Disease burden of mental disorders among children and adolescents considering both co-morbidities and suicide in Northeastern China.

BACKGROUND: Few studies have assessed the burden of mental disorders among children and adolescents considering the impact of co-morbidities and suicide on disability adjusted life years (DALYs). METHODS: This was a multicenter cross-sectional study. Our survey data in Liaoning Province (LN) were used to estimate the burden of six mental disorders, supplemented with data from other investigative studies conducted in China to assess four other disorders. DALYs were derived from the sum of years lived with a disability (YLDs) adjusted for co-morbidities, and the years of life lost (YLLs) adjusted for suicide. The changes in DALYs, YLDs, and YLLs were compared with and without adjustment for co-morbidities and suicide. RESULTS: The DALYs rate of mental disorders among children and adolescents in LN decreased from 1579.6/105 to 1391.4/105, after adjusting for both co-morbidities and suicide (-11.9%). The DALYs rate for major depression, anxiety disorder, and conduct disorder (-80.8/105, -75.0/105 and -30.2/105, respectively) were the top three contributors to the DALYs reduction (-188.2/105). The YLDs decreased from 72724.8 to 62478.5 after co-morbidity adjustment (-17.8%), mainly due to the reduction by major depression (-35.3%) and attention deficit/hyperactivity disorder [ADHD] (-34.2%). The YLLs increased from 130 to 1697.8 after adjusting for suicides (+ 56.9% of all suicide YLLs), mainly due to the contribution of major depression (+ 32.4%) and anxiety disorder (+ 10.4%). Compared to GBD 2010, the estimated DALY rate for mental disorders in LN was to be about 80%, with the proportion of DALYs and DALY rates explained by major depressive disorder accounted for only approximately one-third (14.6% vs. 41.9% and 202.6 vs. 759.9, respectively). But the proportion and absolute level of DALY rates explained by anxiety disorders were approximately 2-fold higher (39.7% vs. 19.6% and 552.2 vs. 323.3, respectively). CONCLUSIONS: The DALYs of mental disorders among Chinese children and adolescents were approximately 80% of the global level, with anxiety disorders imposing about 2 times the global level. Co-morbidity and suicide must be adjusted when calculating DALYs.

Echocardiographic assessment of pediatric heart transplantation: A single-center experience in China.

BACKGROUND: Pediatric heart transplant (HT) has become the standard of care for end-stage heart failure in children worldwide. Serial echocardiographic evaluations of graft anatomy and function during follow-up are crucial for post-HT management. However, evolution of cardiac structure and function after pediatric HT has not been well described, especially during first year post-HT. This study aimed to characterize the evolution of cardiac structure and function after pediatric HT and investigate the correlation between biventricular function with adverse clinical outcomes. METHODS: A single-center retrospective study of echocardiographic data obtained among 99 pediatric HT patients was conducted. Comprehensive echocardiographic examination was performed in all patients at 1-, 3-, 6-, 9- and 12-months post-HT. We obtained structural, functional and hemodynamic parameters from both left- and right-side heart, such as left ventricular stroke volume (LVSV), left ventricular ejection fraction (LVEF), right ventricular fractional area change (RVFAC), etc. The cardiac evolution of pediatric HT patients during first post-HT year was described and compared between different time points. We also explored the correlation between cardiac function and major adverse transplant events (MATEs). RESULTS: 1) Evolution of left heart parameters: left atrial length, mitral E velocity, E/A ratio, LVSV and LVEF significantly increased while mitral A velocity significantly decreased over the first year after HT (P < .05). Compared with 1 month after HT, interventricular septum (IVS) and left ventricular posterior wall (LVPW) decreased at 3 months but increased afterwards. (2) Evolution of right heart parameters: right ventricular base diameter and mid-diameter; right ventricular length diameter, tricuspid E velocity, E/A ratio, tricuspid annular velocity e' at free wall, and RVFAC increased, while tricuspid A velocity decreased over the first year after HT (P < .05). (3) Univariate logistic regression model suggests that biventricular function parameters at 1-year post-HT (LVEF, RVFAC, tricuspid annular plane systolic excursion and tricuspid lateral annular systolic velocity) were associated with MATEs. CONCLUSION: Gradual improvement of LV and RV function was seen in pediatric HT patients within the first year. Biventricular function parameters associated with MATEs. The results of this study pave way for designing larger and longer follow-up of this population, potentially aiming at using multiparameter echocardiographic prediction of adverse events.

