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With the wide application of deep learning technology in disease diagnosis, especially the outstanding performance of convolutional neural network (CNN) in computer vision and image processing, more and more studies have proposed to use this algorithm to achieve the classification of Alzheimer's disease (AD), mild cognitive impairment (MCI) and normal cognition (CN). This article systematically reviews the application progress of several classic convolutional neural network models in brain image analysis and diagnosis at different stages of Alzheimer's disease, and discusses the existing problems and gives the possible development directions in order to provide some references.Image-guided radiation therapy using magnetic resonance imaging (MRI) is a new technology that has been widely studied and developed in recent years. The technology combines the advantages of MRI imaging, and can offer online real-time tracking of tumor and adjacent organs at risk, as well as real-time optimization of radiotherapy plan. In order to provide a comprehensive understanding of this technology, and to grasp the international development and trends in this field, this paper reviews and summarizes related researches, so as to make the researchers and clinical personnel in this field to understand recent status of this technology, and carry out corresponding researches. This paper summarizes the advantages of MRI and the research progress of MRI linear accelerator (MR-Linac), online guidance, adaptive optimization, and dosimetry-related research. Possible development direction of these technologies in the future is also discussed. It is expected that this review can provide a certain reference value for clinician and related researchers to understand the research progress in the field.The cold chain safety of vaccines is a global issue. The electronic vaccine vial monitor (eVVM) label can monitor the temperature of vaccines in real time and provide "early warning" prompts. In order to comprehensively evaluate the monitoring efficiency of eVVM, this study selected 75 eVVM labels and distributed them with a total of 600 vaccine vial monitor (VVM) labels of four different types in different experimental environment (2-8℃, -20℃ and 40℃), and used a temperature recorder as "gold standard". The results showed that the accuracy of the eVVM labels and VVM labels in high temperature environment was as same as that of the temperature recorder ( P = 0.195). The accuracy of low temperature anomalies report and high temperature anomalies report of eVVM labels was 100%, which was better than those reported by VVM labels. Therefore, eVVM labels have high monitoring accuracy, which is suitable not only for ordinary environments, but also for severe temperature environments. It should be helpful for the improvement of the efficiency and accuracy of cold chain monitoring.The anterior cruciate ligament (ACL) reconstruction mostly relies on the experience of surgeons. To improve the effectiveness and adaptability of the tension after ACL reconstruction in knee joint rehabilitation, this paper establishes a lateral force measurement model with relaxation characteristics and designs an on-line stiffness measurement system of ACL. In this paper, we selected 20 sheep knee joints as experimental material for the knee joint stability test before the ACL reconstruction operation, which were divided into two groups for a comparative test of single-bundle ACL reconstruction through the anterolateral approach. The first group of surgeons carried out intraoperative detection with routine procedures. The second group used ACL on-line stiffness measurement system for intraoperative detection. After that, the above two groups were tested for postoperative stability. The study results show that the tension accuracy is (- 2.3 ± 0.04)%, and the displacement error is (1.5 ± 1.8)%. The forward stability, internal rotation stability, and external rotation stability of the two groups were better than those before operation ( P 0.05). The system established in this paper is expected to help clinicians judge the ACL reconstruction tension in the operation process and effectively improve the surgical effect.Auscultation of heart sounds is an important method for the diagnosis of heart conditions. For most people, the audible component of heart sound are the first heart sound (S1) and the second heart sound (S2). Different diseases usually generate murmurs at different stages in a cardiac cycle. Segmenting the heart sounds precisely is the prerequisite for diagnosis. S1 and S2 emerges at the beginning of systole and diastole, respectively. Locating S1 and S2 accurately is beneficial for the segmentation of heart sounds. This paper proposed a method to classify the S1 and S2 based on their properties, and did not take use of the duration of systole and diastole. S1 and S2 in the training dataset were transformed to spectra by short-time Fourier transform and be feed to the two-stream convolutional neural network. The classification accuracy of the test dataset was as high as 91.135%. The highest sensitivity and specificity were 91.156% and 92.074%, respectively. Extracting the features of the input signals artificially can be avoid with the method proposed in this article. The calculation is not complicated, which makes this method effective for distinguishing S1 and S2 in real time.As a novel technology, wearable physiological parameter monitoring technology represents the future of monitoring technology. However, there are still many problems in the application of this kind of technology. In this paper, a pilot study was conducted to evaluate