Single-cell analysis of refractory anti-SRP necrotizing myopathy treated with anti-BCMA CAR-T cell therapy.

Immune-mediated necrotizing myopathy (IMNM) is an autoimmune disorder associated with the presence of autoantibodies, characterized by severe clinical presentation with rapidly progressive muscular weakness and elevated levels of creatine kinase, while traditional pharmacological approaches possess varying and often limited effects. Considering the pathogenic role of autoantibodies, chimeric antigen receptor (CAR)-T cells targeting B cell maturation antigen (BCMA) have emerged as a promising therapeutic strategy. We reported here a patient with anti-signal recognition particle IMNM refractory to multiple available therapies, who was treated with BCMA-targeting CAR-T cells, exhibited favorable safety profiles, sustained reduction in pathogenic autoantibodies, and persistent clinical improvements over 18 mo. Longitudinal single-cell RNA, B cell receptor, T cell receptor sequencing analysis presented the normalization of immune microenvironment after CAR-T cell infusion, including reconstitution of B cell lineages, replacement of T cell subclusters, and suppression of overactivated immune cells. Analysis on characteristics of CAR-T cells in IMNM demonstrated a more active expansion of CD8+ CAR-T cells, with a dynamic phenotype shifting pattern similar in CD4+ and CD8+ CAR-T cells. A comparison of CD8+ CAR-T cells in patients with IMNM and those with malignancies collected at different timepoints revealed a more NK-like phenotype with enhanced tendency of cell death and neuroinflammation and inhibited proliferating ability of CD8+ CAR-T cells in IMNM while neuroinflammation might be the distinct characteristics. Further studies are warranted to define the molecular features of CAR-T cells in autoimmunity and to seek higher efficiency and longer persistence of CAR-T cells in treating autoimmune disorders.

Optical Encryption Using Attention-Inserted Physics-Driven Single-Pixel Imaging.

Optical encryption based on single-pixel imaging (SPI) has made great advances with the introduction of deep learning. However, the use of deep neural networks usually requires a long training time, and the networks need to be retrained once the target scene changes. With this in mind, we propose an SPI encryption scheme based on an attention-inserted physics-driven neural network. Here, an attention module is used to encrypt the single-pixel measurement value sequences of two images, together with a sequence of cryptographic keys, into a one-dimensional ciphertext signal to complete image encryption. Then, the encrypted signal is fed into a physics-driven neural network for high-fidelity decoding (i.e., decryption). This scheme eliminates the need for pre-training the network and gives more freedom to spatial modulation. Both simulation and experimental results have demonstrated the feasibility and eavesdropping resistance of this scheme. Thus, it will lead SPI-based optical encryption closer to intelligent deep encryption.

Multi-omics analysis reveals changes in tryptophan and cholesterol metabolism before and after sexual maturation in captive macaques.

Rhesus macaques (Macaca mulatta, RMs) are widely used in sexual maturation studies due to their high genetic and physiological similarity to humans. However, judging sexual maturity in captive RMs based on blood physiological indicators, female menstruation, and male ejaculation behavior can be inaccurate. Here, we explored changes in RMs before and after sexual maturation based on multi-omics analysis and identified markers for determining sexual maturity. We found that differentially expressed microbiota, metabolites, and genes before and after sexual maturation showed many potential correlations. Specifically, genes involved in spermatogenesis (TSSK2, HSP90AA1, SOX5, SPAG16, and SPATC1) were up-regulated in male macaques, and significant changes in gene (CD36), metabolites (cholesterol, 7-ketolithocholic acid, and 12-ketolithocholic acid), and microbiota (Lactobacillus) related to cholesterol metabolism were also found, suggesting the sexually mature males have stronger sperm fertility and cholesterol metabolism compared to sexually immature males. In female macaques, most differences before and after sexual maturity were related to tryptophan metabolism, including changes in IDO1, IDO2, IFNGR2, IL1Β, IL10, L-tryptophan, kynurenic acid (KA), indole-3-acetic acid (IAA), indoleacetaldehyde, and Bifidobacteria, indicating that sexually mature females exhibit stronger neuromodulation and intestinal immunity than sexually immature females. Cholesterol metabolism-related changes (CD36, 7-ketolithocholic acid, 12-ketolithocholic acid) were also observed in female and male macaques. Exploring differences before and after sexual maturation through multi-omics, we identified potential biomarkers of sexual maturity in RMs, including Lactobacillus (for males) and Bifidobacterium (for females) valuable for RM breeding and sexual maturation research.

