Assessment on malvidin-3-glucoside interaction with TLR4 via multi-spectroscopic analysis and molecular docking.

As one innate immune pattern recognition receptor, Toll-like receptor 4 (TLR4) recently has been considered as a critical player in glucolipid metabolism. Blueberries contain high level of anthocyanins, especially malvidin-3-glucoside (Mv-3-glc), which contribute the anti-inflammatory, hypoglycemic, and hypolipidemic effects. It is speculated that Mv-3-glc is able to possess these functions by binding to TLR4. Here, the noncovalent interactions of Mv-3-glc and TLR4 was explored through multi-techniques including fluorescence and ultraviolet-visible (UV-Vis) absorption spectroscopy, as well as molecular docking. The results demonstrated that Mv-3-glc was able to quench TLR4 intrinsic fluorescence effectively. A stable complex was formed spontaneously and the reaction was exothermic. The degree of binding of Mv-3-glc to TLR4 showed a strong dependence on the chemical concentration, temperature, and pH values. The negative signs for enthalpy (ΔH = -69.1 ± 10.8 kJ/mol) and entropy (ΔS = -105.0 ± 12.3 J/mol/K) from the interaction of the Mv-3-glc and TLR4 shows that the major driving forces are the hydrogen bonding and van der Waals' force, which is consistent with the molecular docking results. In addition, molecular docking predicted that the active center with specific amino acid residues, Phe126, Ser127, Leu54, Ile153, and Tyr131 was responsible for the site of Mv-3-glc binding to TLR4/myeloid differentiation protein-2 (MD-2). These findings confirmed that Mv-3-glc could bind to TLR4, which would be beneficial to understand the target therapeutic effects of blueberry anthocyanins on TLR4 in regulating glucolipid metabolism.

Spatialtemporal evolution characteristics of ozone in China and its response to urbanization.

Based on the background of urbanization in China, we used the dynamic spatial panel Durbin model to study the driving mechanism of ozone pollution empirically. We also analyzed the spatial distribution of ozone driving factors using the GTWR. The results show that: i) The average annual increase of ozone concentration in ambient air in China from 2015 to 2019 was 1.68µg/m3, and 8.39µg/m3 elevated the year 2019 compared with 2015. ii) The Moran's I value of ozone in ambient air was 0.027 in 2015 and 0.209 in 2019, showing the spatial distribution characteristics of "east heavy and west light" and "south low and north high". iii) Per capita GDP industrial structure, population density, land expansion, and urbanization rate have significant spillover effects on ozone concentration, and the regional spillover effect is greater than the local effect. R&D intensity and education level have a significant negative impact on ozone concentration. iv) There is a decreasing trend in the inhibitory effect of educational attainment and R&D intensity on ozone concentration, and an increasing trend in the promotional effect of population urbanization rate, land expansion, and economic development on ozone concentration. Empirical results suggest a twofold policy meaning: i) to explore the causes behind the distribution of ozone from the new perspective of urbanization, and to further the atmospheric environmental protection system and ii) to eliminate the adverse impacts of ozone pollution on nature and harmonious social development.

Advancing Automatic Gastritis Diagnosis: An Interpretable Multilabel Deep Learning Framework for the Simultaneous Assessment of Multiple Indicators.

The evaluation of morphologic features, such as inflammation, gastric atrophy, and intestinal metaplasia, is crucial for diagnosing gastritis. However, artificial intelligence analysis for nontumor diseases like gastritis is limited. Previous deep learning models have omitted important morphologic indicators and cannot simultaneously diagnose gastritis indicators or provide interpretable labels. To address this, an attention-based multi-instance multilabel learning network (AMMNet) was developed to simultaneously achieve the multilabel diagnosis of activity, atrophy, and intestinal metaplasia with only slide-level weak labels. To evaluate AMMNet's real-world performance, a diagnostic test was designed to observe improvements in junior pathologists' diagnostic accuracy and efficiency with and without AMMNet assistance. In this study of 1096 patients from seven independent medical centers, AMMNet performed well in assessing activity [area under the curve (AUC), 0.93], atrophy (AUC, 0.97), and intestinal metaplasia (AUC, 0.93). The false-negative rates of these indicators were only 0.04, 0.08, and 0.18, respectively, and junior pathologists had lower false-negative rates with model assistance (0.15 versus 0.10). Furthermore, AMMNet reduced the time required per whole slide image from 5.46 to only 2.85 minutes, enhancing diagnostic efficiency. In block-level clustering analysis, AMMNet effectively visualized task-related patches within whole slide images, improving interpretability. These findings highlight AMMNet's effectiveness in accurately evaluating gastritis morphologic indicators on multicenter data sets. Using multi-instance multilabel learning strategies to support routine diagnostic pathology deserves further evaluation.

