Using artificial intelligence (AI) to prevent and treat diseases is an ultimate goal in computational medicine. Although AI has been developed for screening and assisted decision-making in disease ...prevention and management, it has not yet been validated for systematic application in the clinic. In the context of rare diseases, the main strategy has been to build specialized care centres; however, these centres are scattered and their coverage is insufficient, which leaves a large proportion of rare-disease patients with inadequate care. Here, we show that an AI agent using deep learning, and involving convolutional neural networks for diagnostics, risk stratification and treatment suggestions, accurately diagnoses and provides treatment decisions for congenital cataracts in an in silico test, in a website-based study, in a ‘finding a needle in a haystack’ test and in a multihospital clinical trial. We also show that the AI agent and individual ophthalmologists perform equally well. Moreover, we have integrated the AI agent with a cloud-based platform for multihospital collaboration, designed to improve disease management for the benefit of patients with rare diseases.An artificial intelligence agent integrated with a cloud-based platform for multihospital collaboration performs equally as well as ophthalmologists in the diagnosis of congenital cataracts in a series of online tests and a multihospital clinical trial.
ObjectivesDepression and depressive symptoms are common mental disorders that have a considerable effect on patients’ health-related quality of life and satisfaction with medical care, but the ...prevalence of these conditions varies substantially between published studies. The aim of this study is to conduct a systematic review and meta-analysis to provide a precise estimate of the prevalence of depression or depressive symptoms among outpatients in different clinical specialties.DesignSystematic review and meta-analysis.Data sources and eligibility criteriaThe PubMed and PsycINFO, EMBASE and Cochrane Library databases were searched to identify observational studies that contained information on the prevalence of depression and depressive symptoms in outpatients. All studies included were published before January 2016. Data characteristics were extracted independently by two investigators. The point prevalence of depression or depressive symptoms was measured using validated self-report questionnaires or structured interviews. Assessments were pooled using a random-effects model. Differences in study-level characteristics were estimated by meta-regression analysis. Heterogeneity was assessed using standard χ2 tests and the I2 statistic. The study protocol has been registered with PROSPERO under number CRD42017054738.ResultsEighty-three cross-sectional studies involving 41 344 individuals were included in this study. The overall pooled prevalence of depression or depressive symptoms was 27.0% (10 943/41 344 individuals; 95% CI 24.0% to 29.0%), with significant heterogeneity between studies (p<0.0001, τ2=0.3742, I2=96.7%). Notably, a significantly higher prevalence of depression and depressive symptoms was observed in outpatients than in the healthy controls (OR 3.16, 95% CI 2.66 to 3.76, I2=72.0%, χ 2 =25.33). The highest depression/depressive symptom prevalence estimates occurred in studies of outpatients from otolaryngology clinics (53.0%), followed by dermatology clinics (39.0%) and neurology clinics (35.0%). Subgroup analyses showed that the prevalence of depression and depressive symptoms in different specialties varied from 17.0% to 53.0%. The prevalence of depression and depressive symptoms was higher among outpatients in developing countries than in outpatients from developed countries. Moreover, the prevalence of depression and depressive symptoms in outpatients slightly decreased from 1996 to 2010. Regarding screening instruments, the Beck Depression Inventory led to a higher estimate of the prevalence of depression and depressive symptoms (1316/4702, 36.0%, 95% CI 29.0% to 44.0%, I2=94.8%) than the Hospital Anxiety and Depression Scale (1003/2025, 22.0%, 95% CI 12.0% to 35.0%, I2=96.6%).ConclusionOur study provides evidence that a significant proportion of outpatients experience depression or depressive symptoms, highlighting the importance of developing effective management strategies for the early identification and treatment of these conditions among outpatients in clinical practice. The substantial heterogeneity between studies was not fully explained by the variables examined.
