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The mechanism of cold-activated TRPM8 channel activation remains unclear. Here, authors have determined structures of mouse TRPM8 in apo or ligand-bound states, providing insights into the activation of TRPM8 structures in different states.

Disruption of colonic homeostasis caused by aberrant M1/M2 macrophage polarization contributes to the development of inflammatory bowel disease (IBD). Lycium barbarum polysaccharide (LBP) is the primary active constituent of traditional Chinese herbal Lycium barbarum L., which has been widely demonstrated to have important functions in regulating immune activity and anti-inflammatory. Thus, LBP may protect against IBD. To test this hypothesis, the DSS-induced colitis model was established in mice, then the mice were treated with LBP. The results indicated that LBP attenuated the weight loss, colon shortening, disease activity index (DAI), and histopathological scores of colon tissues in colitis mice, suggesting that LBP could protect against IBD. Besides, LBP decreased the number of M1 macrophages and the protein level of Nitric oxide synthase 2(NOS2) as a marker of M1 macrophages and enhanced the number of M2 macrophages and the protein level of Arginase 1(Arg-1) as a marker of M2 macrophages in colon tissues from mice with colitis, suggesting that LBP may protect against IBD by regulating macrophage polarization. Next, the mechanistic studies in RAW264.7 cells showed that LBP inhibited M1-like phenotype by inhibiting the phosphorylation of STAT1, and promoted M2-like phenotype by promoting the phosphorylation of STAT6. Finally, immunofluorescence double-staining results of colon tissues showed that LBP regulated STAT1 and STAT6 pathways in vivo. The results in the study demonstrated that LBP could protect against IBD by regulating macrophage polarization through the STAT1 and STAT6 pathways.

The epigenetic regulation of glioblastoma stem cell (GSC) function remains poorly understood. Here, the authors compare the chromatin accessibility landscape of GSC cultures from mice and patients and suggest that the epigenome of GSCs is cell lineage-regulated and could predict patient survival.

Abstract Background Recent studies have demonstrated that airway basal stem cells (BCs) transplantation can ameliorate bleomycin-induced idiopathic pulmonary fibrosis (IPF) through lung regeneration promotion. However, BCs under oxidative stress in the alveolar microenvironment are poor in survival, causing unsatisfied efficacy of BCs transplantation. In this study, we investigated whether Coenzyme Q10(CoQ10) counteracts oxidative stress in the alveolar microenvironment, thus improved the efficacy of BCs transplantation for IPF treatment. Methods The protective effects of CoQ10 on H2O2-induced BCs apoptosis and cytoplasmic reactive oxygen species (ROS) level were tested by flow cytometry in vitro. The therapeutic effects of BCs combined with CoQ10 were compared to a single BCs transplantation protocol in IPF treatment after 2 weeks and were evaluated by parameters including changes of body weight and survival rate, as well as various levels of pulmonary inflammation, α-SMA expression and hydroxyproline (HYP) in IPF mouse lung tissues. Results CoQ10 preincubation with BCs (10 mM, 24 h) significantly reduced the late apoptosis of BCs and the number of oxidative stressful BCs as a result of H2O2 stimulation (1 mM, 6 h) in vitro. IPF mouse model was constructed through bleomycin (5 mg/kg) intratracheal instillation. Bleomycin-induced IPF mice showed weight loss continuously and mortality increased progressively during modeling. Serious pulmonary inflammatory cell infiltration, collagen fiber proliferation, and collagen protein deposition were observed in lung tissues of IPF mice. Though BCs transplantation alone improved indicators above in bleomycin-induced IPF mice to some extent, the combination with CoQ10 improved the transplantation efficacy and obtained better therapeutic effects. Conclusion CoQ10 blocked H2O2-induced apoptosis of BCs and ROS production in vitro, and enhanced the efficacy of BCs transplantation against bleomycin-induced IPF in mice.

Sparse general matrix multiplication (SpGEMM) is an important and expensive computation primitive in many real-world applications. Due to SpGEMM’s inherent irregularity and the vast diversity of its input matrices, developing high-performance SpGEMM implementation on modern processors such as GPUs is challenging. The state-of-the-art SpGEMM libraries (i.e., $nsparse$ and $spECK$ ) adopt several algorithms to tackle the challenges of global load balance, local load balance, and allocation of the result matrix. While these libraries focus on the high-level algorithm design for SpGEMM, they neglect several low-level architecture-specific optimizations, which causes inefficient implementations in their libraries. In this paper, we classify their inefficient implementations into several categories. Based on our observations, we propose a highly optimized SpGEMM library called $OpSparse$ . The optimizations in $OpSparse$ include 1) optimizing the binning method by improving the utilization of the shared memory, 2) optimizing the hashing method by reducing the accesses to the hash tables, 3) improving the trade-off between hash collision rate and hardware utilization in the hashing method by setting appropriate binning ranges, 4) reducing the overheads of global memory utilization by minimizing the global memory usage of the metadata, and 5) improving the execution parallelism by overlapping global memory allocation with kernel execution. Performance evaluations with 26 commonly used matrices on an Nvidia Tesla V100 GPU show that $OpSparse$ achieves on average $7.35\times $ (up to $27.8\times $ ), $1.43\times $ (up to $1.81\times $ ), and $1.52\times $ (up to $2.04\times $ ) speedups over three state-of-the-art SpGEMM libraries: $\mathit {cuSPARSE}$ , $nsparse$ , and $spECK$ , respectively.

Sparse general matrix multiplication (SpGEMM) is a fundamental building block for many real-world applications. Since SpGEMM is a well-known memory-bounded application with vast and irregular memory accesses, considering the memory access efficiency is of critical importance for SpGEMM’s performance. Yet, the existing methods put less consideration into the memory subsystem and achieved suboptimal performance. In this paper, we thoroughly analyze the memory access patterns of SpGEMM and their influences on the memory subsystem. Based on the analysis, we propose a novel and more efficient accumulation method named BRMerge for the multi-core CPU architectures. The BRMerge accumulation method follows the row-wise dataflow. It first accesses the $B$ matrix, generates the intermediate lists for one output row, and stores these intermediate lists in a consecutive memory space, which is implemented by a ping-pong buffer. It then immediately merges these intermediate lists generated in the previous phase two by two in a tree-like hierarchy between two ping-pong buffers. The architectural benefits of BRMerge are 1) streaming access patterns, 2) minimized TLB cache miss rate, and 3) reasonably high L1/L2 cache hit rates, which result in both low access latency and high bandwidth utilization when performing SpGEMM. Based on the BRMerge accumulation method, we propose two SpGEMM libraries named BRMerge-Upper and BRMerge-Precise, which use different allocation methods. Performance evaluations with 26 commonly used benchmarks on two CPU servers show that the proposed SpGEMM libraries significantly outperform the state-of-the-art SpGEMM libraries.

Abstract In this study, amorphous hydrous titanium dioxide was synthesized by a facile precipitation method at room temperature, aiming to effectively remove thallium(I) from water. The titanium dioxide prepared using ammonia as precipitant (TiO2 I) is more effective for thallium(I) uptake than the one synthesized with sodium hydroxide (TiO2 II). The TiO2 obtained particles are amorphous, aggregates of many nanoparticles and irregular in shape. The thallium(I) uptake increases with the rise of solution pH value. Under neutral pH conditions, the maximal thallium(I) adsorption capacities of TiO2 I and TiO2 II are 302.6 and 230.3 mg/g, respectively, outperforming most of the reported adsorbents. The amorphous TiO2 has high selectivity towards thallium(I) in the presence of multiple cations such as K+, Ca2+, Mg2+, Zn2+ and Ni2+. Moreover, the TiO2 I is efficient in removing thallium(I) from real river water and mining wastewater. Additionally, the spent TiO2 I can be regenerated using hydrochloric acid solution and reused. The Tl(I) adsorption is achieved via replacing the H+ in hydroxyl group on the surface of TiO2 and forming inner-sphere surface complexes. Owing to its high efficiency, facile synthesis and environmental friendliness, the TiO2 I has the potential to be used as an alternative adsorbent to remove Tl(I) from water.