Toward Better and Healthier Air Quality: Global PM2.5 and O3 Pollution Status and Risk Assessment Based on the New WHO Air Quality Guidelines for 2021.

To reduce the high burden of disease caused by air pollution, the World Health Organization (WHO) released new Air Quality Guidelines (AQG) on September 22, 2021. In this study, the daily fine particulate matter (PM2.5) and surface ozone (O3) data of 618 cities around the world is collected from 2019 to 2022. Based on the new AQG, the number of attainment days for daily average concentrations of PM2.5 (≤ 15 µg m-3) and O3 (≤ 100 µg m-3) is approximately 10% and 90%, respectively. China and India exhibit a decreasing trend in the number of highly polluted days (> 75 µg m-3) for PM. Every year over 68% and 27% of cities in the world are exposed to harmful PM2.5 (> 35 µg m-3) and O3 (> 100 µg m-3) pollution, respectively. Combined with the United Nations Sustainable Development Goals (SDGs), it is found that more than 35% of the world's cities face PM2.5-O3 compound pollution. Furthermore, the exposure risks in these cities (China, India, etc.) are mainly categorized as "High Risk", "Risk", and "Stabilization". In contrast, economically developed cities are mainly categorized as "High Safety", "Safety", and "Deep Stabilization." These findings indicate that global implementation of the WHO's new AQG will minimize the inequitable exposure risk from air pollution.

Development of an Interpretable Deep Learning Model for Pathological Tumor Response Assessment After Neoadjuvant Therapy.

BACKGROUND: Neoadjuvant therapy followed by surgery has become the standard of care for locally advanced esophageal squamous cell carcinoma (ESCC) and accurate pathological response assessment is critical to assess the therapeutic efficacy. However, it can be laborious and inconsistency between different observers may occur. Hence, we aim to develop an interpretable deep-learning model for efficient pathological response assessment following neoadjuvant therapy in ESCC. METHODS: This retrospective study analyzed 337 ESCC resection specimens from 2020-2021 at the Pudong-Branch (Cohort 1) and 114 from 2021-2022 at the Puxi-Branch (External Cohort 2) of Fudan University Shanghai Cancer Center. Whole slide images (WSIs) from these two cohorts were generated using different scanning machines to test the ability of the model in handling color variations. Four pathologists independently assessed the pathological response. The senior pathologists annotated tumor beds and residual tumor percentages on WSIs to determine consensus labels. Furthermore, 1850 image patches were randomly extracted from Cohort 1 WSIs and binarily classified for tumor viability. A deep-learning model employing knowledge distillation was developed to automatically classify positive patches for each WSI and estimate the viable residual tumor percentages. Spatial heatmaps were output for model explanations and visualizations. RESULTS: The approach achieved high concordance with pathologist consensus, with an R^2 of 0.8437, a RAcc_0.1 of 0.7586, a RAcc_0.3 of 0.9885, which were comparable to two senior pathologists (R^2 of 0.9202/0.9619, RAcc_0.1 of 8506/0.9425, RAcc_0.3 of 1.000/1.000) and surpassing two junior pathologists (R^2 of 0.5592/0.5474, RAcc_0.1 of 0.5287/0.5287, RAcc_0.3 of 0.9080/0.9310). Visualizations enabled the localization of residual viable tumor to augment microscopic assessment. CONCLUSION: This work illustrates deep learning's potential for assisting pathological response assessment. Spatial heatmaps and patch examples provide intuitive explanations of model predictions, engendering clinical trust and adoption (Code and data will be available at https://github.com/WinnieLaugh/ESCC_Percentage once the paper has been conditionally accepted). Integrating interpretable computational pathology could help enhance the efficiency and consistency of tumor response assessment and empower precise oncology treatment decisions.