In a typical intracellular electroanalytical measurement, a nanoelectrode is located inside a living cell and a reference electrode outside the cell. This setup faces a problem to drop a certain ...potential across the cellular plasma membrane that might interrupt the cellular activity. To solve this problem, a self-referenced nanopipette is assembled by incorporating a reference electrode inside the nanocapillary, with a Pt ring at the tip as the electrochemical surface. The potential applied between the Pt ring and the reference electrode is restricted inside the capillary and thus has a negligible effect on the surrounding cellular environment. Using this new setup, the nanopipette pierces into the nucleus of a single living cell for the measurement of hydrogen peroxide under oxidative stress. It is found that a lesser amount of hydrogen peroxide is measured in the nucleus compared with the cytoplasm, revealing uneven oxidative stress inside the cell. The result will not only greatly improve the current setup for intracellular electrochemical analysis but also provide biological information of the compartment inside the living cell.
Electronic medical records provide large-scale real-world clinical data for use in developing clinical decision systems. However, sophisticated methodology and analytical skills are required to ...handle the large-scale datasets necessary for the optimisation of prediction accuracy. Myopia is a common cause of vision loss. Current approaches to control myopia progression are effective but have significant side effects. Therefore, identifying those at greatest risk who should undergo targeted therapy is of great clinical importance. The objective of this study was to apply big data and machine learning technology to develop an algorithm that can predict the onset of high myopia, at specific future time points, among Chinese school-aged children.
Real-world clinical refraction data were derived from electronic medical record systems in 8 ophthalmic centres from January 1, 2005, to December 30, 2015. The variables of age, spherical equivalent (SE), and annual progression rate were used to develop an algorithm to predict SE and onset of high myopia (SE ≤ -6.0 dioptres) up to 10 years in the future. Random forest machine learning was used for algorithm training and validation. Electronic medical records from the Zhongshan Ophthalmic Centre (a major tertiary ophthalmic centre in China) were used as the training set. Ten-fold cross-validation and out-of-bag (OOB) methods were applied for internal validation. The remaining 7 independent datasets were used for external validation. Two population-based datasets, which had no participant overlap with the ophthalmic-centre-based datasets, were used for multi-resource validation testing. The main outcomes and measures were the area under the curve (AUC) values for predicting the onset of high myopia over 10 years and the presence of high myopia at 18 years of age. In total, 687,063 multiple visit records (≥3 records) of 129,242 individuals in the ophthalmic-centre-based electronic medical record databases and 17,113 follow-up records of 3,215 participants in population-based cohorts were included in the analysis. Our algorithm accurately predicted the presence of high myopia in internal validation (the AUC ranged from 0.903 to 0.986 for 3 years, 0.875 to 0.901 for 5 years, and 0.852 to 0.888 for 8 years), external validation (the AUC ranged from 0.874 to 0.976 for 3 years, 0.847 to 0.921 for 5 years, and 0.802 to 0.886 for 8 years), and multi-resource testing (the AUC ranged from 0.752 to 0.869 for 4 years). With respect to the prediction of high myopia development by 18 years of age, as a surrogate of high myopia in adulthood, the algorithm provided clinically acceptable accuracy over 3 years (the AUC ranged from 0.940 to 0.985), 5 years (the AUC ranged from 0.856 to 0.901), and even 8 years (the AUC ranged from 0.801 to 0.837). Meanwhile, our algorithm achieved clinically acceptable prediction of the actual refraction values at future time points, which is supported by the regressive performance and calibration curves. Although the algorithm achieved balanced and robust performance, concerns about the compromised quality of real-world clinical data and over-fitting issues should be cautiously considered.
To our knowledge, this study, for the first time, used large-scale data collected from electronic health records to demonstrate the contribution of big data and machine learning approaches to improved prediction of myopia prognosis in Chinese school-aged children. This work provides evidence for transforming clinical practice, health policy-making, and precise individualised interventions regarding the practical control of school-aged myopia.