Graph Neural Networks have drawn tremendous attention in the past few years due to their convincing performance and high interpretability in various graph-based tasks like link prediction and node classification. With the ever-growing graph size in the real world, especially for industrial graphs at a billion-level, the storage of graphs can easily consume Terabytes so that the process of GNNs has to be processed in a distributed manner. As a result, the execution could be inefficient due to the expensive cross-node communication and irregular memory access. Various GNN accelerators have been proposed for efficient GNN processing. They, however, mainly focused on small and medium-size graphs, which is not applicable to large-scale distributed graphs. In this paper, we present a practical Near-Data-Processing architecture based on a memory-pool system for large-scale distributed GNNs. We propose a customized memory fabric interface to construct the memory pool for low-latency and high throughput cross-node communication, which can provide flexible memory allocation and strong scalability. A practical Near-Data-Processing design is proposed for efficient work offloading and bandwidth utilization improvement. Moreover, we also introduce a partition and scheduling scheme to further improve performance and achieve workload balance. Comprehensive evaluations demonstrate that the proposed architecture can achieve up to $27\times $ and $8\times $ higher training speed compared to two state-of-the-art distributed GNN frameworks: Deep Graph Library and $P^{3}$ , respectively.

Background: Intra-articular injections of hyaluronic acid (HA), the United States Food and Drug Administration approved treatment and widely utilized to delay or reserve the progression of the osteoarthritis (OA) involves. However, this treatment has shown controversial results through various clinical practice guidelines and meta-analysis evaluations, warrants more advanced researches on its safety and effectiveness. Methods: A novel strategy of integrating medical informatics and bioinformatics was utilized. An updated meta-analysis of 16 randomized controlled trials (RCTs) out of 1820 articles was conducted, in combination with a high throughput body-wide-organ-transcriptomic (BOT) RNA-sequencing (RNA-seq) and in vitro and vivo experiments to evaluate the effect of HA at local and systemic levels, revealing the underlying mechanism. Results: A sensitivity analysis was performed restricting to high quality RCTs, no significant effect of HA treatment was found on pain relief and functional improvement. Descriptive analysis of RNA-seq using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis revealed biological process related to innate immune responses and apoptosis; BOT analysis revealed differential gene expressions (DEGs) in cartilage, lymph node, spleen, kidney, and liver, with immune cell proliferation in immune-related organs. In vitro, HA-coated plates were shown to induce macrophage responses; in vivo histological images revealed knee joint, liver, and kidney with damaged/abnormal morphologies, while immune cell proliferation was observed in the lymph node and spleen and it was found that there was no significant difference in the treatment effect for OA animal model. Conclusion: Conclusively, integration of meta-analysis with bioinformatics analysis exhibited that HA induces inflammatory responses both locally and systematically and not benefit for OA treatment, thus limiting the regeneration and leading to some organ-specific pathogenesis. The strategy and findings will be of important for guiding future long-term clinical studies. The Translational potential of this article: This study illustrated that the administered HA activated both systemic and local pro-inflammatory immune responses, possibly limiting its efficacy. This novel unique strategy proposed in this study can be utilized and adapted for future meta-analysis and bioinformatics study.

BackgroundThere are controversies on the pathophysiological alteration in patients with atrial fibrillation (AF) undergoing pulmonary vein isolation using different energy sources.ObjectivesWe evaluated the changes in plasma proteins in acute phase post-ablation in patients receiving cryoballoon ablation, radiofrequency balloon ablation, or radiofrequency ablation.MethodsBlood samples from eight healthy controls and 24 patients with AF were taken on the day of admission, day 1, and day 2 post-ablation and analyzed by the Olink proximity extension assay. Proteins were identified and performed with enrichment analysis. Protein–protein interaction network and module analysis were conducted using Cytoscape software.ResultsOf 181 proteins, 42 proteins in the cryoballoon group, 46 proteins in the radiofrequency balloon group, and 43 proteins in the radiofrequency group significantly changed after ablation. Most of the proteins altered significantly on the first day after ablation. Altered proteins were mainly involved in cytokine–cytokine receptor interaction. Both balloon-based ablations showed a similar shift toward enhancing cell communication and regulation of signaling while inhibiting neutrophil chemotaxis. However, radiofrequency ablation presented a different trend. Seed proteins, including osteopontin, interleukin-6, interleukin-10, C-C motif ligand 8, and matrix metalloproteinase-1, were identified. More significant proteins associated with hemorrhage and coagulation were selected in balloon-based ablations by machine learning.ConclusionPlasma protein response after three different ablations in patients with AF mainly occurred on the first day. Radiofrequency balloon ablation shared similar alteration in protein profile as cryoballoon ablation compared with radiofrequency ablation, suggesting that lesion size rather than energy source is the determinant in pathophysiological responses to the ablation.

Abstract Clusteroluminogens (CLgens), nonconjugated structures with visible luminescence at the clustering state, have recently received remarkable attention due to their great theoretical significance and practical values. In carbonyl‐based aliphatic polymers, (n, π*) transition of carbonyl groups and the through‐space interactions have been demonstrated to play an important role in their clusteroluminescence (CL) properties, but it is still a big challenge to manipulate their CL at the molecular level. In this work, six nonconjugated carbonyl‐based polymers with different heteroatoms and steric hindrances were synthesized, and their photophysical properties were systematically studied. These polymers all showed CL but with different emission efficiency and wavelength. Experimental and theoretical studies indicated that the CL properties could be manipulated by changing the electronic structures of carbonyl groups and the rigidity of polymer chains. This work not only gains further insights into the CL mechanism but also provides reliable strategies to design and manipulate non‐conjugated luminescent materials.

Action recognition is an exciting research avenue for artificial intelligence since it may be a game changer in emerging industrial fields such as robotic visions and automobiles. However, current deep learning (DL) faces major challenges for such applications because of the huge computational cost and inefficient learning. Hence, we developed a novel brain-inspired spiking neural network (SNN) based system titled spiking gating flow (SGF) for online action learning. The developed system consists of multiple SGF units which are assembled in a hierarchical manner. A single SGF unit contains three layers: a feature extraction layer, an event-driven layer, and a histogram-based training layer. To demonstrate the capability of the developed system, we employed a standard dynamic vision sensor (DVS) gesture classification as a benchmark. The results indicated that we can achieve 87.5% of accuracy which is comparable with DL, but at a smaller training/inference data number ratio of 1.5:1. Only a single training epoch is required during the learning process. Meanwhile, to the best of our knowledge, this is the highest accuracy among the non-backpropagation based SNNs. Finally, we conclude the few-shot learning (FSL) paradigm of the developed network: 1) a hierarchical structure-based network design involves prior human knowledge; 2) SNNs for content-based global dynamic feature detection.

Abstract Background Dysregulated gut microbiota is one of major pathogenic factors in the development of colitis. Dock2 acts as a guanine nucleotide exchange factor (GEF) and activates small G protein RAC1. Our previous study showed that, compared to wild type (WT) mice, Dock2 −/− mice were more susceptible to colitis induced by Citrobacter rodentium infection. However, it is not clear whether gut microbiota affects the host susceptibility to enteric bacterial infection in Dock2 −/− mice. Results In this study, we demonstrated that Dock2 regulated the gut microbiota and affected the host susceptibility to C. rodentium infection by co-housing, fecal microbiota transfer and antibiotic treatment methods. Microbiota analysis by 16 S rRNA gene sequencing showed that Dock2 increased the abundance of prevotellaceae-NK3B31-group and Lactobacillus but decreased that of Helicobacter. Conclusions These results suggest that Dock2 regulates the composition of gut microbiota and affects the host susceptibility to C. rodentium infection.

