Discharge protocol in acute pancreatitis: an international survey and cohort analysis.

There are several overlapping clinical practice guidelines in acute pancreatitis (AP), however, none of them contains suggestions on patient discharge. The Hungarian Pancreatic Study Group (HPSG) has recently developed a laboratory data and symptom-based discharge protocol which needs to be validated. (1) A survey was conducted involving all members of the International Association of Pancreatology (IAP) to understand the characteristics of international discharge protocols. (2) We investigated the safety and effectiveness of the HPSG-discharge protocol. According to our international survey, 87.5% (49/56) of the centres had no discharge protocol. Patients discharged based on protocols have a significantly shorter median length of hospitalization (LOH) (7 (5;10) days vs. 8 (5;12) days) p < 0.001), and a lower rate of readmission due to recurrent AP episodes (p = 0.005). There was no difference in median discharge CRP level among the international cohorts (p = 0.586). HPSG-protocol resulted in the shortest LOH (6 (5;9) days) and highest median CRP (35.40 (13.78; 68.40) mg/l). Safety was confirmed by the low rate of readmittance (n = 35; 5%). Discharge protocol is necessary in AP. The discharge protocol used in this study is the first clinically proven protocol. Developing and testifying further protocols are needed to better standardize patients' care.

Hybrid Deep Neural Network Framework Combining Skeleton and Gait Features for Pathological Gait Recognition.

Human skeleton data obtained using a depth camera have been used for pathological gait recognition to support doctor or physician diagnosis decisions. Most studies for skeleton-based pathological gait recognition have used either raw skeleton sequences directly or gait features, such as gait parameters and joint angles, extracted from raw skeleton sequences. We hypothesize that using skeleton, joint angles, and gait parameters together can improve recognition performance. This study aims to develop a deep neural network model that effectively combines different types of input data. We propose a hybrid deep neural network framework composed of a graph convolutional network, recurrent neural network, and artificial neural network to effectively encode skeleton sequences, joint angle sequences, and gait parameters, respectively. The features extracted from three different input data types are fused and fed into the final classification layer. We evaluate the proposed model on two different skeleton datasets (a simulated pathological gait dataset and a vestibular disorder gait dataset) that were collected using an Azure Kinect. The proposed model, with multiple types of input, improved the pathological gait recognition performance compared to single input models on both datasets. Furthermore, it achieved the best performance among the state-of-the-art models for skeleton-based action recognition.

Antiproliferative Activity of Krukovine by Regulating Transmembrane Protein 139 (TMEM139) in Oxaliplatin-Resistant Pancreatic Cancer Cells.

Krukovine (KV) is an alkaloid isolated from the bark of Abuta grandifolia (Mart.) Sandw. (Menispermaceae) with anticancer potential in some cancers with KRAS mutations. In this study, we explored the anticancer efficacy and mechanism of KV in oxaliplatin-resistant pancreatic cancer cells and patient-derived pancreatic cancer organoids (PDPCOs) with KRAS mutation. After treatment with KV, mRNA and protein levels were determined by RNA-seq and Western blotting, respectively. Cell proliferation, migration, and invasion were measured by MTT, scratch wound healing assay, and transwell analysis, respectively. Patient-derived pancreatic cancer organoids (PDPCOs) with KRAS mutations were treated with KV, oxaliplatin (OXA), and a combination of KV and OXA. KV suppresses tumor progression via the downregulation of the Erk-RPS6K-TMEM139 and PI3K-Akt-mTOR pathways in oxaliplatin-resistant AsPC-1 cells. Furthermore, KV showed an antiproliferative effect in PDPCOs, and the combination of OXA and KV inhibited PDPCO growth more effectively than either drug alone.

A Deep Learning-Based Semantic Segmentation Model Using MCNN and Attention Layer for Human Activity Recognition.

