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Supervisor: Ministry of Education of the People's Republic of China
Sponsor: Sichuan University
Host unit: Editorial Board of Journal of Sichuan University (Medical Science Edition)
Editor-in-Chief: Zhang Lin
CN 51-1644/R
ISSN 1672-173X
Postal code: 62-72
Establishment Time: 1959
Address: 17,Section 3,Renmin Nanlu,Chengdu,Sichuan,610041,People's Republic of China
Tel: 028-85501320
Email: scuxbyxb@scu.edu.cn
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2021, 52(2): 153-155.
doi: 10.12182/20210360206
Abstract
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Abstract:
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.
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.
2021, 52(2): 156-161.
doi: 10.12182/20210360101
Abstract:
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.
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.
2021, 52(2): 162-165.
doi: 10.12182/20210360102
Abstract:
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.
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.
2021, 52(2): 166-169.
doi: 10.12182/20210360501
Abstract:
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.
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.
2021, 52(2): 170-175.
doi: 10.12182/20210360207
Abstract:
At present, bacterial infections are mainly treated with antibiotics, but new treatment methods are urgently needed because of growing problems with antibiotic resistance. Therefore, phage therapy will be a potential solution to the problem of bacterial drug resistance, and the combined use of bacteriophage and antibiotics is also considered a potential treatment option. However, there has not been any well-designed clinical controlled trials on phage therapy. More future research needs to be done to solve the problems of phage therapy, for example, its narrow antibacterial spectrum, the uncertainty regarding treatment safety, and the bacterial resistance. Some refractory diseases such as breast cancer and alcoholic hepatitis are difficult to treat clinically. The successful experimental research on bacteriophages reported in these fields provides new ideas of treatment for more refractory diseases in the future. In addition, bacteriophages also showed promising performance in vaccine applications and osteanagenesis. 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.
At present, bacterial infections are mainly treated with antibiotics, but new treatment methods are urgently needed because of growing problems with antibiotic resistance. Therefore, phage therapy will be a potential solution to the problem of bacterial drug resistance, and the combined use of bacteriophage and antibiotics is also considered a potential treatment option. However, there has not been any well-designed clinical controlled trials on phage therapy. More future research needs to be done to solve the problems of phage therapy, for example, its narrow antibacterial spectrum, the uncertainty regarding treatment safety, and the bacterial resistance. Some refractory diseases such as breast cancer and alcoholic hepatitis are difficult to treat clinically. The successful experimental research on bacteriophages reported in these fields provides new ideas of treatment for more refractory diseases in the future. In addition, bacteriophages also showed promising performance in vaccine applications and osteanagenesis. 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.
2021, 52(2): 176-181.
doi: 10.12182/20210360503
Abstract:
Cellular senescence is a permanent state of cell cycle arrest, combined with the acquisition of a variety of secretory phenotypes. In addition to apoptosis, the induction of cellular senescence is an important mechanism that chemo- and radiotherapies and some targeted therapies rely on to produce an anti-tumor effect. However, being a self-protective mechanism of cells, cellular senescence can produce both positive and negative effects in tumor treatment. It remains a challenge to effectively utilize the anti-tumor effect of cellular senescence while averting the pro-tumor effect. How to improve the sensitivity of tumor treatment and to prevent tumor recurrence and metastasis has become the bottleneck in cellular senescence research. We summarize in this review the “double-edged-sword” effect of cellular senescence in tumor treatment. We summarize and discuss the cell autonomous and non-autonomous mechanisms that senescent cells use to affect tumor treatment, hoping to provide information that will help improve the outcome of tumor treatment and promote further research in basic and clinical application of cellular senescence in tumor treatment.
Cellular senescence is a permanent state of cell cycle arrest, combined with the acquisition of a variety of secretory phenotypes. In addition to apoptosis, the induction of cellular senescence is an important mechanism that chemo- and radiotherapies and some targeted therapies rely on to produce an anti-tumor effect. However, being a self-protective mechanism of cells, cellular senescence can produce both positive and negative effects in tumor treatment. It remains a challenge to effectively utilize the anti-tumor effect of cellular senescence while averting the pro-tumor effect. How to improve the sensitivity of tumor treatment and to prevent tumor recurrence and metastasis has become the bottleneck in cellular senescence research. We summarize in this review the “double-edged-sword” effect of cellular senescence in tumor treatment. We summarize and discuss the cell autonomous and non-autonomous mechanisms that senescent cells use to affect tumor treatment, hoping to provide information that will help improve the outcome of tumor treatment and promote further research in basic and clinical application of cellular senescence in tumor treatment.
2021, 52(2): 182-187.
doi: 10.12182/20210260301
Abstract:
According to Healthy China, a national strategy of the Government of China, new requirements were put forward for high-quality medical education, high-level surgical research, and precise clinical diagnosis and treatment. In the context of Emerging Medical Discipline, a strategic blueprint of medical education in China, this paper reviews the concept and core value of virtual reality (VR) and its significant role in the medical industry. On that basis, we explore the role of VR technology in medical training against the background of Emerging Medicine Discipline. Furthermore, typical cases are presented to help analyze and illustrate in detail the important role of VR technology in the teaching and training of stomatological and clinical procedures, skills assessment, online self-directed training, and clinical thinking skills training. We herein summarize useful information from past experience so as to help build innovative models of medical education in the context of Emerging Medical Discipline.