Increasing evidence indicates that neuroinflammation contributes to and exacerbates the pathogenesis of Alzheimer’s disease (AD). Neuroinflammation is thought to be primarily driven by glial cells (microglia and astrocytes) and escalates with neurodegenerative progression in AD. However, the spatiotemporal change patterns of glial reactivity and neuroinflammatory response during different stages of neurodegeneration, especially early in disease, remain unknown. Here we found that gliosis and the up-regulation of substantial neuroinflammatory genes were primarily initiated in the cortex of presenilin 1/2 conditional double knockout (cDKO) mice, rather than in the hippocampus. Specifically, astrocyte activation preceding microglial activation was found in the somatosensory cortex (SS) of cDKO mice at 6 weeks of age. Over time, both astrocyte and microglial activation were found in the whole cortex, and age-related increases in gliosis activation were more pronounced in the cortex compared to hippocampus. Moreover, the age-associated increase in glial activation was accompanied by a gradual increase in the expression of cell chemokines Ccl3 and Ccl4, complement related factors C1qb, C3 and C4, and lysosomal proteases cathepsin S and Z. These findings suggest that astrocyte and microglial activation with a concurrent increase in inflammatory mediators such as chemokines might be an early event and contribute to the pathogenesis of neurodegeneration due to presenilin deficiency.

Transforming growth factor β receptor (TGF-β1R) and receptor tyrosine kinases (RTKs), such as VEGFRs, PDGFRs and FGFRs are considered important therapeutic targets in blocking myofibroblast migration and activation of idiopathic pulmonary fibrosis (IPF). To screen and design innovative prodrug to simultaneously target these four classes of receptors, we proposed an approach based on network pharmacology combining virtual screening and machine learning activity prediction, followed by efficient in vitro and in vivo models to evaluate drug activity. We first constructed Collagen1A2-A549 cells with type I collagen as the main biomarker and evaluated the activity of compounds to inhibit collagen expression at the cellular level. The data from the first round of Collagen1A2-A549 cell screening were substituted into the machine learning model, and the model was optimized accordingly. As a result, the false positive rate of the model was reduced from 85.0% to 66.7%, and two prospective compounds, Z103080500 and Z104578368, were finally selected. Collagen levels were reduced effectively by both Z103080500 (67.88% reduction) and Z104578368 (69.54% reduction). Moreover, these two compounds showed low cellular cytotoxicity. Subsequently, the effect of Z103080500 and Z104578368 was evaluated in a bleomycin-induced C57BL/6 mouse IPF model. These results showed that 50 mg/kg Z103080500 and Z104578368 could effectively reduce the number of inflammatory cells and the expression level of α-SMA. Meanwhile, Z103080500 and Z104578368 reduced the expression of major markers and inflammatory factors of IPF, such as collagen, IFN-γ, IL-17 and HYP, indicating that these screened Z103080500 and Z104578368 effectively delayed lung tissue inflammation and had a potential therapeutic effect on IPF. Our findings demonstrate that a screening and evaluation model for prodrug against IPF has been successfully established. It is of great significance to further modify these compounds to enhance their potency and activity.

With the increasing automation of mobile phone assembly, industrial robots are gradually being used in production lines for loading and unloading operations. At present, industrial robots are mainly used in online teaching mode, in which the robot’s movement and path are set by teaching in advance and then repeat the point-to-point operation. This mode of operation is less flexible and requires high professionalism in teaching and offline programming. When positioning and grasping different materials, the adjustment time is long, which affects the efficiency of production changeover. To solve the problem of poor adaptability of loading robots to differentiated products in mobile phone automatic assembly lines, it is necessary to quickly adjust the positioning and grasping of different models of mobile phone middle frames. Therefore, this paper proposes a highly adaptive grasping and positioning method for vision-guided right-angle robots.

Ultraviolet (UV) radiation can penetrate the basal layer of the skin and induce profound alterations in the underlying dermal tissues, including skin pigmentation, oxidative stress, photoaging, glycation, and skin cancer. Idebenone (IDB), an effective antioxidant that suppresses melanin biosynthesis and glycation, can protect the skin from UV-induced damage, accounting for its use in commercial anti-aging formulations. Ideally, IDB formulations should retain IDB inside the skin for a sufficient period, despite disturbances such as sweating or swimming. Herein, we present an IDB topical formulation based on Tris (tris(hydroxymethyl)-aminomethane)-modified bioadhesive nanoparticles (Tris-BNPs) and microneedle-assisted delivery. We found that Tris-BNPs loaded with IDB (IDB/Tris-BNPs) effectively reached the basal layer of the skin and were retained for at least 4 days with a slow and continuous drug release profile, unlike non-bioadhesive nanoparticles (NNPs) and bioadhesive nanoparticles (BNPs) of similar sizes (ranging from 120–142 nm) and zeta-potentials (above −20 mV), which experienced a significant reduction in concentration within 24 h. Notably, IDB/Tris-BNPs showed superior performance against UV-induced damage relative to IDB/NNPs and IDB/BNPs. This effect was demonstrated by lower levels of reactive oxygen species and advanced glycation end-products in skin tissues, as well as suppressed melanogenesis. Therefore, the proposed IDB delivery strategy provided long-term protective effects against UV-induced skin damage.

Building occupancy profiles are critical in thermal and energy simulations. However, determining an accurate occupancy profile is difficult due to its stochastic nature. In most simulations, the occupant activities are usually represented by fixed yearly schedules, which are often derived from guides and other similar sources and may not represent the simulated building accurately. Therefore, an inaccuracy in defining occupancy profiles can be a source of error in building simulations. Over the past few years machine learning has become very popular due to its ability to reveal hidden patterns and relationships between data and this makes it suitable for investigating patterns in occupancy data. This study proposes a novel hybrid model combining the Graph Neural Network and the Long Short-term Memory neural network (LSTM) to predict the occupancy of individual rooms on a typical office floor. The proposed Graph LSTM model can produce high-resolution occupancy profiles of an office that are in good agreement with the reference occupancy profiles of the same office. The reference occupancy profiles for this office were derived from an agent-based model using AnyLogic and were not used in the training of the neural network. The proposed Graph LSTM model outperformed other neural networks tested such as the Recurrent Neural Network (RNN), the Gated Recurrent Unit (GRU) and LSTM. When Graph LSTM is compared to the other neural networks tested, there is a range of improvement between 13.5 and 14.6% in the index of agreement, 38.3 and 46.8% in mean absolute error and 34.4 and 40.0% in root mean square error, when averaging the differences over the whole office.

Goal-oriented movement is a fundamental animal behaviour. Here, the authors show that neurons in the superior colliculus project to the substantia nigra pars compacta, regulating dopaminergic signaling and specifically appetitive locomotion in mice.

Abstract Visual simultaneous localization and mapping (vSLAM), one of the most important applications in autonomous vehicles and robots to estimate the position and pose using inexpensive visual sensors, suffers from error accumulation for long‐term navigation without loop closure detection. Recently, deep neural networks (DNNs) are leveraged to achieve high accuracy for loop closure detection, however the execution time is much slower than those employing handcrafted visual features. In this paper, a parallel loop searching and verifying method for loop closure detection with both high accuracy and high speed, which combines two parallel tasks using handcrafted and DNN features, respectively, is proposed. A fast loop searching is proposed to link the bag‐of‐words features and histogram for higher accuracy, and it splits the images into multiple grids for high parallelism; meanwhile, a DNN feature extractor is utilized for further verification. A loop state control method based on a finite state machine to control these tasks is designed, wherein the loop closure detection is described as a context‐related procedure. The framework is implemented on a real machine, and the top‐2 best accuracy and fastest execution time of 80‐543 frames per second (min: 1.84ms, and max: 12.45ms) are achieved on several public benchmarks compared with some existing algorithms.

