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Quan Zhang,1,2 Zhiang Liu,3 Jiaxu Li,4 Guohua Liu1,2 1College of Electronic Information and Optical Engineering, Nankai University, Tianjin 300350, People’s Republic of China; 2Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network Technology, Nankai University, Tianjin 300350, People’s Republic of China; 3College of Computer Science, Nankai University, Tianjin 300350, People’s Republic of China; 4The Second Affiliated Hospital of Harbin Medical University, Department of Plastic and Cosmetic Surgery, Harbin, Heilongjiang, 150081, People’s Republic of ChinaCorrespondence: Guohua Liu Tel +86-22-85358565Email liugh@nankai.edu.cnPurpose: Diabetic Macular Edema has been one of the research hotspots all over the world. But as the global population continues to grow, the number of OCT images requiring manual analysis is becoming increasingly unaffordable. Medical images are often fuzzy due to the inherent physical processes of acquiring them. It is difficult for traditional algorithms to use low-quality data. And traditional algorithms usually only provide diagnostic results, which makes the reliability and interpretability of the model face challenges. To solve problem above, we proposed a more intuitive and robust diagnosis model with self-enhancement ability and clinical triage patients’ ability.Methods: We used 38,057 OCT images (Drusen, DME, CNV and Normal) to establish and evaluate the model. All data are OCT images of fundus retina. There were 37,457 samples in the training dataset and 600 samples in the validation dataset. In order to diagnose these images accurately, we propose a multiscale transfer learning algorithm. Firstly, the sample is sent to the automatic self-enhancement module for edge detection and enhancement. Then, the processed data are sent to the image diagnosis module to determine the disease type. This process makes more data more effective and can be accurately classified. Finally, we calculated the accuracy, precision, sensitivity and specificity of the model, and verified the performance of the model from the perspective of clinical application.Results: The model proposed in this paper can provide the diagnosis results and display the detection targets more intuitively. The model reached 94.5% accuracy, 97.2% precision, 97.7% sensitivity and 97% specificity in the independent testing dataset.Conclusion: Comparing the performance of relevant work and ablation test, our model achieved relatively good performance. It is proved that the model proposed in this paper has a stronger ability to recognize diseases even in the face of low-quality images. Experiment results also demonstrate its clinical referral capability. It can reduce the workload of medical staff and save the precious time of patients.Keywords: optical coherence tomography, clinical triage, self-reinforcing, assisted diagnostics, deep learning, diabetic macular edema, retina diseases

Yuliang Liu,1,* Quan Zhang,1,* Geng Zhao,2,* Guohua Liu,3,4 Zhiang Liu5 1College of Electronic Information and Automation, Tianjin University of Science and Technology, Tianjin 300222, People’s Republic of China; 2Tianjin Medical University Hospital for Metabolic Disease, Tianjin 300134, People’s Republic of China; 3College of Electronic Information and Optical Engineering, Nankai University, Tianjin 300350, People’s Republic of China; 4Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network Technology, Nankai University, Tianjin 300350, People’s Republic of China; 5School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, People’s Republic of China*These authors contributed equally to this workCorrespondence: Yuliang Liu; Guohua Liu Email ylliu@tust.edu.cn; liugh@nankai.edu.cnIntroduction: The research of auxiliary diagnosis has always been one of the hotspots in the world. The implementation of auxiliary diagnosis support algorithm for medical text data faces challenges with interpretability and creditability. The improvement of clinical diagnostic techniques means not only the improvement of diagnostic accuracy but also the further study of diagnostic basis. Traditional research methods for diagnostic markers often require a large amount of time and economic costs. Research objects are often dozens of samples, and it is, therefore, difficult to synthesize large amounts of data. Therefore, the comprehensiveness and reliability of traditional methods have yet to be improved. Therefore, the establishment of a model that can automatically diagnose diseases and automatically provide a diagnostic basis at the same time has a positive effect on the improvement of medical diagnostic techniques.Methods: Here, we established an auxiliary diagnostic tool based on attention deep learning algorithm to diagnostic hyperlipemia and automatically predict the corresponding diagnostic markers using hematological parameters. In this paper, we not only demonstrated the ability of the proposed model to automatically diagnose diseases using text-based medical data, such as physiological parameters, but also demonstrated its ability to forecast disease diagnostic markers. Human physiological parameters are used as input to the model, and the doctor’s diagnosis results as an output. Through the attention layer, the degree of attention of the model to different physiological parameters can be obtained, that is, the model provides a diagnostic basis.Results: It achieved 94% ACC, 97.48% AUC, 96% sensitivity and 92% specificity with the test dataset. All the above samples are drawn from clinical practice. Moreover, the model predicted the diagnostic markers of hyperlipidemia by the attention mechanism, and the results were fully agreeable to the golden criteria.Discussion: The auxiliary diagnosis system proposed in this paper not only achieves the accurate and robust performance, and can be used for the preliminary diagnosis of patients, but also showing its great potential to discover new diagnostic markers. Therefore, it not only can improve the efficiency of clinical diagnosis but also shorten the research period of researching a diagnosis basis to an extent. It has a positive significance to the development of the medical diagnosis level.Keywords: automatic predictive diagnostic markers, automatic diagnosis, attention mechanism, hyperlipemia, artificial intelligence

miR-135b is expressed abnormally in various tumors and plays an important role in the occurrence and development of tumors bydifferent pathways. The role of miR-135b in osteosarcoma (OS) and its mechanisms were uncertain. The study aimed to clarify the role of miR-135b in osteosarcoma (OS) cells and explore the effect of miR-135b inhibitor on the biological characteristics of OS cells. Firstly, Compared with adjacent bone tissues, the expression of miR-135b was increased and inversely correlated with potential target-PPM1A mRNA, while the expression of PPM1A was decreased in OS tissues. Dual luciferase reporter assay was used to verify that PPM1A was a target gene of miR-135b and RT-PCR and western blot were used to detect that miR-135b negatively regulated expression of PPM1A in MG63 and U2OS cell lines. After transfection, miR-135b inhibitor significantly inhibited cell proliferation and invasion both in MG63 and U2OS cells and blocked cell cycle in the G2/M phase and induced cells apoptosis, while PPM1A knockdown abolished the inhibition of miR-135b inhibitor on the proliferation and invasion of OS cells. In conclusion, miR-135b is up-regulated in OS cells and down regulating miR-135b expression could inhibit the proliferation and invasion of OS cells by up-regulating PPM1A. miR-135b might be a new therapeutic target of OS.