Linna Zhao

Beijing University of Technology

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Xi Xu

Beijing University of Technology

Jianqiang Li

Beijing University of Technology

Wenxiu Cheng

Beijing University of Technology

Suqin Liu

Central South University

Linlin Bao

Institute of Laboratory Animal Science

Guanpeng Wang

University of Science and Technology Liaoning

Chuan Qin

Chinese Academy of Medical Sciences & Peking Union Medical College

Haisheng Yu

Zhongnan Hospital of Wuhan University

Qi Lv

Institute of Laboratory Animal Science

Jiangning Liu

Model Animal Research Center

Cooperative Institutions

Chinese Academy of Medical Sciences & Peking Union Medical College

Beijing University of Technology

China Agricultural University

Institute of Laboratory Animal Science

Sun Yat-sen University

Peking Union Medical College Hospital

Chinese Academy of Sciences

Academy of Medical Sciences

Capital Medical University

Model Animal Research Center

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Multi-view Information Fusion Network for Pneumonia Diagnosis from Full Sequence CTs

Guangling Qi Linna Zhao Yuanhang Di

Pneumonia diagnosis based on CT scans is crucial for the effective treatment. Existing deep leanring-based methods mainly focus on the global struction of the whole lung organs, while ignore the information of local detailed lesions. This can easily lead to errors in pneumonia decisions and a decline in classification accuracy. Actully, the diagnosis process of specialists in practice involves basically two steps glancing the whole lung organs to capture global information (global view) and gazing at local regions for observing detailed lesion (local view). To mimic this behaviour, we propose a multi-view information fusion network for pneumonia diagnosis from full sequence CTs. First, we design a multi-view information fusion network to extract spatial features from lung CT slices from global and local perspectives. Then, a recursive neural network (RNN) is utilized to solve the problem of dependency between slices and the continuity of lesions. Extensive experiments on real-world datasets are conducted and the results demonstrate the effectiveness of our proposed method.

Sequence (biology)

Information fusion

10.1109/iciba56860.2023.10164992

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Attention-based Multi-flow Network for COVID-19 Classification and Lesion Localization from Chest CT

2021 IEEE International Conference on Networking, Sensing and Control (ICNSC) (2022)

Jianqiang Li Yuanhang Di Guangling Qi Linna Zhao Fujiao Ju

COVID-19 has been rapidly spreading worldwide and infected more than 1 million people with over 690k deaths reported. It is urgent and crucial to identify COVID-19-infected patients by computed tomography (CT) accurately and rapidly. However, we found that two problems, weak supervision and lack of interpretability, hindered its development. To address these challenges, we propose an attention-based multi-flow network for COVID-19 classification and lesion localization from chest CT. In the proposed model, we built a Resnet-based multi-flow network to learn the local information and the longitudinal information from the full chest sequence slice. To assist doctors in decision-making, the attention mechanism integrated into the network, which can locate the key slices and key parts from a full chest CT sequence of patients. We have systematically evaluated our method on the CT images of 1031 cases, including 420 COVID-19 cases, 311CAP cases, and 300 non-pneumonia cases. Our method could obtain an average accuracy of 82.3%, with 85.7% sensitivity and 86.4 % specificity, which outperformed previous works.

Interpretability

2019-20 coronavirus outbreak

10.1109/icnsc55942.2022.10004191

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Cell Death: Mechanisms and Potential Targets in Breast Cancer Therapy

International Journal of Molecular Sciences (2024)

J Qian Linna Zhao Ling Xu Jin Zhao Yongxu Tang

Breast cancer (BC) has become the most life-threatening cancer to women worldwide, with multiple subtypes, poor prognosis, and rising mortality. The molecular heterogeneity of BC limits the efficacy and represents challenges for existing therapies, mainly due to the unpredictable clinical response, the reason for which probably lies in the interactions and alterations of diverse cell death pathways. However, most studies and drugs have focused on a single type of cell death, while the therapeutic opportunities related to other cell death pathways are often neglected. Therefore, it is critical to identify the predominant type of cell death, the transition to different cell death patterns during treatment, and the underlying regulatory mechanisms in BC. In this review, we summarize the characteristics of various forms of cell death, including PANoptosis (pyroptosis, apoptosis, necroptosis), autophagy, ferroptosis, and cuproptosis, and discuss their triggers and signaling cascades in BC, which may provide a reference for future pathogenesis research and allow for the development of novel targeted therapeutics in BC.