Ginsenoside Rh2 (G-Rh2), a rare protopanaxadiol (PPD)-type triterpene saponin, from Panax ginseng has anti-proliferation, anti-invasion, and anti-metastatic activity. However, the mechanisms by which G-Rh2 induces apoptosis of lung cancer cells are unclear. In the present work, a G-Rh2 target-lung cancer network was constructed and analyzed by the network pharmacology approach. A total of 91 compound-targets of G-Rh2 was obtained based on the compound-target network analysis, and 217 targets were identified for G-Rh2 against lung cancer by PPI network analysis. The 217 targets were significantly enriched in 103 GO terms with FDR

Purpose: This study aimed to quantify the different quadrants of the optic nerve head (ONH) and macular parameters and their changes during exposure to high altitude, and to assess their correlation with high-altitude headache (HAH).Methods: Spectral-domain optical coherence tomography (OCT) was used to quantify changes in the retinal structure in 109 healthy subjects during acute exposure to high altitude (3,700 m). Self-reported symptoms of HAH and acute mountain sickness AMS were assessed using Lake Louise Score (LLS), alongside measurements of physiological parameters (oxygen saturation [SpO2], heart rate [HR], hemoglobin level [Hb], and red blood cell [RBC] count). Measurements were taken before and after exposure to the high-altitude environment. The correlations of these parameters and changes at ONH were examined.Results: With the exposure to high altitude, the incidence of AMS was 44.0% and the frequency of HAH was 67.0% (54.1% mild, 12.9% moderate-severe). As for systemic parameters measured at high altitude, the participants exhibited significantly lower SpO2, higher resting HR, higher Hb, and a higher RBC (all p < 0.05). Key stereometric parameters used to describe ONH [superior, inferior, nasal, temporal, and mean retinal nerve fiber layer (RNFL) thickness] and macula (macular thickness) increased at high altitude compared with baseline. Most parameters of ONH changed, especially superior, inferior, and mean RNFL thickness (p < 0.05). There was a significant correlation between the ratios of RNFL at ONH and HAH [mean thickness (r = 0.246, p = 0.01); inferior (r = 0.216, p = 0.02); nasal (r = 0.193, p = 0.04)]. No associations between parameters of ONH and AMS or LLS were observed.Conclusion: The high-altitude environment can increase RNFL thickness at ONH. Furthermore, we found that the ratios of mean thickness, inferior area, and nasal area correlated positively with HAH, which provides new insights for understanding of the underlying pathological mechanisms of high-altitude retinopathy (HAR).

Objective: To explore immune-related feature genes in patients with dilated cardiomyopathy (DCM).Methods: Expression profiles from three datasets (GSE1145, GSE21610 and GSE21819) of human cardiac tissues of DCM and healthy controls were downloaded from the GEO database. After data preprocessing, differentially expressed genes (DEGs) were identified by the ‘limma’ package in R software. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were then performed to identify biological functions of the DEGs. The compositional patterns of stromal and immune cells were estimated using xCell. Hub genes and functional modules were identified based on protein-protein interaction (PPI) network analysis by STRING webtool and Cytoscape application. Correlation analysis was performed between immune cell subtypes and hub genes. Hub genes with |correlation coefficient| > 0.5 and p value

The quality and safety of sufu fermented using Mucor racemosa M2 was studied and compared with naturally fermented sufu. After 90 days post-fermentation, both naturally fermented and inoculated fermented sufu reached the maturity standard of sufu, and the degree of protein hydrolysis of natural sufu (WP/TP: 34% ± 1%; AAN/TN: 33% ± 1%) was slightly higher than that of the inoculated sufu (WP/TP: 28.2% ± 0.4%; AAN/TN: 27% ± 1%). The hardness and adhesiveness of inoculated sufu (Hadness: 1063 g ± 211 g; Adhesiveness: −80 g ± 47 g) were significantly greater than those of natural sufu (Hadness: 790 g ± 57 g; Adhesiveness: −23 g ± 28 g), and the internal structure of natural sufu was denser and more uniform than that of inoculated sufu. A total of 50 aroma compounds were detected in natural and inoculated sufu. The total number of bacterial colonies in naturally fermented sufu was significantly higher than that in inoculated sufu, and the pathogenic bacteria in both types of fermented sufu were lower than the limit of pathogenic bacteria required in fermented soybean products. The content of biogenic amines in sufu was determined by high performance liquid chromatography (HPLC), and the results showed that the content of biogenic amines (Putrescine, Cadaverine, Histamine, Tyramine, etc.) in naturally fermented sufu was significantly higher than that in inoculated fermented sufu. Especially the histamine content, after 90 days of fermentation, was found to be 64.95 ± 4.55 for inoculated fertilization and 44.24 ± 0.71 for natural fertilization. Overall, the quality of inoculated sufu was somewhat better than that of natural sufu, and the M2 strain can be used to ferment sufu.

This study aimed to investigate the properties of acidic whey tofu gelatin generated from two acidic whey coagulants by pure fermentation of Lactiplantibacillus paracasei and L. plantarum, as well as the characteristics of acidic whey tofu. The optimal holding temperature and the amount of coagulants added were determined based on the pH, water-holding capacity, texture, microstructure, and rheological properties of tofu gelation. Then, the differences in quality between tofu produced by pure bacterial fermentation and by natural fermentation were investigated under optimal tofu gelatin preparation conditions. The tofu gelatin presented the best texture at 37 °C with a 10% addition of coagulants fermented by both L. paracasei and L. plantarum. Under these conditions, the coagulant produced by the fermentation of L. plantarum resulted in a shorter formation time and stronger tofu gelatin compared with that produced from L. paracasei. Tofu produced by the fermentation of L. paracasei had higher pH, less hardness, and a rougher network structure, whereas tofu produced by the fermentation of L. plantarum was closer to tofu produced by natural fermentation in terms of pH, texture, rheology, and microstructure.

The human AAA+protein p97 plays an important role in cellular protein homeostasis. Here, the authors use cryo-EM to obtain further insights into how p97 interacts with its co-factor Npl4 and they observe three distinct conformational states of Npl4 in complex with human p97, which suggests that a seesaw motion is essential for the unfolding activity of the p97 complex.

Abstract Background Incomplete aneurysmal occlusion is a common feature of immediate posttreatment angiography. The safety and outcomes of acutely ruptured intracranial aneurysms (RIAs) with incomplete occlusion after stent-assisted coiling (SAC) and no-stent coiling (NSC) have not been well clarified. Progressive occlusion of stents can promote the complete occlusion of intracranial aneurysms (IAs), but it remains to be determined if progressive occlusion in acutely RIAs with incomplete occlusion after coiling may be enhanced by protective stenting. This study aimed to evaluate the safety and outcomes of those aneurysms after SAC and NSC; And to discover whether the stents can promote progressive aneurysm occlusion in such lesions or not. Methods We reviewed 199 patients with acutely RIAs underwent endovascular coiling and developed incomplete occlusion in the past seven years. The patients’ clinical and imaging information were recorded and analyzed. Univariate and multivariate analyses were performed to determine the association of recurrence rate with potential risk factors. Results SAC group had wider aneurysms neck (3.471 mm vs 2.830 mm, P = 0.009) and smaller dome-to-neck ratio (1.536 vs 2.111, P = 0.001) than in NSC group. There was no significant difference between the two groups in total procedure-related complications rate (31.7% vs 23.5%, P = 0.195), procedure-related mortality (6.9% vs 2.0%, P = 0.170) and modified Rankin Scale (mRS) score at 6-month follow-up (P > 0.05). However, SAC group had significantly higher ischemic complications rate (21.8% vs 8.2%, P = 0.007) and complete occlusion rate (65.6% vs 48.3%, P = 0.020), and lower recurrence rate (15.6% vs 28.1%, P = 0.042) than NSC group based on 6-month follow-up angiograms. Additionally, Multivariable analysis showed NSC was an independent risk factor for aneurysm recurrence (Odds Ratio [OR]: 4.061; P = 0.018). Conclusions Acutely RIAs with incomplete occlusion after SAC is associated with higher complications rate and mortality, but has an acceptable safety profile and similar clinical outcome compared to NSC, as well as gives patients superior angiography outcome by progressive occlusion of stents.

