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Ying Zhou

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EAAI Journal 2026 Journal Article

Interpretable hybrid learning for fracturing optimization in deep coalbed methane under data scarcity

  • Jie Liu
  • Cong Xiao
  • Xiaolun Yan
  • Shicheng Zhang
  • Tong Zhou
  • Lei Zou
  • Gui Cao
  • Ying Zhou

The efficient development of deep coalbed methane (CBM) faces challenges including complex geological conditions, sensitivity of fracturing parameters, and data scarcity. This study focuses on a block within the Ordos Basin and proposes a hybrid Artificial Intelligence modeling methodology integrating data augmentation, ensemble learning, and interpretability analysis. The Synthetic Minority Over-sampling Technique (SMOTE) was employed for data enhancement. Based on 17 geological and engineering parameters, a Stacked Generalization ensemble model integrating multiple algorithms including Random Forest, Support Vector Machine, and Gradient Boosting was constructed through randomized search hyperparameter optimization. Furthermore, the Shapley Additive Explanations (SHAP) method was introduced to identify dominant controlling factors, combined with Particle Swarm Optimization (PSO) to achieve collaborative optimization of fracturing parameters. Results demonstrate that geological parameters are the primary controlling factors for post-fracturing productivity. Among geological parameters, gas content, reservoir pressure, and Young's modulus show significant influence, while among engineering parameters, low-viscosity slickwater volume, pad fluid volume, high-viscosity slickwater volume, and pumping rate exhibit considerable impact. After SMOTE and Stacking integration modeling, the production prediction model achieved acceptable prediction accuracy. The optimal fracturing parameter intervals were determined as: low-viscosity slickwater volume 100–150 cubic meters (m3), pad fluid volume 200–400 m3, high-viscosity slickwater volume 100–300 m3, and pumping rate 18–20 cubic meters per minute (m3/min). This study provides an interpretable and scalable methodological framework for fracturing optimization under data-scarce conditions in deep CBM development, offering valuable references for intelligent development of unconventional oil and gas resources.

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YNIMG Journal 2026 Journal Article

Temporal dynamics of flexible cognitive control

  • Chengyuan Wu
  • Carol A. Seger
  • Yixuan Ku
  • Canhuang Luo
  • Ying Zhou
  • Jiefeng Jiang
  • Qi Chen

In dynamic environments, flexible cognitive control adaptively adjusts processing through proactive mechanisms deployed in advance and reactive mechanisms engaged upon conflict. Previous studies have primarily focused on identifying neural networks supporting specific control components, while less is known about how multiple components interact over time to support adaptive control. To characterize these temporal dynamics, we combined electroencephalography (EEG) recordings with a face-word Stroop paradigm under changing conflict environment. A hierarchical Bayesian model was used to estimate trial-wise learning rate, predicted conflict level, and prediction error, providing computational indices of cognitive control flexibility. Neural correlation analysis indicated that these variables correlated with Theta, Alpha, and Beta oscillations in distinct brain regions. Granger causality analyses revealed connectivity patterns among these regions that varied across different task phase. Furthermore, connections reflecting updates to predicted conflict level prior to stimulus onset indexed individual strength in proactive control, while connections reflecting learning rate updates after stimulus onset indexed reactive control. These findings highlight how oscillatory dynamics coordinate multiple control components and provide new insight into how proactive and reactive control emerge as distinct modes within this interconnected neural architecture of flexible cognitive control.

