Systematic data quality assessment of electronic health record data to evaluate study-specific fitness: Report from the PRESERVE research study.

Study-specific data quality testing is an essential part of minimizing analytic errors, particularly for studies making secondary use of clinical data. We applied a systematic and reproducible approach for study-specific data quality testing to the analysis plan for PRESERVE, a 15-site, EHR-based observational study of chronic kidney disease in children. This approach integrated widely adopted data quality concepts with healthcare-specific evaluation methods. We implemented two rounds of data quality assessment. The first produced high-level evaluation using aggregate results from a distributed query, focused on cohort identification and main analytic requirements. The second focused on extended testing of row-level data centralized for analysis. We systematized reporting and cataloguing of data quality issues, providing institutional teams with prioritized issues for resolution. We tracked improvements and documented anomalous data for consideration during analyses. The checks we developed identified 115 and 157 data quality issues in the two rounds, involving completeness, data model conformance, cross-variable concordance, consistency, and plausibility, extending traditional data quality approaches to address more complex stratification and temporal patterns. Resolution efforts focused on higher priority issues, given finite study resources. In many cases, institutional teams were able to correct data extraction errors or obtain additional data, avoiding exclusion of 2 institutions entirely and resolving 123 other gaps. Other results identified complexities in measures of kidney function, bearing on the study's outcome definition. Where limitations such as these are intrinsic to clinical data, the study team must account for them in conducting analyses. This study rigorously evaluated fitness of data for intended use. The framework is reusable and built on a strong theoretical underpinning. Significant data quality issues that would have otherwise delayed analyses or made data unusable were addressed. This study highlights the need for teams combining subject-matter and informatics expertise to address data quality when working with real world data.

Extending the Grading of Recommendations Assessment, Development and Evaluation (GRADE) in Traditional Chinese Medicine (TCM): The GRADE-TCM.

AIM: To extend and form the "Grading of Recommendations Assessment, Development and Evaluation in Traditional Chinese Medicine" (GRADE-TCM). METHODS: Methodologies were systematically reviewed and analyzed concerning evidence-based TCM guidelines worldwide. A survey questionnaire was developed based on the literature review and open-end expert interviews. Then, we performed expert consensus, discussion meeting, opinion collection, external examination, and the GRADE-TCM was formed eventually. RESULTS: 265 Chinese and English TCM guidelines were included and analyzed. Five experts completed the open-end interviews. Ten methodological entries were summarized, screened and selected. One round of consensus was conducted, including a total of 22 experts and 220 valid questionnaire entries, concerning 1) selection of the GRADE, 2) GRADE-TCM upgrading criteria, 3) GRADE-TCM evaluation standard, 4) principles of consensus and recommendation, and 5) presentation of the GRADE-TCM and recommendation. Finally, consensus was reached on the above 10 entries, and the results were of high importance (with voting percentages ranging from 50 % to 81.82 % for "very important" rating) and strong reliability (with the Cr ranging from 0.93 to 0.99). Expert discussion meeting (with 40 experts), opinion collection (in two online platforms) and external examination (with 14 third-party experts) were conducted, and the GRADE-TCM was established eventually. CONCLUSION: GRADE-TCM provides a new extended evidence-based evaluation standard for TCM guidelines. In GRADE-TCM, international evidence-based norms, characteristics of TCM intervention, and inheritance of TCM culture were combined organically and followed. This is helpful for localization of the GRADE in TCM and internationalization of TCM guidelines.

A comprehensive review and shiny application on the matching-adjusted indirect comparison.

Population-adjusted indirect comparison (PAIC) is an increasingly used technique for estimating the comparative effectiveness of different treatments for the health technology assessments when head-to-head trials are unavailable. Three commonly used PAIC methods include matching-adjusted indirect comparison (MAIC), simulated treatment comparison (STC), and multilevel network meta-regression (ML-NMR). MAIC enables researchers to achieve balanced covariate distribution across two independent trials when individual participant data are only available in one trial. In this article, we provide a comprehensive review of the MAIC methods, including their theoretical derivation, implicit assumptions, and connection to calibration estimation in survey sampling. We discuss the nuances between anchored and unanchored MAIC, as well as their required assumptions. Furthermore, we implement various MAIC methods in a user-friendly R Shiny application Shiny-MAIC. To our knowledge, it is the first Shiny application that implements various MAIC methods. The Shiny-MAIC application offers choice between anchored or unanchored MAIC, choice among different types of covariates and outcomes, and two variance estimators including bootstrap and robust standard errors. An example with simulated data is provided to demonstrate the utility of the Shiny-MAIC application, enabling a user-friendly approach conducting MAIC for healthcare decision-making. The Shiny-MAIC is freely available through the link: https://ziren.shinyapps.io/Shiny_MAIC/.

