MiR-181a protects the heart against myocardial infarction by regulating mitochondrial fission via targeting programmed cell death protein 4.

Worldwide, myocardial infarction (MI) is the leading cause of death and disability-adjusted life years lost. Recent researches explored new methods of detecting biomarkers that can predict the risk of developing myocardial infarction, which includes identifying genetic markers associated with increased risk. We induced myocardial infarction in mice by occluding the left anterior descending coronary artery and performed TTC staining to assess cell death. Next, we performed ChIP assays to measure the enrichment of histone modifications at the promoter regions of key genes involved in mitochondrial fission. We used qPCR and western blot to measure expression levels of relative apoptotic indicators. We report that miR-181a inhibits myocardial ischemia-induced apoptosis and preserves left ventricular function after MI. We show that programmed cell death protein 4 (PDCD4) is the target gene involved in miR-181a-mediated anti-ischemic injury, which enhanced BID recruitment to the mitochondria. In addition, we discovered that p53 inhibits the expression of miR-181a via transcriptional regulation. Here, we discovered for the first time a mitochondrial fission and apoptosis pathway which is controlled by miR-181a and involves PDCD4 and BID. This pathway may be controlled by p53 transcriptionally, and we presume that miR-181a may lead to the discovery of new therapeutic and preventive targets for ischemic heart diseases.

F Doping-Induced Multicomponent Synergistic Active Site Construction toward High-Efficiency Bifunctional Oxygen Electrocatalysis for Rechargeable Zn-Air Batteries.

The commercialization of rechargeable Zn-air batteries (ZABs) relies on the material innovation to accelerate the sluggish oxygen electrocatalysis kinetics. Due to the differentiated mechanisms of reverse processes, i.e., oxygen reduction reaction (ORR) and oxygen evolution reaction (OER), rationally integrating dual sites for bifunctional oxygen electrocatalysis is prerequisite yet remains challenging. Herein, multicomponent synergistic active sites within highly graphitic carbon substrate are exquisitely constructed, which is accomplished by fluorine (F) modulation strategy. The incorporation of F dopants facilitates pyridinic N formation for anchoring single metal sites, thus guaranteeing the coexistence of sufficient M-Nx sites and metal nanoparticles toward bifunctional oxygen electrocatalysis. As a result, the optimal catalyst, denoted as F NH2 -FeNi-800, outperforms commercial Pt/C+RuO2 with smaller gap between Ej = 10 and E1/2 (ΔE) of 0.63 V (vs 0.7 V for Pt/C+RuO2 ), demonstrating its superior bifunctionality. Beyond that, its superiority is validated in homemade rechargeable ZABs. ZABs assembled using F NH2 -FeNi-800 as the air cathode delivers higher peak power density (123.8 mW cm-2 ) and long-cycle lifetime (over 660 cycles) in comparison with Pt/C@RuO2 (68.8 mW cm-2 ; 300 cycles). The finding not only affords a highly promising oxygen electrocatalyst, but also opens an avenue to constructing multifunctional active sites for heterogeneous catalysts.

An artificial intelligence-assisted diagnosis modeling software (AIMS) platform based on medical images and machine learning: a development and validation study.

