Atom-ProteinQA: Atom-level protein model quality assessment through fine-grained joint learning.

MOTIVATION: Protein model quality assessment (ProteinQA) is a fundamental task that is essential for biologically relevant applications, i.e., protein structure refinement, protein design, etc. Previous works aimed to conduct ProteinQA only on the global structure or per-residue level, ignoring potentially usable and precise cues from a fine-grained per-atom perspective. In this study, we propose an atom-level ProteinQA model, named Atom-ProteinQA, in which two innovative modules are designed to extract geometric and topological atom-level relationships respectively. Specifically, on the one hand, a geometric perception module exploits 3D sparse convolution to capture the geometric features of the input protein, generating fine-grained atom-level predictions. On the other hand, natural chemical bonds are utilized to construct an atom-level graph, then message passing from a topological perception module is applied to output residue-level predictions in parallel. Eventually, through a cross-model aggregation module, features from different modules mutually interact, enhancing performance on both the atom and residue levels. RESULTS: Extensive experiments show that our proposed Atom-ProteinQA outperforms previous methods by a large margin, regardless of residue-level or atom-level assessment. Concretely, we achieved state-of-the-art performance on CATH-2084, Decoy-8000, public benchmarks CASP13 & CASP14, and the CAMEO. AVAILABILITY: The repository of this project is released on: https://github.com/luyfcandy/Atom_ProteinQA.

Research on the interplay between green finance and manufacturing sustainability outcomes: insights for low-carbon economy in the post-COVID-19 era.

In the quest to strengthen resilient and sustainable recovery in the post-COVID-19 era, there is a huge requirement for manufacturing firms to adopt green finance which is dominated by green bond issuance. Nevertheless, published studies that provide insights on factors that influence the issuance of green bonds within manufacturing firms in the post-COVID-19 era and the impact on sustainable outcomes are currently non-existent. Therefore, this study analyzed the interrelationships that exist between the influencing factors of green bond issuance within manufacturing firms using decision-making trial and evaluation laboratory (DEMATEL) and data from Nigerian manufacturing firms. Then, a structural model of their importance levels was illustrated using interpretive structural modeling (ISM) while their impact on manufacturing sustainability outcomes was estimated with the aid of evaluation based on distance from average solution (EDAS). The study results highlight the key influencing factors of green bond issuance as environmental competencies, policy framing, low corruption, public awareness, and government support thereby signifying the criticality of strong institutions in facilitating green finance in the post-pandemic era. Besides, the study results demonstrate that green finance can significantly strengthen manufacturing sustainability in the post-COVID-19 era via green bonds by enhancing sustainable waste management, technological growth, and quality improvement as well as reducing carbon emissions. The study findings can provide a reference to decision-makers in manufacturing enterprises to predict scenarios and enact policies that facilitate the success of green finance in the post-COVID-19 era to further develop a low-carbon economy and increase competitive edge.

Comparison of different prediction models for estimation of walking and running energy expenditure based on a wristwear three-axis accelerometer.

Objective: Objectively and efficiently measuring physical activity is a common issue facing the fields of medicine, public health, education, and sports worldwide. In response to the problem of low accuracy in predicting energy consumption during human motion using accelerometers, a prediction model for asynchronous energy consumption in the human body is established through various algorithms, and the accuracy of the model is evaluated. The optimal energy consumption prediction model is selected to provide theoretical reference for selecting reasonable algorithms to predict energy consumption during human motion. Methods: A total of 100 subjects aged 18-30 years participated in the study. Experimental data for all subjects are randomly divided into the modeling group (n = 70) and validation group (n = 30). Each participant wore a triaxial accelerometer, COSMED Quark pulmonary function tester (Quark PFT), and heart rate band at the same time, and completed the tasks of walking (speed range: 2 km/h, 3 km/h, 4 km/h, 5 km/h, and 6 km/h) and running (speed range: 7 km/h, 8 km/h, and 9 km/h) sequentially. The prediction models were built using accelerometer data as the independent variable and the metabolic equivalents (METs) as the dependent variable. To calculate the prediction accuracy of the models, root mean square error (RMSE) and bias were used, and the consistency of each prediction model was evaluated based on Bland-Altman analysis. Results: The linear equation, logarithmic equation, cubic equation, artificial neural network (ANN) model, and walking-and-running two-stage model were established. According to the validation results, our proposed walking-and-running two-stage model showed the smallest overall EE prediction error (RMSE = 0.76 METs, Bias = 0.02 METs) and the best performance in Bland-Altman analysis. Additionally, it had the lowest error in predicting EE during walking (RMSE = 0.66 METs, Bias = 0.03 METs) and running (RMSE = 0.90 METs, Bias < 0.01 METs) separately, as well as high accuracy in predicting EE at each single speed. Conclusion: The ANN-based walking-and-running two-stage model established by separating walking and running can better estimate the walking and running EE, the improvement of energy consumption prediction accuracy will be conducive to more accurate to monitor the energy consumption of PA.