New RNN Algorithms for Different Time-Variant Matrix Inequalities Solving Under Discrete-Time Framework.

A series of discrete time-variant matrix inequalities is generally regarded as one of the challenging problems in science and engineering fields. As a discrete time-variant problem, the existing solving schemes generally need the theoretical support under the continuous-time framework, and there is no independent solving scheme under the discrete-time framework. The theoretical deficiency of solving scheme greatly limits the theoretical research and practical application of discrete time-variant matrix inequalities. In this article, new discrete-time recurrent neural network (RNN) algorithms are proposed, analyzed, and investigated for solving different time-variant matrix inequalities under the discrete-time framework, including discrete time-variant matrix vector inequality (discrete time-variant MVI), discrete time-variant generalized matrix inequality (discrete time-variant GMI), discrete time-variant generalized-Sylvester matrix inequality (discrete time-variant GSMI), and discrete time-variant complicated-Sylvester matrix inequality (discrete time-variant CSMI), and all solving processes are based on the direct discretization thought. Specifically, first of all, four discrete time-variant matrix inequalities are presented as the target problems of these researches. Second, for solving such problems, we propose corresponding discrete-time recurrent neural network (RNN) (DT-RNN) algorithms (termed DT-RNN-MVI algorithm, DT-RNN-GMI algorithm, DT-RNN-GSMI algorithm, and DT-RNN-CSMI algorithm), which are different from the traditional DT-RNN design thought because second-order Taylor expansion is applied to derive the DT-RNN algorithms. This creative process avoids the intervention of continuous-time framework. Then, theoretical analyses are presented, which show the convergence and precision of the DT-RNN algorithms. Abundant numerical experiments are further carried out, which further confirm the excellent properties of the DT-RNN algorithms.

Artificial intelligence-assisted system for the assessment of Forrest classification of peptic ulcer bleeding: a multicenter diagnostic study.

BACKGROUND: Inaccurate Forrest classification may significantly affect clinical outcomes, especially in high risk patients. Therefore, this study aimed to develop a real-time deep convolutional neural network (DCNN) system to assess the Forrest classification of peptic ulcer bleeding (PUB). METHODS: A training dataset (3868 endoscopic images) and an internal validation dataset (834 images) were retrospectively collected from the 900th Hospital, Fuzhou, China. In addition, 521 images collected from four other hospitals were used for external validation. Finally, 46 endoscopic videos were prospectively collected to assess the real-time diagnostic performance of the DCNN system, whose diagnostic performance was also prospectively compared with that of three senior and three junior endoscopists. RESULTS: The DCNN system had a satisfactory diagnostic performance in the assessment of Forrest classification, with an accuracy of 91.2% (95%CI 89.5%-92.6%) and a macro-average area under the receiver operating characteristic curve of 0.80 in the validation dataset. Moreover, the DCNN system could judge suspicious regions automatically using Forrest classification in real-time videos, with an accuracy of 92.0% (95%CI 80.8%-97.8%). The DCNN system showed more accurate and stable diagnostic performance than endoscopists in the prospective clinical comparison test. This system helped to slightly improve the diagnostic performance of senior endoscopists and considerably enhance that of junior endoscopists. CONCLUSION: The DCNN system for the assessment of the Forrest classification of PUB showed satisfactory diagnostic performance, which was slightly superior to that of senior endoscopists. It could therefore effectively assist junior endoscopists in making such diagnoses during gastroscopy.

Trophodynamics and potential health risk assessment of heavy metals in the mangrove food web in Yanpu Bay, China.