Background: Supervised machine learning methods [both radiomics and convolutional neural network (CNN)-based deep learning] are usually employed to develop artificial intelligence models with medical images for computer-assisted diagnosis and prognosis of diseases. A classical machine learning-based modeling workflow involves a series of interconnected components and various algorithms, but this makes it challenging, tedious, and labor intensive for radiologists and researchers to build customized models for specific clinical applications if they lack expertise in machine learning methods. Methods: We developed a user-friendly artificial intelligence-assisted diagnosis modeling software (AIMS) platform, which supplies standardized machine learning-based modeling workflows for computer-assisted diagnosis and prognosis systems with medical images. In contrast to other existing software platforms, AIMS contains both radiomics and CNN-based deep learning workflows, making it an all-in-one software platform for machine learning-based medical image analysis. The modular design of AIMS allows users to build machine learning models easily, test models comprehensively, and fairly compare the performance of different models in a specific application. The graphical user interface (GUI) enables users to process large numbers of medical images without programming or script addition. Furthermore, AIMS also provides a flexible image processing toolkit (e.g., semiautomatic segmentation, registration, morphological operations) to rapidly create lesion labels for multiphase analysis, multiregion analysis of an individual tumor (e.g., tumor mass and peritumor), and multimodality analysis. Results: The functionality and efficiency of AIMS were demonstrated in 3 independent experiments in radiation oncology, where multiphase, multiregion, and multimodality analyses were performed, respectively. For clear cell renal cell carcinoma (ccRCC) Fuhrman grading with multiphase analysis (sample size =187), the area under the curve (AUC) value of the AIMS was 0.776; for ccRCC Fuhrman grading with multiregion analysis (sample size =177), the AUC value of the AIMS was 0.848; for prostate cancer Gleason grading with multimodality analysis (sample size =206), the AUC value of the AIMS was 0.980. Conclusions: AIMS provides a user-friendly infrastructure for radiologists and researchers, lowering the barrier to building customized machine learning-based computer-assisted diagnosis models for medical image analysis.

CAPNet: Context attention pyramid network for computer-aided detection of microcalcification clusters in digital breast tomosynthesis.

BACKGROUND AND OBJECTIVE: Computer-aided detection (CADe) of microcalcification clusters (MCs) in digital breast tomosynthesis (DBT) is crucial in the early diagnosis of breast cancer. Although convolutional neural network (CNN)-based detection models have achieved excellent performance in medical lesion detection, they are subject to some limitations in MC detection: 1) Most existing models employ the feature pyramid network (FPN) for multi-scale object detection; however, the rough feature sharing between adjacent layers in the FPN may limit the detection ability for small and low-contrast MCs; and 2) the MCs region only accounts for a small part of the annotation box, so the features extracted indiscriminately within the whole box may easily be affected by the background. In this paper, we develop a novel CNN-based CADe method to alleviate the impacts of the above limitations for the accurate and rapid detection of MCs in DBT. METHODS: The proposed method has two parts: a novel context attention pyramid network (CAPNet) for intra-layer MC detection in two-dimensional (2D) slices and a three-dimensional (3D) aggregation procedure for aggregating 2D intra-layer MCs into a 3D result according to their connectivity in 3D space. The proposed CAPNet is based on an anchor-free and one-stage detection architecture and contains a context feature selection fusion (CFSF) module and a microcalcification response (MCR) branch. The CFSF module can efficiently enrich shallow layers' features by the complementary selection of local context features, aiming to reduce the missed detection of small and low-contrast MCs. The MCR branch is a one-layer branch parallel to the classification branch, which can alleviate the influence of the background region within the annotation box on feature extraction and enhance the ability of the model to distinguish MCs from normal breast tissue. RESULTS: We performed a comparison experiment on an in-house clinical dataset with 648 DBT volumes, and the proposed method achieved impressive performance with a sensitivity of 91.56 % at 1 false positive per DBT volume (FPs/volume) and 93.51 % at 2 FPs/volume, outperforming other representative detection models. CONCLUSIONS: The experimental results indicate that the proposed method is effective in the detection of MCs in DBT. This method can provide objective, accurate, and quick diagnostic suggestions for radiologists, presenting potential clinical value for early breast cancer screening.

Multi-phase-combined CECT radiomics models for Fuhrman grade prediction of clear cell renal cell carcinoma.

Objective: This study aimed to evaluate the effectiveness of multi-phase-combined contrast-enhanced CT (CECT) radiomics methods for noninvasive Fuhrman grade prediction of clear cell renal cell carcinoma (ccRCC). Methods: A total of 187 patients with four-phase CECT images were retrospectively enrolled and then were categorized into training cohort (n=126) and testing cohort (n=61). All patients were confirmed as ccRCC by histopathological reports. A total of 110 3D classical radiomics features were extracted from each phase of CECT for individual ccRCC lesion, and contrast-enhanced variation features were also calculated as derived radiomics features. These features were concatenated together, and redundant features were removed by Pearson correlation analysis. The discriminative features were selected by minimum redundancy maximum relevance method (mRMR) and then input into a C-support vector classifier to build multi-phase-combined CECT radiomics models. The prediction performance was evaluated by the area under the curve (AUC) of receiver operating characteristic (ROC). Results: The multi-phase-combined CECT radiomics model showed the best prediction performance (AUC=0.777) than the single-phase CECT radiomics model (AUC=0.711) in the testing cohort (p value=0.039). Conclusion: The multi-phase-combined CECT radiomics model is a potential effective way to noninvasively predict Fuhrman grade of ccRCC. The concatenation of first-order features and texture features extracted from corticomedullary phase and nephrographic phase are discriminative feature representations.

