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Abstract There is considerable uncertainty regarding the associations between various risk factors and Parkinson’s Disease (PD). This study systematically screened and validated a wide range of potential PD risk factors from 502,364 participants in the UK Biobank. Baseline data for 1851 factors across 11 categories were analyzed through a phenome-wide association study (PheWAS). Polygenic risk scores (PRS) for PD were used to diagnose Parkinson’s Disease and identify factors associated with PD diagnosis through PheWAS. Two-sample Mendelian randomization (MR) analysis was employed to assess causal relationships. PheWAS results revealed 267 risk factors significantly associated with PD-PRS among the 1851 factors, and of these, 27 factors showed causal evidence from MR analysis. Compelling evidence suggests that fluid intelligence score, age at first sexual intercourse, cereal intake, dried fruit intake, and average total household income before tax have emerged as newly identified risk factors for PD. Conversely, maternal smoking around birth, playing computer games, salt added to food, and time spent watching television have been identified as novel protective factors against PD. The integration of phenotypic and genomic data may help to identify risk factors and prevention targets for PD.

Extensive use of the neonicotinoid insecticides acetamiprid (ACE) and flonicamid (FLO) in agriculture poses severe environmental and ecological risks. Microbial remediation is considered a feasible approach to address these issues. Many ACE-and FLO-degrading microorganisms have been isolated and characterized, but few reviews have concentrated on the underlying degradation mechanisms. In this review, we describe the microbial degradation pathways of ACE and FLO and assess the toxicity of ACE, FLO and their metabolites. Especially, we focus on the enzymes involved in degradation of ACE and FLO, including cytochrome P450s, nitrile hydratases, amidases, and nitrilases. Those studies reviewed here further our understanding of the enzymatic mechanisms of microbial degradation of ACE and FLO, and aid in the application of microbes to remediate environmental ACE and FLO contamination.

Abstract Transient ischemic attack (TIA) was clinically divided into anterior circulation (AC) or posterior circulation (PC). Previous study reported that ABCD2 score could predict the stroke risk after AC-TIA but might have limitation for PC-TIA. We aimed to classify TIA depending on neuroimaging and assess the value of ABCD2 score for predicting stroke risk in different territories. Research data was from TIA database of the First Affiliated Hospital of Zhengzhou University. TIA patients with acute infarction on diffuse weighted imaging [that is, transient symptoms with infarction (TSI)] were divided into anterior and posterior circulation groups according to the location of infarction. The outcome was recurrent stroke within 7 and 90 days. The predictive power of ABCD2 score was determined using area under receiver operator characteristic curve (AUC) analyses. Overall, 382 AC-TSI and 112 PC-TSI patients were included. There were 38 (9.9%) AC-TSI patients and 11(9.8%) PC-TSI patients who had recurrent stroke at 7 days, and 66 (17.3%) AC-TSI patients and 19 (17.0%) PC-TSI patients who had recurrent stroke within 90 days. At 7 days, the AUC for ABCD2 score was 0.637 (95% confidence interval CI 0.554–0.720) in anterior circulation and 0.683 (95% CI 0.522–0.845) in posterior circulation. The C statistics for ABCD2 score in the two groups were not statistically significant (Z = − 0.499; P = 0.62). Similar result was found when the outcome time-point was set at 90 days. ABCD2 score could predict the short-term risk of recurrent stroke after AC-TSI and PC-TSI, and had similar predictive abilities for AC-TSI and PC-TSI.