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JBHI Journal 2025 Journal Article

EDSRNet: An Enhanced Decoder Semantic Recovery Network for 2D Medical Image Segmentation

  • Feng Sun
  • Ying Zhou
  • Longxiangfeng Hu
  • Yongyan Li
  • Dan Zhao
  • Yufeng Chen
  • Yu He

In recent years, with the advancement of medical imaging technology, medical image segmentation has played a key role in assisting diagnosis and treatment planning. Current deep learning-based medical image segmentation methods mainly adopt encoder-decoder architecture design and have received wide attention. However, these methods still have some limitations, including: (1) Existing methods are often influenced by the significant semantic information gap when supplementing features for the decoder. (2) Existing methods do not simultaneously consider global and local information interaction during decoding, resulting in ineffective semantic recovery. Therefore, this paper proposes a novel Enhanced Decoder Semantic Recovery Network to address these challenges. Firstly, the Multi-Level Semantic Fusion (MLSF) module is introduced, which effectively fuses low-level features of the original image, encoder features, high-level features of the deepest network layer, and decoder features, and assigns weights based on semantic gaps. Secondly, the Multiscale Spatial Attention (MSSA) and Cross Convolution Channel Attention (CCCA) modules are employed to obtain richer feature information. Finally, the Global-Local Semantic Recovery (GLSR) module is designed to achieve better semantic recovery. Experiments on public datasets such as BUSI, CVC-ClinicDB, and Kvasir-SEG demonstrate that the proposed model improves IoU compared to suboptimal algorithms by 0. 81%, 0. 85% and 1. 98%, respectively, significantly enhancing the performance of 2D medical image segmentation. This method provides effective technical support for further development in the field of medical image.

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AAAI Conference 2025 Conference Paper

MonoBox: Tightness-Free Box-Supervised Polyp Segmentation Using Monotonicity Constraint

  • Qiang Hu
  • Zhenyu Yi
  • Ying Zhou
  • Fan Huang
  • Mei Liu
  • Qiang Li
  • Zhiwei Wang

We propose MonoBox, an innovative box-supervised segmentation method constrained by monotonicity to liberate its training from the user-unfriendly box-tightness assumption. In contrast to conventional box-supervised segmentation, where the box edges must precisely touch the target boundaries, MonoBox leverages imprecisely-annotated boxes to achieve robust pixel-wise segmentation. The 'linchpin' is that, within the noisy zones around box edges, MonoBox discards the traditional misguiding multiple-instance learning loss, and instead optimizes a carefully-designed objective, termed monotonicity constraint. Along directions transitioning from the foreground to background, this new constraint steers responses to adhere to a trend of monotonically decreasing values. Consequently, the originally unreliable learning within the noisy zones is transformed into a correct and effective monotonicity optimization. Moreover, an adaptive label correction is introduced, enabling MonoBox to enhance the tightness of box annotations using predicted masks from the previous epoch and dynamically shrink the noisy zones as training progresses. We verify MonoBox in the box-supervised segmentation task of polyps, where satisfying box-tightness is challenging due to the vague boundaries between the polyp and normal tissues. Experiments on both public synthetic and in-house real noisy datasets demonstrate that MonoBox exceeds other anti-noise state-of-the-arts by improving Dice by at least 5.5% and 3.3%, respectively.

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JBHI Journal 2025 Journal Article

MOPDDI: Predicting Drug–Drug Interaction Events Based on Multimodal Mutual Orthogonal Projection and Intermodal Consistency Loss

  • Qi Xia
  • Zhenghong Xiao
  • Shaopeng Liu
  • Jianhua Guo
  • Ying Zhou
  • Wanlu Hu
  • Xu Lu

Predicting accurately the mechanisms of drug–drug interaction (DDI) events is crucial in drug research and development. Existing methods used to predict these events are primarily based on deep learning and have achieved satisfactory results. However, they rarely consider the presence of redundant co-information between the multimodal data of a drug and the need for consistency in the predicted features of each drug modality. Herein, we propose a new method for drug interaction event prediction based on multimodal mutual orthogonal projection and intermodal consistency loss. Our method obtains the features of each modality through a multimodal mutual orthogonal projection module, which eliminates redundant common information with other modalities. In addition, we use the consistency loss between modalities and make the predicted features of each modality more similar. In comparative experiments, our proposed method achieves a prediction accuracy of 0. 9500, and an area under the precision–recall (AUPR) curve is 0. 9833 for known DDIs. This method outperforms existing methods. The results show that the proposed method is capable of accurately predicting DDIs.