CREB-binding protein and HIF-1α/ß-catenin to upregulate miR-322 and alleviate myocardial ischemia-reperfusion injury.

Myocardial ischemia/reperfusion injury (MIRI) is a prevalent condition associated with numerous critical clinical conditions. miR-322 has been implicated in MIRI through poorly understood mechanisms. Our preliminary analysis indicated potential interaction of CREB-binding protein (CBP), a transcriptional coactivator and acetyltransferase, with HIF-1α/ß-catenin, which might regulate miR-322 expression. We, therefore, hypothesized that CBP/HIF-1α/ß-catenin/miR-322 axis might play a role in MIRI. Rat cardiomyocytes subjected to oxygen-glucose deprivation /reperfusion (OGD/R) and Langendorff perfused heart model were used to model MIRI in vitro and in vivo, respectively. We used various techniques such as CCK-8 assay, transferase dUTP nick end labeling staining, western blotting, RT-qPCR, chromatin immunoprecipitation (ChIP), dual-luciferase assay, co-immunoprecipitation (Co-IP), hematoxylin and eosin staining, and TTC staining to assess cell viability, apoptosis, and the levels of CBP, HIF-1α, ß-catenin, miR-322, and acetylation. Our results indicate that OGD/R in cardiomyocytes decreased CBP/HIF-1α/ß-catenin/miR-322 expression, increased cell apoptosis and cytokines, and reduced cell viability. However, overexpression of CBP or miR-322 suppressed OGD/R-induced cell injury, while knockdown of HIF-1α/ß-catenin further exacerbated the damage. HIF-1α/ß-catenin bound to miR-322 promoter to promote its expression, while CBP acetylated HIF-1α/ß-catenin for stabilization. Overexpression of CBP attenuated MIRI in rats by acetylating HIF-1α/ß-catenin to stabilize their expression, resulting in stronger binding of HIF-1α/ß-catenin with the miR-322 promoter and subsequent increased miR-322 levels. Therefore, activating CBP/HIF-1α/ß-catenin/miR-322 signaling may be a potential approach to treat MIRI.

HIF-1α-induced upregulated miR-322 forms a feedback loop by targeting Smurf2 and Smad7 to activate Smad3/ß-catenin/HIF-1α, thereby improving myocardial ischemia-reperfusion injury.

Myocardial ischemia/reperfusion injury (MIRI) is a major cause of heart failure after myocardial infarction. It has been reported that miR-322 is involved in MIRI progression, while the molecular mechanism of miR-322 in regulating MIRI progression needs to be further probed. MIRI cell model was established by oxygen-glucose deprivation/reoxygenation (OGD/R). Cell viability was assessed using MTS assay. Flow cytometry and terminal deoxynucleotidyl transferase-mediated dUTP nick-end labeling staining were employed to analyze cell apoptosis. In addition, the interactions between miR-322, Smad7/Smurf2, hypoxia-inducible factor alpha (HIF-1α), and ß-catenin were verified by dual-luciferase reporter gene assay. Our results displayed that miR-322 was significantly downregulated in OGD/R-treated H9c2 cells, and its overexpression resulted in increased cell viability and reduced the apoptosis. Smurf2 and Smad7 were identified as the direct targets of miR-322. Smad7 knockdown or Smurf2 knockdown increased OGD/R-treated H9c2 cell viability and suppressed the apoptosis. Meanwhile, miR-322 mimics abolished the mitigating effect of Smad7 or Smurf2 overexpression on MIRI. In addition, the Smad3/ß-catenin pathway was identified as the downstream pathway of Smurf2/Smad7. Moreover, it was found that HIF-1α interacted with the miR-322 promoter, and ß-catenin interacted with the HIF-1α promoter to form a loop. HIF-1α-induced upregulated miR-322 activated the Smad3/ß-catenin pathway by targeting Smurf2 and Smad7 to improve MIRI; meanwhile, ß-catenin/HIF-1α formed a positive feedback loop to continuously improve MIRI.

