Toxicity assessment of Cucurbita pepo cv Dayangua and its effects on gut microbiota in mice.

BACKGROUND: Cucurbita pepo cv Dayangua (CPD) is an edible plant with diverse pharmacological properties. The current research on CPD has primarily focused on initial investigations of its chemical composition and pharmacological effects, and no comprehensive toxicity assessment has been conducted to date. METHODS: In the present study, the toxicity of CPD was evaluated through both acute and sub-chronic oral toxicity tests in mice. 16S rDNA sequencing was used to analyze the composition of the gut microbiota of mice at different time points to observe the effect of CPD on these microbial communities. RESULTS: In the acute toxicity test, CPD exhibited low toxicity, with a median lethal dose (LD50) > 2000 mg/kg. The sub-chronic toxicity test indicated that CPD administration at doses of 200, 400, and 600 mg/kg did not cause mortality or significant organ damage in mice. Furthermore, analysis of the gut microbiota after gavage administration of CPD at 400 and 600 mg/kg revealed an improved abundance of some beneficial gut bacteria. CONCLUSIONS: In summary, no acute or sub-chronic toxic effects were observed in mice following the oral administration of CPD. CPD did not affect the structure and diversity of the gut microbiota and may contribute to an increase in the number of beneficial gut bacteria.

The longitudinal association between adverse childhood experiences, childhood socioeconomic status, and lung function among middle-aged and older adults.

OBJECTIVES: This study aims to investigate the association between adverse childhood experiences (ACEs), childhood socioeconomic status (SES) with lung function among general Chinese middle-aged and older adults. METHODS: Participants at baseline were 9052 individuals aged 45 years old and above from the China Health and Retirement Longitudinal Study (CHARLS), a population-based cohort of Chinese adults. Analyses were conducted with data from three waves (2011, 2013, and 2015). The ACEs included five threat-related indicators (i.e., physical abuse, household substance abuse, domestic violence, unsafe neighborhood, and bullying) and five deprivation-related adversities (i.e., emotional neglect, household mental illness, incarcerated household member, parental separation or divorce, and parental death). The cumulative score of threat-related and deprivation-related ACEs was used for analysis. Lung function was assessed by peak expiratory flow (PEF). Two-level linear mixed growth models were used to evaluate the longitudinal association between and ACEs, childhood SES, and PEF. RESULTS: Participants with more than three deprivation-related ACEs were significantly associated with lower PEF (b = -11.45 L/min, 95%CI: -18.40, -4.49) after adjusting for multiple confounding factors. Threat-related ACEs were not associated with PFF. Father's illiterate education predicted lower lung function (b = -8.49 L/min, 95%CI: -11.68, -5.31) for all middle-aged and older adults while mother's illiterate education was only significantly associated with PEF among the men (b = -9.21 L/min, 95%CI: -18.20, -0.22), and middle-aged adults (b = -7.96 L/min, 95%CI: -14.35, -1.57). DISCUSSION: ACEs and disadvantaged childhood SES are important predictors of lower lung function during adulthood. Reducing ACEs and improving childhood SES may be beneficial for long-term health development.

Anlotinib in Chinese patients aged ≥70 years with advanced non-squamous non-small cell lung cancer without prior chemotherapy: a multicenter, single-arm pilot trial.