Examining the Impact of the Current Reimbursement Regulation on Patient Access to Innovative Medical Devices in Taiwan: Insights From 8 Years' Reimbursement Data.

OBJECTIVES: This study aimed to assess the impact of the reimbursement regulation of medical devices (Regulation), introduced by the National Health Insurance Administration (NHIA) in 2013, on patients' access to innovative medical devices in Taiwan. METHODS: Analysis of the amount of time needed from application for NHIA reimbursement for new medical devices to receiving the decision from NHIA was done using the nonreimbursement product list featured on the NHIA website. Additionally, Welch analysis of variance was used to compare the amount of time it took from application to NHIA with reimbursement decisions made by the NHIA for different nonreimbursement code categories. Further, related Pharmaceutical Benefit Reimbursement Scheme meeting minutes were analyzed to obtain more detailed information concerning medical devices' reimbursement or not. RESULTS: From December 2012 to June 2021, the overall reimbursement percentage was 56.7%, and the average amount of time between application and reimbursement was 856.7 ± 474.7 days. The mandatory reimbursement rate was about 45%. NHIA reimbursement decisions as special medical devices also take a longer amount of time, because the applicants need to agree to the decision (P < .05). The NHIA decision-making process for nonreimbursement medical devices requires a significantly longer amount of time than for general materials (eg, suture, etc) decisions. CONCLUSIONS: Although the Regulation resolves payment issues, it also increases the amount of time to reach reimbursement decisions, thus hindering patient access to innovative medical devices. The study suggests that the review process needs to be simplified concerning reimbursement notification, using local real-world data to support reimbursement decisions.

Combining pathological risk factors and T, N staging to optimize the assessment for risk stratification and prognostication in low-risk stage III colon cancer.

BACKGROUND: This study aimed to investigate the combined pathological risk factors (PRFs) to stratify low-risk (pT1-3N1) stage III colon cancer (CC), providing a basis for individualized treatment in the future. PATIENTS AND METHODS: PRFs for low-risk stage III CC were identified using COX model. Low-risk stage III CC was risk-grouped combining with PRFs, and survival analysis were performed using Kaplan-Meier. The Surveillance, Epidemiology, and End Results (SEER) databases was used for external validation. RESULTS: Nine hundred sixty-two stage III CC patients were included with 634 (65.9%) as low risk and 328 (34.1%) as high risk. Poor differentiation (OS: P = 0.048; DFS: P = 0.011), perineural invasion (OS: P = 0.003; DFS: P < 0.001) and tumor deposits (OS: P = 0.012; DFS: P = 0.003) were identified as PRFs. The prognosis of low-risk CC combined with 2 PRFs (OS: HR = 3.871, 95%CI, 2.004-7.479, P < 0.001; DFS: HR = 3.479, 95%CI, 2.158-5.610, P < 0.001) or 3 PRFs (OS: HR = 5.915, 95%CI, 1.953-17.420, P = 0.002; DFS: HR = 5.915, 95%CI, 2.623-13.335, P < 0.001) was similar to that of high-risk CC (OS: HR = 3.927, 95%CI, 2.317-6.656, P < 0.001; DFS: HR = 4.132, 95%CI, 2.858-5.974, P < 0.001). In the SEER database, 18,547 CC patients were enrolled with 10,023 (54.0%) as low risk and 8524 (46.0%) as high risk. Low-risk CC combined with 2 PRFs (OS: HR = 1.857, 95%CI, 1.613-2.139, P < 0.001) was similar to that of high-risk CC without PRFs (HR = 1.876, 95%CI, 1.731-2.033, P < 0.001). CONCLUSION: Combined PRFs improved the risk stratification of low-risk stage III CC, which could reduce the incidence of undertreatment and guide adjuvant chemotherapy.