The benchmarking of CO2 emissions serves as the foundation for the accurate assessment of the environmental impact of air traffic. To calculate the environmental benchmarks of arrival traffic flows with Point Merge System (PMS) patterns, this study proposes a 4D trajectory optimization method that combines data-driven and optimal control models. First, the predominant arrival routes of traffic flows are identified using the trajectory spectral clustering method, which provides the horizontal reference for 4D trajectory optimization. Second, an optimal control model for vertical profiles with point merging topology is established, with the objective of minimizing the fuel–time cost. Finally, considering the complex structure of the PMS, a flexible and adaptable genetic algorithm-based vertical profile nonlinear optimization model is created. The experimental results demonstrate that the proposed method is adaptable to variations in aircraft type and cost index parameters, enabling the generation of different 4D trajectories. The results also indicate an environmental efficiency gap of approximately 10% between the actual CO2 emissions of the arrival traffic flow example and the obtained benchmark. With this benchmark trajectory generation methodology, the environmental performance of PMSs and associated arrival aircraft scheduling designs can be assessed on the basis of reliable data.

Abstract The accuracy of registration in image stitching task directly affects the performance of subsequent stages. Traditional registration methods rely heavily on the quality of the features when calculating the homography matrix, resulting in alignment failures in low‐texture or low‐overlap scenes due to extracting insufficient features. On the other hand, existing DNN‐based methods for homography estimation are more robust in multiple scenes but previous work usually employs an overly simple convolutional network structure to directly regress the homography, ignoring the redundant information contained in the feature maps so that their prediction accuracy is inferior to the traditional methods in simple scenarios. To overcome the disadvantages of the two methods, a Multi‐scale structure is proposed to extract feature maps at three scales and design two modules to handle the matrix prediction respectively. The DFA‐T module analyzes semantic information on the high‐level features to accomplish coarse‐grained alignment while the Contextual Correlation module on the bottom level to accomplish more accurate alignment. Experiments demonstrate that this method provides more accurate alignment results than the existing state‐of‐the‐art DNN‐based methods and outperforms traditional algorithms with more stable results in some extreme scenarios.

Abstract It is essential to identify high risk transient ischemic attack (TIA) patients. The previous study reported that the CSR (comprehensive stroke recurrence) model, a neuroimaging model, had a high predictive ability of recurrent stroke. The aims of this study were to validate the predictive value of CSR model in TIA patients and compare the predictive ability with ABCD3-I score. Data were analyzed from the prospective hospital-based database of patients with TIA which defined by the World Health Organization time-based criteria. The predictive outcome was stroke occurrence at 90 days. The receiver-operating characteristic (ROC) curves were plotted and the C statistics were calculated as a measure of predictive ability. Among 1186 eligible patients, the mean age was 57.28 ± 12.17 years, and 474 (40.0%) patients had positive diffusion-weighted imaging (DWI). There were 118 (9.9%) patients who had stroke within 90 days. In 1186 TIA patients, The C statistic of CSR model (0.754; 95% confidence interval [CI] 0.729–0.778) was similar with that of ABCD3-I score (0.717; 95% CI 0.691–0.743; Z = 1.400; P = 0.1616). In 474 TIA patients with positive DWI, C statistic of CSR model (0.725; 95% CI 0.683–0.765) was statistically higher than that of ABCD3-I score (0.626; 95% CI 0.581–0.670; Z = 2.294; P = 0.0245). The CSR model had good predictive value for assessing stroke risk after TIA, and it had a higher predictive value than ABCD3-I score for assessing stroke risk for TIA patients with positive DWI.

Abstract Background TOAST subtype classification is important for diagnosis and research of ischemic stroke. Limited by experience of neurologist and time-consuming manual adjudication, it is a big challenge to finish TOAST classification effectively. We propose a novel active deep learning architecture to classify TOAST. Methods To simulate the diagnosis process of neurologists, we drop the valueless features by XGB algorithm and rank the remaining ones. Utilizing active learning framework, we propose a novel causal CNN, in which it combines with a mixed active selection criterion to optimize the uncertainty of samples adaptively. Meanwhile, KL-focal loss derived from the enhancement of Focal loss by KL regularization is introduced to accelerate the iterative fine-tuning of the model. Results To evaluate the proposed method, we construct a dataset which consists of totally 2310 patients. In a series of sequential experiments, we verify the effectiveness of each contribution by different evaluation metrics. Experimental results show that the proposed method achieves competitive results on each evaluation metric. In this task, the improvement of AUC is the most obvious, reaching 77.4. Conclusions We construct a backbone causal CNN to simulate the neurologist process of that could enhance the internal interpretability. The research on clinical data also indicates the potential application value of this model in stroke medicine. Future work we would consider various data types and more comprehensive patient types to achieve fully automated subtype classification.