Customized reaction route for ruthenium oxide towards stabilized water oxidation in high-performance PEM electrolyzers.

The poor stability of Ru-based acidic oxygen evolution (OER) electrocatalysts has greatly hampered their application in polymer electrolyte membrane electrolyzers (PEMWEs). Traditional understanding of performance degradation centered on influence of bias fails in describing the stability trend, calling for deep dive into the essential origin of inactivation. Here we uncover the decisive role of reaction route (including catalytic mechanism and intermediates binding strength) on operational stability of Ru-based catalysts. Using MRuOx (M = Ce4+, Sn4+, Ru4+, Cr4+) solid solution as structure model, we find the reaction route, thereby stability, can be customized by controlling the Ru charge. The screened SnRuOx thus exhibits orders of magnitude lifespan extension. A scalable PEMWE single cell using SnRuOx anode conveys an ever-smallest degradation rate of 53 µV h-1 during a 1300 h operation at 1 A cm-2.

Regulating the MXene-Zinc Interfacial Structure toward a Highly Revisable Metal Anode of Zinc-Air Batteries.

Rechargeable aqueous Zn-air batteries have been regarded as one of the most promising systems for flexible energy storage devices due to their high specific energy, safety, and cost effectiveness. However, Zn metal anodes exposed to strong alkaline electrolytes suffer from several issues such as corrosion, dissolution, and passivation, resulting in extremely poor cycle reversibility. Motivated by this challenge, we herein strategically design an MXene/Zn metal anode interfacial structure with single/few-layer Ti3C2Tx MXene as a protective layer. Such a design not only isolates the direct contact between Zn metal anodes and electrolytes but also inhibits zincate dissolution due to the ion screening function of Ti3C2Tx, potentially addressing the stubborn issues that Zn anodes faced with. As a result, the Ti3C2Tx-protected Zn metal anode exhibits superior cycle stability (stable for more than 400 cycles) to the bare Zn counterpart (20 cycles) at a high current density of 5.0 mA cm-2. When integrated into Zn-air coin cells, it has a high depth of discharge of 91% and operates stably for 140 cycles with small resistance. More interestingly, the excellent flexibility of the as-designed Ti3C2Tx-protected Zn metal anode endows the quasi-solid-state batteries with admirable voltage stability at different bending angles from 0 to 180°.

Challenges and Strategies of Anion Exchange Membranes in Hydrogen-electricity Energy Conversion Devices.

Alkaline hydrogen-electricity energy conversion technologies, involving anion exchange membrane fuel cells (AEMFCs) and anion exchange membrane water electrolyzers (AEMWEs) are more appealing than the acidic counterparts due to the elimination of precious metal catalysts. However, the physicochemical properties of anion exchange membrane (AEMs), i.e., ionic conductivity, mechanical strength, stability, etc., are inferior to that of proton exchange membranes (PEMs), thus hindering these alkaline technologies from practical employment. To promote their development, we summarize the main challenges and the corresponding strategies of AEMs for the application of AEMFCs and AEMWEs in this review. The hydroxide transportation mechanism, ion exchange capacity, hydration and microscopic morphology that are relevant to the ionic conductivity are discussed firstly. Following the ionic conductivity, another obstacle, stability of AEMs is comprehensively described in terms of alkaline stability, mechanical stability and electrochemical stability. Upon integrating into the devices, water management, carbonation effect and membrane-electrode interface that are critical to the cell performance are highlighted as well. This review is anticipated to provide insights into the AEM design for hydrogen-electric energy conversion devices, thus accelerating the widespread commercialization of these promising technologies.

Atorvastatin-induced tolerogenic dendritic cells improve cardiac remodeling by suppressing TLR-4/NF-κB activation after myocardial infarction.