MH6D: Multi-Hypothesis Consistency Learning for Category-Level 6-D Object Pose Estimation.

Six-degree-of-freedom (6DoF) object pose estimation is a crucial task for virtual reality and accurate robotic manipulation. Category-level 6DoF pose estimation has recently become popular as it improves generalization to a complete category of objects. However, current methods focus on data-driven differential learning, which makes them highly dependent on the quality of the real-world labeled data and limits their ability to generalize to unseen objects. To address this problem, we propose multi-hypothesis (MH) consistency learning (MH6D) for category-level 6-D object pose estimation without using real-world training data. MH6D uses a parallel consistency learning structure, alleviating the uncertainty problem of single-shot feature extraction and promoting self-adaptation of domain to reduce the synthetic-to-real domain gap. Specifically, three randomly sampled pose transformations are first performed in parallel on the input point cloud. An attention-guided category-level 6-D pose estimation network with channel attention (CA) and global feature cross-attention (GFCA) modules is then proposed to estimate the three hypothesized 6-D object poses by extracting and fusing the global and local features effectively. Finally, we propose a novel loss function that considers both the process and the final result information allowing MH6D to perform robust consistency learning. We conduct experiments under two different training data settings (i.e., only synthetic data and synthetic and real-world data) to verify the generalization ability of MH6D. Extensive experiments on benchmark datasets demonstrate that MH6D achieves state-of-the-art (SOTA) performance, outperforming most data-driven methods even without using any real-world data. The code is available at https://github.com/CNJianLiu/MH6D.

Detection of Image Artifacts Using Improved Cascade Region-Based CNN for Quality Assessment of Endoscopic Images.

Endoscopy is a commonly used clinical method for gastrointestinal disorders. However, the complexity of the gastrointestinal environment can lead to artifacts. Consequently, the artifacts affect the visual perception of images captured during endoscopic examinations. Existing methods to assess image quality with no reference display limitations: some are artifact-specific, while others are poorly interpretable. This study presents an improved cascade region-based convolutional neural network (CNN) for detecting gastrointestinal artifacts to quantitatively assess the quality of endoscopic images. This method detects eight artifacts in endoscopic images and provides their localization, classification, and confidence scores; these scores represent image quality assessment results. The artifact detection component of this method enhances the feature pyramid structure, incorporates the channel attention mechanism into the feature extraction process, and combines shallow and deep features to improve the utilization of spatial information. The detection results are further used for image quality assessment. Experimental results using white light imaging, narrow-band imaging, and iodine-stained images demonstrate that the proposed artifact detection method achieved the highest average precision (62.4% at a 50% IOU threshold). Compared to the typical networks, the accuracy of this algorithm is improved. Furthermore, three clinicians validated that the proposed image quality assessment method based on the object detection of endoscopy artifacts achieves a correlation coefficient of 60.71%.

Can financial technology development reduce household energy consumption? Evidence from China.

This paper examines whether financial technology (FinTech) development affect household energy consumption. The proposed point that FinTech can reduce household energy consumption is theoretically discussed and empirically tested using data from the 2017 Digital Financial Inclusion Index, the 2018 China Family Panel Studies (CFPS), the 2018 China Environmental Statistical Yearbook and the 2018 China Science and Technology Statistical Yearbook. The results show that FinTech contributes to reducing household energy consumption. Several retests, including the instrumental variable, replacement sample and propensity score matching methods, prove its robustness. Mechanism tests show that investment in environmental governance and technological innovation promotion are the two main transmission channels. We also find that the reducing effect is more significant in the following groups: the low-middle income level classes, the eastern regional residents, those with bachelor's degrees and above, the those aged over 60 and rural residents. The outcomes of this paper call for government departments to positively guide FinTech development to reduce household energy consumption. From another perspective, the conclusions drawn from our analysis make a great reference value for countries and provide new ideas for Chinese carbon peaking and carbon neutralisation goals.