Abstract Background Lower prognostic nutritional index (PNI) is related to the poor prognosis of cardiovascular diseases. However, little is known about PNI and its relationship with the prognosis of cerebral venous sinus thrombosis (CVST). Methods CVST patients were retrospectively identified from January 2013 till June 2019. Patients in the acute / subacute phase were selected as subjects. Poor prognosis was defined as a modified Rankin Scale (mRS) of 3–6. Multivariate logistic regression analysis was used to confirm if lower PNI was associated with a poor prognosis. Results A total of 297 subjects with follow-up data were enrolled. Thirty-three (11.1%) had a poor outcome. Multivariate logistic regression analysis suggested that PNI was an important predictive factor of poor outcome in acute/subacute CVST (odds ratio, 0.903; 95% CI, 0.833–0.978; P = 0.012). The optimal cut-off value for predicting the poor prognosis of PNI was 44.2. Kaplan-Meier analysis and log-rank test suggested that the lower the PNI value, the higher the mortality rate (P

Lipids are implicated in inflammatory responses affecting acute ischaemic stroke prognosis. Therefore, we aimed to develop a predictive model that considers neutrophils and high-density lipoprotein cholesterol to predict its prognosis. This prospective study enrolled patients with acute ischaemic stroke within 24 h of onset between January 2015 and December 2017. The main outcome was a modified Rankin Scale score ≥3 at the 90th day of follow-up. Patients were divided into training and testing sets. The training set was divided into four states according to the median of neutrophils and high-density lipoprotein cholesterol levels in all patients. Through binary logistic regression analysis, the relationship between factors and prognosis was determined. A nomogram based on the results was developed; its predictive value was evaluated through internal and external validations. Altogether, 1,090 patients were enrolled with 872 (80%) and 218 (20%) in the training and testing sets, respectively. In the training set, the major outcomes occurred in 24 (10.4%), 24 (11.6%), 37 (17.2%), and 49 (22.3%) in states 1–4, respectively (P = 0.002). Validation of calibration and decision curve analyses showed that the nomogram showed better performance. The internal and external testing set receiver operating characteristics verified the predictive value [area under the curve = 0.794 (0.753–0.834), P < 0.001, and area under the curve = 0.973 (0.954–0.992), P < 0.001, respectively]. A nomogram that includes neutrophils and high-density lipoprotein cholesterol can predict the prognosis of acute ischaemic stroke, thus providing us with an effective visualization tool.

Aim: The atherogenic index of plasma (AIP) was significantly related to adverse outcomes in patients with cardiovascular disease. Our aim was to investigate the association between AIP and adverse outcomes in acute ischemic stroke.Methods: Patients with acute ischemic stroke (AIS) admitted between 2015 and 2018 were prospectively enrolled in this study. Functional outcomes were evaluated by the modified Rankin Scale (mRS). Poor outcomes were defined as mRS 3–6. The relationship of AIP with the risk of outcomes was analyzed by multivariate logistic regression models.Results: A total of 1,463 patients with AIS within 24 h of symptom onset were enrolled. The poor outcome group had a significantly higher level of AIP [0.09 (−0.10 to 0.27) vs. 0.04 (−0.09 to 0.18), p < 0.001] compared with the good outcome group. Multivariable logistic regression analysis showed that higher AIP was associated with poor outcomes in all the stroke patients (OR 1.84, 95% CI, 1.23–2.53, p = 0.007), which was more evident in patients with large-artery atherosclerosis subtype (OR 1.90, 95% CI, 1.53–2.62, p = 0.002), but not in the other subtypes. Receiver operating curve (ROC) analysis revealed that the best predictive cutoff value of AIP was 0.112, with a sensitivity of 70.8% and a specificity of 59.2%, and the area under the ROC curves for AIP was 0.685.Conclusion: AIP may be an important and independent predictor of the outcome of dysfunction in patients with AIS, especially the stroke subtype of large-artery atherosclerosis.