With the development of wearable devices such as smartwatches, several studies have been conducted on the recognition of various human activities. Various types of data are used, e.g., acceleration data collected using an inertial measurement unit sensor. Most scholars segmented the entire timeseries data with a fixed window size before performing recognition. However, this approach has limitations in performance because the execution time of the human activity is usually unknown. Therefore, there have been many attempts to solve this problem through the method of activity recognition by sliding the classification window along the time axis. In this study, we propose a method for classifying all frames rather than a window-based recognition method. For implementation, features extracted using multiple convolutional neural networks with different kernel sizes were fused and used. In addition, similar to the convolutional block attention module, an attention layer to each channel and spatial level is applied to improve the model recognition performance. To verify the performance of the proposed model and prove the effectiveness of the proposed method on human activity recognition, evaluation experiments were performed. For comparison, models using various basic deep learning modules and models, in which all frames were classified for recognizing a specific wave in electrocardiography data were applied. As a result, the proposed model reported the best F1-score (over 0.9) for all kinds of target activities compared to other deep learning-based recognition models. Further, for the improvement verification of the proposed CEF method, the proposed method was compared with three types of SW method. As a result, the proposed method reported the 0.154 higher F1-score than SW. In the case of the designed model, the F1-score was higher as much as 0.184.

Probing Interfacial Charge Transfer between Amyloid-ß and Graphene during Amyloid Fibrillization Using Raman Spectroscopy.

Charge transfer plays a key role in the structural transformation of amyloid-ß proteins (Aßs), as it fibrillizes from small monomers to intermediate oligomers and to ordered fibrils. While the protein fibrillization states have been identified using cryo-electron microscopy, X-ray diffraction, Raman, infrared, terahertz spectroscopies, etc., there is little known about the electronic states during the fibrilization of Aß protein. Here, we probe the charge transfer of Aß42 proteins at different aggregation stages adsorbed on monolayer graphene (Gr) and molybdenum disulfide (MoS2) using Raman spectroscopy. Monomers, oligomers, and fibrils prepared in buffer solutions were deposited and dried separately on Gr and MoS2 where well-established characteristic Raman modes (G, 2D for Gr and E2g, A1g for MoS2) were monitored. The shifts in Raman parameters showed that the small Aß monomers withdraw electrons, whereas fibrils donate electrons to Gr and MoS2. Oligomers undergo transient charge states near the neutrality point. This is explained in terms of modulated carrier concentration in Gr and MoS2. This finding provides insight into the electronic properties of Aßs that could be essential to identifying the onset of toxic fibril forms and developing a straightforward, label-free diagnosis using Gr and MoS2.

Transformer transfer learning emotion detection model: synchronizing socially agreed and self-reported emotions in big data.

Tactics to determine the emotions of authors of texts such as Twitter messages often rely on multiple annotators who label relatively small data sets of text passages. An alternative method gathers large text databases that contain the authors' self-reported emotions, to which artificial intelligence, machine learning, and natural language processing tools can be applied. Both approaches have strength and weaknesses. Emotions evaluated by a few human annotators are susceptible to idiosyncratic biases that reflect the characteristics of the annotators. But models based on large, self-reported emotion data sets may overlook subtle, social emotions that human annotators can recognize. In seeking to establish a means to train emotion detection models so that they can achieve good performance in different contexts, the current study proposes a novel transformer transfer learning approach that parallels human development stages: (1) detect emotions reported by the texts' authors and (2) synchronize the model with social emotions identified in annotator-rated emotion data sets. The analysis, based on a large, novel, self-reported emotion data set (n = 3,654,544) and applied to 10 previously published data sets, shows that the transfer learning emotion model achieves relatively strong performance.

Markerless 3D Skeleton Tracking Algorithm by Merging Multiple Inaccurate Skeleton Data from Multiple RGB-D Sensors.