Preliminary assessment of portable sleep monitoring for diagnosis of obstructive sleep apnea in children.

OBJECTIVE: By observing the differences in sleep parameters between portable sleep monitoring (PM) and polysomnography (PSG) in children, we aimed to investigate the diagnostic value and feasibility of PM in children with suspected obstructive sleep apnea (OSA). STUDY DESIGN: This prospective study enrolled consecutive children (aged 3-14 years) with suspected OSA in Shenzhen Children's Hospital. They had PSG and PM in the sleep laboratory. Clinical parameters of the two sleep monitoring methods were compared. RESULTS: A total of 58 children participated. They were classified into two groups according to age: 28 children aged 3 to 5 years and 30 children aged 6 to 14 years. No significant differences were observed in apnea-hypopnea index (AHI), lowest oxygen saturation (LSaO2), and mean oxygen saturation (MSaO2) between PM and PSG, but the sleep efficiency with PM was significantly higher (3-5 years age: 92.2 ± 11.3% vs 85.2 ± 14.3%, 6-14 years age: 93.2 ± 14.5% vs 84.8 ± 16.3%, both P < 0.05) than the sleep efficiency with PSG. Pearson correlation analysis indicated a strong correlation between AHI, LSaO2, MSaO2, and sleep efficiency measured by PSG and PM. Receiver operating characteristic curve (ROC) analysis showed that PM was a reliable diagnostic tool for OSA. PM has high sensitivity (3-5 years age: 95.8%, 6-14 years age: 96.3%) and low specificity (3-5 years age: 25.0%, 6-14 years age: 33.3%) for OSA in children. Thus, there is a low rate of missed diagnoses, but there is some inaccuracy in excluding children who do not have OSA. CONCLUSION: The results showed that PM has a good correlation with the various parameters of PSG. PM may be a reliable tool for diagnosing moderate and severe OSA in children, especially those who cannot cooperate with PSG or who have limited access to PSG.

Development, assessment and validation of a novel prediction nomogram model for risk identification of tracheobronchial tuberculosis in patients with pulmonary tuberculosis.

OBJECTIVE: Tracheobronchial tuberculosis (TBTB), a specific subtype of pulmonary tuberculosis (PTB), can lead to bronchial stenosis or bronchial occlusion if not identified early. However, there is currently no available means for predicting the risk of associated TBTB in PTB patients. The objective of this study was to establish a risk prediction nomogram model for estimating the associated TBTB risk in every PTB patient. METHODS: A retrospective cohort study was conducted with 2153 PTB patients. Optimised characteristics were selected using least absolute shrinkage and selection operator regression. Multivariate logistic regression was applied to build a predictive nomogram model. Discrimination, calibration and clinical usefulness of the prediction model were assessed using C-statistics, receiver operator characteristic curves, calibration plots and decision analysis. The developed model was validated both internally and externally. RESULTS: Among all PTB patients who underwent bronchoscopies (n=2153), 40.36% (n=869) were diagnosed with TBTB. A nomogram model incorporating 11 predictors was developed and displayed good discrimination with a C-statistics of 0.782, a sensitivity of 0.661 and a specificity of 0.762 and good calibration with a calibration-in-the-large of 0.052 and a calibration slope of 0.957. Model's discrimination was favourable in both internal (C-statistics, 0.782) and external (C-statistics, 0.806) validation. External validation showed satisfactory accuracy (sensitivity, 0.690; specificity, 0.804) in independent cohort. Decision curve analysis showed that the model was clinically useful when intervention was decided on at the exacerbation possibility threshold of 2.3%-99.2%. A clinical impact curve demonstrated that our model predicted high-risk estimates and true positives. CONCLUSION: We developed a novel and convenient risk prediction nomogram model that enhances the risk assessment of associated TBTB in PTB patients. This nomogram can help identify high-risk PTB patients who may benefit from early bronchoscopy and aggressive treatment to prevent disease progression.

CDK4/6 inhibitors for hormone receptor-positive/human epidermal growth factor receptor 2 negative advanced breast cancer: A rapid health technology assessment.