Socioeconomic inequality in teenage pregnancy in Papua New Guinea: a decomposition analysis.

BACKGROUND: Teenage pregnancy is a global public health issue, and it poses a serious threat to the health and socioeconomic status of mothers and their newborn children. Although Papua New Guinea has recorded one of the highest teenage pregnancy rates among Asia-Pacific countries, few studies have conducted research on the related inequality in the country. Therefore, this study aimed to assess socioeconomic inequality in teenage pregnancy and its contributing factors in Papua New Guinea. METHODS: Data for this cross-sectional study were obtained from the 2016-2018 Papua New Guinea Demographic and Health Survey. The analytical sample consisted of 2,864 girls aged 15-19 years. We employed Erreygers normalized concentration index (ECI) and concentration curves to measure and depict socioeconomic inequality in teenage pregnancy. Decomposition analysis was likewise performed to identify the contributions of determinants to the observed inequality. RESULTS: Weighted ECI for teenage pregnancy was - 0.0582 (P < 0.001), thereby indicating that teenage pregnancy in Papua New Guinea is disproportionately concentrated among poor girls. Decomposition analysis suggested that education level (65.2%), wealth index (55.2%), early sexual debut (25.1%), region (8.5%), and sex of household head (4.1%) are the main determinants explaining the pro-poor socioeconomic inequality in teenage pregnancy. CONCLUSIONS: A pro-poor socioeconomic inequality of teenage pregnancy was present in Papua New Guinea. This inequality may be alleviated by such interventions as ensuring that teenage girls receive education; implementing poverty alleviation projects, eliminating child, early, and forced marriages; strengthening promotion for household head to support teenagers in accessing sexual and reproductive health education; improving geographical accessibility to health facilities on contraceptive services, and taking necessary precautions and responses to sexual misconduct.

Multi-parametric MRI assessment of melatonin regulating the polarization of microglia in rats after cerebral ischemia/reperfusion injury.

OBJECTIVES: Multi-parametric magnetic resonance imaging (MRI) can provide comprehensive and valuable information for precise diagnosis and treatment evaluation of a number of diseases. In this study, the neuroprotective effects of melatonin (Mel) on a rat model of cerebral ischemia/reperfusion injury (CIRI) were assessed by multi-parametric MRI combined with histopathological techniques for longitudinal monitoring of the lesion microenvironment. METHODS: Sixty Sprague Dawley (SD) rats were randomly divided into three groups: the Sham, CIRI and CIRI + Mel groups. At multiple time points after ischemia, MRI scanning was performed on a 7.0 Tesla MRI scanner. Multi-parametric MRI includes T2-weighted imaging (T2WI), diffusion weighted imaging (DWI), and chemical exchange saturation transfer (CEST)-MRI. CEST effects were calculated by the Lorentzian difference method, 3.5 ppm indicates amide protons of mobile proteins/peptide (Amide-CEST) and 2.0 ppm indicates amine protons (Guan-CEST). Multiple histopathological techniques were used to examine the histopathological changes and explore the therapeutic effects of Mel. RESULTS: T2WI and DWI-MRI could localize the infarct foci and areas in CIRI rats, which was further validated by staining, 2, 3, 5-triphenyl tetrazolium chloride (TTC) staining, hematoxylin and eosin (H&E) staining, and terminal deoxynucleotidyl transferase-mediated deoxyuridine triphosphate-biotin nick end labelling (TUNEL) staining. After Mel treatment, T2WI and DWI-MRI showed smaller infarct volume, and neurons displayed improved morphology with less apoptosis rates. Notably, Amide-CEST and Guan-CEST signal decreased as early as 2 h after CIRI (all P <0.001), reflecting the change of pH after ischemia. After Mel treatment, both Amide-CEST and Guan-CEST signal increased in ischemic cortex and striatum compared with control group (all P < 0.001). The immunofluorescence staining and western blotting analysis suggested the expression of M2 microglia increased after Mel treatment; While，after Mel treatment the inflammatory factor interleukin-1ß (IL-1ß) decreased compared with control CIRI rats. CONCLUSIONS: Multi-parametric MRI was shown to be an effective method to monitor the brain damage in a rat model of CIRI and assess the therapeutic effects of Mel treatment. Amide-CEST and Guan-CEST were especially sensitive to the changes in brain microenvironment during the early stage after CIRI. Furthermore, the neuroprotective effect of Mel treatment is associated with its promotion of the microglia polarized to M2 type in CIRI rats.

