ObjectiveTo summarize current patient-derived organoids as preclinical cancer models, and its potential clinical application prospects. MethodsCurrent patient-derived organoids as preclinical cancer models were reviewed according to the results searched from PubMed database. In addition, how cancer-derived human tumor organoids of pancreatic cancer could facilitate the precision cancer medicine were discussed. ResultsThe cancer-derived human tumor organoids show great promise as a tool for precision medicine of pancreatic cancer, with potential applications for oncogene modeling, gene discovery and chemosensitivity studies. ConclusionThe cancer-derived human tumor organoids can be used as a tool for precision medicine of pancreatic cancer.
Tuberculosis (TB) is one of the major public health concerns worldwide. Since the development of precision medicine, the filed regarding TB control and prevention has been brought into the era of precision medicine. Although great progress has been achieved in the accurate diagnosis, treatment and management of TB patients, we have to face several challenges. We should seize the opportunity, and develop and improve novel measures in TB prevention on the basis of precision medicine. The accurate diagnosis criteria, treatment regimen and management of TB patients should be carried out according to the standard of precision medicine. We aim to improve the treatment of TB patients and prevent the transmission of TB in the community, thereby contributing to the achievement of the End TB Strategy by 2035.
ObjectiveTo review the progress of radiomics in the field of colorectal cancer in recent years and summarize its value in the imaging diagnosis of colorectal cancer.MethodsEighty English and seven Chinese articles were retrieved through PUBMED, OVID, CNKI, Weipu and Wanfang. The structure and content of these literatures were classified and analyzed.ResultsIn five studies predicting the preoperative stages of colorectal cancer based on CT radiomics, the area under curve (AUC) ranged from 0.736 to 0.817; in two studies predicting the preoperative stages of colorectal cancer based on MRI radiomics, the AUC were 0.87 and 0.827 respectively. In two studies about radiomics analysis for evaluation of pathological complete response to neoadjuvant chemoradiotherapy based on CT, the AUC were 0.79 and 0.72 respectively; in four studies about radiomics analysis for evaluation of pathological complete response to neoadjuvant chemoradiotherapy based on MRI, the AUC ranged from 0.84 to 0.979. In one study evaluating the sensitivity of neoadjuvant chemotherapy based on MRI radiomics, the AUC was 0.79. In one study predicting the postoperative survival rate based on MRI radiomics, the AUC value of the final model was 0.827. In one study, the accuracy of the model based on PET/CT radiomics in 4-year disease-free survival (DSS), progression-free survival (DFS) and overall survival (OS) were 0.87, 0.79 and 0.79 respectively.ConclusionAt present, radiomics has a valuable impact on preoperative staging, neoadjuvant therapy evaluation, and survival analysis of colorectal cancer.
Lung cancer is a most common malignant tumor of the lung and is the cancer with the highest morbidity and mortality worldwide. For patients with advanced non-small cell lung cancer who have undergone epidermal growth factor receptor (EGFR) gene mutations, targeted drugs can be used for targeted therapy. There are many methods for detecting EGFR gene mutations, but each method has its own advantages and disadvantages. This study aims to predict the risk of EGFR gene mutation by exploring the association between the histological features of the whole slides pathology of non-small cell lung cancer hematoxylin-eosin (HE) staining and the patient's EGFR mutant gene. The experimental results show that the area under the curve (AUC) of the EGFR gene mutation risk prediction model proposed in this paper reached 72.4% on the test set, and the accuracy rate was 70.8%, which reveals the close relationship between histomorphological features and EGFR gene mutations in the whole slides pathological images of non-small cell lung cancer. In this paper, the molecular phenotypes were analyzed from the scale of the whole slides pathological images, and the combination of pathology and molecular omics was used to establish the EGFR gene mutation risk prediction model, revealing the correlation between the whole slides pathological images and EGFR gene mutation risk. It could provide a promising research direction for this field.
In recent years, deep learning has provided a new method for cancer prognosis analysis. The literatures related to the application of deep learning in the prognosis of cancer are summarized and their advantages and disadvantages are analyzed, which can be provided for in-depth research. Based on this, this paper systematically reviewed the latest research progress of deep learning in the construction of cancer prognosis model, and made an analysis on the strengths and weaknesses of relevant methods. Firstly, the construction idea and performance evaluation index of deep learning cancer prognosis model were clarified. Secondly, the basic network structure was introduced, and the data type, data amount, and specific network structures and their merits and demerits were discussed. Then, the mainstream method of establishing deep learning cancer prognosis model was verified and the experimental results were analyzed. Finally, the challenges and future research directions in this field were summarized and expected. Compared with the previous models, the deep learning cancer prognosis model can better improve the prognosis prediction ability of cancer patients. In the future, we should continue to explore the research of deep learning in cancer recurrence rate, cancer treatment program and drug efficacy evaluation, and fully explore the application value and potential of deep learning in cancer prognosis model, so as to establish an efficient and accurate cancer prognosis model and realize the goal of precision medicine.
This article introduces the exploration and establishment of “grass-roots Party building + targeted poverty alleviation” model by the Party Branch of Emergency Department of West China Hospital of Sichuan University, and discusses how to establish the “trinity mode” of management support, personnel training, and on-site guidance under the leading of grass-roots Party building through a series of the branches combined activities, according to the core idea of “strengthening the Party construction, bringing people closer together, and promoting development”. The aim is to form a long-term mechanism of grass-roots Party building and targeted medical poverty alleviation through continuously implementing this model, which can benefit more people in remote and ethnic minority areas and contribute to “Healthy China 2030”.
The "All of Us" research program is a research project supported by the National Institutes of Health. By recruiting over 1 million volunteers residing in the United States, the project builds a strong research resource to promote the exploration of biological, clinical, social, and environmental determinants of health and disease. This paper introduced the design plan of the "All of Us" research program systematically and provided information that can be used for the construction of a million natural population cohort of precision medicine in China.
Retinitis pigmentosa (RP) is an inherited retinal disease characterized by degeneration of retinal pigment epithelial cells. Precision medicine is a new medical model that applies modern genetic technology, combining living environment, clinical data of patients, molecular imaging technology and bio-information technology to achieve accurate diagnosis and treatment, and establish personalized disease prevention and treatment model. At present, precise diagnosis of RP is mainly based on next-generation sequencing technology and preimplantation genetic diagnosis, while precise therapy is mainly reflected in gene therapy, stem cell transplantation and gene-stem cell therapy. Although the current research on precision medicine for RP has achieved remarkable results, there are still many problems in the application process that is needed close attention. For instance, the current gene therapy cannot completely treat dominant or advanced genetic diseases, the safety of gene editing technology has not been solved, the cells after stem cell transplantation cannot be effectively integrated with the host, gene sequencing has not been fully popularized, and the big data information platform is imperfect. It is believed that with the in-depth research of gene sequencing technology, regenerative medicine and the successful development of clinical trials, the precision medicine for RP will be gradually improved and is expected to be applied to improve the vision of patients with RP in the future.
Mixed reality technology is new digital holographic imaging technology that generates three-dimensional simulation images through computers and anchors the virtual images to the real world. Compared with traditional imaging diagnosis and treatment methods, mixed reality technology is more conducive to the advantages of precision medicine, helps to promote the development of medical clinical application, teaching and scientific research in the field of orthopedics, and will further promote the progress of clinical orthopedics toward standardization, digitization and precision. This article briefly introduces the mixed reality technology, reviews its application in the perioperative period, teaching and diagnosis and treatment standardization and dataization in the field of orthopedics, and discusses its technical advantages, aiming to provide a reference for the better use of mixed reality technology in orthopedics.