Trem2 deficiency attenuates microglial phagocytosis and autophagic-lysosomal activation in white matter hypoperfusion.

Chronic cerebral hypoperfusion leads to sustained demyelination and a unique response of microglia. Triggering receptor expressed on myeloid cells 2 (Trem2), which is expressed exclusively on microglia in the central nervous system (CNS), plays an essential role in microglial response in various CNS disorders. However, the specific role of Trem2 in chronic cerebral hypoperfusion has not been elucidated. In this study, we investigated the specific role of Trem2 in a mouse model of chronic cerebral hypoperfusion induced by bilateral carotid artery stenosis (BCAS). Our results showed that chronic hypoperfusion induced white matter demyelination, microglial phagocytosis, and activation of the microglial autophagic-lysosomal pathway, accompanied by an increase in Trem2 expression. After Trem2 knockout, we observed attenuation of white matter lesions and microglial response. Trem2 deficiency also suppressed microglial phagocytosis and relieved activation of the autophagic-lysosomal pathway, leading to microglial polarization towards anti-inflammatory and homeostatic phenotypes. Furthermore, Trem2 knockout inhibited lipid droplet accumulation in microglia in vitro. Collectively, these findings suggest that Trem2 deficiency ameliorated microglial phagocytosis and autophagic-lysosomal activation in hypoperfusion-induced white matter injury, and could be a promising target for the treatment of chronic cerebral hypoperfusion.

TREM2 deficiency inhibits microglial activation and aggravates demyelinating injury in neuromyelitis optica spectrum disorder.

Neuromyelitis optica spectrum disorder (NMOSD) is an inflammatory demyelinating disorder of the central nervous system (CNS) triggered by autoimmune mechanisms. Microglia are activated and play a pivotal role in response to tissue injury. Triggering receptor expressed on myeloid cells 2 (TREM2) is expressed by microglia and promotes microglial activation, survival and phagocytosis. Here, we identify a critical role for TREM2 in microglial activation and function during AQP4-IgG and complement-induced demyelination. TREM2-deficient mice had more severe tissue damage and neurological impairment, as well as fewer oligodendrocytes with suppressed proliferation and maturation. The number of microglia clustering in NMOSD lesions and their proliferation were reduced in TREM2-deficient mice. Moreover, morphology analysis and expression of classic markers showed compromised activation of microglia in TREM2-deficient mice, which was accompanied by suppressed phagocytosis and degradation of myelin debris by microglia. These results overall indicate that TREM2 is a key regulator of microglial activation and exert neuroprotective effects in NMOSD demyelination.

Acute exposure of triclocarban affects early embryo development in mouse through disrupting maternal-to-zygotic transition and epigenetic modifications.

Triclocarban (TCC) is a broad-spectrum antibacterial agent used globally, and high concentrations of this harmful chemical exist in the environment. The human body is directly exposed to TCC through skin contact. Moreover, TCC is also absorbed through diet and inhaled through breathing, which results in its accumulation in the body. The safety profile of TCC and its potential impact on human health are still not completely clear; therefore, it becomes imperative to evaluate the reproductive toxicity of TCC. Here, we explored the effect of TCC on the early embryonic development of mice and its associated mechanisms. We found that acute exposure of TCC affected the early embryonic development of mice in a dose-dependent manner. Approximately 7600 differentially expressed genes (DEGs) were obtained by sequencing the transcriptome of 2-cell mouse embryos; of these, 3157 genes were upregulated and 4443 genes were downregulated in the TCC-treated embryos. GO and KEGG analysis revealed that the enriched genes were mainly involved in redox processes, RNA synthesis, DNA damage, apoptosis, mitochondria, endoplasmic reticulum, Golgi apparatus, cytoskeleton, peroxisome, RNA polymerase, and other components or processes. Moreover, the Venn analysis showed that the zygotic genome activation (ZGA) was affected and the degradation of maternal effector genes was inhibited. TCC induced changes in the epigenetic modification of 2-cell embryos. The level of DNA methylation increased significantly. Further, the levels of H3K27ac, H3K9ac, and H3K27me3 histone modifications decreased significantly, whereas those of H3K4me3 and H3K9me3 modifications increased significantly. Additionally, TCC induced oxidative stress and DNA damage in the 2-cell embryos. In conclusion, acute exposure of TCC affected early embryo development, destroyed early embryo gene expression, interfered with ZGA and maternal gene degradation, induced changes in epigenetic modification of early embryos, and led to oxidative stress and DNA damage in mouse early embryos.