OBJECT: Based on the best available evidence, rapid health technology was used to assess 4 CDK4/6 inhibitors approved for marketing in China. This assessment aims to provide a reference basis for the selection of drugs by medical institutions in China and to promote the rational use of drugs in the clinic. METHODS: Depending on the Rapid Guidelines for Drug Evaluation and Selection in Chinese Medical Institutions (the Second Edition), a percentage quantitative scoring approach was used to objectively score the pharmacological properties, efficacy, safety, economy, and other attributes of CDK4/6 inhibitors. RESULTS: The composite score rankings were, in descending order, 78.09 points for abemaciclib, 78.04 points for palbociclib, 72.15 points for dalpiciclib, and 69.24 points for ribociclib by integrating the result of the 5 dimensions. CONCLUSION: Until the clinical studies, guideline recommendations, prices, and many other aspects of this assessment are updated, abemaciclib and palbociclib, which have the top 2 scores, can be used as a priority recommendation for Chinese medical institutions to select CDK4/6 inhibitors and optimize the use of the drug catalog based on the scoring results of this assessment.

Economic Growth, Income Inequality and Food Safety Risk.

Food safety risk, as an implicit cost of social and economic development, endangers the health of global residents, including China. To systematically understand the impact of socioeconomic development on food safety risk and to establish a sound modern governance system of food safety in China, this paper uses provincial panel data from 2011 to 2020 to explore the relationship between food safety risk and socio-economic development factors such as economic growth and income inequality by employing a two-way fixed effect model and moderating effect model. The results show that the food safety risk is a Kuznets curve, and the turning point is about RMB 58,104.59 per capita GDP (based on prices in 2011). However, under the moderating effect of income inequality, the turning point of the Kuznets curve of food safety risk will shift to the right, and the curve will be flattened. In other words, income inequality has a negative moderating effect on the "inverted U-shaped" relationship between economic growth and food safety risk. When dealing with food safety problems, the goal of stable and sustained economic growth and common prosperity should be incorporated into policy formulation to enhance the governance effectiveness of food safety risk.

Assessment of the sustainability of groundwater utilization and crop production under optimized irrigation strategies in the North China Plain under future climate change.

Over-exploitation of groundwater due to intensive irrigation and anticipated climate change pose severe threats to the water and food security worldwide, particularly in the North China Plain (NCP). Limited irrigation has been recognized as an effective way to improve crop water productivity and slow the rapid decline of groundwater levels. Whether optimized limited irrigation strategies could achieve a balance between groundwater pumping and grain production in the NCP under future climate change deserves further study. In this study, an improved Soil and Water Assessment Tool (SWAT) model was used to simulate climate change impacts on shallow groundwater levels and crop production under limited irrigation strategies to suggest optimal irrigation management practices under future climate conditions in the NCP. The simulations of eleven limited irrigation strategies for winter wheat with targeted irrigations at different growth stages and with irrigated or rainfed summer maize were compared with future business-as-usual management. Climate change impacts showed that mean wheat (maize) yield under adequate irrigation was expected to increase by 13.2% (4.9%) during the middle time period (2041-2070) and by 11.2% (4.6%) during the late time period (2071-2100) under three SSPs compared to the historical period (1971-2000). Mean decline rate of shallow groundwater level slowed by approximately 1 m a-1 during the entire future period (2041-2100) under three SSPs with a greater reduction for SSP5-8.5. The average contribution rate of future climate toward the balance of shallow groundwater pumping and replenishment was 62.9%. Based on the simulated crop yields and decline rate of shallow groundwater level under the future climate, the most appropriate limited irrigation was achieved by applying irrigation during the jointing stage of wheat with rainfed maize, which could achieve the groundwater recovery and sustainable food production.

DGA3-Net: A parameter-efficient deep learning model for ASPECTS assessment for acute ischemic stroke using non-contrast computed tomography.