Mangroves are the cradle of coastal water biodiversity and are susceptible to heavy metal pollution. However, the trophic transfer mechanism of heavy metals in the mangrove food web and the resulting human health risks are not fully understood. Heavy metal concentration (Cr, Ni, Cu, Zn, As, Cd, Pb, V, Co) and stable isotope ratios of carbon and nitrogen (δ13C and δ15N) were evaluated in sediments and particulate organic matter, litter, and aquatic organisms (plankton, arthropods, mollusks, omnivorous fish, and carnivorous fish) from the Yanpu Bay mangroves. The results revealed that heavy metals exhibited different trophic transfer patterns. As and Hg were efficiently biomagnified, with trophic magnification factors of 1.17 and 1.42, respectively; while Cr, Ni, Cu, Cd, Pb, V, and Co were efficiently biodiluted. Zn exhibited a trophic magnification factor > 1 and was not significantly correlated with δ15N (p > 0.05), suggesting no biomagnification or biodilution. The heavy metals in the important fishery species (omnivorous fish and carnivorous fish) were below the permissible limits, except for Zn in Ophichthus apicalis. The assessment of probabilistic health risks revealed that fish consumption in adults and children posed an acceptable risk (total target hazard quotient <1).

Histological and functional assessment of a Takotsubo cardiomyopathy model established by immobilization stress.

INTRODUCTION: Takotsubo cardiomyopathy (TTC), also known as stress-induced cardiomyopathy, resembles acute heart failure syndrome but lacks disease-specific diagnosis and treatment strategies. TTC accounts for approximately 5-6% of all suspected cases of acute coronary syndrome in women. At present, animal models of TTC are often created by large amounts of exogenous catecholamines such as isoproterenol. However, isoproterenol injection cannot fully simulate the onset of stress-induced cardiomyopathy in humans since stress is not an instantaneous event. METHODS: Rats were immobilized for 6 h per day for 1-14 days. To examine whether the TTC model was successful, echocardiography was employed; Elisa detected serum sympathetic activation markers; and the Open-Field test (OFT) was used to analyze behavioral changes in rats after stress. Western blot and histology were used to assess sympathetic remodeling, inflammation levels, and fibrosis; qRT-PCR was used to explore the levels of fibrosis and myocardial hypertrophy. The electrical stability of ventricular was determined by electrophysiological testing. RESULTS: The rats showed severe stress behavior and local sympathetic remodeling of the heart after only 1 day of stress. After 3 days of stress, the induction of ventricular tachyarrhythmia increased prominently. The highest incidence of TTC in rats was at 5 days of immobilization stress. The pathological left ventricular remodeling caused by immobilization (IMO) stress includes inflammatory infiltration, fibrosis, and myocardial hypertrophy. CONCLUSIONS: Our study confirms the hypothesis that IMO stress can mimic Takotsubo cardiomyopathy, and the various effects on the heart depending on the duration of IMO stress. We observed the highest incidence of TTC occurred after 5 days of stress. Furthermore, there is a gradual occurrence of electrical and structural remodeling as the stress duration prolongs.

Spatiotemporal patterns of surface ozone exposure inequality in China.

Rising surface ozone (O3) levels in China are increasingly emphasizing the potential threats to public health, ecological balance, and economic sustainability. Using a 1 km × 1 km dataset of O3 concentrations, this research employs subpopulation demographic data combined with a population-weighted quality model. Its aim is to evaluate quantitatively the differences in O3 exposure among various subpopulations within China, both at a provincial and urban cluster level. Additionally, an exposure disparity indicator was devised to establish unambiguous exposure risks among significant urban agglomerations at varying O3 concentration levels. The findings reveal that as of 2018, the population-weighted average concentration of O3 for all subgroups has experienced a significant uptick, surpassing the average O3 concentration (118 µg/m3). Notably, the middle-aged demographic exhibited the highest O3 exposure level at 135.7 µg/m3, which is significantly elevated compared to other age brackets. Concurrently, there exists a prominent positive correlation between educational attainment and O3 exposure levels, with the medium-income bracket showing the greatest susceptibility to O3 exposure risks. From an industrial vantage point, the secondary sector demographic is the most adversely impacted by O3 exposure. In terms of urban-rural structure, urban groups in all regions had higher levels of exposure to O3 than rural areas, with North and East China having the most significant levels of exposure. These findings not only emphasize the intricate interplay between public health and environmental justice but further highlight the indispensability of segmented subgroup strategies in environmental health risk assessment. Moreover, this research furnishes invaluable scientific groundwork for crafting targeted public health interventions and sustainable air quality management policies.