Exploration on the scientific rationality of low-carbon investment projects from the perspective of supply chain: a case study of CS thermal power plant.

Climate warming has gradually become a major problem threatening human survival, and countries have begun to pay attention to carbon emissions. Energy conservation and emission reduction has become a central task in China's economic development since the 14th Five-Year Plan. As the main force of carbon emissions in China, thermal power industry is bound to become the focus of attention in China's low-carbon development strategy and energy conservation and emission reduction. Moreover, with the marketization of the power industry, the state has joined the market competition at the power generation sectors and the power sale sectors, and implemented the "opening the middle of the two pipes." Therefore, the coverage of influence of carbon emissions and carbon investment behavior of power generation companies is not limited to itself, but will also be extended to the supply chain level. Based on the above background, this paper evaluates the scientific rationality of low-carbon investment projects of thermal power enterprises from the perspective of low-carbon supply chain, which not only can help enterprises achieve a win-win situation of economic and environmental benefits, but also contribute to the carbon emission reduction of the entire supply chain, thereby promoting China's entire social and economic energy conservation and emission reduction work.

Health systems efficiency in China and ASEAN, 2015-2020: a DEA-Tobit and SFA analysis application.

OBJECTIVE: To evaluate the health systems efficiency in China and Association of Southeast Asian Nations (ASEAN) countries from 2015 to 2020. DESIGN: Health efficiency analysis using data envelopment analysis (DEA) and stochastic frontier approach analysis. SETTING: Health systems in China and ASEAN countries. METHODS: DEA-Malmquist model and SFA model were used to analyse the health system efficiency among China and ASEAN countries, and the Tobit regression model was employed to analyse the factors affecting the efficiency of health system among these countries. RESULTS: In 2020, the average technical efficiency, pure technical efficiency and scale efficiency of China and 10 ASEAN countries' health systems were 0.700, 1 and 0.701, respectively. The average total factor productivity (TFP) index of the health systems in 11 countries from 2015 to 2020 was 0.962, with a decrease of 1.4%, among which the average technical efficiency index was 1.016, and the average technical progress efficiency index was 0.947. In the past 6 years, the TFP index of the health system in Malaysia was higher than 1, while the TFP index of other countries was lower than 1. The cost efficiency among China and ASEAN countries was relatively high and stable. The per capita gross domestic product (current US$) and the urban population have significant effects on the efficiency of health systems. CONCLUSIONS: Health systems inefficiency is existing in China and the majority ASEAN countries. However, the lower/middle-income countries outperformed high-income countries. Technical efficiency is the key to improve the TFP of health systems. It is suggested that China and ASEAN countries should enhance scale efficiency, accelerate technological progress and strengthen regional health cooperation according to their respective situations.

Assessment of Joint Toxicity of Arsenate and Lead by Multiple Endpoints in Chlamydomonas reinhardtii.

In aquatic ecosystems, arsenate (As(V)) and lead (Pb(II)) frequently coexist but their joint toxicity on microalgae remains unknown. In this study, Chlamydomonas reinhardtii was exposed to various levels of combined As(V) and Pb(II) treatments. The cell growth, respiration, pigment synthesis, polysaccharides and protein secretion as well as As speciation of C. reinhardtii were analyzed. The low-level coexistence of As(V) and Pb(II) had a stimulatory effect, as indicated by enhanced cell proliferation. In the middle-level coexistence, the cells resisted the toxicity by significant increasing protein secretion. Under high-level coexistence, the presence of Pb(II) inhibited the efflux of As and caused the decline of cell numbers and occurrence of cell lysis, indicating that the interaction mode between As(V) and Pb(II) switched to synergistic. Taken together, the above findings may deepen the understanding of detoxification mechanisms of algae upon exposure to combined metal(loid)s in aqueous environments.

