Current liver fibrosis scoring by computer-assisted image analytics is not fully automated as it requires manual preprocessing (segmentation and feature extraction) typically based on domain knowledge in liver pathology. Deep learning-based algorithms can potentially classify these images without the need for preprocessing through learning from a large dataset of images. We investigated the performance of classification models built using a deep learning-based algorithm pre-trained using multiple sources of images to score liver fibrosis and compared them against conventional non-deep learning-based algorithms - artificial neural networks (ANN), multinomial logistic regression (MLR), support vector machines (SVM) and random forests (RF). Automated feature classification and fibrosis scoring were achieved by using a transfer learning-based deep learning network, AlexNet-Convolutional Neural Networks (CNN), with balanced area under receiver operating characteristic (AUROC) values of up to 0.85–0.95 versus ANN (AUROC of up to 0.87–1.00), MLR (AUROC of up to 0.73–1.00), SVM (AUROC of up to 0.69–0.99) and RF (AUROC of up to 0.94–0.99). Results indicate that a deep learning-based algorithm with transfer learning enables the construction of a fully automated and accurate prediction model for scoring liver fibrosis stages that is comparable to other conventional non-deep learning-based algorithms that are not fully automated.

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Bile canaliculi expand and contract in response to the amount of secreted bile, and resistance from the surrounding actin bundles. Further expansion due to bile duct blockade leads to the formation of inward blebs, which carry away excess bile to prevent bile build up in the canaliculi.

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Integrated circuits (ICs) and optoelectronic chips are the foundation stones of the modern information society. The IC industry has been driven by the so-called "Moore's law" in the past 60 years, and now has entered the post Moore's law era. In this paper, we review the recent progress of ICs and optoelectronic chips. The research status, technical challenges and development trend of devices, chips and integrated technologies of typical IC and optoelectronic chips are focused on. The main contents include the development law of IC and optoelectronic chip technology, the IC design and processing technology, emerging memory and chip architecture, brain-like chip structure and its mechanism, heterogeneous integration, quantum chip technology, silicon photonics chip technology, integrated microwave photonic chip, and optoelectronic hybrid integrated chip.

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Objective: To explore potential risk factors of isolated diastolic hypertension (IDH) among young and middle-aged Chinese. Methods: A community-based cross-sectional study was conducted among 338 subjects, aged 25 years and above, using random sampling technique. There were 68 cases of IDH, 46 cases of isolated systolic hypertension (ISH), 89 cases of systolic and diastolic hypertension (SDH), and 135 of subjects with normal blood pressure. Cases and controls were matched on sex by frequency matching. Demographic characteristics, blood pressure and other relevant information were collected.Results: Compared with controls, patients with IDH and ISH had significant higher level of triglyceride, high density lipoprotein, blood glucose and body mass index (BMI) (p < 0.05); while patients with SDH had significantly higher level of total cholesterol, triglyceride, glucose and BMI (p < 0.05). Linear mixed effects model showed that drinking tea, family history of hypertension (FHH), higher blood glucose, triglyceride and low density lipoprotein were related with elevated diastolic blood pressure (DBP) (p < 0.01); HFH, blood glucose, creatinine and BMI have positive effect on systolic blood pressure (SBP) (p < 0.05). Conclusions: Drinking tea, FHH, high levels of triglyceride, high density lipoprotein, blood glucose and BMI are associated with IDH among young and middle-aged Chinese.