the quality of electrocardiogram (ECG) signals of the wearable physiological monitoring system (SensEcho-5B). Firstly, an evaluation algorithm of ECG signal quality was developed based on template matching method, which was used for automatic and quantitative evaluation of ECG signals. The algorithm performance was tested on a randomly selected 100 h dataset of ECG signals from 100 subjects (15 healthy subjects and 85 patients with cardiovascular diseases). On this basis, 24-hour ECG data of 30 subjects (7 healthy subjects and 23 patients with cardiovascular diseases) were collected synchronously by SensEcho-5B and ECG Holter. The evaluation algorithm was used to evaluate the quality of ECG signals recorded synchronously by the two systems. Algorithm validation results sensitivity was 100%, specificity was 99.51%, and accuracy was 99.99%. Results of controlled test of 30 subjects the median (Q1, Q3) of ECG signal detected by SensEcho-5B with poor signal quality time was 8.93 (0.84, 32.53) minutes, and the median (Q1, Q3) of ECG signal detected by Holter with poor signal quality time was 14.75 (4.39, 35.98) minutes (Rank sum test, P=0.133). The results show that the ECG signal quality algorithm proposed in this paper can effectively evaluate the ECG signal quality of the wearable physiological monitoring system. Compared with signal measured by Holter, the ECG signal measured by SensEcho-5B has the same ECG signal quality. Follow-up studies will further collect physiological data of large samples in real clinical environment, analyze and evaluate the quality of ECG signals, so as to continuously optimize the performance of the monitoring system.Human chromosomes karyotyping is an important means to diagnose genetic diseases. Chromosome image type recognition is a key step in the karyotyping process. Accurate and efficient identification is of great significance for automatic chromosome karyotyping. In this paper, we propose a model named segmentally recalibrated dense convolutional network (SR-DenseNet). In each stage of the model, the dense connected network layers is used to extract the features of different abstract levels of chromosomes automatically, and then the concatenation of all the layers which extract different local features is recalibrated with squeeze-and-excitation (SE) block. SE blocks explicitly construct learnable structures for importance of the features. Then a model fusion method is proposed and an expert group of chromosome recognition models is constructed. On the public available Copenhagen chromosome recognition dataset (G-bands) the proposed model achieves error rate of only 1.60%, and with model fusion the error further drops to 0.99%. On the Padova chromosome dataset (Q-bands) the model gets the corresponding error rate of 6.67%, and with model fusion the error further drops to 5.98%. The experimental results show that the method proposed in this paper is effective and has the potential to realize the automation of chromosome type recognition.The emergence of regular short repetitive palindromic sequence clusters (CRISPR) and CRISPR- associated proteins 9 (Cas9) gene editing technology has greatly promoted the wide application of genetically modified pigs. Efficient single guide RNA (sgRNA) is the key to the success of gene editing using CRISPR/Cas9 technology. For large animals with a long reproductive cycle, such as pigs, it is necessary to screen out efficient sgRNA in vitro to avoid wasting time and resource costs before animal experiments. In addition, how to efficiently obtain positive gene editing monoclonal cells is a difficult problem to be solved. In this study, a rapid sgRNA screening method targeting the pig genome was established and we rapidly obtained Fah gene edited cells, laying a foundation for the subsequent production of Fah knockout pigs as human hepatocyte bioreactor. At the same time, the method of obtaining monoclonal cells using pattern microarray culture technology was explored.Subject recruitment is a key component that affects the progress and results of clinical trials, and generally conducted with eligibility criteria (includes inclusion criteria and exclusion criteria). The semantic category analysis of eligibility criteria can help optimizing clinical trials design and building automated patient recruitment system. This study explored the automatic semantic categories classification of Chinese eligibility criteria based on artificial intelligence by academic shared task. We totally collected 38 341 annotated eligibility criteria sentences and predefined 44 semantic categories. A total of 75 teams participated in competition, with 27 teams having submitted system outputs. Based on the results, we found out that most teams adopted mixed models. The mainstream resolution was applying pre-trained language models capable of providing rich semantic representation, which were combined with neural network models and used to fine-tune the models with reference to classifier tasks, and finally improved classification performance could be obtained by ensemble modeling. The best-performing system achieved a macro F1 score of 0.81 by using a pre-trained language model, i.e. bidirectional encoder representations from transformers (BERT) and ensemble modeling. With the error analysis we found out that from the point of data processing steps the data pre-processing and post-processing were very important for classification, while from the point of data volume these categories with less data volume showed lower classification performance. Finally, we hope that this study could provide a valuable dataset and state-of-the-art result for the research of Chinese medical short text classification.