Vaccines and antibodies are currently effective intervention strategies for preventing and treating COVID-19. Angiotensin-converting enzyme 2 (ACE2), a primary binding receptor for SARS-CoV-2, is a potential target for the prevention and treatment of COVID-19. At present, therapeutics based on the strategies that block the binding of SARS-CoV-2 Spike protein to the ACE2 receptor have entered clinical trials. On December 5, 2022, the journal Nature published the results of a pharmacological intervention to downregulate ACE2 expression for prevention of SARS-CoV-2 infections. The study found that a nuclear receptor, farnesoid X receptor for bile acids, regulated ACE2 expression. The clinical drug ursodeoxycholic acid that was used to treat liver diseases could downregulate ACE2 expression and prevent SARS-CoV-2 infections, showing a new potential pathway in managing COVID-19.
In recent years, with the rapid development of machine learning techniques,the deep learning algorithm has been widely used in one-dimensional physiological signal processing. In this paper we used electroencephalography (EEG) signals based on deep belief network (DBN) model in open source frameworks of deep learning to identify emotional state (positive, negative and neutrals), then the results of DBN were compared with support vector machine (SVM). The EEG signals were collected from the subjects who were under different emotional stimuli, and DBN and SVM were adopted to identify the EEG signals with changes of different characteristics and different frequency bands. We found that the average accuracy of differential entropy (DE) feature by DBN is 89.12%±6.54%, which has a better performance than previous research based on the same data set. At the same time, the classification effects of DBN are better than the results from traditional SVM (the average classification accuracy of 84.2%±9.24%) and its accuracy and stability have a better trend. In three experiments with different time points, single subject can achieve the consistent results of classification by using DBN (the mean standard deviation is1.44%), and the experimental results show that the system has steady performance and good repeatability. According to our research, the characteristic of DE has a better classification result than other characteristics. Furthermore, the Beta band and the Gamma band in the emotional recognition model have higher classification accuracy. To sum up, the performances of classifiers have a promotion by using the deep learning algorithm, which has a reference for establishing a more accurate system of emotional recognition. Meanwhile, we can trace through the results of recognition to find out the brain regions and frequency band that are related to the emotions, which can help us to understand the emotional mechanism better. This study has a high academic value and practical significance, so further investigation still needs to be done.
With the development of image-guided surgery and radiotherapy, the demand for medical image registration is stronger and the challenge is greater. In recent years, deep learning, especially deep convolution neural networks, has made excellent achievements in medical image processing, and its research in registration has developed rapidly. In this paper, the research progress of medical image registration based on deep learning at home and abroad is reviewed according to the category of technical methods, which include similarity measurement with an iterative optimization strategy, direct estimation of transform parameters, etc. Then, the challenge of deep learning in medical image registration is analyzed, and the possible solutions and open research are proposed.
In the field of artificial intelligence (AI) medical imaging, data annotation is a key factor in all AI development. In the traditional manual annotation process, there are prominent problems such as difficult data acquisition, high manual labor intensity, strong professionalism and low labeling quality. Therefore, an intelligent multimodal medical image annotation system is urgently needed to meet the requirements of labeling. Based on the image cloud, West China Hospital of Sichuan University collected the multimodal image data of hospital and allied hospitals, and designed a multi-modal image annotation system through information technology, which integrated various image processing algorithms and AI models to simplify the image data annotation. With the construction of annotation system, the efficiency of data labeling in the hospitals is improved, which provides necessary data support for the AI image research and related industry construction in the hospital, so as to promote the implementation of artificial intelligence industry related to medical images in the hospital.
