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IntroductionVehicle emissions have become an important source of urban air pollution, and the assessment of air pollution emission characteristics and health effects caused by specific pollution sources can provide scientific basis for air quality management.MethodsIn this paper, vehicle PM2.5 pollution in typical urban agglomerations of China (the Beijing-Tianjin-Hebei urban agglomeration (BTHUA), the triangle of the Central China urban agglomeration (TCCUA) and the Chengdu-Chongqing urban agglomeration (CCUA)) were used as research samples to evaluate the emission characteristics, health effects and economic losses of vehicle PM2.5 pollution based on the emission inventory, air quality model and exposure-response function from 2010 to 2020.ResultsThe results indicated that PM2.5 emissions from vehicles in the three urban agglomerations during 2010-2020 first showed an upward yearly trend and then showed a slow decrease in recent years. Heavy-duty trucks and buses are the main contribution vehicles of PM2.5, and the contribution rates of light-duty vehicles to PM2.5 is increasing year by year. The contribution rate of PM2.5 in Beijing decreased significantly. In addition to capital cities and municipalities directly under the central Government, the emission of pollutants in other cities cannot be ignored. The evaluation results of the impact of PM2.5 pollution from vehicles on population health show that: the number of each health endpoint caused by PM2.5 pollution from vehicles in the BTHUA and CCUA showed an overall upward trend, while the TCCUA showed a downward trend in recent years. Among them, PM2.5 pollution from vehicles in the three major urban agglomerations cause about 78,200 (95% CI: 20,500-138,800) premature deaths, 122,800 (95% CI: 25,600-220,500) inpatients, and 628,400 (95% CI: 307,400-930,400) outpatients and 1,332,400 (95% CI: 482,700-2,075,600) illness in 2020. The total health economic losses caused by PM2.5 pollution from vehicles in the three major urban agglomerations in 2010, 2015 and 2020 were 68.25 billion yuan (95% CI: 21.65-109.16), 206.33 billion yuan (95% CI: 66.20-326.20) and 300.73 billion yuan (95% CI: 96.79-473.16), accounting for 0.67% (95% CI: 0.21-1.07%), 1.19% (95% CI: 0.38%-1.88%) and 1.21% (95% CI: 0.39%-1.90%) of the total GDP of these cities.DiscussionDue to the differences in vehicle population, PM2.5 concentration, population number and economic value of health terminal units, there are differences in health effects and economic losses among different cities in different regions. Among them, the problems of health risks and economic losses were relatively prominent in Beijing, Chengdu, Chongqing, Tianjin and Wuhan.

IntroductionAs one of the key areas for air pollution prevention and control in China, the Fenwei Plain is experiencing serious near-surface O3 pollution, which is a key issue that needs to be solved urgently.MethodsBased on pollutant concentration monitoring data and meteorological and health data over the same period, this study analyzed the temporal and spatial characteristics, the relationships with meteorological factors of O3 pollution, and the health effects and economic losses caused by exposure to O3 pollution using environmental health risk and environmental value assessment methods in 11 cities on the Fenwei Plain in China from 2014 to 2020.ResultsThe results showed that O3 pollution has become increasingly serious on the Fenwei Plain in recent years. The annual average concentration of O3_8h_max showed an overall upwards trend, with an increase of 32.39% in 2020 compared to 2014. The mean concentrations observed in summer were the highest, followed by spring and autumn, and the lowest was in winter. The O3 concentration had a significant positive correlation with air temperature and sunshine hours. The evaluation results of the impact of air pollution on population health showed that the number of premature deaths caused by O3 pollution fluctuated and increased during 2014–2020. In 2020, the numbers of total, cardiovascular and respiratory deaths attributable to O3 pollution on the Fenwei Plain were 6,867 (95% CI: 3,739–9,965), 3,652 (95% CI: 1,363–5,905), and 1,257 (95% CI: 747–2,365), respectively, and the total number of premature deaths related to O3 exposure increased by 48.05% compared with 2014. The health and economic losses attributed to O3 pollution on the Fenwei Plain during the study period were 44.22 (95% CI: 22.17–69.18), 47.16 (95% CI: 23.64–73.77), 68.28 (95% CI: 34.27–106.31), 114.44 (95% CI: 57.42–177.76), 110.85 (95% CI: 55.45–172.52), 116.41 (95% CI: 58.24–180.74), and 116.81 (95% CI: 58.00–180.88) billion yuan, respectively. In Linfen City, the increasing rate of the number of premature deaths reached 283.39% because the O3 concentration increased greatly.DiscussionDue to high O3 concentrations and obvious population growth in Xi’an, the problems of premature death and health and economic losses attributed to O3 concentrations exceeding the standard value are prominent.

Green innovation culture is essential to the Chinese 14th five-year plan aligned with sustainable development goal 9. This study examines the relationship between green innovation culture and market performance of Chinese small and medium-sized enterprises (SMEs). We evaluated hypothesis by taking a sample of 564 SMEs employees in China through an online survey. The structural equation modelling (SEM) method is used to analyze data. The findings showed that green innovation culture positively influence product and marketing innovation. Similarly, marketing innovation positively affects product innovation and market performance. In addition, product innovation has a substantial effect on market performance. The outcomes of this study imply that SMEs in emerging economies should concentrate on green innovation culture to improve their market performance. In addition, the identification of study limitations and suggestions for further research are also addressed for all stakeholders involved with SMEs.