Does environmental governance matter to the relationship between digital transformation and high-quality development? Evidence from manufacturing sector in China.

Environmental governance has emerged as a crucial tactic to support the sustainable development of human civilization in light of the serious environmental issues. Meanwhile, during the process of promoting the high-quality development of China's economy, digital transformation plays a significant role in improving the total factor productivity of the manufacturing sector. We seek to find out whether urban environmental governance has an impact on micro-enterprises, and therefore, the study selects the data of A-share listed manufacturing companies from 2012 to 2020 to study the effect of digital transformation on the total factor productivity of the manufacturing industry and how the effect of environmental governance affects the relationship between digital transformation and total factor productivity. The results unveil that digital transformation can significantly contribute to the total factor productivity of the manufacturing industry. At the same time, digital transformation can promote the high-quality development of enterprises by promoting the fulfillment of corporate social responsibility. Additionally, it is shown that poor environmental governance will weaken the promoting effect of digital transformation on total factor productivity. Furthermore, in state-owned companies and non-heavy polluting industries, environmental governance has a more significant moderating influence on digital transformation and total factor productivity. This study enriches the literature on urban environmental governance and micro-enterprise development, and they support the notion that, from the standpoints of environmental protection and economic development, the level of environmental governance should be continuously optimized and the development of ecological civilization should be strengthened.

Subjective and Objective Audio-Visual Quality Assessment for User Generated Content.

In recent years, User Generated Content (UGC) has grown dramatically in video sharing applications. It is necessary for service-providers to use video quality assessment (VQA) to monitor and control users' Quality of Experience when watching UGC videos. However, most existing UGC VQA studies only focus on the visual distortions of videos, ignoring that the perceptual quality also depends on the accompanying audio signals. In this paper, we conduct a comprehensive study on UGC audio-visual quality assessment (AVQA) from both subjective and objective perspectives. Specially, we construct the first UGC AVQA database named SJTU-UAV database, which includes 520 in-the-wild UGC audio and video (A/V) sequences collected from the YFCC100m database. A subjective AVQA experiment is conducted on the database to obtain the mean opinion scores (MOSs) of the A/V sequences. To demonstrate the content diversity of the SJTU-UAV database, we give a detailed analysis of the SJTU-UAV database as well as other two synthetically-distorted AVQA databases and one authentically-distorted VQA database, from both the audio and video aspects. Then, to facilitate the development of AVQA fields, we construct a benchmark of AVQA models on the proposed SJTU-UAV database and other two AVQA databases, of which the benchmark models consist of AVQA models designed for synthetically distorted A/V sequences and AVQA models built through combining the popular VQA methods and audio features via support vector regressor (SVR). Finally, considering benchmark AVQA models perform poorly in assessing in-the-wild UGC videos, we further propose an effective AVQA model via jointly learning quality-aware audio and visual feature representations in the temporal domain, which is seldom investigated by existing AVQA models. Our proposed model outperforms the aforementioned benchmark AVQA models on the SJTU-UAV database and two synthetically distorted AVQA databases. The SJTU-UAV database and the code of the proposed model will be released to facilitate further research.

Deep Learning-Assisted Gait Parameter Assessment for Neurodegenerative Diseases: Model Development and Validation.