Research on Traffic Flow Prediction at Intersections Based on DT-TCN-Attention.

Traditional nonintelligent signal control systems are typically used in road traffic signal systems, which cannot provide optimal guidance and have low traffic efficiency during rush hour. This study proposes a traffic signal phase dynamic timing optimization strategy based on a time convolution network and attention mechanism to improve traffic efficiency at intersections. The corresponding optimization was performed after predicting traffic conditions with different impacts using the digital twinning technique. This method uses a time-convolution network to extract the cross-time nonlinear characteristics of traffic data at road intersections. An attention mechanism was introduced to capture the relationship between the importance distribution and duration of the historical time series to predict the traffic flow at an intersection. The interpretability and prediction accuracy of the model was effectively improved. The model was tested using traffic flow data from a signalized intersection in Shangrao, Jiangxi Province, China. The experimental results indicate that the model generated by training has a strong learning ability for the temporal characteristics of traffic flow. The model has high prediction accuracy, good optimization results, and wide application prospects in different scenarios.

CSF sTREM2 in neurological diseases: a two-sample Mendelian randomization study.

BACKGROUND: Soluble triggering receptor expressed on myeloid cells 2 (sTREM2) in cerebrospinal fluid (CSF) has been described as a biomarker for microglial activation, which were observed increased in a variety of neurological disorders. OBJECTIVE: Our objective was to explore whether genetically determined CSF sTREM2 levels are causally associated with different neurological diseases by conducting a two-sample Mendelian randomization (MR) study. METHODS: Single nucleotide polymorphisms significantly associated with CSF sTREM2 levels were selected as instrumental variables to estimate the causal effects on clinically common neurological diseases, including stroke, Alzheimer's diseases, Parkinson's diseases, amyotrophic lateral sclerosis, multiple sclerosis, and epilepsy and their subtypes. Summary-level statistics of both exposure and outcomes were applied in an MR framework. RESULTS: Genetically predicted per 1 pg/dL increase of CSF sTREM2 levels was associated with higher risk of multiple sclerosis (OR = 1.038, 95%CI = 1.014-1.064, p = 0.002). Null association was found in risk of other included neurological disorders. CONCLUSIONS: These findings provide support for a potential causal relationship between elevated CSF sTREM2 levels and higher risk of multiple sclerosis.

Intravenous Alteplase in a Patient With Acute Ischemic Stroke Subsequent to Purulent Meningitis: A Case Report and Literature Review.

This report highlights the outcome of intravenous alteplase in a patient with acute ischemic stroke subsequent to purulent meningitis. This type of meningitis has not been defined in the guideline for early treatment of acute ischemic stroke. A 58-year-old woman with purulent meningitis developed a sudden stroke and was admitted to our emergency department. She received 0.6 mg/kg of alteplase intravenously 90 minutes after stroke onset. At 1 hour after thrombolysis, the patient's National Institute of Health Stroke Scale score improved from 9 to 4. At 2 hours, she developed a sudden severe headache that progressed to coma, and her National Institute Health Stroke Scale score rapidly deteriorated to 20. Cranial computed tomography revealed subarachnoid hemorrhage and multiple bilateral lobar brain hemorrhages. She died of a cerebellar tonsillar hernia. Intravenous alteplase might be hazardous in patients with acute ischemic stroke subsequent to purulent meningitis.

