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We herein summarize the existing weaknesses of phage therapy and its application prospects in treating systemic diseases, hoping to promote further clinical application research of phage therapy.The incidence of gastric cancer is the highest among all kinds of malignant tumors in China. Because gastric cancer is very hard to identify in its early stage, the early diagnosis rate of gastric cancer in China is relatively low. At present, the pathological diagnosis of gastric cancer mainly depends on the diagnosis of pathologists. However, the gradual improvement of people's living standards and the growing demand for medical and health care have exacerbated the shortage of medical resources, which has become a even more serious problem. Therefore, there is an urgent need for new technologies to help deal with this challenge. In recent years, with the rapid development of artificial intelligence (AI) and digital pathology, AI-aided pathological diagnosis based on convolutional neural network (CNN) as the core technology is showing promises for improving the diagnostic efficiency of gastric cancer. It is also of great significance for the early diagnosis and treatment of the disease and the reduction of its high incidence and mortality. We herein summarize the application and progress of deep-learning CNN in pathological diagnosis of gastric cancer, as well as the existing problems and prospects of future development.One of the most important application of artificial intelligence (AI) in pathology is prediction, using morphological features, of patient prognosis and response to specific treatments. As one of the most common kinds of malignancies in the world and the crucial important cause of death due to malignant tumor among women, breast cancer has become the center of attention in clinical services. Axillary lymph node metastasis is an important prognostic factor in breast cancer. The accuracy of the assessment of axillary lymph node metastasis bears heavily on clinical diagnosis and treatment. At present, based on the principle of non-invasive procedures, many studies have been done to develop models that can be used to predict sentinel lymph node metastasis of breast cancer. However, different clinical and pathological parameters are used in these predictive models. How to analyze the clinical and pathological data of breast cancer patients in a more comprehensive way and how to establish a prediction model with better precision have become the future direction of development. In this paper, we describe the research progress of AI in pathology and the current status of its use in breast cancer research. We have conducted in-depth reflection and looked into the future of ways to predict effectively breast cancer lymph node metastasis and to establish more accurate and effective deep-learning algorithm based on AI assistance so as to continuously improve the diagnosis and treatment of breast cancer.In recent years, with the progress of image processing and network transmission technology, digital pathology (DP) is being more and more extensive applied in clinical practice, and new artificial-intelligence-assisted diagnosis technology based on digital imaging is emerging. Being a widely-used mature field, telepathology is changing the temporal and spatial scope of pathological diagnosis through remote electronic transmission of digital images. Fully digitized pathology departments are realizing the transformation of diagnostic modes and workflow from microscopic diagnosis to digital image computer review, and there have already been successful examples of large-scale fully digitized pathology departments. However, there are still many problems in the implementation of DP, for example, the quality stability and cost of the scanner, the validation of the system, the reengineering of the workflow, the training of pathologists and the change of their perception of DP, which all await further improvement. Although artificial intelligence diagnostic technology is showing great potential, its application in pathological work is still limited to the field of auxiliary diagnostics, and there is still a long way to go to the realization of comprehensive intelligent pathology. The rise of DP will bring about a profound change in the way of how pathological work is done and become a solid foundation for intelligent pathology.Precision pathological diagnosis plays a vital role in precision medicine. Both the limited resources available to pathologists and the incessant demands for further refinement and quantification of clinical diagnosis are posing new challenges for pathologists to meet the needs for precision pathological diagnosis. It is expected that artificial intelligence (AI) will be the powerful tool that will help find solutions to this problem from different angles. The author of this article elaborated on a number of ways in which AI can help promote precision pathological diagnosis, including AI-assisted precision extraction of tissue samples, AI-assisted precision histopathologic diagnosis, AI-assisted histological grading and quantitative scoring, AI-assisted precision assessment of tumor biomarkers, AI-assisted prediction of molecular features and precision interpretation of biological information based on hematoxylin-eosin (HE) stained images, the realization of in-depth precision diagnosis based on AI-assisted information integration, and AI-assisted accurate prediction of patient survival and prognosis based on HE-stained images. The paper presents to the readers the future of smart pathology that AI will help usher in.

To (i) introduce the deformed complex vertebral osteotomy (DCVO) technique for the treatment of severe congenital angular spinal kyphosis; (ii) evaluate the sagittal correction efficacy of the DCVO technique; and (iii) discuss the advantages and limitations of the DCVO technique.

Multiple malformed vertebrae were considered a malformed complex, and large-range and angle wedge osteotomy was performed within the complex using the DCVO technique. Patients with local kyphosis greater than 80° who were treated with DCVO and did not have tumors, infections, or a history of surgery were included. A retrospective case study was performed in these patients with severe angular kyphosis who underwent the DCVO technique from 2008 to 2016. Avexitide Demographic data, the operating time, and the volume of intraoperative blood loss were collected. Spinopelvic parameters (pelvic incidence [PI], pelvic tilt [PT], and sacral slope [SS]), local and global sagittal parameters (deformity angle, thoracic kyphosis [TK], and lumbar lordosis [LL]), visual analog scale (VAS) score, and Oswestry disability index (ODI) score were recorded pre- and postoperatively.