Pyroptosis

10.3390/ijms25179703

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Reinfection could not occur in SARS-CoV-2 infected rhesus macaques

bioRxiv (Cold Spring Harbor Laboratory) (2020)

Linlin Bao Wei Deng Hong Gao Chong Xiao Jiayi Liu

Abstract An outbreak of the Corona Virus Disease 2019 (COVID-19), caused by the severe acute respiratory syndrome CoV-2 (SARS-CoV-2), began in Wuhan and spread globally. Recently, it has been reported that discharged patients in China and elsewhere were testing positive after recovering. However, it remains unclear whether the convalescing patients have a risk of “relapse” or “reinfection”. The longitudinal tracking of re-exposure after the disappeared symptoms of the SARS-CoV-2-infected monkeys was performed in this study. We found that weight loss in some monkeys, viral replication mainly in nose, pharynx, lung and gut, as well as moderate interstitial pneumonia at 7 days post-infection (dpi) were clearly observed in rhesus monkeys after the primary infection. After the symptoms were alleviated and the specific antibody tested positively, the half of infected monkeys were rechallenged with the same dose of SARS-CoV-2 strain. Notably, neither viral loads in nasopharyngeal and anal swabs along timeline nor viral replication in all primary tissue compartments at 5 days post-reinfection (dpr) was found in re-exposed monkeys. Combined with the follow-up virologic, radiological and pathological findings, the monkeys with re-exposure showed no recurrence of COVID-19, similarly to the infected monkey without rechallenge. Taken together, our results indicated that the primary SARS-CoV-2 infection could protect from subsequent exposures, which have the reference of prognosis of the disease and vital implications for vaccine design.

Viral Shedding

Viral Pneumonia

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The Pathogenicity of SARS-CoV-2 in hACE2 Transgenic Mice

bioRxiv (Cold Spring Harbor Laboratory) (2020)

Linlin Bao Wei Deng Baoying Huang Hong Gao Jiangning Liu

Abstract Severe acute respiratory syndrome CoV-2 (SARS-CoV-2) caused the Corona Virus Disease 2019 (COVID-19) cases in China has become a public health emergency of international concern (PHEIC). Based on angiotensin converting enzyme 2 (ACE2) as cell entry receptor of SARS-CoV, we used the hACE2 transgenic mice infected with SARS-CoV-2 to study the pathogenicity of the virus. Weight loss and virus replication in lung were observed in hACE2 mice infected with SARS-CoV-2. The typical histopathology was interstitial pneumonia with infiltration of significant lymphocytes and monocytes in alveolar interstitium, and accumulation of macrophages in alveolar cavities. Viral antigens were observed in the bronchial epithelial cells, alveolar macrophages and alveolar epithelia. The phenomenon was not found in wild type mice with SARS-CoV-2 infection. The pathogenicity of SARS-CoV-2 in hACE2 mice was clarified and the Koch’s postulates were fulfilled as well, and the mouse model may facilitate the development of therapeutics and vaccines against SARS-CoV-2.

Alveolar cells

Histopathology

10.1101/2020.02.07.939389

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Knowledge Guided Feature Aggregation for the Prediction of Chronic Obstructive Pulmonary Disease With Chinese EMRs

IEEE/ACM Transactions on Computational Biology and Bioinformatics (2022)