BACKGROUND: Neurodegenerative diseases (NDDs) are prevalent among older adults worldwide. Early diagnosis of NDD is challenging yet crucial. Gait status has been identified as an indicator of early-stage NDD changes and can play a significant role in diagnosis, treatment, and rehabilitation. Historically, gait assessment has relied on intricate but imprecise scales by trained professionals or required patients to wear additional equipment, causing discomfort. Advancements in artificial intelligence may completely transform this and offer a novel approach to gait evaluation. OBJECTIVE: This study aimed to use cutting-edge machine learning techniques to offer patients a noninvasive, entirely contactless gait assessment and provide health care professionals with precise gait assessment results covering all common gait-related parameters to assist in diagnosis and rehabilitation planning. METHODS: Data collection involved motion data from 41 different participants aged 25 to 85 (mean 57.51, SD 12.93) years captured in motion sequences using the Azure Kinect (Microsoft Corp; a 3D camera with a 30-Hz sampling frequency). Support vector machine (SVM) and bidirectional long short-term memory (Bi-LSTM) classifiers trained using spatiotemporal features extracted from raw data were used to identify gait types in each walking frame. Gait semantics could then be obtained from the frame labels, and all the gait parameters could be calculated accordingly. For optimal generalization performance of the model, the classifiers were trained using a 10-fold cross-validation strategy. The proposed algorithm was also compared with the previous best heuristic method. Qualitative and quantitative feedback from medical staff and patients in actual medical scenarios was extensively collected for usability analysis. RESULTS: The evaluations comprised 3 aspects. Regarding the classification results from the 2 classifiers, Bi-LSTM achieved an average precision, recall, and F1-score of 90.54%, 90.41%, and 90.38%, respectively, whereas these metrics were 86.99%, 86.62%, and 86.67%, respectively, for SVM. Moreover, the Bi-LSTM-based method attained 93.2% accuracy in gait segmentation evaluation (tolerance set to 2), whereas that of the SVM-based method achieved only 77.5% accuracy. For the final gait parameter calculation result, the average error rate of the heuristic method, SVM, and Bi-LSTM was 20.91% (SD 24.69%), 5.85% (SD 5.45%), and 3.17% (SD 2.75%), respectively. CONCLUSIONS: This study demonstrated that the Bi-LSTM-based approach can effectively support accurate gait parameter assessment, assisting medical professionals in making early diagnoses and reasonable rehabilitation plans for patients with NDD.

Global, regional, and national burden of kidney dysfunction from 1990 to 2019: a systematic analysis from the global burden of disease study 2019.

OBJECTIVE: We aim to explore the prevalence and temporal trends of the burden of kidney dysfunction (KD) in global, regional and national level, since a lack of related studies. DESIGN: Cross-sectional study. MATERIALS: The data of this research was obtained from Global Burden of Diseases Study 2019. The estimation of the prevalence, which was measured by the summary exposure value (SEV), and attributable burden of KD was performed by DisMod-MR 2.1, a Bayesian meta-regression tool. The Spearman rank order correlation method was adopted to perform correlation analysis. The temporal trends were represented by the estimated annual percentage change (EAPC). RESULTS: In 2019, there were total 3.16 million deaths and 76.5 million disability-adjusted life years (DALYs) attributable to KD, increased by 101.1% and 81.7% compared with that in 1990, respectively. From 1990 to 2019, the prevalence of KD has increased in worldwide, but decreased in High-income Asia Pacific. Nearly 48.5% of countries globally, such as South Africa, Egypt and Mexico had increased mortality rates of KD from 1990 to 2019 while 44.6% for disability rate. Countries with lower socio-demographic index (SDI) are facing a higher prevalence as well as mortality and disability rate compared with those with higher SDI. Compared with females, the prevalence of KD was lower in males, however the attributable mortality and disability rate were higher in all years from 1990 to 2019. CONCLUSION: With the progress of senescent, we will face more severe challenges of reducing the prevalence and attributable burden of KD, especially in regions with lower SDI. Effective measures are urgently required to alleviate the prevalence and burden of KD.

Attention-Guided Neural Networks for Full-Reference and No-Reference Audio-Visual Quality Assessment.