'There is always good fortune in misfortune to encourage us in coping with difficulties'-The lived experiences of family caregivers of people with dementia during the COVID-19 outbreak in China: A phenomenological study.

AIM: To describe the lived experiences of family caregivers of individuals with dementia during the coronavirus disease (COVID-19) outbreak in China. DESIGN: This study used a descriptive phenomenological research method. METHODS: Between May and September 2021, semi-structured interviews were conducted with 22 family caregivers of people with dementia. Colaizzi's method was used for manual analysis. RESULTS: Qualitative data revealed an overarching experience of finding 'There is always good fortune in misfortune to encourage us in coping with difficulties'. Three themes emerged: family reactions to the COVID-19 outbreak, feeling supported by multiple resources performing respective functions and resilient adaptation to new situations. CONCLUSION: During the COVID-19 outbreak, family caregivers of people living with dementia in China looked for positive aspects among difficulties and experienced corresponding reactions, social support resources and resilient adapted coping styles. IMPLICATIONS FOR THE PROFESSION AND/OR PATIENT CARE: Nurses in China and other countries facing similar pandemic characteristics, cultures or economic development levels, can guide family caregivers to look at family hardships from a positive perspective, develop interventions to rapidly respond to families' reactions after a disaster and help them identify social support resources and form adapted coping styles. IMPACT: We identified the resilience and the positive experiences of Chinese family caregivers of individuals with dementia during the COVID-19 outbreak. The results can inform countries with similar cultures and economic levels, offering measures to support their adaptation to pandemics. REPORTING METHOD: This study followed the COREQ guidelines. PATIENT OR PUBLIC CONTRIBUTION: Family caregivers of people with dementia who met the inclusion criteria and who were interested in sharing their understanding of their experiences, participated in the study.

[Research on Cost Control and Rationalization Application Supervision of Medical Equipment Consumables Based on Analytic Hierarchy Process].

OBJECTIVE: To analyze the medical equipment operation data of 44 clinical departments in the hospital from three aspects: materials and consumables, operation and maintenance depreciation, and operation management. METHODS: To formulate the evaluation standards and scoring criteria for the operation indicators, the lowest score is 0 points, and the highest score is 5 points. Based on the operation indicators of medical equipment, establish a hierarchical structure model, determine the criterion layer and sub-criteria layer, construct a judgment matrix, normalize it, and calculate the weight coefficient. RESULTS: Count equipment operation data in 2021 and 2022. Score according to the assessment standards, assign weights through the analytic hierarchy process, calculate the total score and sort, and making a special analysis on the top 10 departments and departments with a score below 4 points, and formulate a rectification plan. CONCLUSIONS: The establishment of index assessment standards and the weight distribution of AHP can effectively enhance the control of equipment operating costs.

Prospective assessment of breast lesions AI classification model based on ultrasound dynamic videos and ACR BI-RADS characteristics.

Introduction: AI-assisted ultrasound diagnosis is considered a fast and accurate new method that can reduce the subjective and experience-dependent nature of handheld ultrasound. In order to meet clinical diagnostic needs better, we first proposed a breast lesions AI classification model based on ultrasound dynamic videos and ACR BI-RADS characteristics (hereafter, Auto BI-RADS). In this study, we prospectively verify its performance. Methods: In this study, the model development was based on retrospective data including 480 ultrasound dynamic videos equivalent to 18122 static images of pathologically proven breast lesions from 420 patients. A total of 292 breast lesions ultrasound dynamic videos from the internal and external hospital were prospectively tested by Auto BI-RADS. The performance of Auto BI-RADS was compared with both experienced and junior radiologists using the DeLong method, Kappa test, and McNemar test. Results: The Auto BI-RADS achieved an accuracy, sensitivity, and specificity of 0.87, 0.93, and 0.81, respectively. The consistency of the BI-RADS category between Auto BI-RADS and the experienced group (Kappa:0.82) was higher than that of the juniors (Kappa:0.60). The consistency rates between Auto BI-RADS and the experienced group were higher than those between Auto BI-RADS and the junior group for shape (93% vs. 80%; P = .01), orientation (90% vs. 84%; P = .02), margin (84% vs. 71%; P = .01), echo pattern (69% vs. 56%; P = .001) and posterior features (76% vs. 71%; P = .0046), While the difference of calcification was not significantly different. Discussion: In this study, we aimed to prospectively verify a novel AI tool based on ultrasound dynamic videos and ACR BI-RADS characteristics. The prospective assessment suggested that the AI tool not only meets the clinical needs better but also reaches the diagnostic efficiency of experienced radiologists.