Assessment of compensated advanced chronic liver disease based on serum bile acids in chronic hepatitis B patients.

Patients with chronic liver disease progressed to compensated advanced chronic liver disease (cACLD), the risk of liver-related decompensation increased significantly. This study aimed to develop prediction model based on individual bile acid (BA) profiles to identify cACLD. This study prospectively recruited 159 patients with hepatitis B virus (HBV) infection and 60 healthy volunteers undergoing liver stiffness measurement (LSM). With the value of LSM, patients were categorized as three groups: F1 [LSM ≤ 7.0 kilopascals (kPa)], F2 (7.1 < LSM ≤ 8.0 kPa), and cACLD group (LSM ≥ 8.1 kPa). Random forest (RF) and support vector machine (SVM) were applied to develop two classification models to distinguish patients with different degrees of fibrosis. The content of individual BA in the serum increased significantly with the degree of fibrosis, especially glycine-conjugated BA and taurine-conjugated BA. The Marco-Precise, Marco-Recall, and Marco-F1 score of the optimized RF model were all 0.82. For the optimized SVM model, corresponding score were 0.86, 0.84, and 0.85, respectively. RF and SVM models were applied to identify individual BA features that successfully distinguish patients with cACLD caused by HBV. This study provides a new tool for identifying cACLD that can enable clinicians to better manage patients with chronic liver disease.

Purely Organic Fluorescence Afterglow: Visible-Light-Excitation, Inherent Mechanism, Tunable Color, and Practical Applications with Very Low Cost.

Purely organic materials with visible light excitable fluorescence afterglow are promising for applications. Herein, fluorescence afterglow with various intensity and duration was observed on fluorescent dyes once being dispersed in polymer matrix, thanks to the slow reverse intersystem crossing rate (kRISC ) and long delayed fluorescence lifetime (τDF ) derived from the coplanar and rigid chemical structure of the dyes. To verify the mechanism, different polymers were used to tune singlet-triplet splitting energy based on solvent effect. And commercial acriflavine (Acf) film showed blue shifted fluorescence compared to purified one, with slower kRISC (≈100  s-1 ) and longer τDF (0.6 s). Via energy transfer from Acf to rhodamine B, the afterglow color was further regulated, with the largest fluorescence quantum yield of 42.4 %. It was demonstrated that the materials worked on color tunable light sources, and low-cost ($2 for 50 000 labels) anti-counterfeit labels recognized by white light.

Quality assessment of traditional Chinese medicine using quality biomarkers: Compound DanShen dripping pills as an example.

BACKGROUND: The quality control of traditional Chinese medicine (TCM) is one of the main topics in TCM modernisation research. To date, the overwhelming majority of research has focused on chemical ingredients in the quality control of TCM. However, detecting a single or multiple chemical components cannot fully demonstrate the specificity and correlation between quality and efficacy. PURPOSE: To solve the problem that the association between quality control and efficacy is lacking. The present study was designed to establish a methodology for quality control based on quality biomarkers (Q-biomarkers) and the vasodilatation efficacy of compound DanShen dripping pills (CDDP) as a case. METHODS: Guided by the basic principles of Q-biomarkers, the compounds in TCM were determined by ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometry. Predicted targets were screened through network pharmacology. The potential Q-biomarkers were further screened through proteomics and partial least squares regression analysis. The protein-protein interaction network that combines both predicted targets and potential Q-biomarkers was constructed to screen Q-biomarkers. RESULTS: There were 32 components and 79 predictive targets for CDDP. Proteomic results indicated that the expression of 23 differential proteins changed as pharmacodynamic and componential changes. CPSF6, RILP11, TMEM209, COQ7, VPS18, PPPP1CA, NF2, and ARFRP1 highly correlated with vasodilation. Protein interaction network analysis showed that NF2 and PPPP1CA were closely related to predicted proteins. Thus, NF2 and PPPP1CA could be considered as Q-biomarkers of CDDP. CONCLUSION: Our preliminary study suggested the feasibility of the Q-biomarkers theory in the quality of TCM. The concept of Q-biomarkers provided a powerful method to strengthen the link between clinical efficacy and the quality of TCM. In conclusion, a novel, more scientific, and standard quality control method was established in this study.

A novel physiological feature selection method for emotional stress assessment based on emotional state transition.

