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Objective To understand the application and research progress of Mendelian randomization (MR) studies related to gastric cancer and provide a scientific basis for gastric cancer prevention. Methods Published studies on risk factors of gastric cancer based on MR methods were searched in PubMed, Web of Science, EMBASE, CNKI, and WANFANG DATA from the establishment of each database to November 19th, 2022. Two researchers examined the eligibility of studies, extracted key information, and assessed the research quality independently. Results A total of 30 publications published from 2016 to 2022 were included in the study, and 20 were judged to be of high quality. These studies examined the relationship between behaviors and lifestyle factors, anthropometric characteristics, indicators of biological exposure, and other pathological conditions and gastric cancer, and the results suggest potential causal associations between smoking and other factors and the risk of gastric cancer. Conclusion Previous MR studies extensively investigated the causal association between internal and external exposures or traits and gastric cancer and provided positive evidence of gastric cancer etiology. However, MR studies may be subject to methodological limitations. Interpretation of results needs to be approached with caution, which necessitates the integration with biological plausibility and evidence from observation studies.
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ObjectiveTo investigate the current trend of non-drug therapy for gait abnormality of Parkinson's disease. MethodsThe clinical trials about non-drug therapy for gait abnormality of Parkinson's disease were retrieved from the clinical trial registration platform ClinicalTrials.gov, from inception to October 25th, 2022. The records were screened by two researchers independently, and the registration time, registration count, sample sizes, interventions, primary outcome measurements and study design, etc., were summarized and analyzed, according to the PICOS principle. ResultsA total of 218 eligible records were included. The registration count increased in recently years. Almost all of the trials (93.6%) were with a relatively small sample size less than 100, mainly 21 to 30 cases. The major intervention approach was the neuromodulation technique, however, virtual reality and robot-assisted gait training were coming to use in recent years. The primary outcome measurements were the clinical scales, the questionnaires and the exercise examinations. Randomized parallel controlled trials were the most (111, 50.9%). ConclusionThe number of non-drug therapy for gait abnormality of Parkinson's disease increased year by year. The new technologies such as virtual reality and robot-assisted gait training may be used more in the future.
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Objective@#To investigate the factors affecting the depressive symptoms among patients with colorectal cancer in Chengdu City, so as to provide insights into mental health improvement among colorectal cancer patients.@*Methods@#Patients with colorectal cancer were enrolled from three hospitals in Chengdu City using the convenient sampling method. The basic information, life styles and cancer diagnosis and therapy were collected, and the depressive symptoms were evaluated using the Zung Self-Rating Depression Scale ( SDS ). In addition, factors affecting the depressive symptoms were identified using the multivariable logistic regression analysis.@*Results@#A total of 384 patients with colorectal cancer were enrolled, including 247 men ( 64.32% ), and the subjects had a mean age of ( 57.47±11.39 ) years and a mean SDS score of 37.73±9.15. The detection rate of depressive symptoms was 15.36%. Multivariable logistic regression analysis showed that colorectal cancer patients with a history of radiotherapy had a high risk of developing depressive symptoms ( OR=0.468, 95%CI: 0.245-0.895 ), while patients with alcohol consumption ( OR=0.407, 95%CI: 0.172-0.963 ) and drinking tea ( OR=0.470, 95%CI: 0.244-0.904 ) had a low risk of developing depressive symptoms.@*Conclusions@#The detection of depressive symptoms is 15.36% among patients with colorectal cancer in Chengdu City, and a history of radiotherapy, alcohol consumption and drinking tea affect the development of depressive symptoms among patients with colorectal cancer.
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With the rapid development of network structure, convolutional neural networks (CNN) consolidated its position as a leading machine learning tool in the field of image analysis. Therefore, semantic segmentation based on CNN has also become a key high-level task in medical image understanding. This paper reviews the research progress on CNN-based semantic segmentation in the field of medical image. A variety of classical semantic segmentation methods are reviewed, whose contributions and significance are highlighted. On this basis, their applications in the segmentation of some major physiological and pathological anatomical structures are further summarized and discussed. Finally, the open challenges and potential development direction of semantic segmentation based on CNN in the area of medical image are discussed.