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AAAI Conference 2025 Conference Paper

QuARF: Quality-Adaptive Receptive Fields for Degraded Image Perception

  • Fei Gao
  • Ying Zhou
  • Ziyun Li
  • Wenwang Han
  • Jiaqi Shi
  • Maoying Qiao
  • Jinlan Xu
  • Nannan Wang

Advanced Deep Neural Networks (DNNs) perform well for high-quality images, but their performance dramatically decreases for degraded images. Data augmentation is commonly used to alleviate this problem, but using too much perturbed data might seriously decrease the performance on pristine images. To tackle this challenge, we take our cue from the assumption of spatial coincidence in human visual perception, i.e. multiscale and varying receptive fields are required for understanding pristine and degraded images. Correspondingly, we propose a novel plug-and-play network architecture, dubbed Quality-Adaptive Receptive Fields (QuARF), to automatically select the optimal receptive fields based on the quality of the input image. To this end, we first design a multi-kernel convolutional block, which comprises multiscale continuous receptive fields. Afterward, we design a quality-adaptive routing network to predict the significance of each kernel, based on the quality features extracted from the input image. In this way, QuARF automatically selects the optimal inference route for each image. To further boost efficiency and effectiveness, the input feature map is split into multiple groups, with each group independently learning its quality-adaptive routing parameters. We apply QuARF to a variety of DNNs and conduct experiments in both discriminative and generation tasks, including semantic segmentation, image translation, and restoration. Thorough experimental results show that QuARF significantly and robustly improves the performance for degraded images, and outperforms data augmentation in most cases.

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NeurIPS Conference 2025 Conference Paper

Table2LaTeX-RL: High-Fidelity LaTeX Code Generation from Table Images via Reinforced Multimodal Language Models

  • Jun Ling
  • Yao Qi
  • Tao Huang
  • Shibo Zhou
  • Yanqin Huang
  • Jiang Yang
  • Ziqi Song
  • Ying Zhou

In this work, we address the task of table image to LaTeX code generation, with the goal of automating the reconstruction of high-quality, publication-ready tables from visual inputs. A central challenge of this task lies in accurately handling complex tables—those with large sizes, deeply nested structures, and semantically rich or irregular cell content—where existing methods often fail. We begin with a comprehensive analysis, identifying key challenges and highlighting the limitations of current evaluation protocols. To overcome these issues, we propose a reinforced multimodal large language model (MLLM) framework, where a pre-trained MLLM is fine-tuned on a large-scale table-to-LaTeX dataset. To further improve generation quality, we introduce a dual-reward reinforcement learning strategy based on Group Relative Policy Optimization (GRPO). Unlike standard approaches that optimize purely over text outputs, our method incorporates both a structure-level reward on LaTeX code and a visual fidelity reward computed from rendered outputs, enabling direct optimization of the visual output quality. We adopt a hybrid evaluation protocol combining TEDS-Structure and CW-SSIM, and show that our method achieves state-of-the-art performance, particularly on structurally complex tables, demonstrating the effectiveness and robustness of our approach.

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JBHI Journal 2025 Journal Article

TBE-Net: A Deep Network Based on Tree-Like Branch Encoder for Medical Image Segmentation