Detecting the early signs of stroke using non-contrast computerized tomography (NCCT) is essential for the diagnosis of acute ischemic stroke (AIS). However, the hypoattenuation in NCCT is difficult to precisely identify, and accurate assessments of the Alberta Stroke Program Early CT Score (ASPECTS) are usually time-consuming and require experienced neuroradiologists. To this end, this study proposes DGA3-Net, a convolutional neural network (CNN)-based model for ASPECTS assessment via detecting early ischemic changes in ASPECTS regions. DGA3-Net is based on a novel parameter-efficient dihedral group CNN encoder to exploit the rotation and reflection symmetry of convolution kernels. The bounding volume of each ASPECTS region is extracted from the encoded feature, and an attention-guided slice aggregation module is used to aggregate features from all slices. An asymmetry-aware classifier is then used to predict stroke presence via comparison between ASPECTS regions from the left and right hemispheres. Pre-treatment NCCTs of suspected AIS patients were collected retrospectively, which consists of a primary dataset (n = 170) and an external validation dataset (n = 90), with expert consensus ASPECTS readings as ground truth. DGA3-Net outperformed two expert neuroradiologists in regional stroke identification (F1 = 0.69) and ASPECTS evaluation (Cohen's weighted Kappa = 0.70). Our ablation study also validated the efficacy of the proposed model design. In addition, class-relevant areas highlighted by visualization techniques corresponded highly with various well-established qualitative imaging signs, further validating the learned representation. This study demonstrates the potential of deep learning techniques for timely and accurate AIS diagnosis from NCCT, which could substantially improve the quality of treatment for AIS patients.

Identification and hazard analysis of heavy metal sources in agricultural soils in ancient mining areas: A quantitative method based on the receptor model and risk assessment.

Industry in ancient mining areas caused significant heavy metal pollution (HMP) in agricultural soils. This study measured the hazards of specific sources of heavy metals (HMs) in an ancient mining areas agricultural soil. Firstly, we identified the major pollution sources based on the PMF model. Then, the proposed single-factor pollution load index (SPLIzone) and ecological load index (SELIzone) analyzed the integrated pollution and ecological risks of various elements. Finally, the source-specific soil contamination levels and ecological risks were quantified by combining the source assignment and single-factor assessment processes. SPLIzone and SELIzone showed that Cu and Cd were the most contaminated elements. Five factors were determined as the major sources of HMs, including mining, natural, smelting industry, agricultural and traffic sources. The mining sources contributed the most soil contamination (33.73%). However, the largest contributor to ecological risk was the smelting industrial (42.18%). Lower soil contamination may contain higher ecological risk. Smelting industrial and traffic are the most critical sources that need to be controlled at present. This study proposes a quantitative method for assessing the hazards of HM sources, which provides a beneficial reference for the study and management of HMP.

Comparison of crop productivity, economic benefit and environmental footprints among diversified multi-cropping systems in South China.

Diversified multi-cropping system with high productivity and low environmental costs is crucial for the development of sustainable agriculture in different regions. However, the information on this practice has still been limited in the South China. This study compared different diversified multi-cropping systems including peanut-rice-fallow (P-R-F), peanut-rice-ryegrass (P-R-R), soybean-rice-ryegrass (S-R-R), feed corn-rice-milk vetch (FC-R-M), sweet corn-rice-milk vetch (SC-R-M) and zucchini -rice-milk vetch (Z-R-M), with the conventional double-rice system (CK). A newly proposed agricultural environmental footprint index (EFI) framework was introduced to quantify the comprehensive environmental costs of different systems. Results indicated that the annual productivities of P-R-R and FC-R-M rotation systems significantly increased by 39.91 % and 25.06 %, respectively, compared to the CK. The economic benefits of P-R-R and FC-R-M were 53.71 % and 16.67 % higher than the CK, respectively, with significant differences. The EFIs based on unit farmland area, crop productivity and economic benefit of the P-R-R and FC-R-M systems were 17.07 %-40.68 % lower than the CK, respectively, showing the lower environmental costs. Therefore, the P-R-R and FC-R-M were recommended as alternatives of double-rice cropping in the South China. In addition, the results indicated that the fertilization and irrigation practices were the key points for improving the rotation systems. This study provided valuable information for the transition of rice-based cropping system in South China. It was also a reference for the development of sustainable agriculture in the world's subtropical agricultural system.

Bone age assessment based on deep neural networks with annotation-free cascaded critical bone region extraction.