Endothelial cell (EC) plays critical roles in vascular physiological and pathological processes. With the development of high-throughput technologies, transcriptomics analysis of EC has increased dramatically and a large amount of informative data have been generated. The dynamic patterns of gene expression in ECs under various conditions were revealed. Unfortunately, due to the lack of bioinformatics infrastructures, reuse of these large-scale datasets is challenging for many scientists. Here, by systematic re-analyzing, integrating, and standardizing of 203 RNA sequencing samples from freshly isolated mouse ECs under 71 conditions, we constructed an integrated mouse EC gene expression omnibus (ECO). The ECO database enables one-click retrieval of endothelial expression profiles from different organs under different conditions including disease models, genetic modifications, and clinically relevant treatments in vivo. The EC expression profiles are visualized with user-friendly bar-plots. It also provides a convenient search tool for co-expressed genes. ECO facilitates endothelial research with an integrated tool and resource for transcriptome analysis. The ECO database is freely available at https://heomics.shinyapps.io/ecodb/.

Pancreatic cancer (PACA), which is characterized by an immunosuppressive nature, remains one of the deadliest malignancies worldwide. Aberrant DNA methylation (DNAm) reportedly influences tumor immune microenvironment. Here, we evaluated the role of DNA methylation driven genes (MDGs) in PACA through integrative analyses of epigenomic, transcriptomic, genomic and clinicopathological data obtained from TCGA, ICGC, ArrayExpress and GEO databases. Thereafter, we established a four-MDG signature, comprising GPRC5A, SOWAHC, S100A14, and ARNTL2. High signature risk-scores were associated with poor histologic grades and late TNM stages. Survival analyses showed the signature had a significant predictive effect on OS. WGCNA revealed that the signature may be associated with immune system, while high risk-scores might reflect immune dysregulation. Furthermore, GSEA and GSVA revealed significant enrichment of p53 pathway and mismatch repair pathways in high risk-score subgroups. Immune infiltration analysis showed that CD8+ T cells were more abundant in low score subgroups, while M0 macrophages exhibited an opposite trend. Moreover, negative regulatory genes of cancer-immunity cycle (CIC) illustrated that immunosuppressors TGFB1, VEGFA, and CD274 (PDL1) were all positively correlated with risk-scores. Furthermore, the four signature genes were negatively correlated with CD8+ lymphocytes, but positively associated with myeloid derived suppressor cells (MDSC). Conversely, specimens with high risk-scores exhibited heavier tumor mutation burdens (TMB) and might show better responses to some chemotherapy and targeted drugs, which would benefit stratification of PACA patients. On the other hand, we investigated the corresponding proteins of the four MDGs using paraffin-embedded PACA samples collected from patients who underwent radical surgery in our center and found that all these four proteins were elevated in cancerous tissues and might serve as prognostic markers for PACA patients, high expression levels indicated poor prognosis. In conclusion, we successfully established a four-MDG-based prognostic signature for PACA patients. We envisage that this signature will help in evaluation of intratumoral immune texture and enable identification of novel stratification biomarkers for precision therapies.

Abstract Background Street rabies virus (RABV) usually infects hosts at peripheral sites and migrates from motor or sensory nerves to the central nervous system. Several studies have found that inflammation is mild in a mouse model of street RABV infection. However, the pathogenetic mechanisms of street RABV in naturally infected dogs or humans are not well understood. Methods Brain tissues collected from 3 dogs and 3 humans were used; these tissue samples were collected under the natural condition of rabies-induced death. The inflammatory response and pathway activation in the brain tissue samples of dogs and humans were evaluated by HE, IHC, ARY006, WB and ELISA. The clinical isolate street RABV strains CGS-17 and CXZ-15 from 30 six-week-old ICR mice were used to construct the mouse infection model presented here. Results Neuronal degeneration and increased lymphocyte infiltration in the cerebral cortex, especially marked activation of microglia, formation of glial nodules, and neuronophagy, were observed in the dogs and humans infected with the street RABV strains. The various levels of proinflammatory chemokines, particularly CXCL1, CXCL12, CCL2, and CCL5, were increased significantly in the context of infection with street RABV strains in dogs and humans in relation to healthy controls, and the levels of MAPK and NF-κB phosphorylation were also increased in dogs and humans with natural infection. We also found that the degrees of pathological change, inflammatory response, MAPK and NF-κB signaling pathway activation were obviously increased during natural infection in dogs and humans compared with artificial model infection in mice. Conclusion The data obtained here provide direct evidence for the RABV-induced activation of the inflammatory response in a dog infection model, which is a relatively accurate reflection of the pathogenic mechanism of human street RABV infection. These observations provide insight into the precise roles of underlying mechanisms in fatal natural RABV infection.

The trans-synaptic interaction of the cell-adhesion molecules teneurins (TENs) with latrophilins (LPHNs) promotes excitatory synapse formation. Here authors report the high resolution cryo-EM structure of the TEN2-LPHN3 complex, describe the trimeric TEN2-LPHN3-FLRT3 complex and show how alternative-splicing regulates the TEN2-LPHN3 interaction.

Recently, due to the availability of big data and the rapid growth of computing power, artificial intelligence (AI) has regained tremendous attention and investment. Machine learning (ML) approaches have been successfully applied to solve many problems in academia and in industry. Although the explosion of big data applications is driving the development of ML, it also imposes severe challenges of data processing speed and scalability on conventional computer systems. Computing platforms that are dedicatedly designed for AI applications have been considered, ranging from a complement to von Neumann platforms to a “must-have” and stand-alone technical solution. These platforms, which belong to a larger category named “domain-specific computing,” focus on specific customization for AI. In this article, we focus on summarizing the recent advances in accelerator designs for deep neural networks (DNNs)—that is, DNN accelerators. We discuss various architectures that support DNN executions in terms of computing units, dataflow optimization, targeted network topologies, architectures on emerging technologies, and accelerators for emerging applications. We also provide our visions on the future trend of AI chip designs. Keywords: Deep neural network, Domain-specific architecture, Accelerator

Buildings and the construction sector account substantially for global energy consumption. In hot-humid areas of China, suboptimal thermal comfort in classrooms has heightened their cooling load and energy consumption. It is necessary to renovate the buildings of outdated code according to the current weather conditions to save energy. This study thus aimed to examine the thermal effects of such designs on the cooling load, based on an actual classroom building during summers in hot-humid southern China. Using air temperature and PMV values to evaluate thermal comfort, this study conducted simulation through EnergyPlus and DesignBuilder. The resultant updated typical meteorological year (TMY) and the monthly and hourly analyses of indoor thermal comfort revealed persistent classroom overheating. To mitigate the cooling load, numerous design variables were investigated: space form, roofing, external walls, windows, and shading devices. Evaluative comparisons found that appropriate choice of external windows and shading devices represented the two most effective strategies in mitigating the cooling load. Furthermore, jointly applying effective retrofit strategies to the building yielded a favorable reduction in the annual cooling energy consumption by 16.6%. The findings herein are envisioned to provide evidence-based referential guidance for building designs for classrooms in a hot-humid climate.