Arterial Tortuosity and Its Correlation with White Matter Hyperintensities in Acute Ischemic Stroke.

Introduction: The association between arterial tortuosity and acute ischemic stroke (AIS) has been reported, but showing inconsistent results. We hypothesized that tortuosity of extra- and intracranial large arteries might be higher in AIS patients. Furthermore, we explored the correlation between artery tortuosity and white matter hyperintensity (WMH) severity in AIS patients. Methods: 166 AIS patients identified as large artery atherosclerosis, and 83 control subjects were enrolled. All subjects received three-dimensional computed tomography angiography (CTA). Arterial tortuosity was evaluated using the tortuosity index. WMHs were evaluated using magnetic resonance imaging in all AIS patients. Results: AIS patients showed significantly increased arterial tortuosity index relative to controls, including left carotid artery (CA) (p = 0.001), right CA (p < 0.001), left common carotid artery (CCA) (p < 0.001), right CCA (p < 0.001), left internal carotid artery (p = 0.001), right internal carotid artery (p = 0.01), left extracranial internal carotid artery (EICA) (p < 0.001), right EICA (p = 0.01), and vertebral artery dominance (VAD) (p = 0.001). The tortuosity of all above arteries was associated with the presence of AIS. AIS patients with moderate or severe WMHs had a higher tortuosity index in left CA (p = 0.005), left CCA (p = 0.003), left EICA (p = 0.07), and VAD (p = 0.001). In addition, the tortuosity of left EICA and VAD was associated with WMH severity in AIS patients. Conclusions: Increased extra- and intracranial large arteries tortuosity is associated with AIS. The tortuosity of left carotid artery system and vertebral artery may be the independent risk factors for WMH severity in AIS patients. Clinical Trial Registration. This trial is registered with NCT03122002 (http://www.clinicaltrials.gov).

Playing Ant Forest to promote online green behavior: A new perspective on uses and gratifications.

The popularity of Internet technology has resulted in people's lives being increasingly embedded in this network. The rise in usage of environmental protection apps has become a powerful tool in driving offline environmental protection activities and green lifestyles. However, little is known about the public's online green behavior. To fill this gap, we took Ant Forest, the most influential online environmental protection project in China, as a case study to explore the driving psychology of Ant Forest users' continuous use behaviors (CUBs) by expanding the use and gratifications theory. The proposed hypotheses were tested using a structural equation model based on data from 951 Ant Forest users. The results showed that users' gratification is an important psychological motivation that encourages CUBs in Ant Forest, and the different dimensions of gratification have significant differences in the driving intensity of the three types of CUBs. Moreover, emotional dependence moderates the relationships between gratifications and CUBs. Finally, from the perspective of cultivating gratification, this study suggests promoting the continuous use of Ant Forest as it provides a reference for understanding and developing online green behavior.

How does COVID-19 emergency cognition influence public pro-environmental behavioral intentions? An affective event perspective.

Social impacts and serious damages caused by the COVID-19 pandemic have resulted in public introspection on the issue of ecological environmental protection. However, whether the public cognition of COVID-19 can promote pro-environmental behavioral intentions (PEBI) has not yet been determined; this is crucial for studying the ecological significance of the pandemic. Based on the affective events theory (AET), this study investigated the mechanism by which COVID-19 emergency cognition influences public PEBI. Following an analysis of 873 public questionnaires, the results reveal that public cognition of COVID-19 emergency can significantly promote PEBI. Among them, the effect of emergency coping is stronger than that of emergency relevance. Besides, the positive and negative environmental affective reactions aroused by COVID-19 pandemic play a mediating role between the emergency cognition and PEBI. Moreover, the positive environmental affective reactions show a stronger positive effect on household-sphere PEBI. However, the negative environmental affective reactions are more prominent in promoting public-sphere PEBI. This research aims to bridge a research gap by establishing a link between COVID-19 pandemic and PEBI. The findings can provide useful recommendations for policymakers to find the opportunity behind the COVID-19 emergency to promote public PEBI.