The connection between emotional states and physical health has attracted widespread attention. The emotional stress assessment can help healthcare professionals figure out the patient's engagement toward the diagnostic plan and optimize the rehabilitation program as feedback. It is of great significance to study the changes of physiological features in the process of emotional change and find out subset of one or several physiological features that can best represent the changes of psychological state in a statistical sense. Previous studies had used the differences in physiological features between discrete emotional states to select feature subsets. However, the emotional state of the human body is continuously changing. The conventional feature selection methods ignored the dynamic process of an individual's emotional stress in real life. Therefore, a dedicated experimental was conducted while three peripheral physiological signals, i.e., ElectroCardioGram (ECG), Galvanic Skin Resistance (GSR), and Blood Volume Pulse (BVP), were continuously acquired. This paper reported a novel feature selection method based on emotional state transition, the experimental results show that the number of physiological features selected by the proposed method in this paper is 13, including 5 features of ECG, 4 features of PPG and 4 features of GSR, respectively, which are superior to PCA method and conventional feature selection method based on discrete emotional states in terms of dimension reduction. The classification results show that the accuracy of the proposed method in emotion recognition based on ECG and PPG is higher than the other two methods. These results suggest that the proposed method can serve as a viable alternative to conventional feature selection methods, and emotional state transition deserves more attention to promote the development of stress assessment.

How to effectively prevent alienation behavior of prefabricated construction developers: an optimization analysis of regulatory strategies.

Prefabricated buildings (PBs) contribute to sustainable development, and their development largely depends on the active participation of developers. However, based on the characteristics of different stages of PBs' development and the goals of "14th Five-Year Plan" for architecture in China, it is an urgent practical problem for the government to encourage developers' active participation while regulating their alienation behavior. To address such problem, this paper uses the evolutionary game method to explore the government's reasonable regulatory strategies for developers' behavior in different development stages of PBs. Meanwhile, this paper explores the boundary of government's regulatory strength on PBs based on actual situation in China, which help the government to drive high-quality development of PBs with effective policy resources. Results reveal that the strict regulatory strategies have limited effects in the incubation stage of PBs. In the growth stage, it is necessary to adjust the regulatory strategies appropriately. The dynamic linear regulatory strategy can enable the PBs to achieve the phased goal, and the dynamic nonlinear regulatory strategy can help to achieve the optimal goal of PBs in China. In the maturity stage, the government does not need to deliberately regulate due to the considerable profits of developers. The regulatory strategy of "light reward and heavy punishment" is better when it is adopted to promote the development of PBs in the growth stage. The research also provides valuable suggestions for government regulators to formulate reasonable and dynamic regulatory policies for PBs.

[Lean strategy for data mining and continuous improvement of Chinese pharmaceutical process: a case study of sporoderm-removal Ganoderma lucidum spore powder].

In the digital transformation of Chinese pharmaceutical industry, how to efficiently govern and analyze industrial data and excavate the valuable information contained therein to guide the production of drug products has always been a research hotspot and application difficulty. Generally, the Chinese pharmaceutical technique is relatively extensive, and the consistency of drug quality needs to be improved. To address this problem, we proposed an optimization method combining advanced calculation tools(e.g., Bayesian network, convolutional neural network, and Pareto multi-objective optimization algorithm) with lean six sigma tools(e.g., Shewhart control chart and process performance index) to dig deeply into historical industrial data and guide the continuous improvement of pharmaceutical processes. Further, we employed this strategy to optimize the manufacturing process of sporoderm-removal Ganoderma lucidum spore powder. After optimization, we preliminarily obtained the possible interval combination of critical parameters to ensure the P_(pk) values of the critical quality properties including moisture, fineness, crude polysaccharide, and total triterpenes of the sporoderm-removal G. lucidum spore powder to be no less than 1.33. The results indicate that the proposed strategy has an industrial application value.

Assessment of Rose Bengal Photodynamic Therapy on Viability and Proliferation of Human Keratolimbal Epithelial and Stromal Cells In Vitro.