The role of financial markets in the energy transition: an analysis of investment trends and opportunities in renewable energy and clean technology.

The crucial role that financial markets have played in accelerating the shift to clean energy and renewable sources of energy is examined in this article. Thus, we built global essential mineral trade networks from 1999 to 2020 using a complex network technique to analyze their topological properties quantitatively. The impact of crucial mineral trade patterns on the growth of renewable energy is then examined using the dynamic econometric model, along with the mediating function of technological advancement in renewable energy. It analyzes investment patterns and possibilities in various industries while underlining the critical role that financial systems play in determining the speed and scope of the change. The research uses data from reliable sources and thoroughly analyzes the body of current literature. The data shows that investments in clean technologies and renewable energy have significantly increased recently. This increase may be ascribed to several causes, including favorable governmental regulations, falling renewable energy technology prices, and rising environmental consciousness among the general people. Venture capital, private equity, public markets, and specialist funds are just a few examples of financial markets that have been instrumental in directing funding to these industries. The report also reveals a change in how money is invested in the energy industry, with conventional investments in fossil fuels declining and investments in renewable energy growing significantly. The profitability and appeal of renewable energy projects, which are now competitive with traditional energy sources, are driving this transformation. The report also identifies new investment possibilities in clean technology, including smart infrastructure, grid modernization, and energy storage. Due to their potential to improve the effectiveness, dependability, and sustainability of energy systems, these areas are expanding. The results highlight the need to establish long-term stability and incentives for investment in the clean technology and renewable energy industries. Government assistance has considerably aided investor confidence, including carbon pricing systems, tax incentives, and subsidies for renewable energy sources. This analysis emphasizes how critical financial markets are to accelerating the energy transition. Financial markets may hasten the transition to a sustainable energy system by directing investments into clean technologies and renewable energy industries. To take advantage of the investment possibilities given by the energy transition, policymakers, investors, and industry stakeholders must work together.

Ultra-sensitive photoelectrochemical biosensor for determination of African swine fever virus based on surface plasmon resonance.

Sensitive and specific detection of African swine fever virus (ASFV) is crucial for agricultural production and economic development due to the mortality and infectivity. In this study, a bismuth induced enhanced photoelectrochemical (PEC) biosensor based on in-situ loop mediated isothermal amplification (LAMP) was constructed using deposited bismuth nanoparticles loaded bismuth oxycarbonate (Bi/(BiO)2CO3) as photoactive material, using primers designed according to LAMP as recognition elements, and using in-situ LAMP to achieve nucleic acid amplification of target genes. As the Bi induced surface plasmon resonance (SPR) effect, enhanced light captures and effective electron hole separation, it could effectively enhance the photoelectric activity, so the prepared Bi/(BiO)2CO3 nanohybrid had higher photocurrent intensity and good stability. The constructed PEC biosensor has realized the detection of ASFV in real samples with good sensitivity, specificity and repeatability. In the range from 1.0 × 10-13 to 1.0 × 10-7 g/L, the photoelectric current decreased with the increase of the concentration of ASFV, and the detection limit was 3.0 × 10-14 g/L (about 0.048 copies/µL). Combining the advantages of LAMP with the excellent performance of PEC, it provides a simple, economical and efficient method for nucleic acid diagnosis, and also provides a new idea for biosensor detection.

