Aspirin protects against preeclampsia via p38MAPK signaling pathway

Purpose: To investigate the protective effect of aspirin against preeclampsia and the involvement of p38MAPK signaling pathway in the process.Methods: Sixty pregnant women who underwent antenatal care and delivery at Chancheng Central Hospital from September 2020 to September 2022 were selected and equally assigned to control group (CG) and experimental group (EG). From the 12th week of gestation, EG was administered 100 mg of aspirin and 1000 mg of calcium carbonate daily, while CG was given only 1000 mg of calcium carbonate daily. Both groups were treated up to the 35th week of gestation. Thereafter, blood samples were taken for measurement of serum levels of p38MAPK. In addition, the blood pressure of the women was measured. The incidence of preeclampsia and maternal-infant outcomes were assessed.Results: EG had a lower p38MAPK level at week 35 of pregnancy, and lower blood pressure levels at the 27th and 35th weeks of gestation, than CG (p < 0.05). There were 5 cases of preeclampsia (16.7 %) in EG, and 13 cases (43.3 %) of preeclampsia in CG, with a lower incidence of preeclampsia in EG than in CG (ꭓ2 = 5.079, p < 0.05). The numbers of newborns through premature delivery and cesarean section, as well as Apgar score ≤ 7 were lower in EG than in CG (p < 0.05).Conclusion: Aspirin exerts a protective effect against preeclampsia through via p38MAPK signaling pathway. Therefore, aspirin treatment may be useful in reducing the incidence of preeclampsia and improving maternal-infant outcomes. However, further clinical trials are recommended prior to application in clinical practice

Classification of Alzheimer’s Disease by Combination of Convolutional and Recurrent Neural Networks Using FDG-PET Images

Alzheimer’s disease (AD) is an irreversible brain degenerative disorder affecting people aged older than 65 years. Currently, there is no effective cure for AD, but its progression can be delayed with some treatments. Accurate and early diagnosis of AD is vital for the patient care and development of future treatment. Fluorodeoxyglucose positrons emission tomography (FDG-PET) is a functional molecular imaging modality, which proves to be powerful to help understand the anatomical and neural changes of brain related to AD. Most existing methods extract the handcrafted features from images, and then design a classifier to distinguish AD from other groups. These methods highly depends on the preprocessing of brain images, including image rigid registration and segmentation. Motivated by the success of deep learning in image classification, this paper proposes a new classification framework based on combination of 2D convolutional neural networks (CNN) and recurrent neural networks (RNNs), which learns the intra-slice and inter-slice features for classification after decomposition of the 3D PET image into a sequence of 2D slices. The 2D CNNs are built to capture the features of image slices while the gated recurrent unit (GRU) of RNN is cascaded to learn and integrate the inter-slice features for image classification. No rigid registration and segmentation are required for PET images. Our method is evaluated on the baseline FDG-PET images acquired from 339 subjects including 93 AD patients, 146 mild cognitive impairments (MCI) and 100 normal controls (NC) from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Experimental results show that the proposed method achieves an area under receiver operating characteristic curve (AUC) of 95.3% for AD vs. NC classification and 83.9% for MCI vs. NC classification, demonstrating the promising classification performance

Ensemble sparse classification of Alzheimer's disease

The high-dimensional pattern classification methods, e.g., support vector machines (SVM), have been widely investigated for analysis of structural and functional brain images (such as magnetic resonance imaging (MRI)) to assist the diagnosis of Alzheimer’s disease (AD) including its prodromal stage, i.e., mild cognitive impairment (MCI). Most existing classification methods extract features from neuroimaging data and then construct a single classifier to perform classification. However, due to noise and small sample size of neuroimaging data, it is challenging to train only a global classifier that can be robust enough to achieve good classification performance. In this paper, instead of building a single global classifier, we propose a local patch-based subspace ensemble method which builds multiple individual classifiers based on different subsets of local patches and then combines them for more accurate and robust classification. Specifically, to capture the local spatial consistency, each brain image is partitioned into a number of local patches and a subset of patches is randomly selected from the patch pool to build a weak classifier. Here, the sparse representation-based classification (SRC) method, which has shown effective for classification of image data (e.g., face), is used to construct each weak classifier. Then, multiple weak classifiers are combined to make the final decision. We evaluate our method on 652 subjects (including 198 AD patients, 225 MCI and 229 normal controls) from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database using MR images. The experimental results show that our method achieves an accuracy of 90.8% and an area under the ROC curve (AUC) of 94.86% for AD classification and an accuracy of 87.85% and an AUC of 92.90% for MCI classification, respectively, demonstrating a very promising performance of our method compared with the state-of-the-art methods for AD/MCI classification using MR images

Identifying Informative Imaging Biomarkers via Tree Structured Sparse Learning for AD Diagnosis