Skeleton data, which is often used in the HCI field, is a data structure that can efficiently express human poses and gestures because it consists of 3D positions of joints. The advancement of RGB-D sensors, such as Kinect sensors, enabled the easy capture of skeleton data from depth or RGB images. However, when tracking a target with a single sensor, there is an occlusion problem causing the quality of invisible joints to be randomly degraded. As a result, multiple sensors should be used to reliably track a target in all directions over a wide range. In this paper, we proposed a new method for combining multiple inaccurate skeleton data sets obtained from multiple sensors that capture a target from different angles into a single accurate skeleton data. The proposed algorithm uses density-based spatial clustering of applications with noise (DBSCAN) to prevent noise-added inaccurate joint candidates from participating in the merging process. After merging with the inlier candidates, we used Kalman filter to denoise the tremble error of the joint's movement. We evaluated the proposed algorithm's performance using the best view as the ground truth. In addition, the results of different sizes for the DBSCAN searching area were analyzed. By applying the proposed algorithm, the joint position accuracy of the merged skeleton improved as the number of sensors increased. Furthermore, highest performance was shown when the searching area of DBSCAN was 10 cm.

Accelerated Pancreatobiliary MRI for Pancreatic Cancer Surveillance in Patients With Pancreatic Cystic Neoplasms.

BACKGROUND: Pancreatobiliary MRI is often recommended for patients at risk of developing pancreas cancer. But the surveillance MRI protocol has not yet been widely accepted. PURPOSE: To establish an accelerated MRI protocol targeting the table time of 15 minutes for pancreatic cancer surveillance and test its performance in lesion characterization. STUDY TYPE: Prospective. POPULATION: A total of 30 participants were enrolled, who were undergoing follow-up care for intraductal papillary mucinous neoplasms or newly diagnosed pancreatic cysts (≥10 mm) and were scheduled for or had recently undergone contrast-enhanced CT (CECT). FIELD STRENGTH/SEQUENCE: A 3 T; heavily T2WI, 3D MRCP, DWI, dynamic T1WI, two-point Dixon. ASSESSMENT: In-room time and table time were measured. Seven radiologists independently reviewed image quality of MRI and then the presence of high-risk stigmata and worrisome features in addition to diagnostic confidence for accelerated MRI, CECT, and the noncontrast part of accelerated MRI (NC-MRI). STATISTICAL ANALYSIS: Fisher's exact test was used for categorical variables and either the Student's t-test or Mann-Whitney test was performed for continuous variables. The generalized estimated equation was used to compare the diagnostic performance of examinations on a per-patient basis. Interobserver agreement was evaluated via Fleiss kappa. A P value of <0.05 was considered to be statistically significant. RESULTS: The in-room time was 18.5 ± 2.6 minutes (range: 13.7-24.9) and the table time was 13.9 ± 1.9 minutes (range: 10.7-17.5). There was no significant difference between the diagnostic performances of the three examinations (pooled sensitivity: 75% for accelerated MRI and CECT, 68% for NC-MRI, P = 0.95), with the highest significant diagnostic confidence for accelerated MRI (4.2 ± 0.1). With accelerated MRI, the interobserver agreement was fair to excellent for high-risk stigmata (κ = 0.34-0.98). DATA CONCLUSION: Accelerated MRI protocol affords a table time of 15 minutes, making it potentially suitable for cancer surveillance in patients at risk of developing pancreatic cancer. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY STAGE: 2.

Deep-Learning-Based ADHD Classification Using Children's Skeleton Data Acquired through the ADHD Screening Game.