We present the use of conductive spray polymer ionization mass spectrometry (CPSI-MS) combined with machine learning (ML) to rapidly gain the metabolic fingerprint from 1 μl liquid extraction from the biopsied tissue of triple-negative breast cancer (TNBC) in China. The 76 discriminative metabolite markers are verified at the primary carcinoma site and can also be successfully tracked in the serum. The Lasso classifier featured with 15- and 22-metabolites detected by CPSI-MS achieve a sensitivity of 88.8% for rapid serum screening and a specificity of 91.1% for tissue diagnosis, respectively. Finally, the expression levels of their corresponding upstream enzymes and transporters have been initially confirmed. In general, CPSI-MS/ML serves as a cost-effective tool for the rapid screening, diagnosis, and precise characterization for the TNBC metabolism reprogramming in the clinical practice.

BackgroundHeart failure (HF) is the end stage of various cardiovascular diseases with a high mortality rate. Novel diagnostic and therapeutic biomarkers for HF are urgently required. Our research aims to identify HF-related hub genes and regulatory networks using bioinformatics and validation assays.MethodsUsing four RNA-seq datasets in the Gene Expression Omnibus (GEO) database, we screened differentially expressed genes (DEGs) of HF using Removal of Unwanted Variation from RNA-seq data (RUVSeq) and the robust rank aggregation (RRA) method. Then, hub genes were recognized using the STRING database and Cytoscape software with cytoHubba plug-in. Furthermore, reliable hub genes were validated by the GEO microarray datasets and quantitative reverse transcription polymerase chain reaction (qRT-PCR) using heart tissues from patients with HF and non-failing donors (NFDs). In addition, R packages “clusterProfiler” and “GSVA” were utilized for enrichment analysis. Moreover, the transcription factor (TF)–DEG regulatory network was constructed by Cytoscape and verified in a microarray dataset.ResultsA total of 201 robust DEGs were identified in patients with HF and NFDs. STRING and Cytoscape analysis recognized six hub genes, among which ASPN, COL1A1, and FMOD were confirmed as reliable hub genes through microarray datasets and qRT-PCR validation. Functional analysis showed that the DEGs and hub genes were enriched in T-cell-mediated immune response and myocardial glucose metabolism, which were closely associated with myocardial fibrosis. In addition, the TF–DEG regulatory network was constructed, and 13 significant TF–DEG pairs were finally identified.ConclusionOur study integrated different RNA-seq datasets using RUVSeq and the RRA method and identified ASPN, COL1A1, and FMOD as potential diagnostic biomarkers for HF. The results provide new insights into the underlying mechanisms and effective treatments of HF.

The strategic planning and decision making of the government in the combination of high-quality development and green development has had a far-reaching impact on the development of industrial enterprises. This paper grounded theory to build a theoretical framework model of the factors that influence the development of high-quality green industrial enterprises. The data are based on policy documents, policy interpretations, official public opinion, and other original text materials related to the high-quality green development of Chinese industrial enterprises. The research finds that the factors that influence the high-quality green development of industrial enterprises can be divided into three dimensions, including the environmental dimension, the enterprise dimension, and the process dimension. Among them, the enterprise dimension is the most core dimension and directly affects the high-quality green development of industrial enterprises. Different parts (six parts) in each dimension are connected together to form a circular chain of high-quality green development of industrial enterprises. There is also an interaction mechanism within each part.

Finance is significant support for the low-carbon transformation of the real economy, in which digital finance as a new direction of financial development exerts a significant influence on carbon emissions. Therefore, it is crucial to investigate the association between digital finance and carbon emissions in order to develop carbon reduction strategies from the financial side. For this purpose, using the sample set covering 30 provincial areas during 2011–2020, this paper investigates the direct, indirect, and non-linear effects of digital finance on carbon emissions by applying fixed effects, mediating effects, and threshold effects analysis techniques. The results indicate that: (1) digital finance can significantly mitigate carbon emissions at the national level. (2) Digital finance inhibits carbon emissions as it drives green technological innovation and industrial structure upgrading. (3) Significant regional heterogeneity is observed in the effect of digital finance on carbon emissions, i.e., the effects of digital finance on carbon emissions are higher in the east-central region than in the overall sample, while the opposite is true in the western region. (4) The dampening effect on carbon emissions steadily increases as digital finance levels cross the first and second thresholds, respectively. Based on the above considerations, policymakers shall not only develop differentiated digital finance initiatives, but shall also fully unleash carbon emission reduction potential by rationalizing and optimizing industrial layout and strengthening financial subsidies for green technology innovation.

The improvement of carbon emission efficiency and the realization of the goal of “carbon peaking and carbon neutrality” are the key issues that China needs to solve urgently at this stage. The green and low-carbon transformation of the economy requires sufficient financial support. Whether green finance is an opportunity to improve China’s carbon emission efficiency is worth studying. For the aim, based on the macro-panel data of 30 provinces in China from 2010 to 2019, this paper uses fixed effect model and spatial Durbin model to study the impact of green finance on regional carbon emission efficiency. The results show that: First, the development of green finance can improve the carbon emission efficiency; Second, in addition to the “local effect”, the influence of green finance on carbon emission efficiency has a “neighborhood effect”, that is, it has a spatial spillover effect on carbon emission efficiency in neighboring areas, and this effect only exists in a short time; Third, the impact of green finance on carbon emission efficiency is heterogeneous in different regions with different environmental regulations. This paper has reference significance for green finance development and the implementation of the goal of “carbon peaking and carbon neutrality” in China.