PURPOSE: To investigate the effect of Rose Bengal photodynamic therapy (RB-PDT) on viability and proliferation of human limbal epithelial stem cells (T-LSCs), human corneal epithelial cells (HCE-T), human limbal fibroblasts (LFCs), and human normal and keratoconus fibroblasts (HCFs and KC-HCFs) in vitro. METHODS: T-LSCs and HCE-T cell lines were used in this research. LFCs were isolated from healthy donor corneal limbi (n = 5), HCFs from healthy human donor corneas (n = 5), and KC-HCFs from penetrating keratoplasties of keratoconus patients (n = 5). After cell culture, RB-PDT was performed using 0.001% RB concentration and 565 nm wavelength illumination with 0.14 to 0.7 J/cm2 fluence. The XTT and the BrdU assays were used to assess cell viability and proliferation 24 h after RB-PDT. RESULTS: RB or illumination alone did not change cell viability or proliferation in any of the cell types (p ≥ 0.1). However, following RB-PDT, viability decreased significantly from 0.17 J/cm2 fluence in HCFs (p < 0.001) and KC-HCFs (p < 0.0001), and from 0.35 J/cm2 fluence in T-LSCs (p < 0.001), HCE-T (p < 0.05), and LFCs ((p < 0.0001). Cell proliferation decreased significantly from 0.14 J/cm2 fluence in T-LSCs (p < 0.0001), HCE-T (p < 0.05), and KC-HCFs (p < 0.001) and from 0.17 J/cm2 fluence in HCFs (p < 0.05). Regarding LFCs proliferation, no values could be determined by the BrdU assay. CONCLUSIONS: Though RB-PDT seems to be a safe and effective treatment method in vivo, its dose-dependent phototoxicity on corneal epithelial and stromal cells has to be respected. The data and experimental parameters applied in this study may provide a reliable reference for future investigations.

Development and assessment of a nutrition literacy scale for patients with end-stage kidney disease undergoing dialysis and its correlation with quality of life.

OBJECTIVES: To construct a valid and reliable Nutritional Literacy Scale for patients with end-stage kidney disease (ESKD) receiving dialysis and evaluate associations between nutrition literacy and quality of life. METHODS: A total of 208 ESKD patients receiving dialysis were selected for this study. Nutrition literacy evaluation items were drafted based on dietary guidelines for chronic kidney disease (CKD), Literature reviews and expert consultation. Scale reliability and validity were then assessed. Factors influencing nutrition literacy and the associations among nutrition literacy, nutritional status, and quality of life were evaluated. RESULTS: The scale consists of 28 items with a scale-level content validity index of 0.91 and item-level content validity indices ranging from 0.83 to 1.00. Factor analysis identified 4 common factors (dimensions) named nutrition knowledge, cognitive attitude, behavioral practice, and information acquisition ability that collectively explained 56.31% of literacy score variation. The overall Cronbach's α coefficient of the scale was 0.83, the dimensional Cronbach's α coefficients ranged from 0.79 to 0.87, and the retest reliability was r = 0.73 (p < 0.05). Age, education level, residence (urban vs. Rural) , occupational status and dialysis modalities were significant factors influencing nutrition literacy. Nutrition literacy score was negatively correlated with SGA score and positively correlated with serum albumin and prealbumin concen- trations, and with SF-36 quality of life score (all p < 0.05). CONCLUSIONS: This new Nutrition Literacy Scale demonstrates high reliability and validity for Chinese ESKD patients undergoing dialysis. The nutrition literacy is influenced by age, education level, residence, occupational status and dialysis modalities, associated not only with nutritional status but also with quality of life.

Noninvasive Assessment of the Renal Function, Oxford Classification and Prognostic Risk Stratification of IgAN by Using Intravoxel Incoherent Motion Diffusion-Weighted Imaging and Blood Oxygenation Level-Dependent MRI.