OBJECTIVE: Myocardial infarction (MI) caused by ischemic cardiomyocyte necrosis induces inflammatory responses that strongly affect ventricular remodeling. Tolerogenic dendritic cells (tDCs) can suppress this effect on inflammatory responses. However, the precise role of atorvastatin-induced tDCs in ventricular remodeling after MI remains unclear. METHODS: To explore the effect of necrotic cardiomyocytes (SNC) and/or atorvastatin on DC function, the expression of CD40, CD80, CD86, and MHC-II was determined using flow cytometry. The protein levels of TLR-4/NF-κB-related molecules were evaluated using western blotting. The infarct area after MI was determined via 2,3,5-triphenyltetrazolium chloride staining. The TUNEL assay was employed to evaluate the apoptosis of cardiomyocytes in heart sections. Masson's trichrome method was used to determine the extent of fibrosis. RESULTS: Compared to the DCs co-cultured with PBS (control), cells co-cultured with Supernatant-IM or Supernatant-NH produced higher levels of inflammatory cytokines, including TNF-α, IL-1, IL-6, IL-12P40, and IL-8. This cytokine production was impaired by atorvastatin treatment. SNC treatment induced DC maturation and enhanced inflammatory cytokine secretion and oxidative stress through TLR-4/NF-κB pathway activation. Compared to that in the PBS-treated group, the left ventricular ejection fraction was significantly improved after tDC treatment. Additionally, compared to that in the PBS-treated group, tDC treatment reduced the left ventricular end-diastolic and end-systolic diameters in mice. Furthermore, treatment with tDCs improved the left ventricular systolic function, attenuated inflammatory cell infiltration, and reduced cardiomyocyte apoptosis, myocardial fibrosis, and infarct size compared to those in the control group. CONCLUSIONS: Adoptive transfer of atorvastatin-induced tDCs alleviated post-infarction cardiomyocyte apoptosis and myocardial fibrosis in association with decreased inflammatory cell infiltration and inhibited oxidative stress, likely by suppressing TLR-4/NF-κB activation after myocardial infarction.

N-terminal pro-B-type natriuretic peptide and D-dimer combined with left atrial diameter to predict the risk of ischemic stroke in nonvalvular atrial fibrillation.

OBJECTIVES: We aimed to explore the potential role of N-terminal pro-B-type natriuretic peptide (NT-proBNP), d-dimer, and the echocardiographic parameter left atrial diameter (LAD) in identifying and predicting the occurrence of ischemic stroke (IS) in patients with nonvalvular atrial fibrillation (NVAF). METHODS: We conducted a retrospective study of 445 patients with NVAF in the First Affiliated Hospital of Nanchang University. They were divided into the NVAF (309 cases) and NVAF with stroke (136 cases) groups according to whether acute ischemic stroke (AIS) occurred at admission. Multivariate logistic regression was used to analyze the odds ratio (OR) of NT-proBNP, d-dimer, and LAD for IS. The predictive value of NT-proBNP, d-dimer, and LAD in identifying the occurrence of IS in NVAF was determined by plotting the receiver operating characteristic (ROC) curves. RESULTS: NT-proBNP, d-dimer, and LAD levels were significantly higher in the NVAF with stroke group than in the NVAF group (p < .05). NT-ProBNP, d-dimer, and LAD were independently associated with IS in NVAF patients (odds ratio [OR] = 1.12, 95% confidence interval [CI]: 1.08-1.16; OR = 1.87, 95% CI: 1.37-2.55; OR = 1.21, 95% CI: 1.13-1.28, p < .01). The optimal cutoff points for NT-ProBNP, d-dimer, and LAD levels to distinguish the NVAF group from the NVAF with stroke group were 715.0 pg/ml, 0.515 ng/ml, and 38.5 mm, respectively, with the area under the curve (AUC) being [0.801 (95% CI: 0.76-0.84); 0.770 (95% CI: 0.72-0.85); 0.752 (95% CI: 0.71-0.80), p < .01]. The combined score of NT-proBNP, d-dimer, and LAD improved the predictive efficacy of the single index, with an AUC of 0.846 (95% CI: 0.81-0.88, p < .01), sensitivity of 77.2%, and specificity of 76.4%. CONCLUSION: NT-proBNP, d-dimer, and the echocardiographic parameter LAD have outstanding value in predicting the risk of IS in patients with NVAF.