This study examines the impact of digital financing on the degree of financing constraints and discusses the mediating effect of investor confidence. The data are based on companies listed on the Shanghai Stock Exchange and the Shenzhen Stock Exchange from 2010 to 2019. To investigate the impact of digital financing on the financing constraints of companies in different situations, the heterogeneity of internal control and equity characteristics of different organizations is analyzed. The results using fixed-effects models show that (i) the change in digital finance has a significant negative impact on the level of corporate financing constraints; (ii) investor confidence plays a mediating role between digital finance and financing constraints; and (iii) the level of internal control impacts the relationship between the digital finance and the corporate financing constraints. Specifically, for the organizations with better internal control, there is a significant negative relationship between digital finance and corporate financing constraints while for organizations with poor internal control, digital finance has no significant influence on the extent of financing constraints; and (iv) digital finance of private organizations is significantly negatively correlated with the extent of financing constraints, while for government organizations, a negative relationship is not evident.

Under the new normal of economy, a series of active fiscal policies such as tax reduction and fee reduction and new infrastructure construction in China have reduced the source of local government revenue, while the expansion of fiscal expenditure and the widening gap between revenue and expenditure have further exacerbated the motivation of local governments to borrow. Using the spatial econometric model and based on the data of 30 provinces in China from 2006 to 2018, this article deeply studies the impact of local government debt on economic growth and its spatial spillover effect and discusses the regional location heterogeneity of this growth effect. The research shows that: (1) local government debt has significantly promoted regional economic growth; (2) the growth promoting effect of local government borrowing has significant spatial spillover; (3) borrowing debt has the greatest effect on promoting economic growth in the central region, followed by the east, and the west is almost unaffected. Therefore, the Chinese government needs to continue to implement an active fiscal policy, improve the promotion and assessment mechanism of local officials, and adhere to the goal of balanced regional development.

Climate change is one of the most serious threats facing the world today. Environmental pollution and depletion of natural resources have been highlighted by the United Nations Sustainable Development Goals (SDGs), paving the way for modern concepts such as sustainable growth to be introduced. Therefore, this research explores the relationship between green finance, energy efficiency, and CO2 emissions in the G7 countries. The study uses panel data model technique to examine the dependence structure of green finance, energy efficiency, and CO2 emissions. Moreover, we use DEA to construct an energy efficiency index of G7 countries. A specific interval exists between the values of the energy efficiency indexes. Japan, the United Kingdom, and the United States were named the most energy-efficient countries in the world, based on results obtained for five consecutive years in this category. However, according to the comparative rankings, France and Italy are the most successful of all the G7 members, followed by the United Kingdom and Germany. Our overall findings of the econometric model confirm the negative impact of green finance and energy efficiency on CO2 emissions; however, this relationship varies across the different quantiles of the two variables. The findings in the study confirm that green finance is the best financial strategy for reducing CO2 emissions.

Herpes simplex virus type I (HSV-1) is a member of the Alphaherpesvirinae family, which could initiate labial herpes caused by the reactivation of HSV-1 primary infection, and secondary infection even causes herpes encephalitis. The study presented here demonstrates that Hsp90 inhibitors (AT-533 and 17-AAG) directly targeted the HSV-1 UL42-Hsp90 complex, and Hsp90 interacted with HSV-1 UL42 in silicon and experiment. Interestingly, Hsp90 inhibitors also reduced virus titers of ACV-resistant clinical HSV-1 strains (153 and blue strain), revealing that HSV-1 UL42 would be a new target against ACV-resistant HSV-1 strains. Altogether, this present study indicates that Hsp90 inhibitors prevent HSV-1 proliferation by regulating the interaction between Hsp90 and HSV-1 UL42, suggesting a promising target for anti-HSV-1 therapies in the replication.

Herpes simplex virus 1 (HSV-1) is a common neurotropic virus, the herpes simplex encephalitis (HSE) caused by which is considered to be the most common sporadic but fatal encephalitis. Traditional antiviral drugs against HSV-1 are limited to nucleoside analogs targeting viral factors. Inhibition of heat shock protein 90 (Hsp90) has potent anti-HSV-1 activities via numerous mechanisms, but the effects of Hsp90 inhibitors on HSV-1 infection in neuronal cells, especially in the phase of virus entry, are still unknown. In this study, we aimed to investigate the effects of the Hsp90 inhibitors on HSV-1 infection of neuronal cells. Interestingly, we found that Hsp90 inhibitors promoted viral adsorption but inhibited subsequent penetration in neuronal cell lines and primary neurons, which jointly confers the antiviral activity of the Hsp90 inhibitors. Mechanically, Hsp90 inhibitors mainly impaired the interaction between Hsp90 and cofilin, resulting in reduced cofilin membrane distribution, which led to F-actin polymerization to promote viral attachment. However, excessive polymerization of F-actin inhibited subsequent viral penetration. Consequently, unidirectional F-actin polymerization limits the entry of HSV-1 virions into neuron cells. Our research extended the molecular mechanism of Hsp90 in HSV-1 infection in neuron cells and provided a theoretical basis for developing antiviral drugs targeting Hsp90.