Background: Based on pharmacoeconomics, drug availability and actual treatment, optimal treatment regimens for Chinese non-small-cell lung carcinoma (NSCLC) patients over 70 years old are needed. Methods: This multicenter, single-arm pilot trial enrolled patients with advanced non-squamous NSCLC who refused systemic chemotherapy. Eligible patients received anlotinib (12 mg/day, d1-14, Q3W) until disease progression, intolerant toxicities, or withdrawal from the study. The primary endpoint was progression-free survival (PFS). Results: Forty-nine patients were screened between January 2019 and September 2021, of whom 40 patients were eligible. The median age was 76 years. With a median follow-up period of 16.20 (95% CI: 8.77, 25.10) months, the median PFS was 5.45 months (95% CI: 3.52-9.23) and the median overall survival was 10.32 months (95% CI: 6.44-12.78). Three patients achieved a partial response and 34 had stable disease, with an objective response rate of 7.5% and a disease control rate of 92.5%. Thirty-three (82.5%; 33/40) patients reported treatment-related adverse events (TRAEs) of any grade, and the incidence rate of grade ≥3 TRAEs was 35% (14/40). The most common grade ≥3 TRAEs were hypertension (4/40; 10.0%), hand-foot syndrome (3/40; 7.5%), and proteinuria (2/40; 5.0%). Conclusion: Anlotinib treatment was feasible and safe in Chinese elderly patients with advanced non-squamous NSCLC who did not receive any systemic chemotherapy.

A Comparison of Quality of Life, Cosmesis and Cost-Utility of Open Surgery, Vacuum-Assisted Breast Biopsy and High Intensity Focused Ultrasound for Breast Fibroadenoma.

RATIONALE AND OBJECTIVES: To compare the quality of life (QOL), cosmesis and cost-utility of open surgery (OS), vacuum-assisted breast biopsy (VABB) and high intensity focused ultrasound (HIFU) for fibroadenoma (FA). MATERIALS AND METHODS: A total of 162 patients with 267 FAs were enrolled. Baseline characteristics and treatment information were recorded. Patients were followed up at 3-, 6- and 12-month post-treatment. QOL was evaluated by health survey. Breast cosmesis was evaluated by self-rating survey and Harvard Scale. A decision-analytic model was established and incremental cost was calculated for cost-utility analysis. RESULTS: For QOL evaluation, there was no difference of physical component summary (PCS) score in three groups (P > 0.05), while the mental component summary (MCS) score was significantly higher in HIFU group than the other two groups at 3- and 6-month post-treatment (P < 0.05). The proportion of patients satisfied with breast cosmesis was significantly higher in HIFU group (96.49%) than in VABB group (54.90%) and OS group (49.99%) (P < 0.05). By Harvard Scale, 27.78%, 78.42% and 100.00% of patients were rated as excellent and good in OS group, VABB group and HIFU group, respectively (P < 0.05). To acquire a quality-adjusted life year (QALY), cost of OS, VABB and HIFU was 1034.31 USD, 1776.96 USD and 1277.67 USD, respectively. When compared to OS, incremental cost analysis showed HIFU was cost-effective, while VABB was not. CONCLUSION: OS, VABB and HIFU were all effective and safe for FA, but among these three treatments, HIFU had the best QOL improvement, breast cosmesis and cost-effectiveness.

Aquatic ecological risk assessment of imidacloprid and thiacloprid in an urban river of Qingdao, China.

Imidacloprid and thiacloprid, two neonicotinoid insecticides that are extensively used in urban areas, are potentially toxic to non-target aquatic organisms. In this study, the concentrations of imidacloprid and thiacloprid in surface runoff after rainfall were 20.79-43.77 ng/L and 25.13-63.84 ng/L, respectively, whereas the levels for the Licun River were 10.78-41.70 ng/L and 2.66-39.68 ng/L, respectively. The acute and chronic criteria for imidacloprid and thiacloprid are 0.865, 0.006, 0.83, and 0.012 µg/L, respectively. Tiered ecological risk assessments revealed the chronic ecological risks of these micropollutants to local aquatic species. There was a moderate chronic toxicity risk associated with imidacloprid and thiacloprid in the Licun River, and the joint probability curves showed a probability of chronic ecological risk to 5 % of the aquatic organisms at 68 %-97 %. The results provide evidence of urban surface runoff transporting micropollutants from surface into rivers and estuaries, highlighting the ecological risks to aquatic ecosystems.

Exposure risk assessment of representative phthalate acid esters and associated plastic debris under the agricultural land use in typical Chinese regions.