The identification of attention deficit hyperactivity disorder (ADHD) in children, which is increasing every year worldwide, is very important for early diagnosis and treatment. However, since ADHD is not a simple disease that can be diagnosed with a simple test, doctors require a large period of time and substantial effort for accurate diagnosis and treatment. Currently, ADHD classification studies using various datasets and machine learning or deep learning algorithms are actively being conducted for the screening diagnosis of ADHD. However, there has been no study of ADHD classification using only skeleton data. It was hypothesized that the main symptoms of ADHD, such as distraction, hyperactivity, and impulsivity, could be differentiated through skeleton data. Thus, we devised a game system for the screening and diagnosis of children's ADHD and acquired children's skeleton data using five Azure Kinect units equipped with depth sensors, while the game was being played. The game for screening diagnosis involves a robot first travelling on a specific path, after which the child must remember the path the robot took and then follow it. The skeleton data used in this study were divided into two categories: standby data, obtained when a child waits while the robot demonstrates the path; and game data, obtained when a child plays the game. The acquired data were classified using the RNN series of GRU, RNN, and LSTM algorithms; a bidirectional layer; and a weighted cross-entropy loss function. Among these, an LSTM algorithm using a bidirectional layer and a weighted cross-entropy loss function obtained a classification accuracy of 97.82%.

Deep Learning-Based ADHD and ADHD-RISK Classification Technology through the Recognition of Children's Abnormal Behaviors during the Robot-Led ADHD Screening Game.

Although attention deficit hyperactivity disorder (ADHD) in children is rising worldwide, fewer studies have focused on screening than on the treatment of ADHD. Most previous similar ADHD classification studies classified only ADHD and normal classes. However, medical professionals believe that better distinguishing the ADHD-RISK class will assist them socially and medically. We created a projection-based game in which we can see stimuli and responses to better understand children's abnormal behavior. The developed screening game is divided into 11 stages. Children play five games. Each game is divided into waiting and game stages; thus, 10 stages are created, and the additional waiting stage includes an explanation stage where the robot waits while explaining the first game. Herein, we classified normal, ADHD-RISK, and ADHD using skeleton data obtained through games for ADHD screening of children and a bidirectional long short-term memory-based deep learning model. We verified the importance of each stage by passing the feature for each stage through the channel attention layer. Consequently, the final classification accuracy of the three classes was 98.15% using bi-directional LSTM with channel attention model. Additionally, the attention scores obtained through the channel attention layer indicated that the data in the latter part of the game are heavily involved in learning the ADHD-RISK case. These results imply that for ADHD-RISK, the game is repeated, and children's attention decreases as they progress to the second half.

Adverse postnatal developmental effects in offspring from humidifier disinfectant biocide inhaled pregnant rats.

Inhalation exposure to polyhexamethylene guanidine phosphate (PHMG-P), one of the primary biocides used in humidifier disinfectants, caused a fatal pulmonary disease in Korea. Pregnant women were also exposed to PHMG-P, and subsequent studies showed that PHMG-P inhalation during pregnancy adversely affects their health and embryo-fetal development. However, the postnatal developmental effects after birth on prenatally PHMG-P-exposed offspring have not yet been investigated. Therefore, in this study, we aimed to examine the postnatal development of prenatally PHMG-P-exposed offspring. Pregnant rats (22 or 24 females per group) were exposed to PHMG-P during pregnancy in a whole-body inhalation chamber at the target concentrations of 0, 0.14, 1.60, and 3.20 mg/m3. After parturition, the prenatally exposed offspring were transferred to non-exposed surrogate mothers to minimize the secondary effects of severe maternal toxicities. Postnatal development of offspring was then examined with a modified extended one-generation reproductive toxicity study design. At 3.20 mg/m3 PHMG-P, increased perinatal death rates and decreased viability index (postnatal survival of offspring between birth and postnatal day 4) were observed. In addition, F1 offspring had lower body weight at birth that persisted throughout the study. PHMG-P-exposed pregnant rats also had severe systemic toxicities and increased gestation period. At 1.60 mg/m3 PHMG-P, a decreased viability index was also observed with systemic toxicities of PHMG-P-exposed pregnant rats. These results indicate that prenatal PHMG-P exposure adversely affects the offspring's future health and could be used for human risk assessment.

Clinical Outcomes and Risk Factor Analysis of Patients Presenting with Emphysematous Cystitis: A 15-Year Retrospective Multicenter Study.