We introduce Nafion into the ambient ionization technique of spray mass spectrometry to serve for in situ desalting and direct analysis of biological fluids. Nafion was coated onto the surface of the triangular spray tip as the cation exchange material. Because the sulfonic group from the Nafion membrane effectively exchanges their carried protons with inorganic salt ions (e.g., Na+ and K+), the analyte’s ionization efficiency can be significantly enhanced by reducing ion suppression. The desalting efficiency can reach 90% and the maximum tolerance of the absolute salt amount reaches 100 μmol. The mass spectral profile can also be simplified by removing the multiple adducted ion types from small-molecule drugs and metabolites ([M + Na]+ and [M + K]+), or multiply charged ions formed by proteins ([M + nNa]n+ and [M + nK]n+). Thus, the Nafion coating makes less ambiguous data interpretation collected from spray mass spectrometry for qualitative profiling or quantitative measurement of a target analyte.

Background: Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a multisystem medical condition with heterogeneous symptom expression. Currently, there is no effective cure or treatment for the standard care of patients. A variety of ME/CFS symptoms can be linked to the vital life functions of the brainstem, the lower extension of the brain best known as the hub relaying information back and forth between the cerebral cortex and various parts of the body.Objective/Methods: Over the past decade, Magnetic Resonance Imaging (MRI) studies have emerged to understand ME/CFS with interesting findings, but there has lacked a synthesized evaluation of what has been found thus far regarding the involvement of the brainstem. We conducted this study to review and evaluate the recent MRI findings via a literature search of the MEDLINE database, from which 11 studies met the eligibility criteria.Findings: Data showed that MRI studies frequently reported structural changes in the white and gray matter. Abnormalities of the functional connectivity within the brainstem and with other brain regions have also been found. The studies have suggested possible mechanisms including astrocyte dysfunction, cerebral perfusion impairment, impaired nerve conduction, and neuroinflammation involving the brainstem, which may at least partially explain a substantial portion of the ME/CFS symptoms and their heterogeneous presentations in individual patients.Conclusions: This review draws research attention to the role of the brainstem in ME/CFS, helping enlighten future work to uncover the pathologies and mechanisms of this complex medical condition, for improved management and patient care.

cGAS, a DNA sensor in mammalian cells, catalyzes the generation of 2’-3’-cyclic AMP-GMP (cGAMP) once activated by the binding of free DNA. cGAMP can bind to STING, activating downstream TBK1-IRF-3 signaling to initiate the expression of type I interferons. Although cGAS has been considered a traditional DNA-binding protein, several lines of evidence suggest that cGAS is a potential RNA-binding protein (RBP), which is mainly supported by its interactions with RNAs, RBP partners, RNA/cGAS-phase-separations as well as its structural similarity with the dsRNA recognition receptor 2’-5’ oligoadenylate synthase. Moreover, two influential studies reported that the cGAS-like receptors (cGLRs) of fly Drosophila melanogaster sense RNA and control 3′-2′-cGAMP signaling. In this review, we summarize and discuss in depth recent studies that identified or implied cGAS as an RBP. We also comprehensively summarized current experimental methods and computational tools that can identify or predict RNAs that bind to cGAS. Based on these discussions, we appeal that the RNA-binding activity of cGAS cannot be ignored in the cGAS-mediated innate antiviral response. It will be important to identify RNAs that can bind and regulate the activity of cGAS in cells with or without virus infection. Our review provides novel insight into the regulation of cGAS by its RNA-binding activity and extends beyond its DNA-binding activity. Our review would be significant for understanding the precise modulation of cGAS activity, providing the foundation for the future development of drugs against cGAS-triggering autoimmune diseases such as Aicardi-Gourtières syndrome.

Artemisia argyi H. Lév. and Vaniot is a traditional medical herb that has been used for a long time in China and other Asian counties. Essential oil is the main active fraction of Artemisia argyi H. Lév. and Vaniot, and its anti-inflammatory potential has been observed in vitro and in vivo. Here, we found that the essential oil of Artemisia argyi H. Lév. and Vaniot (EOAA) inhibited monosodium urate (MSU)- and nigericin-induced NLRP3 inflammasome activation. EOAA suppressed caspase-1 and IL-1β processing and pyroptosis. NF-κB p65 phosphorylation and translocation were also inhibited. In addition, EOAA suppressed nigericin-induced NLRP3 inflammasome activation without blocking ASC oligomerization, suggesting that it may inhibit NLRP3 inflammasome activation by preventing caspase-1 processing. Our study thus indicates that EOAA inhibits NLRP3 inflammasome activation and has therapeutic potential against NLRP3-driven diseases.