Dysregulation of Immune Response in Patients With Coronavirus 2019 (COVID-19) in Wuhan, China.

BACKGROUND: In December 2019, coronavirus 2019 (COVID-19) emerged in Wuhan and rapidly spread throughout China. METHODS: Demographic and clinical data of all confirmed cases with COVID-19 on admission at Tongji Hospital from 10 January to 12 February 2020 were collected and analyzed. The data on laboratory examinations, including peripheral lymphocyte subsets, were analyzed and compared between patients with severe and nonsevere infection. RESULTS: Of the 452 patients with COVID-19 recruited, 286 were diagnosed as having severe infection. The median age was 58 years and 235 were male. The most common symptoms were fever, shortness of breath, expectoration, fatigue, dry cough, and myalgia. Severe cases tend to have lower lymphocyte counts, higher leukocyte counts and neutrophil-lymphocyte ratio (NLR), as well as lower percentages of monocytes, eosinophils, and basophils. Most severe cases demonstrated elevated levels of infection-related biomarkers and inflammatory cytokines. The number of T cells significantly decreased, and were more impaired in severe cases. Both helper T (Th) cells and suppressor T cells in patients with COVID-19 were below normal levels, with lower levels of Th cells in the severe group. The percentage of naive Th cells increased and memory Th cells decreased in severe cases. Patients with COVID-19 also have lower levels of regulatory T cells, which are more obviously decreased in severe cases. CONCLUSIONS: The novel coronavirus might mainly act on lymphocytes, especially T lymphocytes. Surveillance of NLR and lymphocyte subsets is helpful in the early screening of critical illness, diagnosis, and treatment of COVID-19.

Stopped-Flow Dynamics Study on the Escape Behavior of Polyelectrolyte Macromolecules from Microgels: The Influence of the Path Length and Size.

We reported the fabrication of several monodispersed poly(2-vinyl pyridine)-poly(N-isopropylacrylamide) (P2VP-PNIPAM) microgels including the P2VP core (non-cross-linked) and PNIPAM (cross-linked) shell by mature emulsion polymerization. The fast escape behavior (diffusion process) of linear P2VP chains through a porous PNIPAM layer was investigated by a pH jump stopped-flow apparatus. The time-dependent dynamic traces (corresponding to the scattered light intensity) decreased at the initial timescale of several seconds and then reached an apparent equilibrium, confirming the efficient escape of P2VP chains from microgels. Compared with the previously reported literature, such an accelerated escape process resulted from the sharply increased internal charge repulsive force caused by the protonation of P2VP moieties under acidic conditions. The obtained characteristic relaxation times by single exponential fitting of these kinetic traces were dependent on the final pH values, equilibrium temperatures, shell thickness (path length), and cross-linking density (mesh size). We believe that this work can provide an efficient way to investigate hindered diffusion, especially the initial rapid diffusion stage. Not only that, the proposed model can also provide theoretical guidance to some practical applications, such as membrane separation and the exocytosis phenomenon of intracellular proteins or macromolecular substances.

Growing networks with communities: A distributive link model.

Evolution and popularity are two keys of the Barabasi-Albert model, which generates a power law distribution of network degrees. Evolving network generation models are important as they offer an explanation of both how and why complex networks (and scale-free networks, in particular) are ubiquitous. We adopt the evolution principle and then propose a very simple and intuitive new model for network growth, which naturally evolves modular networks with multiple communities. The number and size of the communities evolve over time and are primarily subjected to a single free parameter. Surprisingly, under some circumstances, our framework can construct a tree-like network with clear community structures-branches and leaves of a tree. Results also show that new communities will absorb a link resource to weaken the degree growth of hub nodes. Our models have a common explanation for the community of regular and tree-like networks and also breaks the tyranny of the early adopter; unlike the standard popularity principle, newer nodes and communities will come to dominance over time. Importantly, our model can fit well with the construction of the SARS-Cov-2 haplotype evolutionary network.

ANN-Based Estimation of Low-Latitude Monthly Ocean Latent Heat Flux by Ensemble Satellite and Reanalysis Products.