Objective To evaluate the osteogenesis of bi phasic ceramic-l ike biologic bone (BCBB) with tissue engineering in repairing segmental bone defects. Methods BMSCs isolated from the femoral and tibial marrow of 2-weekold Japanese white rabbit were cultured to passage 3. Then 20 μL of the cell suspension at a concentration of 1 × 107 cells/mLwere seeded into 15 mm × 15 mm × 5 mm BCBB block; the construction of tissue engineered BCBB was completed after 8 days of compound culture. Forty-eight adult Japanese white rabbits were randomly divided into groups A, B, C and D, then BCBBs cultured with BMSCs in vitro for 8 days (group A) and only BCBBs without BMSCs (group B) were respectively implanted into the radius segmental bone defects of rabbits, autogenous il iac bone graft (group C) and empty defect (group D) were used as controls. The specimens were examined after 4, 8, 12 and 24 weeks, the osteogenesis was evaluated through X-ray radiograph and histology examination. Results X-ray examination: the border between the material and host’s bone was clear after 4 weeks, and blurred after 8 weeks in group A and group B; the density of some part of the edge of the material was similar to that of radius and there was high density imaging in the materials of group A after 12 weeks; there was much high density imaging in the materials of group B after 12 weeks. The medullary cavity of bone was formed and l ittle high density imaging in the materials of group A after 24 weeks. Some high density imaging still existed in the materials of group B after 24 weeks. The X-ray evaluated scores showed that the scores of group A was higher than that of group B, and there was significant difference between group A and group B after 12 and 24 weeks (P lt; 0.05). Histological examination: there was new bone formation in the materials and also new bone grew adhesively on the surface of BCBB in group A. While in group B only new bone grew and attached to the surface of BCBB. BCBB degraded more with the time and more new bone formed. The histological evaluation showed that the bone forming area in group A was more than that in group B, and there was significant difference between group A and group B (P lt; 0.05). Conclusion The osteogenesis of BCBB with tissue engineering was superior to only BCBB, BCBB could be used as a scaffold of bone tissue engineering.
Objective To investigate the influencing factors for the clinical remission of advanced esophageal squamous cell carcinoma (ESCC) after neoadjuvant chemotherapy, establish an individualized nomogram model to predict the clinical remission of advanced ESCC with neoadjuvant chemotherapy and evaluate its efficacy, providing serve for the preoperative adjuvant treatment of ESCC.Methods The clinical data of patients with esophageal cancer who underwent neoadjuvant chemotherapy (nedaplatin 80 mg/m2, day 3+docetaxel 75 mg/m2, day 1, 2 cycles, 21 days per cycle interval) in the Department of Thoracic Surgery, Affiliated Hospital of North Sichuan Medical College from February 2016 to August 2020 were analyzed retrospectively. According to the WHO criteria for efficacy assessment of solid tumors, tumors were divided into complete remission (CR), partial remission (PR), stable disease (SD) and progressive disease (PD). CR and PR were defined as effective neoadjuvant chemotherapy, and SD and PD were defined as ineffective neoadjuvant chemotherapy. Univariate and multivariate analyses were used to analyze the influencing factors for the short-term efficacy of neoadjuvant chemotherapy. The R software was used to establish a nomogram model for predicting the clinical remission of advanced ESCC with neoadjuvant chemotherapy, and Bootstrap method for internal verification of the model. C-index, calibration curve and receiver operating characteristic (ROC) curve were used to evaluate the predictive performance of the nomogram.Results Finally 115 patients were enrolled, including 93 males and 22 females, aged 40-75 (64.0±8.0) years. After receiving docetaxel+nedaplatin neoadjuvant chemotherapy for 2 cycles, there were 9 patients with CR, 56 patients with PR, 43 patients with SD and 7 patients with PD. Among them, chemotherapy was effective (CR+PR) in 65 patients and ineffective (SD+PD) in 50 patients, with the clinical effective rate of about 56.5% (65/115). Univariate analysis showed that there were statistical differences in smoking history, alcoholism history, tumor location, tumor differentiation degree, and cN stage before chemotherapy between the effective neoadjuvant chemotherapy group and the ineffective neoadjuvant chemotherapy group (P<0.05). Logistic regression analysis showed that low-differentiation advanced ESCC had the worst clinical response to neoadjuvant chemotherapy, moderately-highly differentiated ESCC responded better (P<0.05). Stage cN0 advanced ESCC responded better to neoadjuvant chemotherapy than stage cN1 and cN2 (P<0.05). The C-index and the area under the ROC curve of the nomogram were both 0.763 (95%CI 0.676-0.850), the calibration curve fit well, the best critical value of the nomogram calculated by the Youden index was 70.04 points, and the sensitivity and specificity of the critical value were 80.0% and 58.0%, respectively.ConclusionThe established clinical prediction model has good discrimination and accuracy, and can provide a reference for individualized analysis of the clinical remission of advanced ESCC with neoadjuvant chemotherapy and the screening of new adjuvant treatment subjects.