Until now, the effects of driving factors on the gut microbiota of amphibians are still mostly confounded. Due to a long-term fasting, hibernating amphibians are ideal experimental materials to explore this question. In this study, we characterized the small intestine microbiota of adult hibernating Asiatic toads (Bufo gargarizans) collected from two geographical populations using 16S rRNA amplicon sequencing technique and evaluated the effects of non-dietary factors (e.g., sex and host genetic background). Proteobacteria (0.9196 ± 0.0892) was characterized as the most dominant phylum in the small gut microbiota of hibernating Asiatic toads, among which five core OTUs were identified and three were classified into Pseudomonas. In view of the coincidence between the dominant KEGG pathways (such as the two-component system) and Pseudomonas, Pseudomonas appeared to be a key adaptor for small gut microbiota during hibernation. Furthermore, we detected a greater discrepancy of gut microbiota between geographical populations than between sexes. Both sex and host genetic background showed a minor effect on the gut microbiota variation. Finally, life stage was determined to be the decisive factor driving the gut microbiota discrepancy between populations. However, a large proportion of the gut microbiota variation (∼70%) could not be explained by the measured deterministic factors (i.e., sex, location, body length, and routine blood indices). Therefore, other factors and/or stochastic processes may play key roles in shaping gut bacterial community of hibernating amphibians.

Numerous studies have noted the importance of white matter changes in motor learning, but existing literature only focuses on structural and microstructural MRI changes, as there are limited tools available for in vivo investigations of white matter function. One method that has gained recent prominence is the application of blood oxygen level dependent (BOLD) fMRI to white matter, with high-field scanners now being able to better detect the smaller hemodynamic changes present in this tissue type compared to those in the gray matter. However, fMRI techniques have yet to be applied to investigations of neuroplastic change with motor learning in white matter. White matter function represents an unexplored component of neuroplasticity and is essential for gaining a complete understanding of learning-based changes occurring throughout the whole brain. Twelve healthy, right-handed participants completed fine motor and gross motor tasks with both hands, using an MRI compatible computer mouse. Using a crossover design along with a prior analysis approach to establish WM activation, participants received a baseline scan followed by 2 weeks of training, returning for a midpoint and endpoint scan. The motor tasks were designed to be selectively difficult for the left hand, leading to a training effect only in that condition. Analysis targeted the comparison and detection of training-associated right vs left hand changes. A statistically significant improvement in motor task score was only noted for the left-hand motor condition. A corresponding change in the temporal characteristics of the white matter hemodynamic response was shown within only the right corticospinal tract. The hemodynamic response exhibited a reduction in the dispersion characteristics after the training period. To our knowledge, this is the first report of MRI detectable functional neuroplasticity in white matter, suggesting that modifications in temporal characteristics of white matter hemodynamics may underlie functional neuroplasticity in this tissue.

Background: White matter lesions (WML) have been proved to be significantly associated with many brain diseases. Precise evaluation of burden of WML at early stage could provide insights in the prognosis and assist in intervention. However, acute ischemic lesions (AIL) exhibit hyperintensities on FLAIR images either, and are detected by diffusion weighted imaging (DWI). It is challenging to identify and segment WML in the patients with WML and AIL. Convolutional neural network (CNN) based architecture has been validated as an efficient tool for automatic segmentation. This study aimed to evaluate the performance of U-net in evaluation of WML in the patients with WML and AIL.Methods: A total of 208 cases from Chinese Atherosclerosis Risk Evaluation (CARE II) study were recruited in the present study. All subjects underwent imaging of FLAIR and DWI on 3.0 Tesla scanners. The contours of WML delineated by the observer and its scores rated by the observer were considered as gold standard. Among all 208 cases, 108 were randomly selected as train set, and the remaining 100 cases were used as test set. The performance of lesion segmentation toolbox (LST) and three U-net models were evaluated on three levels: pixel, lesion, and subject levels. The performance of all methods in WML identification and segmentation was also evaluated among the cases with different lesion volumes and between the cases with and without AIL.Results: All U-net models outperformed LST on pixel, lesion, and subject levels, while no differences were found among three U-net models. All segmentation methods performed best in the cases with WML volume (WMLV) > 20 ml but worst in those with WMLV < 5 ml. In addition, all methods showed similar performance between the cases with and without AIL. The scores determined by U-net exhibited a strong correlation with the gold standard (all Spearman correlation coefficients >0.89, ICCs >0.88, p-values

Brain and reproductive organ-expressed protein (BRE) is aberrantly expressed in multiple cancers; however, its expression pattern in human esophageal squamous cell carcinoma (ESCC) and its role in ESCC progression remain unclear. In this study, we aimed to investigate the expression pattern of BRE in human ESCC and its role in ESCC progression. BRE was overexpressed in ESCC tissues compared with that in the adjacent non-tumor tissues. Forced expression of BRE was sufficient to enhance ESCC cell growth by promoting cell cycle progression and anti-apoptosis. Silencing of BRE suppressed these malignant phenotypes of ESCC cells. Mechanistic evaluation revealed that BRE overexpression activated the phosphorylation of AKT, and inhibition of the AKT pathway by MK2206 decreased the BRE-induced cell growth and apoptotic resistance in ESCC cells, highlighting the critical role of AKT signaling in mediating the effects of BRE. Moreover, the effects of BRE on ESCC cell growth and AKT activation were verified in a xenograft model in vivo. The present results show that BRE is overexpressed in ESCC tissues and contributes to the growth of ESCC cells by activating AKT signaling both in vitro and in vivo and provide insight into the role of BRE in AKT signaling and ESCC pathogenesis.