Background: The combined index of hemoglobin, albumin, lymphocyte, and platelet (HALP) is considered a novel score to reflect systemic inflammation and nutritional status. This study aimed to investigate the association between HALP score and poor outcome in patients with acute ischemic stroke (AIS).Methods: Consecutive AIS patients within 24 h after onset were prospectively enrolled. Poor outcome was a combination of a new stroke event (ischemic and hemorrhagic) and all-cause death within 90 days and 1 year. The association between HALP score and poor outcome was analyzed using Cox proportional hazards.Results: A total of 1,337 patients were included. Overall, 60 (4.5%) and 118 (8.8%) patients experienced poor outcome within 90 days and 1 year, respectively. Patients in the highest tertile of HALP score had a lower risk of poor outcome within 90 days and 1 year (hazard ratio: 0.25 and 0.42; 95% confidence intervals: 0.11–0.57 and 0.25–0.69, P for trend

Existing techniques have many limitations in the diagnosis and classification of ischemic stroke (IS). Considering this, we used metabolomics to screen for potential biomarkers of IS and its subtypes and to explore the underlying related pathophysiological mechanisms. Serum samples from 99 patients with acute ischemic stroke (AIS) [the AIS subtypes included 49 patients with large artery atherosclerosis (LAA) and 50 patients with small artery occlusion (SAO)] and 50 matched healthy controls (HCs) were analyzed by non-targeted metabolomics based on liquid chromatography–mass spectrometry. A multivariate statistical analysis was performed to identify potential biomarkers. There were 18 significantly different metabolites, such as oleic acid, linoleic acid, arachidonic acid, L-glutamine, L-arginine, and L-proline, between patients with AIS and HCs. These different metabolites are closely related to many metabolic pathways, such as fatty acid metabolism and amino acid metabolism. There were also differences in metabolic profiling between the LAA and SAO groups. There were eight different metabolites, including L-pipecolic acid, 1-Methylhistidine, PE, LysoPE, and LysoPC, which affected glycerophospholipid metabolism, glycosylphosphatidylinositol-anchor biosynthesis, histidine metabolism, and lysine degradation. Our study effectively identified the metabolic profiles of IS and its subtypes. The different metabolites between LAA and SAO may be potential biomarkers in the context of clinical diagnosis. These results highlight the potential of metabolomics to reveal new pathways for IS subtypes and provide a new avenue to explore the pathophysiological mechanisms underlying IS and its subtypes.

This study evaluated the inhibitory effects of samples extracted by different solvents from bayberry on eight common foodborne pathogens and investigated the main bacteriostatic components of bayberry extracts using ultraviolet and ultraperformance liquid chromatographic-electrospray mass spectrometry. Among the different solvent extracts, the water-extracted sample (BE1) had the greatest average inhibition zone diameter for the tested foodborne pathogens, which reached 22.1 mm. Among them, Salmonella paratyphi, Listeria innocua, and Listeria monocytogenes were most sensitive to BE1, and the lowest minimal inhibitory concentration value was 2.07 mg/mL. The polyphenol content in BE1 was the highest at 24.11 mg/mL. In addition, the main bacteriostatic component in BE1 was identified as cyanidin 3-O-glucoside. As natural antimicrobial agents, the antimicrobial compounds in berries are likely to have many important applications in food and medical industries in the future.