Phthalate acid esters (PAEs) are frequently detected in the global environment and can cause potential health hazards. In this study, quantitative exposure risk assessment was undertaken to derive soil generic assessment criteria (GAC) for six representative PAEs under the agricultural land use in the evaluated Chinese regions, which coupled multi-media transport and human exposure models based on multiple exposure pathways including vegetables consumption, dermal absorption, ingestion of soil and dust, and the exposure from non-soil sources. It is identified that the PAEs in agricultural soil are dominated by DEHP and DnBP representing 72-96% of the total PAEs. The GAC for BBP and DEHP, calculated on the basis of region-specific exposure parameters and soil properties in various locations, are stringent, signifying greater potential health risks from exposure to them, warranting more rigorous contamination management. The proposed soil GAC for plastic debris are 100, 107, 73 and 88 mg kg-1 for Heilongjiang Province, Beijing City, Jiangsu and Guangdong Provinces respectively. Additionally, the potential risks of 1.68 × 10-6 and 7 × 10-6 are identified for BBP and DEHP in Guangdong Province as indicated by the exceedance of target risk level of 1 × 10-6, with the consumption of vegetables being the dominant contributor to the total estimated PAEs exposure. Overall, this methodology based on the coupled contaminant transport and exposure models incorporating region-specific data provides a technical framework to derive science-based soil GAC for representative PAEs for maintaining and assessing soil quality and food safety under the agricultural land use.

Protocol of the development of a core outcome set for stroke in multidimensional value assessment of traditional Chinese medicine.

INTRODUCTION: Stroke is the most common cause of death in China. In Chinese clinical practise, traditional Chinese medicine (TCM) and integrative medicine have been widely used as adjuvant therapies for the treatment of stroke. However, their clinical effectiveness, particularly their clinical value, has been inconsistent in the literature mainly because various outcome measures have been used and reported in clinical research. Hence, obtaining a comprehensive list of outcomes for TCM value assessment is crucial for a multidimensional value assessment. Therefore, the main objective of this protocol was to develop an outcome set used in health technology assessment (HTA) decision-making for TCM treatment of stroke. METHODS AND ANALYSIS: The outcome set will be developed in four phases: (1) we will perform a systematic literature review to identify candidate outcomes that have been previously measured in published studies; (2) we will develop a comprehensive list of outcome measures by conducting a multistakeholder semistructured interview; (3) we will conduct two-round Delphi surveys to prioritise outcomes for each HTA domain; and (4) we will finalise the outcome sets by holding a ratification meeting with multiple stakeholder groups. The developed outcome set should be measured and reported as the minimum set of outcomes for HTA assessment for the TCM treatment of acute ischaemic stroke (AIS). ETHICS AND DISSEMINATION: This protocol was reviewed and approved by the Institutional Review Board of the Minhang Hospital of Fudan University. Our findings will be shared at academic conferences and in peer-reviewed publications.

Low-dose decitabine for previously untreated acute myeloid leukemia ineligible for intensive chemotherapy aged 65 years or older: a prospective study based on comprehensive geriatric assessment.