Early detection is critical for effective management of Alzheimer’s disease (AD) and screening for mild cognitive impairment (MCI) is common practice. Among several deep-learning techniques that have been applied to assessing structural brain changes on magnetic resonance imaging (MRI), convolutional neural network (CNN) has gained popularity due to its superb efficiency in automated feature learning with the use of a variety of multilayer perceptrons. Meanwhile, ensemble learning (EL) has shown to be beneficial in the robustness of learning-system performance via integrating multiple models. Here, we proposed a classifier ensemble developed by combining CNN and EL, i.e., the CNN-EL approach, to identify subjects with MCI or AD using MRI: i.e., classification between (1) AD and healthy cognition (HC), (2) MCIc (MCI patients who will convert to AD) and HC, and (3) MCIc and MCInc (MCI patients who will not convert to AD). For each binary classification task, a large number of CNN models were trained applying a set of sagittal, coronal, or transverse MRI slices; these CNN models were then integrated into a single ensemble. Performance of the ensemble was evaluated using stratified fivefold cross-validation method for 10 times. The number of the intersection points determined by the most discriminable slices separating two classes in a binary classification task among the sagittal, coronal, and transverse slice sets, transformed into the standard Montreal Neurological Institute (MNI) space, acted as an indicator to assess the ability of a brain region in which the points were located to classify AD. Thus, the brain regions with most intersection points were considered as those mostly contributing to the early diagnosis of AD. The result revealed an accuracy rate of 0.84 ± 0.05, 0.79 ± 0.04, and 0.62 ± 0.06, respectively, for classifying AD vs. HC, MCIc vs. HC, and MCIc vs. MCInc, comparable to previous reports and a 3D deep learning approach (3D-SENet) based on a more state-of-the-art and popular Squeeze-and-Excitation Networks model using channel attention mechanism. Notably, the intersection points accurately located the medial temporal lobe and several other structures of the limbic system, i.e., brain regions known to be struck early in AD. More interestingly, the classifiers disclosed multiple patterned MRI changes in the brain in AD and MCIc, involving these key regions. These results suggest that as a data-driven method, the combined CNN and EL approach can locate the most discriminable brain regions indicated by the trained ensemble model while the generalization ability of the ensemble model was maximized to successfully capture AD-related brain variations early in the disease process; it can also provide new insights into understanding the complex heterogeneity of whole-brain MRI changes in AD. Further research is needed to examine the clinical implication of the finding, capability of the advocated CNN-EL approach to help understand and evaluate an individual subject’s disease status, symptom burden and progress, and the generalizability of the advocated CNN-EL approach to locate the most discriminable brain regions in the detection of other brain disorders such as schizophrenia, autism, and severe depression, in a data-driven way.

Background: Frailty is characterized by loss of biological reserves and is associated with an increased risk of adverse health outcomes. Frailty can be operationalized using a Frailty Index (FI) based on the accumulation of health deficits; items under health evaluation in the well-established Comprehensive Geriatric Assessment (CGA) have been used to generate an FI-CGA. Traditionally, constructing the FI-CGA has relied on paper-based recording and manual data processing. As this can be time-consuming and error-prone, it limits widespread uptake of this proven type of frailty assessment. Here, we report the development of an electronic tool, the eFI-CGA, for use on personal computers by frontline healthcare providers, to collect CGA data and automate FI-CFA calculation. The ultimate goal is to support early identification and management of frailty at points-of-care, and make uptake in Electronic Medical Records (EMR) feasible and transparent.Methods: An electronic CGA (eCGA) form was implemented to operate on Microsoft's WinForms platform and coded using C# programming language. Users complete the eCGA form, from which items under the CGA evaluation are automatically retrieved and processed to output an eFI-CGA score. A user-friendly interface and secured data saving methods were implemented. The software was debugged and tested using systematically designed simulation data, addressing different logic, syntax, and application errors, and then tested with clinical assessment. The user manual and manual scoring were used as ground truth to compare eFI-CGA input and automated eFI score calculations. Frontline health-provider user feedback was incorporated to improve the end-user experience.Results: The Standalone eFI-CGA software tool was developed and optimized for use on personal computers. The user interface adapted the design of paper-based CGA form to facilitate familiarity for clinical users. Compared to known scores, the software tool generated eFI-CGA scores with 100% accuracy to four decimal places. The eFI-CGA allowed secure data storage and retrieval of multiple types, including user input, completed eCGA form, coded items, and calculated eFI-CGA scores. It also permitted recording of actions requiring clinical follow-up, facilitating care planning. Application bugs were identified and resolved at various stages of the implementation, resulting in efficient system performance.Discussion: Accurate, robust, and reliable computerized frailty assessments are needed to promote effective frailty assessment and management, as a key tool in health care systems facing up to frailty. Our research has enabled the delivery of the standalone eFI-CGA software technology to empower effective frailty assessment and management by various healthcare providers at points-of-care, facilitating integrated care of older adults.

Despite past controversies, increasing evidence has led to acceptance that white matter activity is detectable using functional magnetic resonance imaging (fMRI). In spite of this, advanced analytic methods continue to be published that reinforce a historic bias against white matter activation by using it as a nuisance regressor. It is important that contemporary analyses overcome this blind spot in whole brain functional imaging, both to ensure that newly developed noise regression techniques are accurate, and to ensure that white matter, a vital and understudied part of the brain, is not ignored in functional neuroimaging studies.