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Diffuse large B-cell lymphoma (DLBCL) is the most common subtype of adult lymphoma and is a group of invasive and heterogeneous diseases. Although rituximab in combination with CHOP regimen (R-CHOP) for DLBCL is better, one third of patients have relapsed/refractory conditions. DLBCL is divided into many subtypes due to its high heterogeneity. Different histological types have different response to treatment. High-risk DLBCL has little effect on R-CHOP treatment. How to further improve the first-line cure rate of high-risk DLBCL has become an important challenge in the field of lymphoma treatment. Currently in the era of precision medicine, in recent years, many new targeted drugs, such as immunosuppressive agents, mammalian target of rapamycin (mTOR) receptor inhibitors and Bruton tyrosine kinase (BTK) inhibitors, have been developed for DLBCL-related pathways and molecular targets, provide more new possibilities for the treatment of DLBCL.
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Diffuse large B-cell lymphoma (DLBCL) is the most common subtype of adult lymphoma and is a group of invasive and heterogeneous diseases.Although rituximab in combination with CHOP regimen (R-CHOP) for DLBCL is better,one third of patients have relapsed/refractory conditions.DLBCL is divided into many subtypes due to its high heterogeneity.Different histological types have different response to treatment.High-risk DLBCL has little effect on R-CHOP treatment.How to further improve the first-line cure rate of high-risk DLBCL has become an important challenge in the field of lymphoma treatment.Currently in the era of precision medicine,in recent years,many new targeted drugs,such as immunosuppressive agents,mammalian target of rapamycin (mTOR) receptor inhibitors and Bruton tyrosine kinase (BTK)inhibitors,have been developed for DLBCL-related pathways and molecular targets,provide more new possibilities for the treatment of DLBCL.
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The human brain deteriorates as we age, and the rate and the trajectories of these changes significantly vary among brain regions and among individuals. Because neuroimaging data are potentially important indicators of individual's brain health, they are commonly used in brain age prediction. In this review, we summarize brain age prediction model from neuroimaging-based studies in the last ten years. The studies are categorized based on their image modalities and feature types. The results indicate that the prediction frameworks based on neuroimaging holds promise toward individualized brain age prediction. Finally, we addressed the challenges in brain age prediction and suggested some future research directions.
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Humans , Aging , Brain , Diagnostic Imaging , Physiology , NeuroimagingABSTRACT
Computer-aided diagnosis based on computed tomography (CT) image can realize the detection and classification of pulmonary nodules, and improve the survival rate of early lung cancer, which has important clinical significance. In recent years, with the rapid development of medical big data and artificial intelligence technology, the auxiliary diagnosis of lung cancer based on deep learning has gradually become one of the most active research directions in this field. In order to promote the deep learning in the detection and classification of pulmonary nodules, we reviewed the research progress in this field based on the relevant literatures published at domestic and overseas in recent years. This paper begins with a brief introduction of two widely used lung CT image databases: lung image database consortium and image database resource initiative (LIDC-IDRI) and Data Science Bowl 2017. Then, the detection and classification of pulmonary nodules based on different network structures are introduced in detail. Finally, some problems of deep learning in lung CT image nodule detection and classification are discussed and conclusions are given. The development prospect is also forecasted, which provides reference for future application research in this field.
Subject(s)
Humans , Deep Learning , Lung Neoplasms , Diagnostic Imaging , Radiographic Image Interpretation, Computer-Assisted , Reproducibility of Results , Solitary Pulmonary Nodule , Diagnostic Imaging , Tomography, X-Ray ComputedABSTRACT
Globally, colorectal cancer (CRC) is a substantial public health burden, and it is increasingly affecting populations in Asian countries. The overall prevalence of CRC is reported to be low in Asia when compared with that in Western nations, yet it had the highest number of prevalent cases. This review described the prevalence of CRC in Asia according to the International Agency for Research on Cancer from World Health Organization (WHO) database and summarized its major risk factors. Non-modifiable factors include genetic factors, ethnicity, age, gender, family history and body height; smoking, alcohol drinking, weight, Westernized diet, physical inactivity, chronic diseases and microbiota were involved in environmental factors. These risk factors were separately discussed in this review according to published literature from Asian countries. CRC screening has been playing an important role in reducing its disease burden. Some recommendations on its screening practices have been formulated in guidelines for Asia Pacific countries.