[Multi-task motor imagery electroencephalogram classification based on adaptive time-frequency common spatial pattern combined with convolutional neural network].

The effective classification of multi-task motor imagery electroencephalogram (EEG) is helpful to achieve accurate multi-dimensional human-computer interaction, and the high frequency domain specificity between subjects can improve the classification accuracy and robustness. Therefore, this paper proposed a multi-task EEG signal classification method based on adaptive time-frequency common spatial pattern (CSP) combined with convolutional neural network (CNN). The characteristics of subjects' personalized rhythm were extracted by adaptive spectrum awareness, and the spatial characteristics were calculated by using the one-versus-rest CSP, and then the composite time-domain characteristics were characterized to construct the spatial-temporal frequency multi-level fusion features. Finally, the CNN was used to perform high-precision and high-robust four-task classification. The algorithm in this paper was verified by the self-test dataset containing 10 subjects (33 ± 3 years old, inexperienced) and the dataset of the 4th 2018 Brain-Computer Interface Competition (BCI competition Ⅳ-2a). The average accuracy of the proposed algorithm for the four-task classification reached 93.96% and 84.04%, respectively. Compared with other advanced algorithms, the average classification accuracy of the proposed algorithm was significantly improved, and the accuracy range error between subjects was significantly reduced in the public dataset. The results show that the proposed algorithm has good performance in multi-task classification, and can effectively improve the classification accuracy and robustness.

Investigation into the protective effects of hypaconitine and glycyrrhetinic acid against chronic heart failure of the rats.

BACKGROUND: The present study aimed to determine the protective effects of hypaconitine (HA) and glycyrrhetinic acid (GA) against chronic heart failure (CHF) in the rats and to explore the underlying molecular mechanisms. METHODS: The CHF rat model was established by transverse-aortic constriction (TAC) operation. Transthoracic echocardiography and hematoxylin eosin (HE) staining were used to evaluate the pathophysiological and histopathological changes of CHF model. The total cholesterol (TCHO) and triglyceride (TG) levels were determined by ELISA assay. The protein expression of fibroblast growth factor 2 (FGF2), vascular endothelial growth factor A (VEGFA) and endothelial nitric oxide synthase (eNOS) in the rat ventricular tissues was determined by immunohistochemistry. The serum metabolites were determined by LC-MS/MS assay. RESULTS: After applied the HA + GA, the cardiac tissue and structure were obviously improved, and the HA + GA treatment also significantly reduced the plasma levels of TCHO and TG in the CHF rats. The expression of FGF2 and VEGFA protein was up-regulated and the expression of eNOS protein was down-regulated in the ventricular tissues of CHF rats, which was significantly restored after HA + GA treatment. HA + GA treatment down-regulated serum isonicotinic acid, phosphatidylcholine, cardiolipin, estrogen glucuronide, and glycocholic acid, up-regulated serum sphingosine and deoxycholic acid in the CHF rats. CONCLUSIONS: In conclusion, HA + GA showed protective effects on CHF in the rats, and the HA + GA may exert protective effects by reducing lipid levels, up-regulating the expression of FGF2 and VEGFA proteins, attenuating eNOS protein expression, and modulating metabolic pathways. However, the molecular mechanisms underlying HA + GA-mediated effects still require further examination.

Extracellular Vesicle-Derived circITGB1 Regulates Dendritic Cell Maturation and Cardiac Inflammation via miR-342-3p/NFAM1.