Quantitative assessment of extraocular muscles in Graves' ophthalmopathy using T1 mapping.

OBJECTIVE: To evaluate the performance of T1 mapping in the characterization of extraocular muscles (EOMs) of Graves' ophthalmopathy (GO) patients and investigate its feasibility in assessing the response to glucocorticoid therapy in active GO patients. METHODS: A total of 133 participants (78 active GO, 23 inactive GO, 18 Graves' disease (GD) patients, and 14 healthy volunteers) were consecutively enrolled from July 2018 to December 2020. Native T1 (nT1) and postcontrast T1 (cT1) values of EOMs were measured and compared. The variations in T1 mapping metrics of EOMs were compared pre/post glucocorticoid treatment in 23 follow-up active GO patients. Logistic regression analysis and receiver operating characteristic (ROC) curve analysis were performed. RESULTS: The nT1 of EOMs in GO patients was higher than that in GD patients and healthy volunteers. The nT1 of superior rectus (SR) in active GO was higher than that in inactive GO patients, and it could be used as a potential marker of GO activity (OR: 1.003; 95% CI: 1.001, 1.004), with a diagnostic sensitivity of 86.3% and specificity of 43.7%. Meanwhile, the cT1 of SR, inferior rectus (IR), and medial rectus (MR) in inactive GO patients were higher than those in active GO patients. The nT1 of EOMs achieved sufficient diagnostic performance in evaluating the response to glucocorticoid therapy for follow-up active GO patients (AUC, 0.797; sensitivity, 71.9%; specificity, 85.7%). CONCLUSIONS: T1 mapping could quantitatively assess the activity of GO and the response to glucocorticoid therapy in active GO patients and may even potentially reflect the fibrosis of EOMs. CLINICAL RELEVANCE STATEMENT: T1 values can reflect the pathological status of the extraocular muscle. T1 mapping could help to quantitatively assess the clinical activity of GO and the response to glucocorticoid therapy in active GO patients. KEY POINTS: • Graves' ophthalmopathy patients had greater nT1 of extraocular muscles than Graves' disease patients and healthy volunteers, and nT1 of the superior rectus could be a potential marker of Graves' ophthalmopathy activity. • The cT1 of extraocular muscles in inactive Graves' ophthalmopathy patients was higher than that in active Graves' ophthalmopathy patients, and it might be associated with muscle fibrosis. • nT1 of extraocular muscles could offer sufficient diagnostic performance in evaluating the response to glucocorticoid therapy for follow-up active Graves' ophthalmopathy patients.

Occurrence, distribution and risk assessment of polycyclic aromatic hydrocarbons in soils around main water source areas of Beijing, China.

Polycyclic aromatic hydrocarbons (PAHs) in urban environments have been globally concerned due to their significant health impacts on residents. However, little is known about potential risks of PAHs from centralized water source areas. In the present study, 326 soils samples from the main water source areas of Beijing were collected and the occurrence, source appointment, and risks of PAHs were systematically investigated based on the monitoring results from high-performance liquid chromatography (HPLC). The total PAHs (∑16 PAHs) concentrations ranged from 5.70 to 1512 ng/g with median value of 44.2 ng/g, in which 4-ring and 5-ring groups were the major components. PAHs concentrations in the cultivated land were significantly higher than other areas, which could reflect significant impact of soil organic matter and total nitrogen contents on the spatial variations of PAHs. Further source identifications through positive matrix factorization model (PMF) revealed that biomass (22.5%), coal (21.4%), gasoline (17.6%) and diesel (16.4%) combustion were dominant sources of soil PAHs in the study area. Moreover, the risk assessment indicated that total ecological and health risk of PAHs were negligible, but individual PAH, including pyrene and benzo(b)fluoranthene, should be concerned due to their potential risks in several monitored stations located in the secondary protection area of four reservoirs. Our study provided new insights into environmental risks of soils in main water source areas from PAHs and could be helpful for organic micropollutant controlling and drinking water safety in rapidly urbanizing cities.

Distribution, source identification, and health risk of emerging organic contaminants in groundwater of Xiong'an New Area, Northern China.