  • Shukai Yang
  • Xiaoqian Zhang
  • Youdong He
  • Yufeng Chen
  • Ying Zhou

In recent years, encoder-decoder-based network structures have been widely used in designing medical image segmentation models. However, these methods still face some limitations: 1) The network's feature extraction capability is limited, primarily due to insufficient attention to the encoder, resulting in a failure to extract rich and effective features. 2) Unidirectional stepwise decoding of smaller-sized feature maps restricts segmentation performance. To address the above limitations, we propose an innovative Tree-like Branch Encoder Network (TBE-Net), which adopts a tree-like branch encoder to better perform feature extraction and preserve feature information. Additionally, we introduce the Depth and Width Expansion (DWE) module to expand the network depth and width at low parameter cost, thereby enhancing network performance. Furthermore, we design a Deep Aggregation Module (DAM) to better aggregate and process encoder features. Subsequently, we directly decode the aggregated features to generate the segmentation map. The experimental results show that, compared to other advanced algorithms, our method, with the lowest parameter cost, achieved improvements in the IoU metric on the TNBC, PH2, CHASE-DB1, STARE, and COVID-19-CT-Seg datasets by 1. 6%, 0. 46%, 0. 81%, 1. 96%, and 0. 86%, respectively.

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YNICL Journal 2023 Journal Article

Low-density lipoprotein cholesterol, statin therapy, and cerebral microbleeds: The CIRCLE study

  • Yuqi Zhao
  • Ying Zhou
  • Huan Zhou
  • Xiaoxian Gong
  • Zhongyu Luo
  • Jiaping Li
  • Jianzhong Sun
  • Min Lou

BACKGROUND: Current evidence suggests a potential association between cerebral microbleeds (CMBs), low-density lipoprotein cholesterol (LDL-C) levels, and statin use, but the exact relationship remains unclear. This study aims to prospectively examine these relationships in a stroke-free population. METHODS: From January 2010 to January 2020, we enrolled stroke-free individuals with at least one cerebral small vessel disease imaging marker from the CIRCLE study (ClinicalTrials.gov ID: NCT03542734). Participants underwent baseline and 1-year follow-up susceptibility-weighted imaging (SWI), and baseline LDL-C testing. New CMBs were categorized as strictly lobar and deep CMBs based on location. RESULTS: A total of 209 individuals were included. Baseline serum LDL-C levels were divided into quartiles: Q1 (≤1.76 mmol/L), Q2 (1.77-2.36 mmol/L), Q3 (2.37-2.93 mmol/L), and Q4 (>2.93 mmol/L). The incidence of new deep CMBs was 30.0%, 11.1%, 10.9%, 8.2% in Q1, Q2, Q3, Q4, respectively. Multivariate logistic model revealed that only LDL-C in Q1 was associated with increased incidence of new deep CMBs (OR = 4.256; 95% CI: 1.156-15.666; p = 0.029). In a subset of 169 participants without prior statin use, the use of atorvastatin was associated with reduced occurrence of new deep CMBs (OR = 0.181; 95% CI: 0.035-0.928; p = 0.040), while it was not found with rosuvastatin (OR = 0.808; 95% CI: 0.174-3.741; p = 0.785). CONCLUSIONS: While lower LDL-C levels were associated with higher CMB development, statin therapy did not increase the risk of new CMBs. Atorvastatin even demonstrated a protective effect.

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YNICL Journal 2022 Journal Article

Impact of different white matter hyperintensities patterns on cognition: A cross-sectional and longitudinal study

  • Junjun Wang
  • Ying Zhou
  • Yaode He
  • Qingqing Li
  • Wenhua Zhang
  • Zhongyu Luo
  • Rui Xue
  • Min Lou