Ocean latent heat flux (LHF) is an essential variable for air-sea interactions, which establishes the link between energy balance, water and carbon cycle. The low-latitude ocean is the main heat source of the global ocean and has a great influence on global climate change and energy transmission. Thus, an accuracy estimation of high-resolution ocean LHF over low-latitude area is vital to the understanding of energy and water cycle, and it remains a challenge. To reduce the uncertainties of individual LHF products over low-latitude areas, four machine learning (ML) methods (Artificial Neutral Network (ANN), Random forest (RF), Bayesian Ridge regression and Random Sample Consensus (RANSAC) regression) were applied to estimate low-latitude monthly ocean LHF by using two satellite products (JOFURO-3 and GSSTF-3) and two reanalysis products (MERRA-2 and ERA-I). We validated the estimated ocean LHF using 115 widely distributed buoy sites from three buoy site arrays (TAO, PIRATA and RAMA). The validation results demonstrate that the performance of LHF estimations derived from the ML methods (including ANN, RF, BR and RANSAC) were significantly better than individual LHF products, indicated by R2 increasing by 3.7-46.4%. Among them, the LHF estimation using the ANN method increased the R2 of the four-individual ocean LHF products (ranging from 0.56 to 0.79) to 0.88 and decreased the RMSE (ranging from 19.1 to 37.5) to 11 W m-2. Compared to three other ML methods (RF, BR and RANSAC), ANN method exhibited the best performance according to the validation results. The results of relative uncertainty analysis using the triangle cornered hat (TCH) method show that the ensemble LHF product using ML methods has lower relative uncertainty than individual LHF product in most area. The ANN was employed to implement the mapping of annual average ocean LHF over low-latitude at a spatial resolution of 0.25° during 2003-2007. The ocean LHF fusion products estimated from ANN methods were 10-30 W m-2 lower than those of the four original ocean products (MERRA-2, JOFURO-3, ERA-I and GSSTF-3) and were more similar to observations.

Estimation of Daily Terrestrial Latent Heat Flux with High Spatial Resolution from MODIS and Chinese GF-1 Data.

Reliable estimates of terrestrial latent heat flux (LE) at high spatial and temporal resolutions are of vital importance for energy balance and water resource management. However, currently available LE products derived from satellite data generally have high revisit frequency or fine spatial resolution. In this study, we explored the feasibility of the high spatiotemporal resolution LE fusion framework to take advantage of the Moderate Resolution Imaging Spectroradiometer (MODIS) and Chinese GaoFen-1 Wide Field View (GF-1 WFV) data. In particular, three-fold fusion schemes based on Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) were employed, including fusion of surface reflectance (Scheme 1), vegetation indices (Scheme 2) and high order LE products (Scheme 3). Our results showed that the fusion of vegetation indices and further computing LE (Scheme 2) achieved better accuracy and captured more detailed information of terrestrial LE, where the determination coefficient (R2) varies from 0.86 to 0.98, the root-mean-square error (RMSE) ranges from 1.25 to 9.77 W/m2 and the relative RSME (rRMSE) varies from 2% to 23%. The time series of merged LE in 2017 using the optimal Scheme 2 also showed a relatively good agreement with eddy covariance (EC) measurements and MODIS LE products. The fusion approach provides spatiotemporal continuous LE estimates and also reduces the uncertainties in LE estimation, with an increment in R2 by 0.06 and a decrease in RMSE by 23.4% on average. The proposed high spatiotemporal resolution LE estimation framework using multi-source data showed great promise in monitoring LE variation at field scale, and may have value in planning irrigation schemes and providing water management decisions over agroecosystems.

Proteomic profiling of plasma biomarkers in acute ischemic stroke due to large vessel occlusion.