Background: The outcome of patients with acute myeloid leukemia (AML) aged ⩾65 years is poor. Effective treatment options are limited for patients with AML who cannot tolerate intensive chemotherapy. Objectives: We aimed to evaluate the efficacy of low-dose decitabine in previously untreated patients with AML aged ⩾65 years who were ineligible for intensive chemotherapy based on a comprehensive geriatric assessment. Design: We performed a prospective, multicenter, open-label, and non-randomized study. Methods: Patients were enrolled at four centers in Beijing between 1 January 2017 and 31 December 2020. They were treated with decitabine at a dose of 6 mg/m2 for 10 days. The treatment was repeated every 28 days for one cycle for a total of six cycles. The primary endpoint of our study was overall survival (OS) at the end of the first year after enrolment. The secondary endpoints included overall response rate, leukemia-free survival, relapse rate, treatment-related mortality (TRM), quality of life, safety, and transfusion dependence. Patients were continuously monitored for toxicity. Results: Overall, 47 patients (30 males and 17 females) participated in this study. The median age of the enrolled patients was 78 (range, 65-90) years. The median follow-up time was 22.2 (range, 4.6-38.8) months. Fifteen (31.9%) patients achieved complete remission (CR), 11 (23.4%) patients achieved partial remission, 3 (6.4%) patients achieved hematological improvement only, and 18 (38.3%) patients did not achieve remission. The median time to obtain CR was 2 months. The median CR was 8.5 months. Of the patients, 36 (76.6%) patients completed six cycles of treatment with low-dose decitabine, and the 1-year OS was 36.1%. According to instrumental activities of daily living scales, age, comorbidities, and albumin (IACA) scores, the median survival was 11.2 months in the unfit group and 6 months in the frail group. The 1-year OS rates in the unfit and frail groups were 49.2% and 23.4%, respectively. Grade ⩾3 non-hematological toxicity was observed in 70.2% (33/47) of the patients. TRM occurred in three patients. No early deaths occurred after treatment. Conclusion: In newly diagnosed older patients with AML whose IACA assessment was unfit or frail for standard chemotherapy, treatment with low-dose decitabine demonstrated clinical activity and good security in our study.

A review on guided-ultrasonic-wave-based structural health monitoring: From fundamental theory to machine learning techniques.

The development of structural health monitoring (SHM) techniques is of great importance to improve the structural efficiency and safety. With advantages of long propagation distances, high damage sensitivity, and economic feasibility, guided-ultrasonic-wave-based SHM is recognized as one of the most promising technologies for large-scale engineering structures. However, the propagation characteristics of guided ultrasonic waves in in-service engineering structures are highly complex, which results in difficulties in developing precise and efficient signal feature mining methods. The damage identification efficiency and reliability of existing guided ultrasonic wave methods cannot meet engineering requirements. With the development of machine learning (ML), numerous researchers have proposed improved ML methods that can be incorporated into guided ultrasonic wave diagnostic techniques for SHM of actual engineering structures. To highlight their contributions, this paper provides a state-of-the-art overview of the guided-wave-based SHM techniques enabled by ML methods. Accordingly, multiple stages required for ML-based guided ultrasonic wave techniques are discussed, including guided ultrasonic wave propagation modeling, guided ultrasonic wave data acquisition, wave signal pre-processing, guided wave data-based ML modeling, and physics-based ML modeling. By placing ML methods in the context of the guided-wave-based SHM for actual engineering structures, this paper also provides insights into future prospects and research strategies.

Assessment of disaster preparedness and related impact factors among emergency nurses in tertiary hospitals: descriptive cross-sectional study from Henan Province of China.

Background: The aim of this study was to investigate the current state of disaster preparedness and to determine associated factors among emergency nurses from tertiary hospitals in Henan Province of China. Methods: This multicenter descriptive cross-sectional study was conducted with emergency nurses from 48 tertiary hospitals in Henan Province of China between September 7, 2022-September 27, 2022. Data were collected through a self-designeds online questionnaire using the mainland China version of the Disaster Preparedness Evaluation Tool (DPET-MC). Descriptive analysis and multiple linear regression analysis were used to evaluate disaster preparedness and to determine factors affecting disaster preparedness, respectively. Results: A total of 265 emergency nurses in this study displayed a moderate level of disaster preparedness with a mean item score of 4.24 out 6.0 on the DPET-MC questionnaire. Among the five dimensions of the DPET-MC, the mean item score for pre-disaster awareness was highest (5.17 ± 0.77), while that for disaster management (3.68 ± 1.36) was the lowest. Female gender (B = -9.638, p = 0.046) and married status (B = -8.618, p = 0.038) were negatively correlated with the levels of disaster preparedness. Five factors positively correlated with the levels of disaster preparedness included having attended in the theoretical knowledge training of disaster nursing since work (B = 8.937, p = 0.043), having experienced the disaster response (B = 8.280, p = 0.036), having participated in the disaster rescue simulation exercise (B = 8.929, p = 0.039), having participated in the disaster relief training (B = 11.515, p = 0.025), as well as having participated in the training of disaster nursing specialist nurse (B = 16.101, p = 0.002). The explanatory power of these factors was 26.5%. Conclusion: Emergency nurses in Henan Province of China need more education in all areas of disaster preparedness, especially disaster management, which needs to be incorporated into nursing education, including formal and ongoing education. Besides, blended learning approach with simulation-based training and disaster nursing specialist nurse training should be considered as novel ways to improve disaster preparedness for emergency nurses in mainland China.