Background: The brain atrophy and lesion index (BALI) has been developed to assess whole-brain structural deficits that are commonly seen on magnetic resonance imaging (MRI) in aging. It is unclear whether such changes can be detected at younger ages and how they might relate to other exposures. Here, we investigate how BALI scores, and the subcategories that make the total score, compare across adulthood and whether they are related to the level of cardiovascular risks, in both young and old adulthood.Methods: Data were from 229 subjects (72% men; 24–80 years of age) whose annual health evaluation included a routine anatomical MRI examination. A BALI score was generated for each subject from T2-weighted MRI. Differences in the BALI total score and categorical subscores were examined by age and by the level of cardiovascular risk factors (CVRFs). Regression analysis was used to evaluate relationships between continuous variables. Relative risk ratios (RRRs) of CVRF on BALI were examined using a multinomial logistic regression. The area under the receiver operating characteristic (ROC) curve was used to estimate the classification accuracy.Results: Nearly 90% of the participants had at least one CVRF. Mean CVRF scores increased with age (slope = 0.03; r = 0.36, 95% confidence intervals: 0.23–0.48; p < 0.001). The BALI total score was closely related to age (slope = 0.18; r = 0.69, 95% confidence intervals: 0.59–0.78; p < 0.001), as so were the categorical subscores (r’s = 0.41–0.61, p < 0.001); each differed by the number of CVRF (t-test: 4.16–14.83, χ2: 6.9–43.9, p’s < 0.050). Multivariate analyses adjusted for age and sex suggested an independent impact of age and the CVRF on the BALI score (for each year of advanced age, RRR = 1.20, 95% CI = 1.11–1.29; for each additional CVRF, RRR = 3.63, 95% CI = 2.12–6.23). The CVRF and BALI association remained significant even in younger adults.Conclusion: The accumulation of MRI-detectable structural brain deficits can be evident from young adulthood. Age and the number of CVFR are independently associated with BALI score. Further research is needed to understand the extent to which other age-related health deficits can increase the risk of abnormalities in brain structure and function, and how these, with BALI scores, relate to cognition.

The critical need for rapid objective, physiological evaluation of brain function at point-of-care has led to the emergence of brain vital signs—a framework encompassing a portable electroencephalography (EEG) and an automated, quick test protocol. This framework enables access to well-established event-related potential (ERP) markers, which are specific to sensory, attention, and cognitive functions in both healthy and patient populations. However, all our applications to-date have used auditory stimulation, which have highlighted application challenges in persons with hearing impairments (e.g., aging, seniors, dementia). Consequently, it has become important to translate brain vital signs into a visual sensory modality. Therefore, the objectives of this study were to: 1) demonstrate the feasibility of visual brain vital signs; and 2) compare and normalize results from visual and auditory brain vital signs. Data were collected from 34 healthy adults (33 ± 13 years) using a 64-channel EEG system. Visual and auditory sequences were kept as comparable as possible to elicit the N100, P300, and N400 responses. Visual brain vital signs were elicited successfully for all three responses across the group (N100: F = 29.8380, p < 0.001; P300: F = 138.8442, p < 0.0001; N400: F = 6.8476, p = 0.01). Initial auditory-visual comparisons across the three components showed attention processing (P300) was found to be the most transferrable across modalities, with no group-level differences and correlated peak amplitudes (rho = 0.7, p = 0.0001) across individuals. Auditory P300 latencies were shorter than visual (p < 0.0001) but normalization and correlation (r = 0.5, p = 0.0033) implied a potential systematic difference across modalities. Reduced auditory N400 amplitudes compared to visual (p = 0.0061) paired with normalization and correlation across individuals (r = 0.6, p = 0.0012), also revealed potential systematic modality differences between reading and listening language comprehension. This study provides an initial understanding of the relationship between the visual and auditory sequences, while importantly establishing a visual sequence within the brain vital signs framework. With both auditory and visual stimulation capabilities available, it is possible to broaden applications across the lifespan.

The importance of the gut microbiome in central nervous system (CNS) diseases has long been recognized; however, research into this connection is limited, in part, owing to a lack of convincing mechanisms because the brain is a distant target of the gut. Previous studies on the brain revealed that most of the CNS diseases affected by the gut microbiome are closely associated with microglial dysfunction. Microglia, the major CNS-resident macrophages, are crucial for the immune response of the CNS against infection and injury, as well as for brain development and function. However, the current understanding of the mechanisms controlling the maturation and function of microglia is obscure, especially regarding the extrinsic factors affecting microglial function during the developmental process. The gut microflora has been shown to significantly influence microglia from before birth until adulthood, and the metabolites generated by the microbiota regulate the inflammation response mediated by microglia in the CNS; this inspired our hypothesis that microglia act as a critical mediator between the gut microbiome and CNS diseases. Herein, we highlight and discuss current findings that show the influence of host microbiome, as a crucial extrinsic factor, on microglia within the CNS. In addition, we summarize the CNS diseases associated with both the host microbiome and microglia and explore the potential pathways by which the gut bacteria influence the pathogenesis of CNS diseases. Our work is thus a comprehensive theoretical foundation for studies on the gut-microglia connection in the development of CNS diseases; and provides great potential for researchers to target pathways associated with the gut-microglia connection and overcome CNS diseases.