Bone age assessment (BAA) from hand radiographs is crucial for diagnosing endocrinology disorders in adolescents and supplying therapeutic investigation. In practice, due to the conventional clinical assessment being a subjective estimation, the accuracy of BAA relies highly on the pediatrician's professionalism and experience. Recently, many deep learning methods have been proposed for the automatic estimation of bone age and had good results. However, these methods do not exploit sufficient discriminative information or require additional manual annotations of critical bone regions that are important biological identifiers in skeletal maturity, which may restrict the clinical application of these approaches. In this research, we propose a novel two-stage deep learning method for BAA without any manual region annotation, which consists of a cascaded critical bone region extraction network and a gender-assisted bone age estimation network. First, the cascaded critical bone region extraction network automatically and sequentially locates two discriminative bone regions via the visual heat maps. Second, in order to obtain an accurate BAA, the extracted critical bone regions are fed into the gender-assisted bone age estimation network. The results showed that the proposed method achieved a mean absolute error (MAE) of 5.45 months on the public dataset Radiological Society of North America (RSNA) and 3.34 months on our private dataset.

Analysis of the Spatial-Temporal Evolution of Urbanization Quality in Zhejiang Province, China.

Taking cities in Zhejiang Province of China from 2011 to 2020 as the research object, a multi-dimensional urbanization quality evaluation index system was constructed using the comprehensive analysis method, and the urbanization quality of 11 cities in Zhejiang Province was quantitatively measured using the entropy weight method. The system classification and time-space evolution analysis were carried out using ArcGIS software (Environmental Systems Research Institute, Inc., RedLands, CA, USA) to comprehensively study the evolution characteristics and influencing factors of the urbanization quality of cities in Zhejiang Province. This study provides a reference for local governments to formulate feasible urbanization development strategies and policies to promote the high-quality development of urbanization and for the construction of new urbanization in other provinces and cities.

A portable thermostatic molecular diagnosis device based on high-efficiency photothermal conversion material for rapid field detection of SARS-CoV-2.

The outbreak of the novel coronavirus (SARS-CoV-2) has seriously harmed human health and economic development worldwide. Studies have shown that timely diagnosis and isolation are the most effective ways to prevent the spread of the epidemic. However, the current polymerase chain reaction (PCR) based molecular diagnostic platform has the problems of expensive equipment, high operation difficulty, and the need for stable power resources support, so it is difficult to popularize in low-resource areas. This study established a portable (<300 g), low-cost (<$10), and reusable molecular diagnostic device based on solar energy photothermal conversion strategy, which creatively introduces a sunflower-like light tracking system to improve light utilization, making the device suitable for both high and low-light areas. The experimental results show that the device can detect SARS-CoV-2 nucleic acid samples as low as 1 aM within 30 min.

Origami plot: a novel multivariate data visualization tool that improves radar chart.

OBJECTIVES: We propose the origami plot, which maintains the original functionality of a radar chart and avoids potential misuse of its connected regions, with newly added features to better assist multicriteria decision-making. STUDY DESIGN AND SETTING: Built upon a radar chart, the origami plot adds additional auxiliary axes and points such that the area of the connected region of all dots is invariant to the ordering of axes. As such, it enables ranking different individuals by the overall performance for multicriteria decision-making while maintaining the intuitive visual appeal of the radar chart. We develop extensions of the origami plot, including the weighted origami plot, which allows reweighting of each attribute to define the overall performance, and the pairwise origami plot, which highlights comparisons between two individuals. RESULTS: We illustrate the different versions of origami plots using the hospital compare database developed by the Centers for Medicare & Medicaid Services (CMS). The plot shows individual hospital's performance on mortality, readmission, complication, and infection, as well as patient experience and timely and effective care, as well as their overall performance across these metrics. The weighted origami plot allows weighing the attributes differently when some are more important than others. We illustrate the potential use of the pairwise origami plot in electronic health records (EHR) system to monitor five clinical measures (body mass index [BMI]), fasting glucose level, blood pressure, triglycerides, and low-density lipoprotein ([LDL] cholesterol) of a patient across multiple hospital visits. CONCLUSION: The origami plot is a useful visualization tool to assist multicriteria decision making. It improves radar charts by avoiding potential misuse of the connected regions. It has several new features and allows flexible customization.