A cost-effective machine learning-based method for preeclampsia risk assessment and driver genes discovery.

BACKGROUND: The placenta, as a unique exchange organ between mother and fetus, is essential for successful human pregnancy and fetal health. Preeclampsia (PE) caused by placental dysfunction contributes to both maternal and infant morbidity and mortality. Accurate identification of PE patients plays a vital role in the formulation of treatment plans. However, the traditional clinical methods of PE have a high misdiagnosis rate. RESULTS: Here, we first designed a computational biology method that used single-cell transcriptome (scRNA-seq) of healthy pregnancy (38 wk) and early-onset PE (28-32 wk) to identify pathological cell subpopulations and predict PE risk. Based on machine learning methods and feature selection techniques, we observed that the Tuning ReliefF (TURF) score hybrid with XGBoost (TURF_XGB) achieved optimal performance, with 92.61% accuracy and 92.46% recall for classifying nine cell subpopulations of healthy placentas. Biological landscapes of placenta heterogeneity could be mapped by the 110 marker genes screened by TURF_XGB, which revealed the superiority of the TURF feature mining. Moreover, we processed the PE dataset with LASSO to obtain 497 biomarkers. Integration analysis of the above two gene sets revealed that dendritic cells were closely associated with early-onset PE, and C1QB and C1QC might drive preeclampsia by mediating inflammation. In addition, an ensemble model-based risk stratification card was developed to classify preeclampsia patients, and its area under the receiver operating characteristic curve (AUC) could reach 0.99. For broader accessibility, we designed an accessible online web server ( http://bioinfor.imu.edu.cn/placenta ). CONCLUSION: Single-cell transcriptome-based preeclampsia risk assessment using an ensemble machine learning framework is a valuable asset for clinical decision-making. C1QB and C1QC may be involved in the development and progression of early-onset PE by affecting the complement and coagulation cascades pathway that mediate inflammation, which has important implications for better understanding the pathogenesis of PE.

A computational framework of routine test data for the cost-effective chronic disease prediction.

Chronic diseases, because of insidious onset and long latent period, have become the major global disease burden. However, the current chronic disease diagnosis methods based on genetic markers or imaging analysis are challenging to promote completely due to high costs and cannot reach universality and popularization. This study analyzed massive data from routine blood and biochemical test of 32 448 patients and developed a novel framework for cost-effective chronic disease prediction with high accuracy (AUC 87.32%). Based on the best-performing XGBoost algorithm, 20 classification models were further constructed for 17 types of chronic diseases, including 9 types of cancers, 5 types of cardiovascular diseases and 3 types of mental illness. The highest accuracy of the model was 90.13% for cardia cancer, and the lowest was 76.38% for rectal cancer. The model interpretation with the SHAP algorithm showed that CREA, R-CV, GLU and NEUT% might be important indices to identify the most chronic diseases. PDW and R-CV are also discovered to be crucial indices in classifying the three types of chronic diseases (cardiovascular disease, cancer and mental illness). In addition, R-CV has a higher specificity for cancer, ALP for cardiovascular disease and GLU for mental illness. The association between chronic diseases was further revealed. At last, we build a user-friendly explainable machine-learning-based clinical decision support system (DisPioneer: http://bioinfor.imu.edu.cn/dispioneer) to assist in predicting, classifying and treating chronic diseases. This cost-effective work with simple blood tests will benefit more people and motivate clinical implementation and further investigation of chronic diseases prevention and surveillance program.