Background and objectives: To investigate the risk factors for emphysematous cystitis (EC) compared to those of acute cystitis (AC) to increase clinicians awareness of the possibility for the aggravation of patient status. Materials and methods: We retrospectively reviewed a total of 54 patients who were hospitalized with a diagnosis of EC by abdominal computed tomography (CT) scan from 2006 to 2020. The control group included 92 patients who were hospitalized for the treatment of AC in the same period. We sought to identify the clinical features and predisposing diseases, such as age, sex, diabetes mellitus (DM), hypertension (HTN), cerebrovascular accident (CVA), chronic kidney disease (CKD), neurogenic bladder (NB), history of urinary tract infection (UTI), and emphysematous pyelonephritis (EPN), that were associated with the development of EC. Results: The median (interquartile range (IQR)) age of the patients with EC was older than that of the patients with AC (78.5 (15.3) years (range: 52-100) vs. 70.0 (26.5) years (range: 28-97 years)). Sepsis and mortality occurred only in the EC group (48.1% and 11.1%, respectively). The univariate analysis of predisposing factors revealed that age, DM, HTN, CVA, CKD, and NB were significantly associated with EC. In the multivariate analysis, DM (OR, 6.251; 95% CI, 2.254-17.250; p < 0.001), CKD (OR, 18.439; 95% CI, 3.421-99.404; p = 0.001), NB (OR, 7.374; 95% CI, 1.993-27.285; p = 0.003) were associated with EC. Conclusions: The results of this study revealed that DM, CKD, and NB were significant risk factors for EC. The tendency toward sepsis and high mortality underscore the need for careful observation while treating patients with EC with the risk noted above.

Subsentence Extraction from Text Using Coverage-Based Deep Learning Language Models.

Sentiment prediction remains a challenging and unresolved task in various research fields, including psychology, neuroscience, and computer science. This stems from its high degree of subjectivity and limited input sources that can effectively capture the actual sentiment. This can be even more challenging with only text-based input. Meanwhile, the rise of deep learning and an unprecedented large volume of data have paved the way for artificial intelligence to perform impressively accurate predictions or even human-level reasoning. Drawing inspiration from this, we propose a coverage-based sentiment and subsentence extraction system that estimates a span of input text and recursively feeds this information back to the networks. The predicted subsentence consists of auxiliary information expressing a sentiment. This is an important building block for enabling vivid and epic sentiment delivery (within the scope of this paper) and for other natural language processing tasks such as text summarisation and Q&A. Our approach outperforms the state-of-the-art approaches by a large margin in subsentence prediction (i.e., Average Jaccard scores from 0.72 to 0.89). For the evaluation, we designed rigorous experiments consisting of 24 ablation studies. Finally, our learned lessons are returned to the community by sharing software packages and a public dataset that can reproduce the results presented in this paper.

The Value of Low Prostate Imaging-Reporting and Data System (PI-RADS) Scores in Preventing Unnecessary Prostate Biopsies.

Background and Objectives: Magnetic resonance imaging (MRI) and the Prostate Imaging-Reporting and Data System (PI-RADS) have become essential tools for prostate cancer evaluation. We evaluated the ability of PI-RADS scores in identifying significant prostate cancer, which would help avoid unnecessary prostate biopsies. Materials and Methods: Patients with prostate-specific antigen (PSA) levels ≤ 20 ng/mL, who underwent prostate MRI for evaluation from January 2018 to November 2019, were analyzed. Among them, 105 patients who received transrectal ultrasonography (TRUS)-guided biopsy were included. PSA, PI-RADS scores (low 1-2, high 3-5), biopsy results, and Gleason scores (GS) were evaluated. Biopsies with GS higher than 3 + 4 were considered as significant cancers and biopsies with no cancer or Gleason 3 + 3 were considered insignificant or no cancers. Results: Among the 105 patients, 45 patients had low PI-RADS and 60 had high PI-RADS scores. There were no patients with significant prostate cancer in the low PI-RADS groups. For the high PI-RADS group, 28 (46.7%) patients had significant cancer and 32 (53.3%) had insignificant or no cancer. The sensitivity and specificity of high PI-RADS to detect significant cancer was 100% and 58.4%, respectively. Positive predictive value was 46.7% and negative predictive value was 100%. Conclusions: Low PI-RADS scores on MRI did not show significant prostate cancer and surveillance should be considered in selected cases to prevent unnecessary invasive procedures and overdiagnosis.