Neuroimaging provides a powerful tool to characterize neurodegenerative progression and therapeutic efficacy in Alzheimer’s disease (AD) and its prodromal stage—mild cognitive impairment (MCI). However, since the disease pathology might cause different patterns of structural degeneration, which is not pre-known, it is still a challenging problem to identify the relevant imaging markers for facilitating disease interpretation and classification. Recently, sparse learning methods have been investigated in neuroimaging studies for selecting the relevant imaging biomarkers and have achieved very promising results on disease classification. However, in the standard sparse learning method, the spatial structure is often ignored, although it is important for identifying the informative biomarkers. In this paper, a sparse learning method with tree-structured regularization is proposed to capture patterns of pathological degeneration from fine to coarse scale, for helping identify the informative imaging biomarkers to guide the disease classification and interpretation. Specifically, we first develop a new tree construction method based on the hierarchical agglomerative clustering of voxel-wise imaging features in the whole brain, by taking into account their spatial adjacency, feature similarity and discriminability. In this way, the complexity of all possible multi-scale spatial configurations of imaging features can be reduced to a single tree of nested regions. Second, we impose the tree-structured regularization on the sparse learning to capture the imaging structures, and then use them for selecting the most relevant biomarkers. Finally, we train a support vector machine (SVM) classifier with the selected features to make the classification. We have evaluated our proposed method by using the baseline MR images of 830 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, which includes 198 AD patients, 167 progressive MCI (pMCI), 236 stable MCI (sMCI), and 229 normal controls (NC). Our experimental results show that our method can achieve accuracies of 90.2 %, 87.2 %, and 70.7 % for classifications of AD vs. NC, pMCI vs. NC, and pMCI vs. sMCI, respectively, demonstrating promising performance compared with other state-of-the-art methods

Hierarchical fusion of features and classifier decisions for Alzheimer's disease diagnosis: AD Diagnosis

Pattern classification methods have been widely investigated for analysis of brain images to assist the diagnosis of Alzheimer’s disease (AD) and its early stage such as mild cognitive impairment (MCI). By considering the nature of pathological changes, a large number of features related to both local brain regions and interbrain regions can be extracted for classification. However, it is challenging to design a single global classifier to integrate all these features for effective classification, due to the issue of small sample size. To this end, we propose a hierarchical ensemble classification method to combine multilevel classifiers by gradually integrating a large number of features from both local brain regions and interbrain regions. Thus, the large-scale classification problem can be divided into a set of small-scale and easier-to-solve problems in a bottom-up and local-to-global fashion, for more accurate classification. To demonstrate its performance, we use the spatially normalized grey matter (GM) of each MR brain image as imaging features. Specifically, we first partition the whole brain image into a number of local brain regions and, for each brain region, we build two low-level classifiers to transform local imaging features and the inter-region correlations into high-level features. Then, we generate multiple high-level classifiers, with each evaluating the high-level features from the respective brain regions. Finally, we combine the outputs of all high-level classifiers for making a final classification. Our method has been evaluated using the baseline MR images of 652 subjects (including 198 AD patients, 225 MCI patients, and 229 normal controls (NC)) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The experimental results show that our classification method can achieve the accuracies of 92.0% and 85.3% for classifications of AD versus NC and MCI versus NC, respectively, demonstrating very promising classification performance compared to the state-of-the-art classification methods

High-Capacity, Dendrite-Free, and Ultrahigh-Rate Lithium-Metal Anodes Based on Monodisperse N-Doped Hollow Carbon Nanospheres

To unlock the great potential of lithium metal anodes for high-performance batteries, a number of critical challenges must be addressed. The uncontrolled dendrite growth and volume changes during cycling (especially, at high rates) will lead to short lifespan, low Coulombic efficiency (CE), and security risks of the batteries. Here it is reported that Li metal anodes, employing the monodisperse, lithiophilic, robust, and large-cavity N-doped hollow carbon nanospheres (NHCNSs) as the host, show remarkable performances—high areal capacity (10 mAh cm−2), high CE (up to 99.25% over 500 cycles), complete suppression of dendrite growth, dense packing of Li anode, and an extremely smooth electrode surface during repeated Li plating/stripping. In symmetric cells, a highly stable voltage hysteresis over a long cycling life >1200 h is achieved, and a low and stable voltage hysteresis can be realized even at an ultrahigh current density of 64 mA cm−2. Furthermore, the NHCNSs-based anodes, when paired with a LiFePO4 (LFP) cathode in full cells, give rise to highly improved rate capability (104 mAh g−1 at 10 C) and cycling stability (91.4% capacity retention for 200 cycles), enabling a promising candidate for the next-generation high energy/power density batteries

Identification of Causal Relationship between Amyloid-beta Accumulation and Alzheimer's Disease Progression via Counterfactual Inference

Alzheimer's disease (AD) is a neurodegenerative disorder that is beginning with amyloidosis, followed by neuronal loss and deterioration in structure, function, and cognition. The accumulation of amyloid-beta in the brain, measured through 18F-florbetapir (AV45) positron emission tomography (PET) imaging, has been widely used for early diagnosis of AD. However, the relationship between amyloid-beta accumulation and AD pathophysiology remains unclear, and causal inference approaches are needed to uncover how amyloid-beta levels can impact AD development. In this paper, we propose a graph varying coefficient neural network (GVCNet) for estimating the individual treatment effect with continuous treatment levels using a graph convolutional neural network. We highlight the potential of causal inference approaches, including GVCNet, for measuring the regional causal connections between amyloid-beta accumulation and AD pathophysiology, which may serve as a robust tool for early diagnosis and tailored care

