantly overexpressed Adamdec1 mRNAs and protein in DSS-induced colitis mice. Adamdec1 was predominantly expressed in CD45- PDGFRα+ cells in DSS-induced colitis mice, with only minimal expression in CD45+ CD64+ macrophages. Additionally, overexpression of both ADAMDEC1 mRNA and protein was consistently observed in PDGFRα+ cells, but not in CD64+ macrophages found in human colonic mucosal tissue affected by Crohn's disease. In summary, PDGFRα+ cells selectively express ADAMDEC1, which is localized to the colon mucosa layer. ADAMDEC1 expression significantly increases in DSS-induced colitis affected mice and Crohn's disease affected human tissue, suggesting that this gene can serve as a diagnostic and/or therapeutic target for intestinal inflammation and Crohn's disease.

Hierarchically Structured Conductive Polymer Binders with Silver Nanowires for High-Performance Silicon Anodes in Lithium-Ion Batteries.

Silicon (Si) anodes in lithium-ion batteries (LIBs) suffer from huge volume changes that lead to a rapid capacity decrease and short cycle life. A conductive binder can be a key factor to overcome this issue, maintaining continuous electron paths under pulverization of Si. Herein, composites of poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) and poly(vinyl alcohol) (PVA) are augmented with poly(ethylene glycol) (PEG) and poly(ethylene oxide) (PEO) as a binder for Si anodes, which forms hierarchical structures due to different chain lengths of PEG and PEO. The integration of PEG and PEO imparts higher electrical conductivity (∼40%) and stretchability (∼60%) through densely spread hydrogen bonding and cross-linking, compared to conductive polymer binders with PEO or PEG. Further, a silver nanowire (AgNW) network combined with the polymer binder supplies an effective three-dimensional (3D) electrical path, sufficient void space to buffer the volume changes, and highly adhesive interaction with the current collector. The fabricated Si anode demonstrates a higher specific capacity of 1066 mAh g-1 at 0.8 A g-1 after 100 cycles and improved rate capability.

Flexible Neural Network Realized by the Probabilistic SiOx Memristive Synaptic Array for Energy-Efficient Image Learning.

The human brain's neural networks are sparsely connected via tunable and probabilistic synapses, which may be essential for performing energy-efficient cognitive and intellectual functions. In this sense, the implementation of a flexible neural network with probabilistic synapses is a first step toward realizing the ultimate energy-efficient computing framework. Here, inspired by the efficient threshold-tunable and probabilistic rod-to-rod bipolar synapses in the human visual system, a 16 × 16 crossbar array comprising the vertical form of gate-tunable probabilistic SiOx memristive synaptic barristor utilizing the Si/graphene heterojunction is designed and fabricated. Controllable stochastic switching dynamics in this array are achieved via various input voltage pulse schemes. In particular, the threshold tunability via electrostatic gating enables the efficient in situ alteration of the probabilistic switching activation (PAct ) from 0 to 1.0, and can even modulate the degree of the PAct change. A drop-connected algorithm based on the PAct is constructed and used to successfully classify the shapes of several fashion items. The suggested approach can decrease the learning energy by up to ≈2,116 times relative to that of the conventional all-to-all connected network while exhibiting a high recognition accuracy of ≈93 %.

Effect of particle size on in vivo performances of long-acting injectable drug suspension.

Herein, entecavir-3-palmitate (EV-P), an ester prodrug of entecavir (EV), was employed as a model drug, and the effect of drug particle size on in vivo pharmacokinetic profiles and local inflammatory responses, and those associations were evaluated following intramuscular (IM) injection. EV-P crystals with different median diameters (0.8, 2.3, 6.3, 15.3 and 22.6 µm) were prepared using the anti-solvent crystallization method, with analogous surface charges (-10.7 ~ -4.7 mV), and crystallinity (melting point, 160-170 °C). EV-P particles showed size-dependent in vitro dissolution profiles under sink conditions, exhibiting a high correlation between the median diameter and Hixon-Crowell's release rate constant (r2 = 0.94). Following IM injection in rats (1.44 mg/kg as EV), the pharmacokinetic profile of EV exhibited marked size-dependency; 0.8 µm-sized EV-P particles about 1.6-, 3.6-, and 5.6-folds higher systemic exposure, compared to 6.3, 15.3, and 22.6 µm-sized particles, respectively. This pharmacokinetic pattern, depending on particle size, was also highly associated with histopathological responses in the injected tissue. The smaller EV-P particles (0.8 or 2.3 µm) imparted the larger inflammatory lesion after 3 days, lower infiltration of inflammatory cells, and thinner fibroblastic bands around depots after 4 weeks. Conversely, severe fibrous isolation with increasing particle size augmented the drug remaining at injection site over 4 weeks, impeding the dissolution and systemic exposure. These findings regarding the effects of formulation variable on the in vivo behaviors of long-acting injectable suspension, provide constructive knowledge toward the improved design in poorly water-soluble compounds.

Extracellular Matrix Biomarkers in Colorectal Cancer.

The cellular microenvironment composition and changes therein play an extremely important role in cancer development. Changes in the extracellular matrix (ECM), which constitutes a majority of the tumor stroma, significantly contribute to the development of the tumor microenvironment. These alterations within the ECM and formation of the tumor microenvironment ultimately lead to tumor development, invasion, and metastasis. The ECM is composed of various molecules such as collagen, elastin, laminin, fibronectin, and the MMPs that cleave these protein fibers and play a central role in tissue remodeling. When healthy cells undergo an insult like DNA damage and become cancerous, if the ECM does not support these neoplastic cells, further development, invasion, and metastasis fail to occur. Therefore, ECM-related cancer research is indispensable, and ECM components can be useful biomarkers as well as therapeutic targets. Colorectal cancer specifically, is also affected by the ECM and many studies have been conducted to unravel the complex association between the two. Here we summarize the importance of several ECM components in colorectal cancer as well as their potential roles as biomarkers.

Tracking nitrate sources in agricultural-urban watershed using dual stable isotope and Bayesian mixing model approach: Considering N transformation by Lagrangian sampling.

A dual isotopes approach and the Bayesian isotope mixing model were applied to trace nitrogen pollution sources and to quantify their relative contribution to river water quality. We focused on two points to enhance the applicability of the method: 1) Direct measurement on the end-members to distinguish "sewage" and "manure" which used to be grouped in one pollution source as their isotope ranges overlap; 2) The Lagrangian sampling method was applied to consider the transport of nitrogen pollutants in a long river so that any fractionation process can be dealt with in the given Bayesian modeling framework. The results of the analysis confirmed the NO3- isotope composition in the river of interest to be within the range of NO3- with origins in "NH4+ in fertilizer", "Soil N", and "Manure and sewage" pollution. This suggests that nitrogen pollution is mostly attributed to anthropogenic sources. The δ18O NO3 value follows the range +2.5∼+15.0‰, implying that NO3- in the river is mainly derived from nitrification, and possible nitrification in groundwater or waterfront other than surface water. The ratio of the concentration of δ15N NO3 to that of δ18O NO3, and the corresponding regression equation indicates that the denitrification effect in surface water was insignificant during the study period. From the results of the contribution ratio of each source, improving the water quality of the discharge from the sewage treatment plants was proved to be the key factor to reduce nitrogen pollution in the river.