Slit-lamp images play an essential role for diagnosis of pediatric cataracts. We present a computer vision-based framework for the automatic localization and diagnosis of slit-lamp images by ...identifying the lens region of interest (ROI) and employing a deep learning convolutional neural network (CNN). First, three grading degrees for slit-lamp images are proposed in conjunction with three leading ophthalmologists. The lens ROI is located in an automated manner in the original image using two successive applications of Candy detection and the Hough transform, which are cropped, resized to a fixed size and used to form pediatric cataract datasets. These datasets are fed into the CNN to extract high-level features and implement automatic classification and grading. To demonstrate the performance and effectiveness of the deep features extracted in the CNN, we investigate the features combined with support vector machine (SVM) and softmax classifier and compare these with the traditional representative methods. The qualitative and quantitative experimental results demonstrate that our proposed method offers exceptional mean accuracy, sensitivity and specificity: classification (97.07%, 97.28%, and 96.83%) and a three-degree grading area (89.02%, 86.63%, and 90.75%), density (92.68%, 91.05%, and 93.94%) and location (89.28%, 82.70%, and 93.08%). Finally, we developed and deployed a potential automatic diagnostic software for ophthalmologists and patients in clinical applications to implement the validated model.
In this article, we examine the nexus between foreign development assistance and the attraction of foreign direct investment (FDI) through a de facto political power, as an aid-seeking and likely ...aid-dependent group. We apply structural equation modelling to investigate the direct and indirect effect of aid on FDI via economic institutions for a sample of 42 African countries from 2002 to 2016. Our results corroborate a direct positive effect of aid and institutions on FDI as a productive financial source. However, an aid-dependent de facto political power does not improve the economic institutions, and within a broad institutional context, it may even worsen them, evidencing the indirect effect of reducing a country’s attractiveness for FDI. This study offers robust evidence under different specifications and variables of institutions in addition to several controls for political and strategic interests and economic conditions. We ultimately develop a model explaining why aid barely makes any contribution to institutional reforms. In countries that are heavily dependent on aid, the beneficiary group is discouraged from improving institutional qualities as the source of benefits would be discontinued.
Flap endonuclease 1 (FEN1) is a structure-specific nuclease that plays a role in a variety of DNA metabolism processes. FEN1 is important for maintaining genomic stability and regulating cell growth ...and development. It is associated with the occurrence and development of several diseases, especially cancers. There is a lack of systematic bibliometric analyses focusing on research trends and knowledge structures related to FEN1.
To analyze hotspots, the current state and research frontiers performed for FEN1 over the past 15 years.
Publications were retrieved from the Web of Science Core Collection (WoSCC) database, analyzing publication dates ranging from 2005 to 2019. VOSviewer1.6.15 and Citespace5.7 R1 were used to perform a bibliometric analysis in terms of countries, institutions, authors, journals and research areas related to FEN1. A total of 421 publications were included in this analysis.
Our findings indicated that FEN1 has received more attention and interest from researchers in the past 15 years. Institutes in the United States, specifically the Beckman Research Institute of City of Hope published the most research related to FEN1. Shen BH, Zheng L and Bambara Ra were the most active researchers investigating this endonuclease and most of this research was published in the Journal of Biological Chemistry. The main scientific areas of FEN1 were related to biochemistry, molecular biology, cell biology, genetics and oncology. Research hotspots included biological activities, DNA metabolism mechanisms, protein-protein interactions and gene mutations. Research frontiers included oxidative stress, phosphorylation and tumor progression and treatment.
This bibliometric study may aid researchers in the understanding of the knowledge base and research frontiers associated with FEN1. In addition, emerging hotspots for research can be used as the subjects of future studies.