According to Healthy China, a national strategy of the Government of China, new requirements were put forward for high-quality medical education, high-level surgical research, and precise clinical diagnosis and treatment. In the context of Emerging Medical Discipline, a strategic blueprint of medical education in China, this paper reviews the concept and core value of virtual reality (VR) and its significant role in the medical industry. On that basis, we explore the role of VR technology in medical training against the background of Emerging Medicine Discipline. Furthermore, typical cases are presented to help analyze and illustrate in detail the important role of VR technology in the teaching and training of stomatological and clinical procedures, skills assessment, online self-directed training, and clinical thinking skills training. We herein summarize useful information from past experience so as to help build innovative models of medical education in the context of Emerging Medical Discipline.
Abstract:
Objective To analyze the correlation between connexin 43 (Cx43) and the expression of P16 and P21, aging-related proteins, and to investigate the possible role of Cx43 in the development of cell senescence with an aging model prepared by D-galactose (D-gal) intervention in the vascular smooth muscle cells (VSMCs) of guinea pig spiral modiolar artery (SMA). Methods The VSMCs of guinea pig SMA were cultured with the adhesion method, and the markers of VSMCs were detected with immunofluorescence technique. The experiment has a control group, a D-gal group, and a group that received D-gal and gap junction agonist AAP10 intervention, hereafter referred to as the AAP10 group. Cell Counting Kit-8 (CCK-8) was used to check VSMC activity and to determine the concentration and duration of D-gal intervention. The mRNA expression of Cx43 in each group was checked with qRT-PCR. The expression of Cx43, P16 and P21 proteins in each group was examined with the Western blot. The expression and distribution of P16 and P21 proteins were examined with immunofluorescence assay. Results Immunofluorescence results showed that the positive expression rate of cell actin (α-SM-actin) was over 90%. CCK-8 results showed that the optimal concentration of D-gal intervention was 30 mg/mL and the intervention duration was 48 h. qRT-PCR test showed that the mRNA expression of Cx43 in VSMCs in the D-gal group was significantly lower than that in the control group (P<0.01), while it is higher in the AAP10 group than that of the D-gal group (P<0.01); Western blot assay showed that the Cx43 expression level in VSMCs in the D-gal group was significantly lower than that in the control group (P<0.01) and the expression of P16 and P21 was significantly higher than that in the control group (P<0.01), the expression of Cx43 protein in AAP10 group was significantly up-regulated compared with that in the D-gal group (P<0.01), while the expression of P16 and P21 was down-regulated significantly (P<0.01); The results of immunofluorescence showed that P16 and P21 were mainly expressed in the cell nucleus. Semi-quantitative analysis of fluorescence intensity showed that the level of P16 and P21 protein in the D-gal group was significantly higher than that in the control group, and the fluorescence intensity of AAP10 group was significantly lower than that in the D-gal group (P<0.01). Conclusion Up-regulation of Cx43 expression can reverse the D-gal-induced abnormal expression of P16 and P21, two aging-related proteins, in SMA. It is suggested that Cx43 on SMA may be involved in D-gal-induced cell senescence, which provides a theoretical basis and possible intervention target for the delay of cell senescence.
Abstract:
Objective To construct eukaryotic and prokaryotic recombinant vectors containing Pepck-Gp63 and to achieve protein expression by selecting the dominant epitope genes of Pepck and Gp63 of Leishmania infantum. Methods The secondary structure and HLA epitopes of phosphoenolpyruvate carboxylase (PEPCK) were predicted by in silico analysis, and the dominant epitopes were picked out. According to the analysis results of glycoprotein of 63×103 (GP63) epitopes identified by the same method in our laboratory, the dominant epitope genes of Pepck and Gp63 were used to construct pET32a-Pepck-Gp63 and pVAX1-Pepck-Gp63 by overlapping PCR and enzyme reaction. Then, for protein expression, the prokaryotic vectors were transfected into E.coil while the eukaryotic vectors were transfected into NIH3T3 cells by liposome transfection. Results There were multiple dominant epitopes in Pepck and there were Pepck-Gp63 sequences in the polyclonal site of expression vector. The expression of Pepck-Gp63 in E.coil appeared in inclusion form and led to 74 kDa band in SDS-PAGE. The immunofluorescence results of NIH3T3 cells transfected by pVAX1-Pepck-Gp63 were positive. Conclusion The recombinant prokaryotic expression plasmids pET32a-Pepck-Gp63
2021, 52(2): 202-206.
doi: 10.12182/20210160506
Abstract:
Objective To examine the infectivity of human adenovirus type 55 (HAdV-55) in human intestinal cells. Methods Caco-2 cells were cultured in vitro, and infected with HAdV-3, 7, 14 and 55. The expression of viral proteins in infected cells was detected with immunofluorescence method. The intracellular and supernatant viral DNA levels were determined with fluorescent quantitative PCR at different points of time. The level of infectious virus particles in the supernatant of Caco-2 cells was determined with adenovirus sensitive HEp-2 infection assay. Results Immunofluorescence assay showed positive result for the expression of HAdV-55 virus protein in Caco-2 cells 48 h post infection. HAdV-3, 7, 14, and 55 showed sustained replication and proliferation in Caco-2 cells. The level of viral DNA in infected cells and the supernatant increased with the infection time, and the viral DNA level of HAdV-55 was significantly higher than those of HAdV-3, 7 and 14. The infectious virus particles of HAdV-55 in Caco-2 supernatant were more than those of HAdV-3, 7 and 14, showing statistically significant difference (P<0.05). Caco-2 cells were infected with low doses of virus (1×TCID50), and the cytopathic effect (CPE) of HAdV-55 infection wells was more significant than that of HAdV-3, 7 and 14 infection wells. Conclusion This study found that human intestinal cells were susceptible to HAdV-55, and the infection level was higher than that of other common respiratory infections caused by adenovirus types 3, 7 and 14.