In view of the present situation of “learning difficulty” in health statistics, this paper proposes a video visualization technology based on the convolutional neural network, which updates parameters by calculating the gradient of loss function to obtain accurate or nearly accurate loss function. Taking the students from 2014 to 2017 in a university in Henan as the research object, this paper analyzes the video visualization technology and its application effect on the teaching of college students’ health statistics from the aspects of students’ course awareness, learning behavior, communication between teachers and students, knowledge mastery, and course satisfaction. The results show that the external model load difference between each explicit variable and latent variable is statistically significant. Learning behavior and communication between teachers and students have a direct impact on the mastery of knowledge, and the degree of influence from high to low is as follows: learning behavior and communication between teachers and students. The teaching effect model of health statistics based on video visualization technology of the convolutional neural network has certain practicability.

Porous organic polymers (POPs) have attracted much attention in numerous areas, including catalysis, adsorption and separation. Herein, POP supported Ziegler–Natta catalysts were designed for preparation of isotactic polypropylene (iPP). The POPs-based Ziegler–Natta catalysts exhibited the characteristic of broad molecular weight distribution (MWD > 11) with or without adding an extra internal electron donor. The added internal electron donor 3-methyl-5-tert-butyl-1,2-phenylene dibenzoate (ID-2) used in cat-2 showed good propylene polymerization activity of 15.3 × 106 g·PP/mol·Ti·h, high stereoregularity with 98.2% of isotacticity index and broad molecular weight distribution (MWD) of 12.3. Compared to the MgCl2-supported Ziegler–Natta catalysts (cat-4) with the same ID-2, cat-2 showed higher chain stereoregularity for propylene polymerization. As seen in the TREF results, the elution peak of PP-2 (124.0 °C, 91.7%) is 1.5 °C higher than the isotactic fraction from PP-4 (122.5 °C, 87.2%), and even 1.2 °C higher than PP-5 prepared from ID-3 with the characteristics of high stereoregularity. Moreover, the pentad methyl sequence mmmm of PP-2 (93.0%) from cat-2 is 0.5% higher than that of PP-4 from cat-4. XPS analysis revealed that the minute difference in binding energy of Ti, Mg, C and O atoms exist between the inorganic MgCl2 and the organic polymer based Z–N catalysts. The plausible interaction mechanism of active sites of Mg and Ti with the functional groups in the POP support and the added ID was proposed, which could be explained by their high stereoregularity and the broad molecular weight distribution of the POP-based Z–N catalysts.

Gastrodin (GAS) is the main bioactive ingredient of Gastrodia, a famous Chinese herbal medicine widely used as an analgesic, but the underlying analgesic mechanism is still unclear. In this study, we first observed the effects of GAS on the vincristine-induced peripheral neuropathic pain by alleviating the mechanical and thermal hyperalgesia. Further studies showed that GAS could inhibit the current density of NaV1.7 and NaV1.8 channels and accelerate the inactivation process of NaV1.7 and NaV1.8 channel, thereby inhibiting the hyperexcitability of neurons. Additionally, GAS could significantly reduce the over-expression of NaV1.7 and NaV1.8 on DRG neurons from vincristine-treated rats according to the analysis of Western blot and immunofluorescence results. Moreover, based on the molecular docking and molecular dynamic simulation, the binding free energies of the constructed systems were calculated, and the binding sites of GAS on the sodium channels (NaV1.7 and NaV1.8) were preliminarily determined. This study has shown that modulation of NaV1.7 and NaV1.8 sodium channels by GAS contributing to the alleviation of vincristine-induced peripheral neuropathic pain, thus expanding the understanding of complex action of GAS as a neuromodulator.

Summary: Transient receptor potential melastatin 2 (TRPM2), a Ca2+-permeable cation channel, is gated by intracellular adenosine diphosphate ribose (ADPR), Ca2+, warm temperature, and oxidative stress. It is critically involved in physiological and pathological processes ranging from inflammation to stroke to neurodegeneration. At present, the channel’s gating and ion permeation mechanisms, such as the location and identity of the selectivity filter, remain ambiguous. Here, we report the cryo-electron microscopy (cryo-EM) structure of human TRPM2 in nanodisc in the ligand-free state. Cryo-EM map-guided computational modeling and patch-clamp recording further identify a quadruple-residue motif as the ion selectivity filter, which adopts a restrictive conformation in the closed state and acts as a gate, profoundly contrasting with its widely open conformation in the Nematostella vectensis TRPM2. Our study reveals the gating of human TRPM2 by the filter and demonstrates the feasibility of using cryo-EM in conjunction with computational modeling and functional studies to garner structural information for intrinsically dynamic but functionally important domains.

Liver has an ability to regenerate itself in mammals, whereas the mechanism has not been fully explained. Here we used a GC/MS-based metabolomic method to profile the dynamic endogenous metabolic change in the serum of C57BL/6J mice at different times after 2/3 partial hepatectomy (PHx), and nine machine learning methods including Least Absolute Shrinkage and Selection Operator Regression (LASSO), Partial Least Squares Regression (PLS), Principal Components Regression (PCR), k-Nearest Neighbors (KNN), Support Vector Machines (SVM), Random Forest (RF), eXtreme Gradient Boosting (xgbDART), Neural Network (NNET) and Bayesian Regularized Neural Network (BRNN) were used for regression between the liver index and metabolomic data at different stages of liver regeneration. We found a tree-based random forest method that had the minimum average Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and the maximum R square (R2) and is time-saving. Furthermore, variable of importance in the project (VIP) analysis of RF method was performed and metabolites with VIP ranked top 20 were selected as the most critical metabolites contributing to the model. Ornithine, phenylalanine, 2-hydroxybutyric acid, lysine, etc. were chosen as the most important metabolites which had strong correlations with the liver index. Further pathway analysis found Arginine biosynthesis, Pantothenate and CoA biosynthesis, Galactose metabolism, Valine, leucine and isoleucine degradation were the most influenced pathways. In summary, several amino acid metabolic pathways and glucose metabolism pathway were dynamically changed during liver regeneration. The RF method showed advantages for predicting the liver index after PHx over other machine learning methods used and a metabolic clock containing four metabolites is established to predict the liver index during liver regeneration.

Methamphetamine (METH) abuse has become a global public health and safety problem. More information is needed to identify the time of drug abuse. In this study, methamphetamine was administered to male C57BL/6J mice with increasing doses from 5 to 30 mg kg−1 (once a day, i.p.) for 20 days. Serum and urine samples were collected for metabolomics studies using gas chromatography–mass spectrometry (GC-MS). Six machine learning models were used to infer the time of drug abuse and the best model was selected to predict administration time preliminarily. The metabolic changes caused by methamphetamine were explored. As results, the metabolic patterns of methamphetamine exposure mice were quite different from the control group and changed over time. Specifically, serum metabolomics showed enhanced amino acid metabolism and increased fatty acid consumption, while urine metabolomics showed slowed metabolism of the tricarboxylic acid (TCA) cycle, increased organic acid excretion, and abnormal purine metabolism. Phenylalanine in serum and glutamine in urine increased, while palmitic acid, 5-HT, and monopalmitin in serum and gamma-aminobutyric acid in urine decreased significantly. Among the six machine learning models, the random forest model was the best to predict the exposure time (serum: MAE = 1.482, RMSE = 1.69, R squared = 0.981; urine: MAE = 2.369, RMSE = 1.926, R squared = 0.946). The potential biomarker set containing four metabolites in the serum (palmitic acid, 5-hydroxytryptamine, monopalmitin, and phenylalanine) facilitated the identification of methamphetamine exposure. The random forest model helped predict the methamphetamine exposure time based on these potential biomarkers.