BACKGROUND: Acute ischemic stroke (AIS) due to large vessel occlusion (LVO) is a devastating cerebrovascular disorder, which could benefit from collateral circulation. Proteins associated with acute LVO pathogenesis and endothelial function may appear in blood samples of AIS patients due to LVO, thus permitting development of blood-based biomarkers for its diagnosis and prognosis. METHODS: This study is a single-center, retrospective, observational case-control trial. Consecutive patients who presented at the Department of Neurology of Tongji Hospital were recruited from July 2016 to April 2018. In the discovery phase, a proteomic approach with iTRAQ-based LC-MS/MS was used to investigate the altered proteomic pattern in plasma from patients with AIS due to LVO. In the validation study, Western blots was used to identify biomarkers associated with stroke diagnosis as well as their prognostic value associated with different collateral statuses. RESULTS: For this exploratory study, the proteomic analysis of plasma from 40 patients with AIS due to LVO and 20 healthy controls revealed seven differentially expressed proteins with a 1.2/0.83-fold or greater difference between groups. The four elevated proteins, PPBP (1.58 ± 0.78 vs 0.98 ± 0.37; P < 0.001), THBS1 (1.13 ± 0.88 vs 0.43 ± 0.26; P < 0.001), LYVE1 (1.61 ± 0.55 vs 0.97 ± 0.50; P < 0.001), and IGF2 (1.19 ± 0.42 vs 0.86 ± 0.24; P < 0.001), were verified by Western blots analysis in an independent cohort including 33 patients and 33 controls. A strong interaction was observed between the four-protein panel and the diagnosis of AIS due to LVO (AUC 0.947; P < 0.001). Furthermore, IGF2, LYVE1, and THBS1 were closely associated with collateral status (IGF2 0.115, 95% CI 0.016-0.841, P = 0.033; LYVE1 0.183, 95% CI 0.036-0.918, P = 0.039; THBS1 4.257, 95% CI 1.273-14.228, P = 0.019), and proved to be independent predictors of good outcome (IGF2 0.115, 95% CI 0.015-0.866, P = 0.036; LYVE1 0.028, 95% CI 0.002-0.334, P = 0.005; THBS1 3.294, 95% CI 1.158-9.372, P = 0.025) at a 3-month follow-up. CONCLUSIONS: The identified 4-biomarker panel could provide diagnostic aid to the existing imaging modalities for AIS due to LVO, and the prognostic value of IGF2, LYVE1, and THBS1 was proved in predicting functional outcomes related to collateral status. Trial registration ClinicalTrials.gov NCT03122002. Retrospectively registered April 20, 2017. URL of trial registry record: https://www.clinicaltrials.gov/ct2/show/NCT03122002?term=NCT+03122002&rank=1.

Link prediction for tree-like networks.

Link prediction is the problem of predicting the location of either unknown or fake links from uncertain structural information of a network. Link prediction algorithms are useful in gaining insight into different network structures from partial observations of exemplars. However, existing link prediction algorithms only focus on regular complex networks and are overly dependent on either the closed triangular structure of networks or the so-called preferential attachment phenomenon. The performance of these algorithms on highly sparse or treelike networks is poor. In this letter, we proposed a method that is based on the network heterogeneity. We test our algorithms for three real large sparse networks: a metropolitan water distribution network, a Twitter network, and a sexual contact network. We find that our method is effective and performs better than traditional algorithms, especially for the Twitter network. We further argue that heterogeneity is the most obvious defining pattern for complex networks, while other statistical properties failed to be predicted. Moreover, preferential attachment based link prediction performed poorly and hence we infer that preferential attachment is not a plausible model for the genesis of many networks. We also suggest that heterogeneity is an important mechanism for online information propagation.

Aquaporin-4 antibody positive neuromyelitis optica spectrum disorder subsequent to rhabdomyolysis: a case report and literature review.

Rhabdomyolysis could be caused by various mechanisms including autoimmune reaction. Here, we reported a case of 76-year-old-woman diagnosed with aquaporin-4 (AQP4) IgG positive neuromyelitis optica spectrum disorder (NMOSD) following rhabdomyolysis. After a review of literature, we propose that physical injury of skeletal muscle cells may lead to the production of AQP4 IgG and this AQP4 IgG might further decrease in the stability of skeletal muscle cells creating a positive feedback loop. HyperCKemia might be an inducement of NMOSD.