OBJECTIVES: White matter hyperintensities (WMH) are highly prevalent in older adults and considered to be a contributor to cognition impairment. However, the strategic WMH lesion distribution related to cognitive impairment is still debated. The aim of this study was to characterize the spatial patterns of WMH associated with cognitive impairment and explore its risk factors. METHODS: We retrospectively analyzed patients who underwent T2 fluid attenuated inversion recovery (FLAIR) and mini-mental state examination (MMSE) in two centers. WHM was classified into four patterns based on T2 FLAIR as follows: (1) multiple subcortical spots (multi-spots); (2) peri-basal ganglia (peri-BG); (3) anterior subcortical patches (anterior SC patches); and (4) posterior subcortical patches (posterior SC patches). We cross-sectionally and longitudinally estimated associations between different WMH patterns and all-cause dementia and cognitive decline. Multivariable logistic regression analysis was followed to identify risk factors of WMH patterns related to cognitive impairment. RESULTS: A total of 442 patients with WMH were enrolled, with average age of 71.6 ± 11.3 years, and MMSE score of 24.1 ± 5.4. Among them, 281 (63.6%), 66 (14.9%), 163 (36.9%) and 197 (44.6%) patients presented multi-spots, peri-BG, anterior SC patches and posterior SC patches, respectively. Patients with anterior SC patches were more likely to have all-cause dementia in cross-sectional study (OR 2.002; 95% CI 1.098-3.649; p = 0.024), and have cognitive decline in longitudinal analysis (OR 3.029; 95% CI 1.270-7.223; p = 0.012). Four patterns of WMH referred to different cognitive domains, and anterior SC patches had the most significant and extensive impact on cognition after Bonferroni multiple comparison correction (all p < 0.0125). In addition, older age (OR 1.054; 95% CI 1.027-1.082; p < 0.001), hypertension (OR 1.956; 95% CI 1.145-3.341; p = 0.014), higher percentage of neutrophils (OR 1.046; 95% CI 1.014-1.080; p = 0.005) and lower concentration of hemoglobin (OR 0.983; 95% CI 0.967-1.000; p = 0.044) were risk factors for the presence of anterior SC patches. CONCLUSIONS: Different patterns of subcortical leukoaraiosis visually identified on MRI might have different impacts on cognitive impairment. Further studies should be undertaken to validate this simple visual classification of WMH in different population.

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AIIM Journal 2022 Journal Article

MF-OMKT: Model fusion based on online mutual knowledge transfer for breast cancer histopathological image classification

  • Guangli Li
  • Chuanxiu Li
  • Guangting Wu
  • Guangxin Xu
  • Ying Zhou
  • Hongbin Zhang

Pathological diagnosis is considered as the benchmark for the detection of breast cancer. With the increasing number of patients, computer-aided histopathological image classification can assist pathologists in improving breast cancer diagnosis accuracy and working efficiency. However, a single model is insufficient for effective diagnosis, and this also does not accord with the principle of centralized decision-making. Starting from the real pathological diagnosis scenario, we propose a novel model fusion framework based on online mutual knowledge transfer (MF-OMKT) for breast cancer histopathological image classification. The OMKT part based on deep mutual learning (DML) imitates the mutual communication and learning between multiple experienced pathologists, which can break the isolation of single models and provides sufficient complementarity among heterogeneous networks for MF. The MF part based on adaptive feature fusion uses the complementarity to train a powerful fusion classifier. MF imitates the centralized decision-making process of these pathologists. We used the MF-OMKT model to classify breast cancer histopathological images (BreakHis dataset) into benign and malignant as well as eight subtypes. The accuracy of our model reaches the range of [99. 27 %, 99. 84 %] for binary classification. And that for multi-class classification reaches the range of [96. 14 %, 97. 53 %]. Additionally, MF-OMKT is applied to the classification of skin cancer images (ISIC 2018 dataset) and achieves an accuracy of 94. 90 %. MF-OMKT is an effective and versatile framework for medical image classification.

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NeurIPS Conference 2022 Conference Paper

Sequence-to-Set Generative Models

  • Longtao Tang
  • Ying Zhou
  • Yu Yang

In this paper, we propose a sequence-to-set method that can transform any sequence generative model based on maximum likelihood to a set generative model where we can evaluate the utility/probability of any set. An efficient importance sampling algorithm is devised to tackle the computational challenge of learning our sequence-to-set model. We present GRU2Set, which is an instance of our sequence-to-set method and employs the famous GRU model as the sequence generative model. To further obtain permutation invariant representation of sets, we devise the SetNN model which is also an instance of the sequence-to-set model. A direct application of our models is to learn an order/set distribution from a collection of e-commerce orders, which is an essential step in many important operational decisions such as inventory arrangement for fast delivery. Based on the intuition that small-sized sets are usually easier to learn than large sets, we propose a size-bias trick that can help learn better set distributions with respect to the $\ell_1$-distance evaluation metric. Two e-commerce order datasets, TMALL and HKTVMALL, are used to conduct extensive experiments to show the effectiveness of our models. The experimental results demonstrate that our models can learn better set/order distributions from order data than the baselines. Moreover, no matter what model we use, applying the size-bias trick can always improve the quality of the set distribution learned from data.