BACKGROUND: Immunoglobulin A nephropathy (IgAN) is the most common primary glomerulonephritis worldwide. Oxford classification including mesangial hypercellularity (M), endothelial hypercellularity (E), segmental sclerosis (S), interstitial fibrosis/tubular atrophy (T), and crescent (C) were recommended to predict the prognosis of IgAN. PURPOSE: To explore whether multiparametric magnetic resonance imaging (MRI) can be applied to assess the renal function, Oxford classification, and risk of progression to end-stage kidney disease within 5 years of IgAN. STUDY TYPE: Prospective. POPULATION: A total of 46 patients with pathologically confirmed IgAN and 20 healthy volunteers. FIELD STRENGTH/SEQUENCE: A 3-T, blood oxygenation level-dependent (BOLD)-MRI, intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI). ASSESSMENT: Two radiologists measured the cortex and medulla T2*, apparent diffusion coefficient (ADC), true diffusion (Dt), pseudo-diffusion (Dp), perfusion fraction (fp). All participants were divided into three groups: group 1, healthy volunteers; group 2, patients with estimated glomerular filtration rate (eGFR) ≥60 mL/min/1.73 m2 ; group 3, patients with eGFR <60 mL/min/1.73 m2 . Or two groups: group A, 5-year risk scores ≤10% and group B, 5-year risk scores >10%. STATISTICAL TESTS: Intraclass correlation coefficient, one-way analysis of variance, least-significant difference, Student's t-test, Pearson product-moment correlation, Spearman's rank correlation, and receiver operating characteristics (ROC) with the area under the curve (AUC). A P value <0.05 was considered statistically significant. RESULTS: Except for cortical Dp, all other MRI parameters showed significant differences between group 1 and group 2. None of the MRI parameters showed a significant correlation with M, E, or C scores. Cortical T2*, Dt, fp, and medullary Dt and fp showed low-to-moderate significant correlations with S scores. Except for cortical and medullary Dp, all other MRI parameters were significantly correlated with T scores. Cortical Dt showed the largest AUC for differentiating group A from group B (AUC = 0.927) and T0 from T1/T2 (AUC = 0.963). DATA CONCLUSION: Imaging by IVIM-DWI and BOLD-MRI could facilitate noninvasive assessment of the renal function, Oxford classification, and prognostic risk of IgAN patients. EVIDENCE LEVEL: 2. TECHNICAL EFFICACY: Stage 3.

Does pollution levy standard reform promotes green innovation? Evidence from China.

Taking advantage of the quasi-natural experiment generated by the adjustment of pollution levy standard in different Chinese provinces and utilizing detailed prefecture-level data from 2004 to 2014, we rigorously examine the effect of an increase in pollution levy standard on green innovation by adopting the difference-in-differences method. We find that an increase in pollution levy standard can significantly promote regional green innovation, with a magnitude of nearly 12.8%, as compared with their control group. Additionally, we find that financial development and intellectual property protection tend to reinforce the positive effects. Our study suggests that the Chinese government should strengthen its institutional structure to promote green innovation.

Noninvasive assessment of renal function and fibrosis in CKD patients using histogram analysis based on diffusion kurtosis imaging.

PURPOSE: To investigate the potential of histogram analysis based on diffusion kurtosis imaging (DKI) in evaluating renal function and fibrosis associated with chronic kidney disease (CKD). MATERIALS AND METHODS: Thirty-six CKD patients were enrolled, and DKI was performed in all patients before the renal biopsy. The histogram parameters of diffusivity (D) and kurtosis (K) were obtained using FireVoxel. The histogram parameters between the stable [estimated glomerular filtration rate (eGFR) ≥ 60 ml/min/1.73 m2] and impaired (eGFR < 60 ml/min/1.73 m2) eGFR group were compared. Besides, patients were classified into mild, moderate, and severe fibrosis group using a semi-quantitative standard. The correlations of histogram parameters with eGFR and fibrosis scores were investigated and the diagnostic performances of histogram parameters in assessing renal dysfunction and fibrosis were analyzed. The added value of combination of most significant parameter with 24 h urinary protein (24 h-UPRO) in evaluating fibrosis was also explored. RESULTS: Seven D histogram parameters in cortex (mean, median, 10th, 25th, 75th, 90th percentiles and entropy), two D histogram parameters in medulla (75th, 90th percentiles), seven K histogram parameters in cortex (mean, min, median, 10th, 25th, 75th, 90th percentiles) and three K histogram parameters in medulla (mean, median, 25th percentile) were significantly different between the two groups. The Dmean of cortex was the most relevant parameter to eGFR (r = 0.648, P < 0.001) and had the largest area under the curve (AUC) for differentiating the stable from impaired eGFR group [AUC = 0.889; 95% confidence interval (CI) 0.728-0.970]. The K90th of cortex presented the strongest correlation with fibrosis scores (r = 0.575, P < 0.001) and achieved the largest AUC for distinguishing the mild from moderate to severe fibrosis group (AUC = 0.849, 95% CI 0.706-0.993). Combining the K90th in cortex with 24 h-UPRO gained statistically higher AUC value (AUC = 0.880, 95% CI 0.763-0.996). CONCLUSION: Histogram analysis based on DKI is practicable for the noninvasive assessment of renal function and fibrosis in CKD patients.