With the popularity of mobile Internet, audio and video (A/V) have become the main way for people to entertain and socialize daily. However, in order to reduce the cost of media storage and transmission, A/V signals will be compressed by service providers before they are transmitted to end-users, which inevitably causes distortions in the A/V signals and degrades the end-user's Quality of Experience (QoE). This motivates us to research the objective audio-visual quality assessment (AVQA). In the field of AVQA, most previous works only focus on single-mode audio or visual signals, which ignores that the perceptual quality of users depends on both audio and video signals. Therefore, we propose an objective AVQA architecture for multi-mode signals based on attentional neural networks. Specifically, we first utilize an attention prediction model to extract the salient regions of video frames. Then, a pre-trained convolutional neural network is used to extract short-time features of the salient regions and the corresponding audio signals. Next, the short-time features are fed into Gated Recurrent Unit (GRU) networks to model the temporal relationship between adjacent frames. Finally, the fully connected layers are utilized to fuse the temporal related features of A/V signals modeled by the GRU network into the final quality score. The proposed architecture is flexible and can be applied to both full-reference and no-reference AVQA. Experimental results on the LIVE-SJTU Database and UnB-AVC Database demonstrate that our model outperforms the state-of-the-art AVQA methods. The code of the proposed method will be publicly available to promote the development of the field of AVQA.

Digital Inclusion, Cultural Capital, and Health Status of Urban and Rural Residents: An Empirical Study Based on 2017 CGSS Database.

China is committed to using digital technology to drive urban-rural integration in health care. This study aims to explore the effect of digital inclusion on health status with the mediating role of cultural capital and the digital health disparities between urban and rural residents in China. Using data from the 2017 Chinese General Social Survey (CGSS), the present study adopted an ordinary least squares (OLS) robust standard error regression model to investigate the impact of digital inclusion on health status. In addition, causal step regression (CSR) and bootstrapping methods were combined to test the mediating effect of cultural capital. The results showed that, first, digital inclusion was related to positive and significant effects on resident health status. Second, cultural capital played a mediating role in the relationship between digital inclusion and health status. Third, urban residents gained more health benefits from digital inclusion than rural residents. Additionally, common method variance (CMV) tests, endogenous tests, and a propensity score matching (PSM) analysis showed that the above conclusions remained robust. The government should therefore focus not only on promoting the population's health by utilizing digital inclusion but also on accelerating digital health equity between urban and rural areas by developing such strategies as a digital infrastructure expansion schedule and digital literacy education and training programs.

Data driven toxicity assessment of organic chemicals against Gammarus species using QSAR approach.

Nowadays, organic chemicals play an essential role in almost all walks of life and have become indispensable to modern society. However, the continually synthesized chemicals and the numerous potential adverse endpoints against living organisms increasingly promote the regulators regarding the computational approach as a crucial supplement and an alternative to the traditional animal tests in chemical risk assessment. In this present research, we evaluated the ecotoxicity of chemicals against four typical Gammarus species, which constituted a critical element in detritus cycle and also the recommended species for water monitoring. We first screened the molecular descriptors based on the Genetic Algorithm and then developed the Quantitative Structure-Activity Relationship models using the Multiple Linear Regression method. The statistical results from various validation metrics suggested that the obtained models were internally robust and externally predictive. The application domain analysis based on the leverage approach and standardized residual method demonstrated the broad application range of each model. The interpretation of molecular descriptors in each model suggested that the chemicals with higher polarity and hydrophilicity tend to be less toxic, whereas the lipophilic moieties would enhance the chemical toxicity. Meanwhile, the other selected descriptors, such as Chi-cluster, heterocyclic, and distance matrix descriptors, manifested that the chemical toxicity was also affected by molecular branching, connectivity, electrotopological state, and other various properties. In summary, the present work proposed well-performed QSAR models and clarified the possible toxic mechanism of chemicals against Gammarus species. The obtained models could help predict the toxicity data and conduct a preliminary risk assessment, thus guiding the subsequent animal tests and reducing the assessment cost.

Measurement of provincial carbon emission efficiency and analysis of influencing factors in China.

The massive use of energy has caused a rapid increase in global carbon dioxide emissions, resulting in a series of environmental problems such as climate warming. Investment in the energy industry can guide funds into green and clean production, reduce carbon emissions in the energy industry, and promote the green development of the energy industry. This paper considers the energy, the environment, the economy, and other factors and focuses on energy consumption and investment structure. Taking 30 provinces in China as research samples, a dynamic spatial Durbin model is established. The results show that the first-order term of carbon emissions has a driving force of 0.5068% for current carbon emissions at a significance level of 1% and that the increase in current carbon emissions will lead to a continued increase in carbon emissions in the next period. The increase in the carbon emissions of neighbouring provinces will increase their carbon emissions through the spatial spillover effect. Whether in the short term or long term, the increase in energy investment and the optimization of the energy investment structure can reduce carbon emissions. The above conclusions can provide a reference for the formulation of government environmental policies.