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AIIM Journal 2021 Journal Article

Convolutional squeeze-and-excitation network for ECG arrhythmia detection

  • Rongjun Ge
  • Tengfei Shen
  • Ying Zhou
  • Chengyu Liu
  • Libo Zhang
  • Benqiang Yang
  • Ying Yan
  • Jean-Louis Coatrieux

Automatic detection of arrhythmia through an electrocardiogram (ECG) is of great significance for the prevention and treatment of cardiovascular diseases. In Convolutional neural network, the ECG signal is converted into multiple feature channels with equal weights through the convolution operation. Multiple feature channels can provide richer and more comprehensive information, but also contain redundant information, which will affect the diagnosis of arrhythmia, so feature channels that contain arrhythmia information should be paid attention to and given larger weight. In this paper, we introduced the Squeeze-and-Excitation (SE) block for the first time for the automatic detection of multiple types of arrhythmias with ECG. Our algorithm combines the residual convolutional module and the SE block to extract features from the original ECG signal. The SE block adaptively enhances the discriminative features and suppresses noise by explicitly modeling the interdependence between the channels, which can adaptively integrate information from different feature channels of ECG. The one-dimensional convolution operation over the time dimension is used to extract temporal information and the shortcut connection of the Se-Residual convolutional module in the proposed model makes the network easier to optimize. Thanks to the powerful feature extraction capabilities of the network, which can effectively extract discriminative arrhythmia features in multiple feature channels, so that no extra data preprocessing including denoising in other methods are need for our framework. It thus improves the working efficiency and keeps the collected biological information without loss. Experiments conducted with the 12-lead ECG dataset of the China Physiological Signal Challenge (CPSC) 2018 and the dataset of PhysioNet/Computing in Cardiology (CinC) Challenge 2017. The experiment results show that our model gains great performance and has great potential in clinical.

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YNIMG Journal 2021 Journal Article

Glymphatic clearance function in patients with cerebral small vessel disease

  • Wenhua Zhang
  • Ying Zhou
  • Jianan Wang
  • Xiaoxian Gong
  • Zhicai Chen
  • Xuting Zhang
  • Jinsong Cai
  • Siyan Chen

Few studies have focused on the connection between glymphatic dysfunction and cerebral small vessel disease (CSVD), partially due to the lack of non-invasive methods to measure glymphatic function. We established modified index for diffusion tensor image analysis along the perivascular space (mALPS-index), which was calculated on diffusion tensor image (DTI), compared it with the classical detection of glymphatic clearance function calculated on Glymphatic MRI after intrathecal administration of gadolinium (study 1), and analyzed the relationship between CSVD imaging markers and mALPS-index in CSVD patients from the CIRCLE study (ClinicalTrials.gov ID: NCT03542734) (study 2). Among 39 patients included in study 1, mALPS-index were significantly related to glymphatic clearance function calculated on Glymphatic MRI ( r = -0.772~-0.844, p < 0.001). A total of 330 CSVD patients were included in study 2. Severer periventricular and deep white matter hyperintensities (β = -0.332, p < 0.001; β = -0.293, p < 0.001), number of lacunas (β = -0.215, p < 0.001), number of microbleeds (β = -0.152, p = 0.005), and severer enlarged perivascular spaces in basal ganglia (β = -0.223, p < 0.001) were related to mALPS-index. Our results indicated that non-invasive mALPS-index might represent glymphatic clearance function, which could be applied in clinic in future. Glymphatic clearance function might play a role in the development of CSVD.