Groundwater contamination in China has been greatly concerned due to dramatically increasing fresh water demand accompanied by economic development. However, little is known about aquifer vulnerability to hazardous matters especially in previously contaminated site of rapidly urbanizing cities. Here, we collected 90 groundwater samples from Xiong'an New Area during wet and dry seasons of 2019 and characterized the composition and distribution of emerging organic contaminants (EOCs) in this strategically developing city. A total of 89 EOCs, assigned to organochlorine pesticides (OCPs), polychlorinated biphenyls (PCBs), and volatile organic compounds (VOCs), were detected with the frequencies ranging from 1.11 %-85.6 %. Methyl tert-butyl ether (16.3 µg/L), Epoxid A (6.15 µg/L), and α-Lindane (5.15 µg/L) could be identified as major contributors to groundwater organic pollution. Significant aggregation of groundwater EOCs along Tang River were found due to historical residue and accumulation from wastewater storage there before 2017. Significant seasonal variations (p < 0.05) in the types and concentrations of EOCs could be attributed to discrepant pollution sources between varying seasons. Human health effects from exposure to groundwater EOCs were further evaluated with negligible risk (<10-4) in most samples (97.8 %) and notable risk (10-6-10-4) in several monitored wells (2.20 %) located along Tanghe Sewage Reservoir. This study provides new evidences for aquifer vulnerability to hazardous matters in historically contaminated sites and is of significant to groundwater pollution controlling and drinking water safety for rapidly urbanizing cities.

Emergency Treatment and Photoacoustic Assessment of Spinal Cord Injury Using Reversible Dual-Signal Transform-Based Selenium Antioxidant.

Spinal cord injury (SCI), following explosive oxidative stress, causes an abrupt and irreversible pathological deterioration of the central nervous system. Thus, preventing secondary injuries caused by reactive oxygen species (ROS), as well as monitoring and assessing the recovery from SCI are critical for the emergency treatment of SCI. Herein, an emergency treatment strategy is developed for SCI based on the selenium (Se) matrix antioxidant system to effectively inhibit oxidative stress-induced damage and simultaneously real-time evaluate the severity of SCI using a reversible dual-photoacoustic signal (680 and 750 nm). Within the emergency treatment and photoacoustic severity assessment (ETPSA) strategy, the designed Se loaded boron dipyrromethene dye with a double hydroxyl group (Se@BDP-DOH) is simultaneously used as a sensitive reporter group and an excellent antioxidant for effectively eliminating explosive oxidative stress. Se@BDP-DOH is found to promote the recovery of both spinal cord tissue and locomotor function in mice with SCI. Furthermore, ETPSA strategy synergistically enhanced ROS consumption via the caveolin 1 (Cav 1)-related pathways, as confirmed upon treatment with Cav 1 siRNA. Therefore, the ETPSA strategy is a potential tool for improving emergency treatment and photoacoustic assessment of SCI.

Quantifying the effect of socio-economic-geo drivers on the change of municipal waste disposal in China by an integrated TWFE-PRF-SDM methodology.

Municipal solid waste management and disposal in China have significantly evolved since 2000. Due to China's vast land area and significant socioeconomic and geographic disparities, nationwide waste management strategies need to be further evaluated. This study quantified the effect of social, economic, and geographic drivers on municipal waste disposal activities in 31 provincial-level administrative regions from 2003 to 2020, by establishing a methodology integrated by two-way fixed effects regression model, panel random forest, and spatial Durbin model. The results showed, in the past two decades, socio-economic-geo indicators significantly co-decided the landfill and incineration activities. In particular, the explanatory ability of GDP per capita was above 45%, while land resources and city size also showed great significance. Spatial autocorrelation analysis showed that the relationship between landfill/incineration rates and socio-economic-geo drivers changed notably from unobvious to significant with economic growth. Furthermore, the local economy and land resources displayed more impact than those of the neighboring regions, while sci-tech and education showed clear spatial spillover effects. Chinese government would carefully assess the full-scale promotion policy of incineration plants, landfill is still hold as a reasonable option for regions with specific socio-economic-geo conditions.