Assessment of artificial intelligence-aided reading in the detection of nasal bone fractures.

BACKGROUND: Artificial intelligence (AI) technology is a promising diagnostic adjunct in fracture detection. However, few studies describe the improvement of clinicians' diagnostic accuracy for nasal bone fractures with the aid of AI technology. OBJECTIVE: This study aims to determine the value of the AI model in improving the diagnostic accuracy for nasal bone fractures compared with manual reading. METHODS: A total of 252 consecutive patients who had undergone facial computed tomography (CT) between January 2020 and January 2021 were enrolled in this study. The presence or absence of a nasal bone fracture was determined by two experienced radiologists. An AI algorithm based on the deep-learning algorithm was engineered, trained and validated to detect fractures on CT images. Twenty readers with various experience were invited to read CT images with or without AI. The accuracy, sensitivity and specificity with the aid of the AI model were calculated by the readers. RESULTS: The deep-learning AI model had 84.78% sensitivity, 86.67% specificity, 0.857 area under the curve (AUC) and a 0.714 Youden index in identifying nasal bone fractures. For all readers, regardless of experience, AI-aided reading had higher sensitivity ([94.00 ± 3.17]% vs [83.52 ± 10.16]%, P< 0.001), specificity ([89.75 ± 6.15]% vs [77.55 ± 11.38]%, P< 0.001) and AUC (0.92 ± 0.04 vs 0.81 ± 0.10, P< 0.001) compared with reading without AI. With the aid of AI, the sensitivity, specificity and AUC were significantly improved in readers with 1-5 years or 6-10 years of experience (all P< 0.05, Table 4). For readers with 11-15 years of experience, no evidence suggested that AI could improve sensitivity and AUC (P= 0.124 and 0.152, respectively). CONCLUSION: The AI model might aid less experienced physicians and radiologists in improving their diagnostic performance for the localisation of nasal bone fractures on CT images.

Muffin: A Framework Toward Multi-Dimension AI Fairness by Uniting Off-the-Shelf Models.

Model fairness (a.k.a., bias) has become one of the most critical problems in a wide range of AI applications. An unfair model in autonomous driving may cause a traffic accident if corner cases (e.g., extreme weather) cannot be fairly regarded; or it will incur healthcare disparities if the AI model misdiagnoses a certain group of people (e.g., brown and black skin). In recent years, there are emerging research works on addressing unfairness, and they mainly focus on a single unfair attribute, like skin tone; however, real-world data commonly have multiple attributes, among which unfairness can exist in more than one attribute, called "multi-dimensional fairness". In this paper, we first reveal a strong correlation between the different unfair attributes, i.e., optimizing fairness on one attribute will lead to the collapse of others. Then, we propose a novel Multi-Dimension Fairness framework, namely Muffin, which includes an automatic tool to unite off-the-shelf models to improve the fairness on multiple attributes simultaneously. Case studies on dermatology datasets with two unfair attributes show that the existing approach can achieve 21.05% fairness improvement on the first attribute while it makes the second attribute unfair by 1.85%. On the other hand, the proposed Muffin can unite multiple models to achieve simultaneously 26.32% and 20.37% fairness improvement on both attributes; meanwhile, it obtains 5.58% accuracy gain.

The economic burden of malaria inpatients and its determinants during China's elimination stage.