Inexact fuzzy-flexible left-hand-side chance-constrained programming for agricultural nonpoint-source water quality management.

In this study, an inexact fuzzy-flexible left-hand-side chance-constrained programming (IFLCCP) method is proposed for optimizing an agricultural nonpoint-source water quality management problem under uncertainty. The developed method can address complex uncertainties resulted from system fuzzy flexible under various level of decision-making requirements and randomness parameters appeared on the left-hand side of the constraints, and deal with the conflict between water quality protection and agricultural system economic development. The IFLCCP model is formulated through incorporating inexact left-hand-sided chance-constrained programming into interval fuzzy flexible programming framework. The decision schemes obtained by the IFLCCP are analyzed under scenarios at different confidence level of environmental constraint. The results demonstrate that the scale of crop planting and breeding industries reduces as the confidence coefficient of environmental constraint (1-pi) increases, in order to satisfy pollutant discharge constraints, which results in the reduction of the system net benefit from scenarios 1 to 3. Meanwhile, the interval control variables λ± are introduced for quantifying the degrees of overall satisfaction for the objective function and the constraints, which get optimal adjustment to guarantee the net benefit to be as close as possible to the upper bound. The IFLCCP is able to provide management schemes with high system benefits under different levels of acceptable environmental risk, taking full consideration of decision makers' environmental management requirements. This study is a new application of the IFLCCP model to agricultural water quality management problem, demonstrating its applicability to practical environmental problems with high complexity and uncertainty.

Regional convergence analysis of sustainable innovation efficiency in European Union countries.

Sustainable innovation strategies have been taken very seriously by the European Union (EU), which aims to reduce energy consumption and environmental pollutants emissions. For the sake of testing the sustainable performance of EU countries empirically, this research evaluates the sustainable innovation efficiency (SIE) of EU countries through a DEA-SBM model and analyzes the convergence trends of the EU regions by convergence analysis. The results show that the EU has attached great importance to sustainable innovation efficiency, indicating that the EU makes a concerted effort in technological innovation, energy saving, and environmental protection. Significant differences exist in SIE among EU regions, even though the southern region has the highest efficiency. In addition, there are distinct convergence trends in regional sustainable innovation efficiency. Control variables have significant impacts on the convergence of SIE in the EU regions. Furthermore, policymakers are also provided with useful decision support for regional sustainable innovation, energy conservation, and emission reduction policies.

Differential characteristics of carbon emission efficiency and coordinated emission reduction pathways under different stages of economic development: Evidence from the Yangtze River Delta, China.

Regional carbon emission efficiency (CEE) has differentiated characteristics under different economic development stages and patterns, and identifying such characteristics is important for formulating corresponding policies for high-quality regional development. Using input‒output data related to economic development and energy consumption, a comprehensive evaluation model of the Super-SBM and Malmquist‒Luenberger (ML) index is constructed to evaluate the spatial and temporal changes and driving forces of CEE. Based on this index, a proposal is designed for collaborative carbon emission reduction zoning. The results indicate that the CEE of the Yangtze River Delta shows a fluctuating upward trend with obvious spatial agglomeration characteristics, and CEE changes are closely related to economic development stages. The annual average CEE values in each stage show positive changes, indicating that economic development gradually evolves to low carbonization levels. Moreover, CEE improvement gradually shifts from being driven by efficiency changes to being driven by technological changes. Finally, according to the characteristics of total carbon emissions and the efficiency of different cities, a synergistic emission reduction path is proposed with four aspects: land use optimization, ecological co-preservation, innovation cooperation and low carbon development.

Assessment of an MR Elastography-Based Nomogram as a Potential Imaging Biomarker for Predicting Microvascular Invasion of Hepatocellular Carcinoma.