Real-space imaging of acoustic plasmons in large-area graphene grown by chemical vapor deposition.

An acoustic plasmon mode in a graphene-dielectric-metal structure has recently been spotlighted as a superior platform for strong light-matter interaction. It originates from the coupling of graphene plasmon with its mirror image and exhibits the largest field confinement in the limit of a sub-nm-thick dielectric. Although recently detected in the far-field regime, optical near-fields of this mode are yet to be observed and characterized. Here, we demonstrate a direct optical probing of the plasmonic fields reflected by the edges of graphene via near-field scattering microscope, revealing a relatively small propagation loss of the mid-infrared acoustic plasmons in our devices that allows for their real-space mapping at ambient conditions even with unprotected, large-area graphene grown by chemical vapor deposition. We show an acoustic plasmon mode that is twice as confined and has 1.4 times higher figure of merit in terms of the normalized propagation length compared to the graphene surface plasmon under similar conditions. We also investigate the behavior of the acoustic graphene plasmons in a periodic array of gold nanoribbons. Our results highlight the promise of acoustic plasmons for graphene-based optoelectronics and sensing applications.

A humidifier disinfectant biocide, polyhexamethylene guanidine phosphate, inhalation exposure during pregnancy induced toxicities in rats.

Biocides are widely used for their effective antiseptic and disinfectant properties, including polyhexamethylene guanidine phosphate (PHMG-P), which is also used as a biocide as it selectively disrupts bacterial cell membrane. It is used to clean humidifiers commonly used in the dry winter season in South Korea, which exposes people to PHMG-P inhalation. However, comprehensive toxicological data on PHMG-P inhalation exposure, including in pregnant women, and the potential occurrence of lung disease is lacking. Therefore, in this study, we investigated PHMG-P inhalation exposure-induced toxicities in pregnant rats and prenatal development of their conceptus. Pregnant rats were exposed to PHMG-P via inhalation at target concentrations of 0, 0.14, 1.60, and 3.20 mg/m3 from implantation to nearly parturition (from gestation day 6-20) and then analyzed for relevant abnormalities. Results showed systemic toxicities in the pregnant rats including respiratory function abnormalities, decreased body weight gain, and decreased food consumption at ≥1.60 mg/m3. Prenatal development toxicities, including decreased fetal weight with ossification retardations of fetal bones, were observed at 3.20 mg/m3. These results will contribute to clarifying the PHMG-P inhalation exposure-induced toxicities during pregnancy and support its risk assessment in humans.

High-mobility junction field-effect transistor via graphene/MoS2 heterointerface.

Monolayer molybdenum disulfide (MoS2) possesses a desirable direct bandgap with moderate carrier mobility, whereas graphene (Gr) exhibits a zero bandgap and excellent carrier mobility. Numerous approaches have been suggested for concomitantly realizing high on/off current ratio and high carrier mobility in field-effect transistors, but little is known to date about the effect of two-dimensional layered materials. Herein, we propose a Gr/MoS2 heterojunction platform, i.e., junction field-effect transistor (JFET), that enhances the carrier mobility by a factor of ~ 10 (~ 100 cm2 V-1 s-1) compared to that of monolayer MoS2, while retaining a high on/off current ratio of ~ 108 at room temperature. The Fermi level of Gr can be tuned by the wide back-gate bias (VBG) to modulate the effective Schottky barrier height (SBH) at the Gr/MoS2 heterointerface from 528 meV (n-MoS2/p-Gr) to 116 meV (n-MoS2/n-Gr), consequently enhancing the carrier mobility. The double humps in the transconductance derivative profile clearly reveal the carrier transport mechanism of Gr/MoS2, where the barrier height is controlled by electrostatic doping.