Background: Malaria burden is still worrisome, while empirical evidence from malaria-eliminated countries including China may provide inspiration for the world. Objective: This study aimed to investigate China's malaria hospitalization costs and explore its determinants. Methods: Stratified multistage sampling across provincial, municipal, and county hospitals was conducted in 2017. All the malaria medical records were retrieved from 2014 to 2016 in 70 hospitals. Parametric and non-parametric methods were employed to estimate hospitalization costs, and the non-parametric bootstrap was used to compare hospitalization costs among sample areas and assessed the uncertainty of its differences. Quantile regressions were conducted to identify the determinants of hospitalization costs. Results: The median hospitalization costs of 1633 malaria inpatients were 628 USD. Medication and laboratory tests accounted for over 70% of total expenditure. The median reimbursement rate was 41.87%, and this number was even lower in higher-level hospitals (<35%) and among the New Rural Cooperative Medical Scheme (<40%). Finally, health insurance type, hospital tier, clinical units, unknown fever, and comorbidity were the main determinants of hospitalization costs. Conclusion: The disparity of health protection for malaria hospitalization between rural and urban areas was noteworthy. Equivocal diagnosis and comorbidity are contributors of high cost as well. A reasonable payment system and enhanced capacities to treat malaria in a cost-effective way are suggested to reassure malaria economic burden.

Application of machine learning model to predict osteoporosis based on abdominal computed tomography images of the psoas muscle: a retrospective study.

BACKGROUND: With rapid economic development, the world's average life expectancy is increasing, leading to the increasing prevalence of osteoporosis worldwide. However, due to the complexity and high cost of dual-energy x-ray absorptiometry (DXA) examination, DXA has not been widely used to diagnose osteoporosis. In addition, studies have shown that the psoas index measured at the third lumbar spine (L3) level is closely related to bone mineral density (BMD) and has an excellent predictive effect on osteoporosis. Therefore, this study developed a variety of machine learning (ML) models based on psoas muscle tissue at the L3 level of unenhanced abdominal computed tomography (CT) to predict osteoporosis. METHODS: Medical professionals collected the CT images and the clinical characteristics data of patients over 40 years old who underwent DXA and abdominal CT examination in the Second Affiliated Hospital of Wenzhou Medical University database from January 2017 to January 2021. Using 3D Slicer software based on horizontal CT images of the L3, the specialist delineated three layers of the region of interest (ROI) along the bilateral psoas muscle edges. The PyRadiomics package in Python was used to extract the features of ROI. Then Mann-Whitney U test and the least absolute shrinkage and selection operator (LASSO) algorithm were used to reduce the dimension of the extracted features. Finally, six machine learning models, Gaussian naïve Bayes (GNB), random forest (RF), logistic regression (LR), support vector machines (SVM), Gradient boosting machine (GBM), and Extreme gradient boosting (XGBoost), were applied to train and validate these features to predict osteoporosis. RESULTS: A total of 172 participants met the inclusion and exclusion criteria for the study. 82 participants were enrolled in the osteoporosis group, and 90 were in the non-osteoporosis group. Moreover, the two groups had no significant differences in age, BMI, sex, smoking, drinking, hypertension, and diabetes. Besides, 826 radiomic features were obtained from unenhanced abdominal CT images of osteoporotic and non-osteoporotic patients. Five hundred fifty radiomic features were screened out of 826 by the Mann-Whitney U test. Finally, 16 significant radiomic features were obtained by the LASSO algorithm. These 16 radiomic features were incorporated into six traditional machine learning models (GBM, GNB, LR, RF, SVM, and XGB). All six machine learning models could predict osteoporosis well in the validation set, with the area under the receiver operating characteristic (AUROC) values greater than or equal to 0.8. GBM is more effective in predicting osteoporosis, whose AUROC was 0.86, sensitivity 0.70, specificity 0.92, and accuracy 0.81 in validation sets. CONCLUSION: We developed six machine learning models to predict osteoporosis based on psoas muscle images of abdominal CT, and the GBM model had the best predictive performance. GBM model can better help clinicians to diagnose osteoporosis and provide timely anti-osteoporosis treatment for patients. In the future, the research team will strive to include participants from multiple institutions to conduct external validation of the ML model of this study.

More synergies or more trade-offs? The interaction among multiple assessment indicators in sustainable urban development in Guangzhou, China.