Qing Zhao Jianqiang Li Linna Zhao Zhichao Zhu

The automatic disease diagnosis utilizing clinical data has been suffering from the issues of feature sparse and high probability of missing values. Since the graph neural network is a effective tool to model the structural information and infer the missing values, it is becoming the dominant method for the predictive model construction from electronic medical records. Existing graph neural network based solutions usually adopt the medical concepts (e.g., symptoms) the feature representation of clinical data without considering their underlying semantic relations. The limited discriminative capability of the medical concept cannot provide sufficient indicative information about the disease. This article proposes a knowledge-guided graph attention network for the disease prediction. Beside extracting the attribute-value structure as a large-size medical concept, the mutual information between multiple medical concepts mentioned in the electronic medical records are taken into account in the graph construction. Meanwhile, the defined diseases and their associations with the medical concepts in the medical knowledge graph are incorporated into the graph, which provides the potentials to enhance the indicative impacts of the medical concepts directly related to a target disease. Then, the spatial and attention based graph encoders are employed to aggregate information from directly neighbor nodes to generate node embeddings as the compact features to be used for disease diagnosis. The approach itself is a general one that can utilized to build the predictive model using Chinese EMRs for different diseases. The empirical experiments for its performance evaluation are conducted on the real-world COPD EMR dataset. The comparison study results show that the proposed model outperforms baseline methods, which illustrates the effectiveness of our proposed model.

Discriminative model

Feature (linguistics)

Medical classification

10.1109/tcbb.2022.3198798

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Citations (9)

Study on the Real-Time PCR Reagent for Detection of Shigella

Xiandai shipin keji (2008)

Linna Zhao

According to the open reading frame of sequence of Shigella-ipaH gene,several couples of specific primer and probe were designed.The concentrations of primer and probe,Mg2+,Taq enzyme dosage,and reaction condition were optimized.This method was specific and sensitive,and will be widely used in areas of import and export inspection and quarantine,food safety and disease control.

Primer (cosmetics)

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Weakly Supervised Collaborative Learning for Airborne Pollen Segmentation and Classification from SEM Images

Life (2023)

Jianqiang Li Qinlan Xu Wenxiu Cheng Linna Zhao Suqin Liu

Existing pollen identification methods heavily rely on the scale and quality of pollen images. However, there are many impurities in real-world SEM images that should be considered. This paper proposes a collaborative learning method to jointly improve the performance of pollen segmentation and classification in a weakly supervised manner. It first locates pollen regions from the raw images based on the detection model. To improve the classification performance, we segmented the pollen grains through a pre-trained U-Net using unsupervised pollen contour features. The segmented pollen regions were fed into a deep convolutional neural network to obtain the activation maps, which were used to further refine the segmentation masks. In this way, both segmentation and classification models can be collaboratively trained, supervised by just pollen contour features and class-specific information. Extensive experiments on real-world datasets were conducted, and the results prove that our method effectively avoids impurity interference and improves pollen identification accuracy (86.6%) under the limited supervision (around 1000 images with image-level labels).

Identification

Supervised Learning

10.3390/life13010247

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Citations (2)

PEGylated lipid bilayer coated mesoporous silica nanoparticles co-delivery of paclitaxel and curcumin leads to increased tumor site drug accumulation and reduced tumor burden

European Journal of Pharmaceutical Sciences (2019)

Jiafeng Gao Kai Fan Yipeng Jin Linna Zhao Qian Wang

Nanocarriers

10.1016/j.ejps.2019.105070

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Citations (50)

Lack of Reinfection in Rhesus Macaques Infected with SARS-CoV-2

bioRxiv (Cold Spring Harbor Laboratory) (2020)

Linlin Bao Wei Deng Hong Gao Chong Xiao Jiayi Liu

Abstract A global pandemic of Corona Virus Disease 2019 (COVID-19) caused by severe acute respiratory syndrome CoV-2 (SARS-CoV-2) is ongoing spread. It remains unclear whether the convalescing patients have a risk of reinfection. Rhesus macaques were rechallenged with SARS-CoV-2 during an early recovery phase from initial infection characterized by weight loss, interstitial pneumonia and systemic viral dissemination mainly in respiratory and gastrointestinal tracts. The monkeys rechallenged with the identical SARS-CoV-2 strain have failed to produce detectable viral dissemination, clinical manifestations and histopathological changes. A notably enhanced neutralizing antibody response might contribute the protection of rhesus macaques from the reinfection by SARS-CoV-2. Our results indicated that primary SARS-CoV-2 infection protects from subsequent reinfection. One Sentence Summary Neutralizing antibodies against SARS-CoV-2 might protect rhesus macaques which have undergone an initial infection from reinfection during early recovery days.

Pandemic

Viral Pneumonia

Rhesus macaque

10.1101/2020.03.13.990226

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Citations (320)