Resectable non-small cell lung cancer (NSCLC) is prone to recurrence and metastasis after simple surgery. Although patients can benefit from preoperative neoadjuvant chemotherapy and postoperative adjuvant chemotherapy, the 5-year survival rate is not significantly improved. In recent years, with the rise of immunotherapy, NSCLC immunotherapy has gradually received attention. Many explorations have been made on resectable NSCLC immunotherapy, and satisfactory results have been obtained. With the release of multiple phase 3 research results, a new chapter in resectable NSCLC immunotherapy has officially opened. However, there are still many problems in the immunotherapy of resectable NSCLC. This article reviews the current relevant research and provides reference for clinical application.
This paper describes a simulation of microwave brain imaging for the detection of hemorrhagic stroke. Firstly, in the research process, the formula of DebyeⅡwas used to study tissues of brain and blood clot so that microwave frequency band was confirmed for imaging. Then a model with electromagnetic characteristics of brain was built on this basis. In addition, an ultra-wideband (UWB) Vivaldi antenna is designed to use for transmitting and receiving microwave signals of widths 1.7 GHz to 4 GHz. Microwave signals were transmitted and received when the antenna revolved around the brain model. Symmetric position de-noising method was used to eliminate the strong background noise signals, and finally confocal imaging method was applied to get brain imaging. Blood clot was distinguished clearly from result of imaging and position error was less than 1 cm.
ObjectiveTo understand the obstacles in the practice of pulmonary rehabilitation between doctors and patients.MethodsMedical staff and patients with chronic obstructive pulmonary disease (COPD) in public hospitals in this region were randomly sampled, and a questionnaire survey was conducted on possible obstacles to the practice of pulmonary rehabilitation.ResultsTotal of 265 medical staff and 120 COPD patients were recruited in this survey. The obstacles of pulmonary rehabilitation practice of medical staff in clinical work are poor cooperation of patients and their families (84.2%), medical staff’s insufficient awareness of pulmonary rehabilitation (82.3%), and lack of practice and guidelines (78.9%), lack of objective conditions such as site, equipment and equipment (75.1%), lack of multidisciplinary teams (74.3%), pulmonary rehabilitation has fallen by the wayside (73.6%) etc. The main obstacles for COPD patients in pulmonary rehabilitation are lack of access to relevant knowledge (52.4%), insufficient knowledge (36.5%), inconvenient transportation, economic problems and other objective conditions (33.3%). After multiple correspondence analysis, there are differences in the degree of correlation between obstacle factors and groups with different characteristics.ConclusionsThere are many factors hindering the development of pulmonary rehabilitation and there are certain differences among different populations, but the lack of understanding of pulmonary rehabilitation between doctors and patients is the primary problem. It is necessary to improve the cognition of both doctors and patients on pulmonary rehabilitation, and then to solve the obstacles in the implementation of pulmonary rehabilitation.
ObjectiveTo prepare platelet-derived growth factor receptor β (PDGFRβ)-targeted near-infrared molecular probe and evaluate its potential in optical molecular imaging of lung cancer.MethodsPDGFRβ-specific affibody Z-tri was recombinantly expressed in Escherichia coli (E. coli) and purified using affinity chromatography. In vitro cell-binding of Z-tri was analyzed by flow cytometry. Cellular distribution of Z-tri in tumor grafts was determined by protein-tracing. The molecular probe CF750-Z-tri was prepared by conjugating near-infrared fluorescent dye CF750 to Z-tri. The optical images of xenografts of lung cancer were obtained by using CF750-Z-tri combined with optical imaging system.ResultsPDGFRβ-specific affibody Z-tri was highly expressed in E. coli and purified to homogeneity. Z-tri could bind PDGFRβ-positive cells but not PDGFRβ-negative cells cultured in vitro. In the tumor xenografts of human lung cancer, intravenously injected Z-tri was predominantly distributed on cells overexpressing PDGFRβ. The near infrared fluorescent dye CF750 was efficiently conjugated to Z-tri. Optical images with high contrast of lung cancer xenografts were produced by using the near-infrared fluorescent probe CF750-Z-tri combined with optical imaging system.ConclusionThe near-infrared fluorescent probe CF750-Z-tri can be used for optical imaging of human lung cancer, which takes great potential in optical imaging-guided surgery of lung cancer.