Acute myocardial infarction (AMI) is a complication of atherosclerosis-related cardiovascular illness that is caused by prolonged ischemia. Circular RNAs (circRNAs) are concentrated in extracellular vesicles (EVs) and have been linked to cardiovascular disease. However, additional research is needed into the expression and function of circRNAs in AMI. In this study, circITGB1 (has_circRNA_0018146), derived from exon 1 of the ITGB1 gene localized on chromosome 10, was shown to be considerably increased in plasma from patients with AMI compared to healthy controls, as demonstrated by the comparison of EV-circRNA expression patterns. Using a luciferase screening assay and a biotin-labeled circITGB1 probe to identify microRNA(s) complementary to circITGB1 sequences, we discovered that circITGB1 competitively binds to miR-342-3p and inhibits its expression, which in turn increase the expression of NFAT activating molecule 1 (NFAM1). Based on western blotting and immunological studies, circITGB1 controls dendritic cell maturation by targeting miR-342-3p and NFAM1. circITGB1 also exacerbated cardiac damage and regulated miR-342-3p and NFAM1 expression in a mouse AMI model. This implies that EV-circITGB1 is involved in dendritic cell maturation and cardiac damage via miR-342-3p/NFAM1, and that is linked to AMI-associated pathogenic processes.

Quasi-Covalently Coupled Ni-Cu Atomic Pair for Synergistic Electroreduction of CO2.

Developing highly active, selective, and stable electrocatalysts for the carbon dioxide reduction reaction (CO2RR) is crucial to establish a CO2 conversion system for industrial implementation and, therefore, to realize an artificially closed carbon loop. This can only be achieved through the rational material design based upon the knowledge of the operational active site at the molecular scale. Enlightened by theoretical screening, herein, we for the first time manipulate a novel Ni-Cu atomic pair configuration toward improved CO2RR performance. Systematic characterizations and theoretical modeling reveal that the secondary Cu metal incorporation positively shifts the Ni 3d orbital energy to the Fermi level and thus accelerates the rate-determining step, *COOH formation. In addition, the intrinsic inactivity of Cu toward the competing hydrogen evolution reaction causes a considerable reaction barrier for water dissociation on the Ni-Cu moiety. Due to these attributes, the as-developed Ni/Cu-N-C catalyst exhibits excellent catalytic activity and selectivity, with a record-high turnover frequency of 20,695 h-1 at -0.6 V (vs RHE) and a maximum Faradaic efficiency of 97.7% for CO production. Furthermore, the dynamic structure evolution monitored by operando X-ray absorption fine-structure spectroscopy unveils the interaction between the Ni center and CO2 molecules and the synergistic effect of the Ni-Cu atomic pair on CO2RR activity.

Functional Covariance Connectivity of Gray and White Matter in Olfactory-Related Brain Regions in Parkinson's Disease.

Before the onset of motor symptoms, Parkinson's disease (PD) involves dysfunction of the anterior olfactory nucleus and olfactory bulb, causing olfactory disturbance, commonly resulting in hyposmia in the early stages of PD. Accumulating evidence has shown that blood oxygen level dependent (BOLD) signals in white matter are altered by olfactory disorders and related stimuli, and the signal changes in brain white matter pathways show a certain degree of specificity, which can reflect changes of early olfactory dysfunction in Parkinson's disease. In this study, we apply the functional covariance connectivity (FCC) method to decode FCC of gray and white matter in olfactory-related brain regions in Parkinson's disease. Our results show that the dorsolateral prefrontal, anterior entorhinal cortex and fronto-orbital cortices in the gray matter have abnormal connectivity with the posterior corona radiata and superior corona radiata in white matter in patients with Parkinson's hyposmia. The functional covariance connection strength (FCS) of the right dorsolateral prefrontal cortex and white matter, and the covariance connection strength of the left superior corona radiata and gray matter function have potential diagnostic value. These results demonstrate that alterations in FCC of gray and white matter in olfactory-related brain regions can reflect the change of olfactory function in the early stages of Parkinson's disease, indicating that it could be a potential neuroimaging marker for early diagnosis.

Outcomes and risk factors of perforating and non-perforating middle cerebral artery infarctions after intravenous thrombolysis.