Spontaneous blinking occurs 15–20 times per minute. Although blinking has often been associated with its physiological role of corneal lubrication, there is now increasing behavioral evidence suggesting that blinks are also modulated by cognitive processes such as attention and information processing. Recent low-density electroencephalography (EEG) studies have reported so-called blink-related oscillations (BROs) associated with spontaneous blinking at rest. Delta-band (0.5–4 Hz) BROs are thought to originate from the precuneus region involved in environmental monitoring and awareness, with potential clinical utility in evaluation of disorders of consciousness. However, the neural mechanisms of BROs have not been elucidated. Using magnetoencephalography (MEG), we characterized delta-band BROs in 36 healthy individuals while controlling for background brain activity. Results showed that, compared to pre-blink baseline, delta-band BROs resulted in increased global field power (p < 0.001) and time-frequency spectral power (p < 0.05) at the sensor level, peaking at ~250 ms post-blink maximum. Source localization showed that spontaneous blinks activated the bilateral precuneus (p < 0.05 FWE), and source activity within the precuneus was also consistent with sensor-space results. Crucially, these effects were only observed in the blink condition and were absent in the control condition, demonstrating that results were due to spontaneous blinks rather than as part of the inherent brain activity. The current study represents the first MEG examination of BROs. Our findings suggest that spontaneous blinks activate the precuneus regions consistent with environmental monitoring and awareness, and provide important neuroimaging support for the cognitive role of spontaneous blinks.

Despite past controversies, increasing evidence has led to acceptance that white matter activity is detectable using functional magnetic resonance imaging (fMRI). In spite of this, advanced analytic methods continue to be published that reinforce a historic bias against white matter activation by using it as a nuisance regressor. It is important that contemporary analyses overcome this blind spot in whole brain functional imaging, both to ensure that newly developed noise regression techniques are accurate, and to ensure that white matter, a vital and understudied part of the brain, is not ignored in functional neuroimaging studies. [ABSTRACT FROM AUTHOR]

Spontaneous blinking occurs 15-20 times per minute. Although blinking has often been associated with its physiological role of corneal lubrication, there is now increasing behavioral evidence suggesting that blinks are also modulated by cognitive processes such as attention and information processing. Recent low-density electroencephalography (EEG) studies have reported so-called blink-related oscillations (BROs) associated with spontaneous blinking at rest. Delta-band (0.5-4Hz) BROs are thought to originate from the precuneus region involved in environmental monitoring and awareness, with potential clinical utility in evaluation of disorders of consciousness. However, the neural mechanisms of BROs have not been elucidated. Using magnetoencephalography (MEG), we characterized delta-band BROs in 36 healthy individuals while controlling for background brain activity. Results showed that, compared to pre-blink baseline, delta-band BROs resulted in increased global field power (p < 0.001) and time-frequency spectral power (p < 0.05) at the sensor level, peaking at ~250ms post-blink maximum. Source localization showed that spontaneous blinks activated the bilateral precuneus (p<0.05 FWE), and source activitywithin the precuneus was also consistent with sensor-space results. Crucially, these effects were only observed in the blink condition and were absent in the control condition, demonstrating that results were due to spontaneous blinks rather than as part of the inherent brain activity. The current study represents the first MEG examination of BROs. Our findings suggest that spontaneous blinks activate the precuneus regions consistent with environmental monitoring and awareness, and provide important neuroimaging support for the cognitive role of spontaneous blinks. [ABSTRACT FROM AUTHOR]

Clinical assessment of brain function relies heavily on indirect behavior-based tests. Unfortunately, behavior-based assessments are subjective and therefore susceptible to several confounding factors. Event-related brain potentials (ERPs), derived from electroencephalography (EEG), are often used to provide objective, physiological measures of brain function. Historically, ERPs have been characterized extensively within research settings, with limited but growing clinical applications. Over the past 20 years, we have developed clinical ERP applications for the evaluation of functional status following serious injury and/or disease. This work has identified an important gap: the need for a clinically accessible framework to evaluate ERP measures. Crucially, this enables baseline measures before brain dysfunction occurs, and might enable the routine collection of brain function metrics in the future much like blood pressure measures today Here, we propose such a framework for extracting specific ERPs as potential "brain vital signs." This framework enabled the translation/transformation of complex ERP data into accessible metrics of brain function for wider clinical utilization. To formalize the framework, three essential ERPs were selected as initial indicators: (1) the auditory N100 (Auditory sensation); (2) the auditory oddball P300 (Basic attention); and (3) the auditory speech processing N400 (Cognitive processing). First step validation was conducted on healthy younger and older adults (age range: 22-82 years). Results confirmed specific ERPs at the individual level (86.81-98.96%), verified predictable age-related differences (P300 latency delays in older adults, p < 0.05), and demonstrated successful linear transformation into the proposed brain vital sign (BVS) framework (basic attention latency sub-component of BVS framework reflects delays in older adults, p < 0.05). The findings represent an initial critical step in developing, extracting, and characterizing ERPs as vital signs, critical for subsequent evaluation of dysfunction in conditions like concussion and/or dementia. [ABSTRACT FROM AUTHOR]