Background and purposeThe relationship between plasma level of total homocysteine (tHcy) and white matter hyperintensities (WMHs), especially in patients with acute ischemic stroke (AIS), is controversial. The present study investigated the association between these two as well as WMH locations in a large cohort of patients with AIS.MethodsConsecutive patients were reviewed from a prospective ischemic stroke database. Clinical data, including tHcy level and WMHs, were assessed. WMHs were assessed using the Fazekas scale and Age-Related White Matter Changes (ARWMC) visual grading scale. The association between tHcy and WMH locations was investigated by using multivariate logistic regression analyses.ResultsA total of 923 out of 1,205 patients were examined. The average age was 58.9 ± 11.9 years; 31.6% were female. Elevated tHcy level was significantly associated with WMHs. For the highest tHcy quartile, the odds ratio (OR) (95% confidence interval; CI) was 1.891 (1.257; 2.843) according to the Fazekas scale and 1.781 (1.185; 2.767) according to the ARWMC scale when compared to the lowest quartile. However, in a subgroup analysis, only WMHs in the periventricular area and left or right frontal areas were found to be independently associated with tHcy level. For the highest tHcy quartile, the OR (95% CI) was 1.761 (1.172; 2.648) for the periventricular WMHs, 1.768 (1.134; 2.756)for the left frontal WMHs, and 1.890 (1.206; 2.960)for the right frontal WMHs.ConclusionsIn patients with AIS, plasma tHcy level is related to WMHs, especially WMHs distributed within the periventricular and frontal areas.

Multiple system atrophy (MSA) is a neurodegenerative disease, and its pathological hallmark is the accumulation of α-synuclein proteins. Homocysteine (Hcy) is an intermediate amino acid generated during the metabolism of methionine. Hcy may contribute to the pathogenesis of neurodegenerative disorders. Vitamin B12 and folate are cofactors necessary for the methylation of homocysteine.This study compared the levels of serum Hcy, vitamin B12 and folate in patients with MSA with those in healthy people to reveal the possible association between MSA and plasma levels of Hcy, vitamin B12 and folate. We enrolled 161 patients with MSA and 161 healthy people in this study. The association between MSA and the levels of Hcy, vitamin B12 and folate were analyzed using binary logistic regression.The mean level of Hcy in patients with MSA was significantly higher than that in healthy controls (16.23 ± 8.09 umol/l vs 14.04 ± 4.25 umol/l, p < 0.05). After adjusting for age, sex and medical history, the odds ratio for Hcy was 1.07 (95% CI = 1.01-1.13, p < 0.05) for patients with MSA. Vitamin B12 and folate levels were not significantly different between patients with MSA and controls.Our data suggest that higher levels of Hcy may be associated with an increased risk for MSA.

Little is known about the effects of induced pluripotent stem cell (iPSC) treatment on acute cerebral inflammation and injuries after intracerebral hemorrhage (ICH), though they have shown promising therapeutic potentials in ischemic stoke.An ICH model was established by stereotactic injection of collagenase VII into the left striatum of male Sprague-Dawley (SD) rats. Six hours later, ICH rats were randomly divided into two groups and received intracerebrally 10 μl of PBS with or without 1 × 10(6) of iPSCs. Subsequently, neural function of all ICH rats was assessed at days 1, 3, 7, 14, 28 and 42 after ICH. Inflammatory cells, cytokines and neural apoptosis in the rats' perihematomal regions, and brain water content were determined on day 2 or 3 post ICH. iPSC differentiation was determined on day 28 post ICH. Nissl(+) cells and glial fibrillary acidic protein (GFAP)(+) cells in the perihematoma and the survival rates of rats in two groups were determined on post-ICH day 42.Compared with control animals, iPSCs treatment not only improved neurological function and survival rate, but also resulted in fewer intracephalic infiltrations of neutrophils and microglia, along with decreased interleukin (IL)-1β, IL-6 and tumour necrosis factor-alpha (TNF-α), and increased IL-10 in the perihematomal tissues of ICH rats. Furthermore, brain oedema formation, apoptosis, injured neurons and glial scar formation were decreased in iPSCs-transplanted rats.Our findings indicate that iPSCs transplantation attenuate cerebral inflammatory reactions and neural injuries after ICH, and suggests that multiple mechanisms including inflammation modulation, neuroprotection and functional recovery might be involved simultaneously in the therapeutic benefit of iPSC treatment against hemorrhagic stroke.