Silicon (Si) is considered to be a plant growth and development regulator element as well as provide the regulatory response against various biotic stressors. However, the potential mechanism of Si enhancement to regulate plant disease resistance remains to be studied. Therefore, the current study evaluated the effects of Si application on the performance of sugarcane against Xanthomonas albilineans (Xa) infection. Si was applied exogenously (0, 3.85 and 7.70 g Si/kg soil) and the results show that plant height, stem circumference and leaf width of siliconized sugarcane have been improved, which effectively reduced the disease index (0.17–0.21) and incidence (58.2%−69.1%) after Xa infection. Lowest values of MDA (348.5 nmol g−1 FW) and H2O2 (3539.4 mmol/L) were observed in 7.70 g Si/kg soil followed by in 3.85 g Si/kg soil (MDA: 392.6 nmol g−1 FW and H2O2: 3134.6 mmol/L) than that of the control. Whereas, PAL enzyme activity (50.8 mmol/L), JA (230.2 mmol/L) and SA (2.7 ug mL−1) contents were significantly higher in 7.70 g Si/kg soil followed by in 3.85 g Si/kg soil (PAL: 46.3 mmol/L, JA: 182.7 mmol/L and SA: 2.4 ug mL−1) as compared to control. The lower MDA, H2O2 level and higher enzymatic activities were associated with the highest expression levels of their metabolic pathway associated genes i.e., ShMAPK1, ShLOX, ShPAL, ShAOS, ShAOC, ShC4H, ShCAT, Sh4CL and ShNPR1 (22.08, 15.56, 10.42, 3.35, 2.54, 2.14, 1.82, 1.67 and 1.22 folds, respectively) in 7.70 g Si/kg soil as compared to other experimental units and control. Overall, the results of current study indicates that siliconized sugarcane more actively regulates disease resistance through modulation of growth and MDA, H2O2, SA and JA associated metabolic pathways.

There is a lack of understanding of the spray characteristics of gas-centered swirl coaxial (GCSC) injectors during self-pulsation occurs. Therefore, the self-pulsation of a GCSC injector was investigated experimentally in this study. Experiments were conducted at atmospheric pressure with filtered water and dried air supplied through a propellant feed system. A back-lighting high-speed photography technique was used to capture unsteady spray features. A laser-based particle size analyzer (LPSA) was used to measure the size of the droplets in the spray. The effects of recess and gas-liquid ratio on spray self-pulsation were analyzed. It was found that the recess of the injector strongly determines the spray pattern. When spray self-pulsation occurs without recess, both the center and periphery of the spray oscillate. With an increase in the mass flow rate of the gas, the boundary between the center and the periphery of the spray becomes more noticeable. Meanwhile, small droplets in the spray center oscillate, with the periphery of the spray being characterized by a periodic “shoulder.” Under the same operating conditions but with a small recess (2 mm), the spray adheres to the injector faceplate. With a larger recess (7 mm), when spray self-pulsation occurs, the spray periodically forms “shoulder” and “neck,” similar to the behavior of self-pulsation in a liquid-centered coaxial injector. Therefore, it can be concluded that spray self-pulsation enhances atomization at the center of the spray to a certain extent. However, atomization becomes worse in the periphery with an oscillating spray.

Background and AimsThe heat shock protein (Hsp) 90α is induced by stress and regulates inflammation through multiple pathways. Elevated serum Hsp90α had been found in nonalcoholic steatohepatitis (NASH). Geranylgeranylacetone (GGA, also called teprenone) is a terpenoid derivative. It was reported to induce Hsp and alleviate insulin resistance. We aimed to evaluate the Hsp90α as a biomarker in predicting metabolic-associated fatty liver disease (MAFLD) and define the therapeutic effects of geranylgeranylacetone for the disease.MethodsA clinical study was conducted to analyze the elements associated with Hsp90α, and a predictive model of MAFLD was developed based on Hsp90α. The histopathological correlation between Hsp90α and MAFLD was investigated through a diet-induced mouse model. Furthermore, GGA was applied to the mouse model.ResultsSerum Hsp90α was increased in patients with MAFLD. A positive linear relationship was found between age, glycosylated hemoglobin (HbA1c), MAFLD, and serum Hsp90α. Meanwhile, a negative linear relationship with body mass index (BMI) was found. A model using Hsp90α, BMI, HbA1c, and ALT was established for predicting MAFLD. The area under the receiver operating characteristic (ROC) curves was 0.94 (95% CI 0.909–0.971, p = 0.000). The sensitivity was 84.1%, and the specificity was 93.1%. In vitro experiments, GGA induced Hsp90α in steatosis cells. In the mice model, Hsp90α decreased in the GGA treatment group. Hepatic steatosis, inflammation, insulin resistance, and glucose intolerance were improved in the GGA-treated group. Serum Hsp90α was positively correlated with steatohepatitis activity according to hepatic histopathology.ConclusionsSerum Hsp90α was elevated in MAFLD, and a positive correlation between serum Hsp90α and the grade of activity of steatohepatitis was observed. The model using BMI, HbA1c, and alanine aminotransferase (ALT) had a good value to predict MAFLD. The findings also revealed the effectiveness of GGA in the treatment of MAFLD.

Passage of systemically delivered pharmacological agents into the brain is largely blocked by the blood-brain-barrier (BBB), an organotypic specialization of brain endothelial cells (ECs). Tumor vessels in glioblastoma (GBM), the most common malignant brain tumor in humans, are abnormally permeable, but this phenotype is heterogeneous and may differ between the tumor’s center and invasive front. Here, through single-cell RNA sequencing (scRNA-seq) of freshly isolated ECs from human glioblastoma and paired tumor peripheral tissues, we have constructed a molecular atlas of human brain ECs providing unprecedented molecular insight into the heterogeneity of the human BBB and its molecular alteration in glioblastoma. We identified 5 distinct EC phenotypes representing different states of EC activation and BBB impairment, and associated with different anatomical locations within and around the tumor. This unique data resource provides key information for designing rational therapeutic regimens and optimizing drug delivery.

White matter stroke (WMS) occurs as small infarcts in deep penetrating blood vessels in the brain and affects the regions of the brain that carry connections, termed the subcortical white matter. WMS progresses over years and has devastating clinical consequences. Unlike large grey matter strokes, WMS disrupts the axonal architecture of the brain and depletes astrocytes, oligodendrocyte lineage cells, axons and myelinating cells, resulting in abnormalities of gait and executive function. An astrocytic cell-based therapy is positioned as a strong therapeutic candidate after WMS. In this study we report, the reliable generation of a novel stem cell-based therapeutic product, glial enriched progenitors (GEPs) derived from human induced pluripotent stem cells (hiPSCs). By transient treatment of hiPSC derived neural progenitors (hiPSC-NPCs) with the small molecule deferoxamine, a prolyl hydroxylase inhibitor, for three days hiPSC-NPCs become permanently biased towards an astrocytic fate, producing hiPSC-GEPs. In preparation for clinical application, we have developed qualification assays to ensure identity, safety, purity, and viability of the cells prior to manufacture. Using tailored q-RT-PCR-based assays, we have demonstrated the lack of pluripotency in our final therapeutic candidate cells (hiPSC-GEPs) and we have identified the unique genetic profile of hiPSC-GEPs that is clearly distinct from the parent lines, hiPSCs and iPSC-NPCs. After completion of the viability assay, we have stablished the therapeutic window of use for hiPSC-GEPs in future clinical applications (7 h). Lastly, we were able to reliably and consistently produce a safe therapeutic final product negative for contamination by any human or murine viral pathogens, selected bacteria, common laboratory mycoplasmas, growth of any aerobes, anaerobes, yeast, or fungi and 100 times less endotoxin levels than the maximum acceptable value. This study demonstrates the reliable and safe generation of patient derived hiPSC-GEPs that are clinically ready as a cell-based therapeutic approach for WMS.