The clinical symptoms of perforating arteries differ, and responses to intravenous thrombolytic therapy are heterogeneous. Here, we investigated the effect of intravenous thrombolytic therapy and the related factors influencing acute perforating and non-perforating middle cerebral artery infarctions. We analyzed 320 patients with acute middle cerebral artery infarction who received alteplase thrombolysis within 4.5 h of onset at two stroke centers from January 2016 to December 2019. Outcome measures included rates of a favorable functional outcome (modified Rankin Scale scores of 0-2), distribution of modified Rankin Scale scores, intracranial hemorrhage, and symptomatic cerebral hemorrhage at 14 days, with comparisons between perforating artery and non-perforating artery cerebral infarction groups. In the perforating vessel disease group, 12 cases (17.4%) of intracranial hemorrhage occurred, with symptomatic cerebral hemorrhage in three cases (4.3%); there were no significant differences between the perforating and non-perforating vessel disease groups (all P > 0.05). In the perforating vessel disease group, the only significant prognostic factor was the National Institutes of Health Stroke Scale score before thrombolysis (Exp(B) = 1.365; 95% confidence interval [CI] 1.124-1.659; P = 0.002), and the only significant risk factor for hemorrhagic transformation was previous perforator disease (Exp(B) = 0.078; P = 0.038). Regardless of whether an acute infarction is perforating or non-perforating, intravenous thrombolytic therapy can yield a favorable outcome. Therefore, intravenous thrombolysis should be actively administered to treat perforating artery infarctions with a high risk of disability.

Improving MRI-based analysis of brain structural changes in patients with hypertension via a privileged information learning algorithm.

Hypertension can lead to changes in the brain structure and function, and different blood pressure levels (2017ACC/AHA) have different effects on brain structure. It is important to analyze these changes by machine learning methods, and various characteristics can provide rich information for the analysis of these changes. However, multiple feature extraction involves complex data processing. How to make a single feature achieve the same diagnosis effect as multiple features do is worth of study. Kernel ridge regression (KRR) is a kind of machine learning method, which shows faster learning speed and generalization ability in classification tasks. In order to knowledge transfer, we use privileged information (PI) to transfer information of multiple types of feature to single feature. This allows only one feature type to be used during the test stage. In the process of feature fusion, we need to consider all the samples' attribution making the classifier better. In this work, we propose a multi-kernel KRR+ framework based on self-paced learning to analyze the changes of the brain structure in patients with different blood pressure levels. Specifically, one kind of a feature is taken as main feature, and other features are input into the multi-kernel KRR as PI. These two inputs are fed into the final KRR classifier together. In addition, a self-paced learning method is introduced into sample selecting to avoid training the classifier using samples with a large loss value firstly, which improves the generalization performance of the classifier. Experimental results show that the proposed method can make full use of the information of various features and achieve better classification performance. This shows self-paced learning based KRR can help analyze brain structure of patients with different blood pressure levels. The discriminative features may help clinicians to make judgments of hypertension degrees on brain MRI images.

Olfactory functional covariance connectivity in Parkinson's disease: Evidence from a Chinese population.

Introduction: Central anosmia is a potential marker of the prodrome and progression of Parkinson's disease (PD). Resting-state functional magnetic resonance imaging studies have shown that olfactory dysfunction is related to abnormal changes in central olfactory-related structures in patients with early PD. Methods: This study, which was conducted at Guanyun People's Hospital, analyzed the resting-state functional magnetic resonance data using the functional covariance connection strength method to decode the functional connectivity between the white-gray matter in a Chinese population comprising 14 patients with PD and 13 controls. Results: The following correlations were observed in patients with PD: specific gray matter areas related to smell (i.e., the brainstem, right cerebellum, right temporal fusiform cortex, bilateral superior temporal gyrus, right Insula, left frontal pole and right superior parietal lobule) had abnormal connections with white matter fiber bundles (i.e., the left posterior thalamic radiation, bilateral posterior corona radiata, bilateral superior corona radiata and right superior longitudinal fasciculus); the connection between the brainstem [region of interest (ROI) 1] and right cerebellum (ROI2) showed a strong correlation. Right posterior corona radiation (ROI11) showed a strong correlation with part 2 of the Unified Parkinson's Disease Rating Scale, and right superior longitudinal fasciculus (ROI14) showed a strong correlation with parts 1, 2, and 3 of the Unified Parkinson's Disease Rating Scale and Hoehn and Yahr Scale. Discussion: The characteristics of olfactory-related brain networks can be potentially used as neuroimaging biomarkers for characterizing PD states. In the future, dynamic testing of olfactory function may help improve the accuracy and specificity of olfactory dysfunction in the diagnosis of neurodegenerative diseases.