Abstract
Age is closely related to human health and disease risks. However, chronologically defined age often disagrees with biological age, primarily due to genetic and environmental variables. ...Identifying effective indicators for biological age in clinical practice and self-monitoring is important but currently lacking. The human lens accumulates age-related changes that are amenable to rapid and objective assessment. Here, using lens photographs from 20 to 96-year-olds, we develop LensAge to reflect lens aging via deep learning. LensAge is closely correlated with chronological age of relatively healthy individuals (R
2
> 0.80, mean absolute errors of 4.25 to 4.82 years). Among the general population, we calculate the LensAge index by contrasting LensAge and chronological age to reflect the aging rate relative to peers. The LensAge index effectively reveals the risks of age-related eye and systemic disease occurrence, as well as all-cause mortality. It outperforms chronological age in reflecting age-related disease risks (
p
< 0.001). More importantly, our models can conveniently work based on smartphone photographs, suggesting suitability for routine self-examination of aging status. Overall, our study demonstrates that the LensAge index may serve as an ideal quantitative indicator for clinically assessing and self-monitoring biological age in humans.
Ocular images play an essential role in ophthalmology. Current research mainly focuses on computer-aided diagnosis using slit-lamp images, however few studies have been done to predict the ...progression of ophthalmic disease. Therefore exploring an effective approach of prediction can help to plan treatment strategies and to provide early warning for the patients. In this study, we present an end-to-end temporal sequence network (TempSeq-Net) to automatically predict the progression of ophthalmic disease, which includes employing convolutional neural network (CNN) to extract high-level features from consecutive slit-lamp images and applying long short term memory (LSTM) method to mine the temporal relationship of features. First, we comprehensively compare six potential combinations of CNNs and LSTM (or recurrent neural network) in terms of effectiveness and efficiency, to obtain the optimal TempSeq-Net model. Second, we analyze the impacts of sequence lengths on model's performance which help to evaluate their stability and validity and to determine the appropriate range of sequence lengths. The quantitative results demonstrated that our proposed model offers exceptional performance with mean accuracy (92.22), sensitivity (88.55), specificity (94.31) and AUC (97.18). Moreover, the model achieves real-time prediction with only 27.6ms for single sequence, and simultaneously predicts sequence data with lengths of 3-5. Our study provides a promising strategy for the progression of ophthalmic disease, and has the potential to be applied in other medical fields.
Accurate feature identification of drought disaster events is required for proper risk management in agriculture. This study improved the crop water deficit index (CWDI) by including the daily ...meteorological, crop development stage, soil moisture content, and yield data for 1981–2020 in northeastern China. Two drought characteristic variables (drought duration and intensity) were extracted using the theory of runs to produce the improved crop water deficit index (CWDIwp). Thresholds for the bivariate indicators were also determined for agricultural drought events of varying severity. A joint distribution model for drought variables was constructed based on five types of Archimedean copulas. The joint probability and the joint recurrence period for agricultural drought events were analyzed for drought events with varying intensities in northeast China. The results suggest that the CWDIwp can reliably identify the onset, duration, and intensity of drought events over the study area and can be used to monitor agricultural drought events. The conditional probability of drought intensity (duration) decreased as the drought duration (intensity) threshold increased, whereas the drought recurrence period increased as the threshold for drought duration and intensity rose. In the period (1981–2020), drought intensity in the three Northeastern provinces showed an increasing trend in the order Jilin Province > Liaoning Province > Heilongjiang Province. The spatial distribution of the joint probability and the joint recurrence period was obvious, and the joint probability showed a decreasing distribution trend from west to east. The distribution trend for the joint probability was opposite to that of the joint recurrence period. Furthermore, the areas with high drought probability values corresponded to the areas with low values for the recurrence period, indicating that the drought occurrence probability was higher, and the recurrence period value was lower in the drought-prone areas. The high-risk drought areas (60–87%) were in western Liaoning and western Jilin, with a recurrence period of 1–3 years, whereas the low-risk areas (<40%) were located in the mountainous areas of eastern Liaoning and eastern Jilin. The joint probability and joint recurrence period for agricultural drought events of varying severity were quite different, with the probability following the order light drought > moderate drought > severe drought > extreme drought. The order for the recurrence period was light drought < moderate drought < severe drought < extreme drought. The results provide technical support for disaster prevention and mitigation in drought risk management.
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