An Efficient Direction Field-Based Method for the Detection of Fasteners on High-Speed Railways

Railway inspection is an important task in railway maintenance to ensure safety. The fastener is a major part of the railway which fastens the tracks to the ground. The current article presents an efficient method to detect fasteners on the basis of image processing and pattern recognition techniques, which can be used to detect the absence of fasteners on the corresponding track in high-speed(up to 400 km/h). The Direction Field is extracted as the feature descriptor for recognition. In addition, the appropriate weight coefficient matrix is presented for robust and rapid matching in a complex environment. Experimental results are presented to show that the proposed method is computation efficient and robust for the detection of fasteners in a complex environment. Through the practical device fixed on the track inspection train, enough fastener samples are obtained, and the feasibility of the method is verified at 400 km/h

Electroacupuncture Improves Cerebral Vasospasm and Functional Outcome of Patients With Aneurysmal Subarachnoid Hemorrhage

Cerebral vasospasm is the major cause of a poor outcome after aneurysmal subarachnoid hemorrhage (aSAH), and effective treatments for vasospasm are limited. The purpose of this study was to research the impact of electroacupuncture (EA) on cerebral vasospasm and the outcomes of patients with aSAH. A total of 60 age- and sex-matched aSAH patients were collected from Ningbo First Hospital between December 2015 and June 2017. All patients were given a basic treatment of nimodipine and randomized into two groups. The study group was treated with EA therapy on the Baihui (GV20) acupoint, and the control group was given mock transcutaneous electrical nerve stimulation. Cerebral vasospasm was measured by computed tomographic perfusion (CTP) and transcranial doppler (TCD). The mean flow velocity (MFV) in the middle cerebral artery (MCA), cerebral blood flow (CBF), cerebral blood volume (CBV), and mean transit time (MTT) of the patients were analyzed. The CBV and MTT exhibited significant differences between the study and control groups on the 1st (p = 0.026 and p = 0.001), 7th (p = 0.020 and p < 0.001), and 14th (p = 0.001 and p < 0.001) day after surgery, whereas CBF exhibited statistical significance only on the 14th day after surgery (p = 0.002). The MFV in MCA were significantly reduced after EA treatment in all patients (all p < 0.001). Additionally, the MFV in the MCA in patients treated with EA were considerably reduced compared with those of the control group (3rd day p = 0.046; 5th day, p = 0.010; 7th day, p < 0.001). Moreover, better outcomes were noted in the EA-treated group for the 1st month (p < 0.001) and 3rd month (p = 0.001) after surgery than in the control group. In conclusion, EA represents a potential method to treat cerebral vasospasm after aSAH and can improve the outcomes of patients with aSAH

Association of inpatient use of angiotensin converting enzyme inhibitors and angiotensin II receptor blockers with mortality among patients with hypertension hospitalized with COVID-19

Rationale: Use of angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin II receptor blockers (ARBs) is a major concern for clinicians treating coronavirus disease 2019 (COVID-19) in patients with hypertension. Objective: To determine the association between in-hospital use of ACEI/ARB and all-cause mortality in COVID-19 patients with hypertension. Methods and Results: This retrospective, multi-center study included 1128 adult patients with hypertension diagnosed with COVID-19, including 188 taking ACEI/ARB (ACEI/ARB group; median age 64 [IQR 55-68] years; 53.2% men) and 940 without using ACEI/ARB (non-ACEI/ARB group; median age 64 [IQR 57-69]; 53.5% men), who were admitted to nine hospitals in Hubei Province, China from December 31, 2019 to February 20, 2020. Unadjusted mortality rate was lower in the ACEI/ARB group versus the non-ACEI/ARB group (3.7% vs. 9.8%; P = 0.01). In mixed-effect Cox model treating site as a random effect, after adjusting for age, gender, comorbidities, and in-hospital medications, the detected risk for all-cause mortality was lower in the ACEI/ARB group versus the non-ACEI/ARB group (adjusted HR, 0.42; 95% CI, 0.19-0.92; P =0.03). In a propensity score-matched analysis followed by adjusting imbalanced variables in mixed-effect Cox model, the results consistently demonstrated lower risk of COVID-19 mortality in patients who received ACEI/ARB versus those who did not receive ACEI/ARB (adjusted HR, 0.37; 95% CI, 0.15-0.89; P = 0.03). Further subgroup propensity score-matched analysis indicated that, compared to use of other antihypertensive drugs, ACEI/ARB was also associated with decreased mortality (adjusted HR, 0.30; 95%CI, 0.12-0.70; P = 0.01) in COVID-19 patients with hypertension. Conclusions: Among hospitalized COVID-19 patients with hypertension, inpatient use of ACEI/ARB was associated with lower risk of all-cause mortality compared with ACEI/ARB non-users. While study interpretation needs to consider the potential for residual confounders, it is unlikely that in-hospital use of ACEI/ARB was associated with an increased mortality risk