Background: We speculated that stimulating the uterine cervix of patients may increase blood flow resistance of the uterine artery more significantly in women with endometriosis than those without. Accordingly, the resistance index (RI) of the uterine artery following stimulation of the cervix may offer a novel and promising non-invasive method for diagnosing endometriosis. In this prospective study, the aim was to determine the performance of the uterine artery resistance index (RI) following stimulation of the cervix in the diagnosis of endometriosis. Methods: This prospective cohort study included 270 patients with non-uterine diseases who were scheduled for laparoscopic surgery. Each patient underwent Doppler sonography for peak systolic velocity (PSV), end diastolic velocity (EDV), and RI before and after stimulation of the cervix, in addition to conventional ultrasound examination one day prior to laparoscopic surgery. The cervix was stimulated using a double convex array electronic vaginal probe for real-time tissue elastography. Results: Laparoscopic surgical evaluation revealed endometriosis in 91 (33.7%) patients. Following stimulation of the cervix, the RI of the uterine artery was significantly increased in the endometriosis group (p = 0.00), but no significant increase in the RI was observed in the control group (p = 0.45). The sensitivity and specificity of the post-stimulation RI for diagnosing endometriosis (using a cut off of 0.83) were 85.71% (95% confidence interval (CI): 76.45–91.88) and 88.83% (95% CI: 83.05–92.87), respectively. For peritoneal endometriosis (PEM), sensitivity and specificity were 86.96% (95% CI: 65.33–96.57) and 88.83% (95% CI: 83.05–92.87), respectively. The sensitivity and specificity of transvaginal ultrasonography (TVU) for diagnosing endometriosis were 63.74% (95% CI: 52.93–73.37) and 94.41% (95% CI: 89.68–97.14), respectively. The positive rate for TVU in diagnosing all endometrioses was 63.7%, and this was lower than that for the post-stimulation RI (84.6%, p = 0.001). Conclusions: Stimulation of the cervix leads to a significant decrease in uterine blood perfusion in women with endometriosis. TVU is a sensitive and specific method for the diagnosis of endometrioma, while the RI is a more sensitive parameter for diagnosing PEM than TVU. For the diagnosis of endometriosis, especially for PEM, the post-stimulation RI may prove to be a promising non-invasive diagnostic technique.

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BackgroundThe mortality of humans due to rabies in China has been declining in recent years, but it is still a significant public health problem. According to the global framework, China strives to achieve the goal of eliminating human rabies before 2030.MethodsWe reviewed the epidemiology of human deaths from rabies in mainland China from 2004 to 2018. We identified high risk regions, age and occupational groups, and used a continuous deterministic susceptibility-exposure-infection-recovery (SEIR) model with periodic transmission rate to explore seasonal rabies prevalence in different human populations. The SEIR model was used to simulate the data of human deaths from rabies reported by the Chinese Center for Disease Control and Prevention (China CDC). We calculated the relative transmission intensity of rabies from canines to different human groups, and they provided a reliable epidemiological basis for further control and prevention of human rabies.ResultsResults showed that human deaths from rabies exhibited regional differences and seasonal characteristics in mainland China. The annual human death from rabies in different regions, age groups and occupational groups decreased steadily across time. Nevertheless, the decreasing rates and the calculated R0s of canines of various human groups were different. The transmission intensity of rabies from canines to human populations was the highest in the central regions of China, in people over 45 years old, and in farmers.ConclusionsAlthough the annual cases of human deaths from rabies have decreased steadily since 2007, the proportion of human deaths from rabies varies with region, age, gender, and occupation. Further enhancement of public awareness and immunization status in high-risk population groups and blocking the transmission routes of rabies from canines to humans are necessary. The concept of One Health should be abided and human, animal, and environmental health should be considered simultaneously to achieve the goal of eradicating human rabies before 2030.

PurposeGlioblastoma (GBM) is the most aggressive and lethal type of brain tumors. Magnetic resonance imaging (MRI) has been commonly used for GBM diagnosis. Contrast enhancement (CE) on T1-weighted sequences are presented in nearly all GBM as a result of high vascular permeability in glioblastomas. Although several radiomics studies indicated that CE is associated with distinct molecular signatures in tumors, the effects of vascular endothelial cells, the key component of blood brain barrier (BBB) controlling vascular permeability, on CE have not been thoroughly analyzed.MethodsEndothelial cell enriched genes have been identified using transcriptome data from 128 patients by a systematic method based on correlation analysis. Distinct endothelial cell enriched genes associated with CE were identified by analyzing difference of correlation score between CE-high and CE–low GBM cases. Immunohistochemical staining was performed on in-house patient cohort to validate the selected genes associated with CE. Moreover, a survival analysis was conducted to uncover the relation between CE and patient survival.ResultsWe illustrated that CE is associated with distinct vascular molecular imprints characterized by up-regulation of pro-inflammatory genes and deregulation of BBB related genes. Among them, PLVAP is up-regulated, whereas TJP1 and ABCG2 are down-regulated in the vasculature of GBM with high CE. In addition, we found that the high CE is associated with poor prognosis and GBM mesenchymal subtype.ConclusionWe provide an additional insight to reveal the molecular trait for CE in MRI images with special focus on vascular endothelial cells, linking CE with BBB disruption in the molecular level. This study provides a potential new direction that may be applied for the treatment optimization based on MRI features.

Abstract Background Cathelicidins are a major group of natural antimicrobial peptides which play essential roles in regulating host defense and immunity. In addition to the antimicrobial and immunomodulatory activities, recent studies have reported the involvement of cathelicidins in cardiovascular diseases by regulating inflammatory response and microvascular dysfunction. However, the role of cathelicidins in myocardial apoptosis upon cardiac ischemia/reperfusion (I/R) injury remains largely unknown. Methods CRAMP (cathelicidin-related antimicrobial peptide) levels were measured in the heart and serum from I/R mice and in neonatal mouse cardiomyocytes treated with oxygen glucose deprivation/reperfusion (OGDR). Human serum cathelicidin antimicrobial peptide (LL-37) levels were measured in myocardial infarction (MI) patients. The role of CRAMP in myocardial apoptosis upon I/R injury was investigated in mice injected with the CRAMP peptide and in CRAMP knockout (KO) mice, as well as in OGDR-treated cardiomyocytes. Results We observed reduced CRAMP level in both heart and serum samples from I/R mice and in OGDR-treated cardiomyocytes, as well as reduced LL-37 level in MI patients. Knockdown of CRAMP enhanced cardiomyocyte apoptosis, and CRAMP KO mice displayed increased infarct size and myocardial apoptosis. In contrast, the CRAMP peptide reduced cardiomyocyte apoptosis and I/R injury. The CRAMP peptide inhibited cardiomyocyte apoptosis by activation of Akt and ERK1/2 and phosphorylation and nuclear export of FoxO3a. c-Jun was identified as a negative regulator of the CRAMP gene. Moreover, lower level of serum LL-37/neutrophil ratio was associated with readmission and/or death in MI patients during 1-year follow-up. Conclusions CRAMP protects against cardiomyocyte apoptosis and cardiac I/R injury via activation of Akt and ERK and phosphorylation and nuclear export of FoxO3a. Increasing LL-37 might be a novel therapy for cardiac ischemic injury.

Underdetermined blind source separation (UBSS) is a hot and challenging problem in signal processing. In the traditional UBSS algorithm, the number of source signals is often assumed to be known, which is very inconvenient in practice. In addition, it is more difficult to obtain the accurate estimation of mixing matrix in the underdetermined case. However, this information has a great influence on the source separation results, which can easily lead to poor separation performance. In this paper, a novel UBSS algorithm is presented to carry out a combined source signal number estimation and source signal separation task. First, in the proposed algorithm, we design a gap-based detection method to detect the number of source signals by eigenvalue decomposition. Then, the estimation of the mixing matrix is processed using a higher-order cumulant-based method so that the uniqueness of the estimated mixing matrix is guaranteed. Furthermore, an improved l1 -norm minimization algorithm is proposed to estimate the source signals. Meanwhile, the pre-conditioned conjugate gradient technology is employed to accelerate the convergence rate such that the computational load is reduced. Finally, a series of simulation experiments with synthetic heart sound data and image reconstruction results demonstrate that the proposed algorithm achieves better separating property than the state-of-the-art algorithms.