BACKGROUND: Microvascular invasion (MVI) is a well-established poor prognostic factor for hepatocellular carcinoma (HCC). Preoperative prediction of MVI is important for both therapeutic and prognostic purposes, but noninvasive methods are lacking. PURPOSE: To develop an MR elastography (MRE)-based nomogram for the preoperative prediction of MVI in HCC. STUDY TYPE: Prospective. SUBJECTS: A total of 111 patients with surgically resected single HCC (52 MVI-positive and 59 MVI-negative), randomly allocated to training and validation cohorts (7:3 ratio). FIELD STRENGTH/SEQUENCE: 2D-MRE and conventional sequences (T1-weighted in-phase and opposed phase gradient echo, T2-weighted fast spin echo, diffusion-weighted single-shot spin echo echo-planar, and dynamic contrast-enhanced T1-weighted gradient echo) at 3.0 T. ASSESSMENT: MRE-stiffness and conventional qualitative and quantitative MRI features were evaluated and compared between MVI-positive and MVI-negative HCCs. STATISTICAL TESTS: Univariable and multivariable logistic regression analyses were applied to identify potential predictors for MVI, and a nomogram was constructed according to the predictive model. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance. Harrell's C-index evaluated the discrimination performance of the nomogram, calibration curves analyzed its diagnostic performance and decision curve analysis determined its clinical usefulness. A P value <0.05 was considered statistically significant. RESULTS: Tumor stiffness >6.284 kPa (odds ratio [OR] = 24.38) and the presence of arterial peritumoral enhancement (OR = 6.36) were independent variables associated with MVI. The areas under the ROC curves for tumor stiffness were 0.81 (95% confidence interval [CI]: 0.70, 0.89) and 0.77 (95% CI: 0.60, 0.90) in the training and validation cohorts, respectively. When both predictive variables were integrated, the best nomogram performance was achieved with C-indices of 0.88 (95% CI: 0.78, 0.94) and 0.87 (95% CI: 0.71, 0.96) in the two cohorts, fitting well in calibration curves. The decision curve exhibited optimal net benefit with a wide range of threshold probabilities for the nomogram. DATA CONCLUSION: An MRE-based nomogram may be a potential noninvasive imaging biomarker for predicting MVI of HCC preoperatively. EVIDENCE LEVEL: 2. TECHNICAL EFFICACY: Stage 2.

The Evaluation Indexes Suitable for Nonhuman Primates can be Extracted from Clinical Consciousness Disorder Assessment Scales: A Hypothesis.

BACKGROUND: Currently, case studies or clinical trials in different patient populations remain the main resource underlying the understanding of disorder of consciousness (DoC). This provides a low efficacy for the derivation of data and the implementation of associated controlled experimental designs. Preclinical models provide precise controls, reduced variability, rich data output and limited ethical complexity. Nonhuman primates are suitable model animals for disorders of consciousness due to their brain structure being very similar to that of humans. Behavioral tests remain the primary standard for assessing the consciousness status of humans. However, there is currently no behavioral assessment scale available for evaluation of the state of consciousness disorder in nonhuman primates. This presents a significant challenge for the establishment of different models of consciousness disorder. Therefore, there is considerable motivation to focus on the development of a proper tool for assessment of the state of consciousness associated with nonhuman primate models that are based on clinically common consciousness assessment scales. METHODS: It is assumed that the Delphi and level analysis methods based on clinical consciousness disorder assessment scales may provide an effective way to select and include assessment indexes for levels of consciousness in nonhuman primates. RESULTS: 8 first-level indicators with 41 second-level indexes were selected preliminary as a pool of evaluation entries of state of consciousness of nonhuman primates. CONCLUSIONS: It may be practicable to extract appropriate indicators for non-human primates from the clinical consciousness disorder assessment scales. Besides, a combination of Delphi method, behavioral analysis, electroencephalography, neuroimaging (such as positron emission tomography-computed tomography) and functional magnetic resonance imaging is necessary to test the reliability and validity of the novel scale reported here.