Information Disclosure Impacts Intention to Consume Man-Made Meat: Evidence from Urban Residents' Intention to Man-Made Meat in China.

Meat substitutes such as man-made meat are emerging to promote low-carbon healthy consumption, mitigate climate change, and assist healthy economic development; however, most consumers seem reluctant to make the transition. While profound social change may be required to make significant progress in this area, limited efforts have been made to understand the psychological processes that may hinder or facilitate this transition. To clearly identify the factors influencing the public's intention to consume man-made meat and their influencing paths, this study analyzes the mechanism by which man-made meat information disclosure affects the public's intention to consume these products based on the social cognitive theory of "awareness-situation-behavior" and using structural equation modeling, with residents of seven Chinese cities as examples (647 respondents). The results of this study yielded three main findings. First, low-carbon awareness, personal social responsibility awareness, and man-made meat risk perception significantly influence the public's intention to consume man-made meat, with risk perception having the greatest influence (-0.434). Second, low-carbon awareness and man-made meat risk perception have a significant interaction effect on the public's intention to consume man-made meat (-0.694). Third, man-made meat information disclosure has the most significant moderating effect on the relationship between low-carbon awareness and the public's intention to consume man-made meat, as well as a moderating effect on the relationship between man-made meat risk perception and the public's intention to consume man-made meat.

Single pulse protoacoustic range verification using a clinical synchrocyclotron.

Objective.Proton therapy as the next generation radiation-based cancer therapy offers dominant advantages over conventional radiation therapy due to the utilization of the Bragg peak; however, range uncertainty in beam delivery substantially mitigates the advantages of proton therapy. This work reports using protoacoustic measurements to determine the location of proton Bragg peak deposition within a water phantom in real time during beam delivery.Approach.In protoacoustics, proton beams have a definitive range, depositing a majority of the dose at the Bragg peak; this dose is then converted to heat. The resulting thermoelastic expansion generates a 3D acoustic wave, which can be detected by acoustic detectors to localize the Bragg peak.Main results.Protoacoustic measurements were performed with a synchrocyclotron proton machine over the exhaustive energy range from 45.5 to 227.15 MeV in clinic. It was found that the amplitude of the acoustic waves is proportional to proton dose deposition, and therefore encodes dosimetric information. With the guidance of protoacoustics, each individual proton beam (7 pC/pulse) can be directly visualized with sub-millimeter (<0.7 mm) resolution using single beam pulse for the first time.Significance.The ability to localize the Bragg peak in real-time and obtain acoustic signals proportional to dose within tumors could enable precision proton therapy and hope to progress towardsin vivomeasurements.

Deep Learning-Based Automatic Detection of ASPECTS in Acute Ischemic Stroke: Improving Stroke Assessment on CT Scans.

(1) Background: The Alberta Stroke Program Early CT Score (ASPECTS) is a standardized scoring tool used to evaluate the severity of acute ischemic stroke (AIS) on non-contrast CT (NCCT). Our aim in this study was to automate ASPECTS. (2) Methods: We utilized a total of 258 patient images with suspected AIS symptoms. Expert ASPECTS readings on NCCT were used as ground truths. A deep learning-based automatic detection (DLAD) algorithm was developed for automated ASPECTS scoring based on 168 training patient images using a convolutional neural network (CNN) architecture. An additional 90 testing patient images were used to evaluate the performance of the DLAD algorithm, which was then compared with ASPECTS readings on NCCT as performed by physicians. (3) Results: The sensitivity, specificity, and accuracy of DLAD for the prediction of ASPECTS were 65%, 82%, and 80%, respectively. These results demonstrate that the DLAD algorithm was not inferior to radiologist-read ASPECTS on NCCT. With the assistance of DLAD, the individual sensitivity of the ER physician, neurologist, and radiologist improved. (4) Conclusion: The proposed DLAD algorithm exhibits a reasonable ability for ASPECTS scoring on NCCT images in patients presenting with AIS symptoms. The DLAD algorithm could be a valuable tool to improve and accelerate the decision-making process of front-line physicians.