ObjectiveTo determine the diagnostic accuracy of telemedicine in various clinical levels of diabetic retinopathy (DR) and diabetic macular oedema (DME).MethodsPubMed, EMBASE and Cochrane databases were searched for telemedicine and DR. The methodological quality of included studies was evaluated using the Quality Assessment for Diagnostic Accuracy Studies (QUADAS-2). Measures of sensitivity, specificity and other variables were pooled using a random effects model. Summary receiver operating characteristic curves were used to estimate overall test performance. Meta-regression and subgroup analyses were used to identify sources of heterogeneity. Publication bias was evaluated using Stata V.12.0.ResultsTwenty articles involving 1960 participants were included. Pooled sensitivity of telemedicine exceeded 80% in detecting the absence of DR, low- or high-risk proliferative diabetic retinopathy (PDR), it exceeded 70% in detecting mild or moderate non-proliferative diabetic retinopathy (NPDR), DME and clinically significant macular oedema (CSME) and was 53% (95% CI 45% to 62%) in detecting severe NPDR. Pooled specificity of telemedicine exceeded 90%, except in the detection of mild NPDR which reached 89% (95% CI 88% to 91%). Diagnostic accuracy was higher with digital images obtained through mydriasis than through non-mydriasis, and was highest when a wide angle (100–200°) was used compared with a narrower angle (45–60°, 30° or 35°) in detecting the absence of DR and the presence of mild NPDR. No potential publication bias was detected.ConclusionsThe diagnostic accuracy of telemedicine using digital imaging in DR is overall high. It can be used widely for DR screening. Telemedicine based on the digital imaging technique that combines mydriasis with a wide angle field (100–200°) is the best choice in detecting the absence of DR and the presence of mild NPDR.
Aims/IntroductionBesides the aging population in China, the following have become serious public health problems: increasing urban population, lifestyle changes and diabetes. We assessed the epidemiology of type 2 diabetes mellitus in China between 2000 and 2014, and analyzed time trends to better determine the prevalence status of diabetes in China and to provide a basis for prevention and decision‐making.Materials and MethodsIn our systematic review, we searched China National Knowledge Infrastructure, Chinese VIP Information, Wanfang and PubMed databases for studies on type 2 diabetes mellitus between 2000 and 2014 in China. Two investigators extracted the data and assessed the quality of the included literature independently. We excluded studies that did not use 1999 World Health Organization criteria for diabetes. We also excluded reviews and viewpoints, studies with insufficient data, studies that were not carried out in mainland China and studies on troops, community, schools or physical examination people. We used stata 12.0 to combine the prevalence of all studies, calculated the pooled prevalence and its 95% confidence interval, and analyzed the differences among men/women, urban/rural areas and year of study. We calculated the prevalence of seven geographic areas of China, respectively, and mapped the distribution in the whole country to estimate the pooled prevalence of each area.ResultsOur search returned 4,572 studies, 77 of which satisfied the inclusion criteria. The included studies had a total of 1,287,251 participants, in which 680,574 cases of type 2 diabetes mellitus were recorded. The overall prevalence (9.1%) has been increasing since the 1970s, and it increased rapidly with age. The prevalence of the 65–74 years group was as high as 14.1%. Meanwhile, the prevalence among men/women and urban/rural areas was significantly different. The prevalence was 9.9% for men and 11.6% for women, which were significantly higher than the average at the end of the last century and the beginning of this century. The prevalence rate in urban areas (11.4%) was significantly higher than that in rural areas and in urban‐rural fringe areas, and the prevalence in rural areas (8.2%) was slightly higher than that in urban‐rural fringe areas (7.5%). In addition, the prevalence in each geographic area were estimated and mapped, which showed a large imbalance in the map.ConclusionsOur analysis suggested that type 2 diabetes mellitus is highly prevalent in China. These results underscore the urgent need for the government to vigorously strengthen the management of diabetes prevention and control.
Immune checkpoint inhibitors (ICIs) exert the antitumor efficacy depending on immune response, which is affected by sex difference, where both biological and sociological factors are involved. The role of sex in ICI trials has been overlooked. How sex correlates with ICI efficacy is incompletely understood. Clinical trials evaluating ICI versus other therapies in male and female patients were included. The hazard ratio (HR) and 95% confidence interval (CI) of overall survival (OS) and progression-free survival (PFS) were used. Six thousand and ninety-six patients from 11 trials were included. More improvement of OS was observed in males (HR, 0.62; 95% CI, 0.53-0.71; p < 0.001) treated with ICI versus controls than females (HR, 0.74; 95% CI, 0.65-0.84; p < 0.001). ICIs improved PFS more in males (HR, 0.57; 95% CI, 0.43-0.71; p < 0.001) than females (HR, 0.71; 95% CI, 0.52-0.91; p < 0.001). The sex difference had more effect on the overall survival in melanoma patients versus NSCLC patients. Overall survival of patients treated with CTLA-4 inhibitor was more influenced by sex variable compared with PD-1 inhibitors. A significant sex-related efficacy difference was observed between female and male melanoma patients. Although male patients had longer OS and PFS than females when treated with ICIs versus controls, the difference was not significant. Sex difference should be more considered in future clinical trials, guidelines and clinical practice.
Recent findings demonstrate that aberrant downregulation of the iron-exporter protein, ferroportin (FPN1), is associated with poor prognosis and osteoclast differentiation in multiple myeloma (MM). Here, we show that FPN1 was downregulated in MM and that clustered regularly interspaced short palindromic repeat (CRISPR)-mediated FPN1 knockout promoted MM cell growth and survival. Using a microRNA target-scan algorithm, we identified miR-17-5p as an FPN1 regulator that promoted cell proliferation and cell cycle progression, and inhibited apoptosis—both in vitro and in vivo. miR-17-5p inhibited retarded tumor growth in a MM xenograft model. Moreover, restoring FPN1 expression at least partially abrogated the biological effects of miR-17-5p in MM cells. The cellular iron concentration regulated the expression of the iron-regulatory protein (IRP) via the 5′-untranslated region of IRP messenger RNA and modulated the post-transcriptional stability of FPN1. Bioinformatics analysis with subsequent chromatin immunoprecipitation-polymerase chain reaction and luciferase activity experiments revealed that the transcription factor Nrf2 drove FPN1 transcription through promoter binding and suppressed miR-17-5p (which also increased FPN1 expression). Nrf2-mediated FPN1 downregulation promoted intracellular iron accumulation and reactive oxygen species. Our study links FPN1 transcriptional and post-transcriptional regulation with MM cell growth and survival, and validates the prognostic value of FPN1 and its utility as a novel therapeutic target in MM.
The automated classification of heart sounds plays a significant role in the diagnosis of cardiovascular diseases (CVDs). With the recent introduction of medical big data and artificial intelligence technology, there has been an increased focus on the development of deep learning approaches for heart sound classification. However, despite significant achievements in this field, there are still limitations due to insufficient data, inefficient training, and the unavailability of effective models. With the aim of improving the accuracy of heart sounds classification, an in-depth systematic review and an analysis of existing deep learning methods were performed in the present study, with an emphasis on the convolutional neural network (CNN) and recurrent neural network (RNN) methods developed over the last five years. This paper also discusses the challenges and expected future trends in the application of deep learning to heart sounds classification with the objective of providing an essential reference for further study.
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