Time Evolution Studies on Strain and Doping of Graphene Grown on a Copper Substrate Using Raman Spectroscopy.

The enhanced growth of Cu oxides underneath graphene grown on a Cu substrate has been of great interest to many groups. In this work, the strain and doping status of graphene, based on the gradual growth of Cu oxides from underneath, were systematically studied using time evolution Raman spectroscopy. The compressive strain to graphene, due to the thermal expansion coefficient difference between graphene and the Cu substrate, was almost released by the nonuniform Cu2O growth; however, slight tensile strain was exerted. This induced p-doping in the graphene with a carrier density up to 1.7 × 1013 cm-2 when it was exposed to air for up to 30 days. With longer exposure to ambient conditions (>1 year), we observed that graphene/Cu2O hybrid structures significantly slow down the oxidation compared to that using a bare Cu substrate. The thickness of the CuO layer on the bare Cu substrate was increased to approximately 270 nm. These findings were confirmed through white light interference measurements and scanning electron microscopy.

Cigarette smoking differentially regulates inflammatory responses in a mouse model of nonalcoholic steatohepatitis depending on exposure time point.

Cigarette smoke (CS) is a risk factor for the development of nonalcoholic fatty liver disease. However, the role of mainstream CS (MSCS) in the pathogenesis of nonalcoholic steatohepatitis (NASH) remains unclear. During the first (early exposure) or last (late exposure) three weeks of methionine-choline deficient with high fat diet feeding (6 weeks), each diet group was exposed to MSCS (300 or 600 µg/L). Hepatic or serum biochemical analysis showed that MSCS differentially modulated hepatic injury in NASH milieu, depending on exposure time points. Consistently, NASH-related hepatocellular apoptosis and fibrosis were increased in the early exposure group, but decreased in the late exposure group, except for steatosis. Ex vivo experiments showed that CS extract differentially regulated inflammatory responses in co-cultured hepatocytes and macrophages isolated from steatohepatitic livers after 10 days or 3 weeks of diet feeding. Furthermore, CS differentially up- and down-regulated the expression levels of M1/M2 polarization markers and peroxisome proliferator-activated receptor-gamma (PPARγ) in livers (29% and 38%, respectively) or co-cultured macrophages (2 and 2.5 fold, respectively). Collectively, our findings indicate that opposite effects of MSCS on NASH progression are mediated by differential modulation of PPARγ and its-associated M1/M2 polarization in hepatic macrophages, depending on exposure time points.

Tunable Negative Differential Resistance in van der Waals Heterostructures at Room Temperature by Tailoring the Interface.

Vertically stacked two-dimensional van der Waals (vdW) heterostructures, used to obtain homogeneity and band steepness at interfaces, exhibit promising performance for band-to-band tunneling (BTBT) devices. Esaki tunnel diodes based on vdW heterostructures, however, yield poor current density and peak-to-valley ratio, inferior to those of three-dimensional materials. Here, we report the negative differential resistance (NDR) behavior in a WSe2/SnSe2 heterostructure system at room temperature and demonstrate that heterointerface control is one of the keys to achieving high device performance by constructing WSe2/SnSe2 heterostructures in inert gas environments. While devices fabricated in ambient conditions show poor device performance due to the observed oxidation layer at the interface, devices fabricated in inert gas exhibit extremely high peak current density up to 1460 mA/mm2, 3-4 orders of magnitude higher than reported vdW heterostructure-based tunnel diodes, with a peak-to-valley ratio of more than 4 at room temperature. Besides, Pd/WSe2 contact in our device possesses a much higher Schottky barrier than previously reported Cr/WSe2 contact in the WSe2/SnSe2 device, which suppresses the thermionic emission current to less than the BTBT current level, enabling the observation of NDR at room temperature. Diode behavior can be further modulated by controlling the electrostatic doping and the tunneling barrier as well.