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YNICL Journal 2019 Journal Article

The relationship between deep medullary veins score and the severity and distribution of intracranial microbleeds

  • Ruiting Zhang
  • Qingqing Li
  • Ying Zhou
  • Shenqiang Yan
  • Minming Zhang
  • Min Lou

BACKGROUND: Microbleeds are frequently detected in normal elderly population, and their presence is associated with an increased risk of intracerebral hemorrhage, ischemic stroke and cognitive impairment. Previous histopathologic findings mainly focused on arteries and capillaries. Nevertheless, few studies investigated the relationship between venous disruption and microbleeds. OBJECTIVE: We aimed to evaluate the extent of venous disruption in vivo and assess the correlation between deep medullary veins (DMVs) disruption and the severity and distribution of intracranial microbleeds in patients with cerebral small vessel disease (cSVD). METHODS: We retrospectively reviewed the clinical, laboratory and imaging data of the patients admitted to our department who received brain MRI and presented with CSVD imaging markers. Susceptibility weighted imaging (SWI) phase images were used to observe characteristics of DMVs and derive a brain region-based DMVs visual score. SWI magnitude images were used to evaluate microbleeds. We recorded the number and distribution (lobar or deep or infratentorial) of microbleeds. One-way ANOVA and logistic-regression analysis were used to examine the association between the DMVs score and microbleeds. RESULTS: A total of 369 cSVD patients were analyzed, including 177 (48.0%) patients with microbleeds, among whom 81(45.8%) patients had 1-2 microbleeds and 96 (54.2%) patients had ≥3 microbleeds (extensive microbleeds). The patients' DMVs score ranged from 0 to18, with a median score of 8(6-12). Higher DMVs score was independently associated with extensive microbleeds (OR = 1.108, 95%Cl: 1.010-1.215, p = 0.03) after adjusting for gender, hypertension, hyperhomocysteinemia, Fazekas score and number of lacunas. According to the distribution, 38 (21.5%) patients were found with strict lobar microbleeds, while 139 (78.5%) patients had non-strict lobar microbleeds. Higher DMVs score was also independently associated with non-strict lobar microbleeds (OR = 1.106, 95% Cl: 1.019-1.200, p = 0.016) after adjusting for gender, hypertension, hyperhomocysteinemia, Fazekas score and number of lacunas. DMVs score was not associated with strict lobar microbleeds (p = 0.307). CONCLUSION: DMVs disruption might be involved in the development of extensive microbleeds, especially non-strict lobar cerebral microbleeds.

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YNICL Journal 2019 Journal Article

Time-dependent infarct volume affects the benefit of recanalization

  • Haitao Hu
  • Shenqiang Yan
  • Ying Zhou
  • Min Lou

OBJECTIVES: The benefit threshold of infarct volume from recanalization remains unclear. We assumed that the threshold decreased over time, and then investigated the benefit curve of infarct volume during different time periods. METHODS: We reviewed prospectively collected clinical and imaging data from acute ischemic stroke patients with internal carotid artery and M1 occlusion who underwent angiography before and 24 h after reperfusion therapy. Ordinal analyses of modified Rankin Scale scores were performed and curves were fitted. RESULTS: Of the included 445 patients, the median age was 71 years and 157 (35.3%) were women. The mean time from onset to treatment (OTT) was 248 ± 142 min. The median baseline infarct core volume was 49 (IQR 22-85) ml. Follow-up angiography revealed recanalization in 265 (59.6%) patients. The fitting curves showed that patients with an OTT ≤3 h would benefit from recanalization no matter how large the infarct volume was, whereas patients with an OTT between 3 and 4.5 h and with an infarct volume ≥ 125 ml, and those with an OTT ≥ 4.5 h and with an infarct volume ≥ 80 ml did not benefit from recanalization. CONCLUSIONS: We established a time-dependent benefit threshold of infarct volume from recanalization, and thus suggested to estimate infarct core volume to select patients for reperfusion therapy in those with an OTT beyond 3 h.

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