Institution
Tianjin Medical University Cancer Institute and Hospital
Healthcare•Tianjin, China•
About: Tianjin Medical University Cancer Institute and Hospital is a healthcare organization based out in Tianjin, China. It is known for research contribution in the topics: Cancer & Breast cancer. The organization has 5352 authors who have published 3936 publications receiving 79440 citations.
Topics: Cancer, Breast cancer, Metastasis, Lung cancer, Cell growth
Papers published on a yearly basis
Papers
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TL;DR: It is demonstrated that miRNAs are present in the serum and plasma of humans and other animals such as mice, rats, bovine fetuses, calves, and horses, and can serve as potential biomarkers for the detection of various cancers and other diseases.
Abstract: Dysregulated expression of microRNAs (miRNAs) in various tissues has been associated with a variety of diseases, including cancers. Here we demonstrate that miRNAs are present in the serum and plasma of humans and other animals such as mice, rats, bovine fetuses, calves, and horses. The levels of miRNAs in serum are stable, reproducible, and consistent among individuals of the same species. Employing Solexa, we sequenced all serum miRNAs of healthy Chinese subjects and found over 100 and 91 serum miRNAs in male and female subjects, respectively. We also identified specific expression patterns of serum miRNAs for lung cancer, colorectal cancer, and diabetes, providing evidence that serum miRNAs contain fingerprints for various diseases. Two non-small cell lung cancer-specific serum miRNAs obtained by Solexa were further validated in an independent trial of 75 healthy donors and 152 cancer patients, using quantitative reverse transcription polymerase chain reaction assays. Through these analyses, we conclude that serum miRNAs can serve as potential biomarkers for the detection of various cancers and other diseases.
4,184 citations
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TL;DR: This study describes possible transmission of novel coronavirus disease 2019 (COVID-19) from an asymptomatic Wuhan resident to 5 family members in Anyang, a Chinese city in the neighboring province of Hubei.
Abstract: This study describes possible transmission of novel coronavirus disease 2019 (COVID-19) from an asymptomatic Wuhan resident to 5 family members in Anyang, a Chinese city in the neighboring province of Hubei.
3,818 citations
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TL;DR: It is reported that secreted miRNAs can serve as signaling molecules mediating intercellular communication and demonstrate that cells can secrete miRNA and deliver them into recipient cells where the exogenous mi RNAs can regulate target gene expression and recipient cell function.
1,122 citations
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TL;DR: It is shown that miR-105, which is characteristically expressed and secreted by metastatic breast cancer cells, is a potent regulator of migration through targeting the tight junction protein ZO-1.
1,106 citations
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TL;DR: The results demonstrate the proof-of-principle for using artificial intelligence to extract radiological features for timely and accurate COVID-19 diagnosis.
Abstract: Background The outbreak of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2) has caused more than 2.5 million cases of Corona Virus Disease (COVID-19) in the world so far, with that number continuing to grow. To control the spread of the disease, screening large numbers of suspected cases for appropriate quarantine and treatment is a priority. Pathogenic laboratory testing is the gold standard but is time-consuming with significant false negative results. Therefore, alternative diagnostic methods are urgently needed to combat the disease. Based on COVID-19 radiographical changes in CT images, we hypothesized that Artificial Intelligence’s deep learning methods might be able to extract COVID-19’s specific graphical features and provide a clinical diagnosis ahead of the pathogenic test, thus saving critical time for disease control. Methods and Findings We collected 1,065 CT images of pathogen-confirmed COVID-19 cases (325 images) along with those previously diagnosed with typical viral pneumonia (740 images). We modified the Inception transfer-learning model to establish the algorithm, followed by internal and external validation. The internal validation achieved a total accuracy of 89.5% with specificity of 0.88 and sensitivity of 0.87. The external testing dataset showed a total accuracy of 79.3% with specificity of 0.83 and sensitivity of 0.67. In addition, in 54 COVID-19 images that first two nucleic acid test results were negative, 46 were predicted as COVID-19 positive by the algorithm, with the accuracy of 85.2%. Conclusion These results demonstrate the proof-of-principle for using artificial intelligence to extract radiological features for timely and accurate COVID-19 diagnosis. Author summary To control the spread of the COVID-19, screening large numbers of suspected cases for appropriate quarantine and treatment measures is a priority. Pathogenic laboratory testing is the gold standard but is time-consuming with significant false negative results. Therefore, alternative diagnostic methods are urgently needed to combat the disease. We hypothesized that Artificial Intelligence’s deep learning methods might be able to extract COVID-19’s specific graphical features and provide a clinical diagnosis ahead of the pathogenic test, thus saving critical time. We collected 1,065 CT images of pathogen-confirmed COVID-19 cases along with those previously diagnosed with typical viral pneumonia. We modified the Inception transfer-learning model to establish the algorithm. The internal validation achieved a total accuracy of 89.5% with specificity of 0.88 and sensitivity of 0.87. The external testing dataset showed a total accuracy of 79.3% with specificity of 0.83 and sensitivity of 0.67. In addition, in 54 COVID-19 images that first two nucleic acid test results were negative, 46 were predicted as COVID-19 positive by the algorithm, with the accuracy of 85.2%. Our study represents the first study to apply artificial intelligence to CT images for effectively screening for COVID-19.
957 citations
Authors
Showing all 5402 results
Name | H-index | Papers | Citations |
---|---|---|---|
Xiang Zhang | 154 | 1733 | 117576 |
Xin Li | 114 | 2778 | 71389 |
Yan Zhang | 107 | 2410 | 57758 |
Bin Zhang | 99 | 435 | 55028 |
Xiang Gao | 92 | 1359 | 42047 |
Chen-Yu Zhang | 85 | 332 | 39724 |
Ke Zen | 71 | 254 | 22538 |
Xiaofeng Li | 68 | 702 | 20128 |
Jiali Han | 62 | 306 | 12775 |
Ning Zhang | 62 | 701 | 16494 |
Min Li | 59 | 310 | 12877 |
Li Jia | 51 | 279 | 9460 |
Thomas Liehr | 51 | 781 | 13221 |
Hong Zheng | 50 | 271 | 7735 |
Qiang Li | 50 | 514 | 10063 |