To achieve sustainable development, local governments are under enormous pressure to simultaneously achieve the three assessment tasks (TATs) of energy saving, CO2 reduction, and pollution reduction. However, the TATs are often managed by different authorities and have three types of measures (TTMs) that correspond to them. The lack of adequate cooperation between these authorities has led to the inefficient investment of policy resources and even to policy conflicts, and the interactions among the TTMs are not yet known. To this end, this paper uses the MCEE model to assess the interactions among the TTMs quantitatively using Guangzhou, China, as a case study. The results showed the following. (1) According to the current development trend, if the authorities managing the TATs continue to work alone, they will not be able to fulfill the corresponding assessment tasks in the future. (2) The TTMs have interactions with each other, and their synergies are far greater than their trade-offs. From 2015 to 2035, it is expected that energy-saving measures can accomplish 54.39% of the CO2 reduction tasks and 32.74% of the pollution reduction tasks indirectly, and low-carbon measures can accomplish 55.53% of the energy saving tasks and 27.20% of the pollution reduction tasks indirectly, However, environmental-protection measures will cause fewer trade-offs (energy demand and CO2 emissions increase by 3.76% and 2.88%, respectively). (3) In some years, the contribution of interactions among the TTMs are even higher than their direct contribution to the TATs. Our findings suggest that intensive cooperation between authorities is necessary, and that the benefits of such cooperation will outweigh the disadvantages.

Phytoremediation plants (ramie) and steel smelting wastes for calcium silicate coated-nZVI/biochar production: Environmental risk assessment and efficient As(V) removal mechanisms.

Arsenic is one of the most common and harmful pollutants in environment throughout the world, especially in aqueous solutions. In this study, two kinds of industrial solid wastes (Oxide scale (OS) and Blast furnace slag (BFS)) and one kind of phytoremediation plant waste (Ramie stalk) were used to prepare an environmentally friendly, low-cost, and efficient calcium silicate coated nano zero-valent iron (nZVI)/biochar composite (BOS) for As(V) adsorption. The potential environmental risks of BOS and their effects on removal of arsenic ions from aqueous media were investigated. The adsorption mechanism was explored and discussed based on XRD, SEM-EDS, XPS, etc. The results suggested that the environmental risk and heavy metals toxicity in BOS by co-pyrolysis were significantly reduced compared to the original materials, and no additional contaminant was observed in the subsequent experiments. Simultaneously, the BOS showed excellent As(V) removal capacity (>99%) and regenerative properties. The As(V) removal mechanisms are mainly ascribed to the complexation and co-precipitation between Fe and As, and the hydrogen bond between CO functional group of BOS and As. The mechanism of enhanced nZVI activity for As(V) removal was revealed. A protective layer of Ca2SiO4 was formed on the surface of nZVI during the co-pyrolysis process to prevent the passivation of nZVI. During the reaction process, the Ca2SiO4 covering the nZVI surface would be continuously detached to expose the fresh surface of nZVI, thus providing more redox activity and adsorption sites. This study provides a new way to treat and recycle industrial steel solid wastes and phytoremediation plant wastes, and the produced calcium silicate coated-nZVI/biochar composite is proposed to be a very promising material for practical remediation of As(V)-contaminated water bodies.

Medical expenditure for strabismus: a hospital-based retrospective survey.

BACKGROUND AND AIMS: The misconception of the purpose of strabismus treatment has, on the one hand, affected the motivation of strabismus patients to seek care and, on the other hand, has resulted in strabismus not being covered by health insurance, both of which interact to limit the motivation of strabismus patients and also impose a financial burden on strabismus patients. Previous studies on the cost of strabismus had only addressed the cost utility and functional and psychosocial benefits of strabismus surgery. The aim of this study was to estimate the direct medical expenditure incurred for strabismus surgery and analyze the trend for the period 2014-2019 using the data collected by local eye hospitals in northeast China. METHODS: This study was based on 6-year strabismus medical expenditure data collected from the eye hospital of the first affiliated hospital of Harbin medical university, covering 3596 strabismus patients who had strabismus surgery. All medical expenditure data were adjusted to 2014 using China's annual consumer price index to remove the effects of inflation. RESULTS: The average direct medical expenditure for strabismus cares (in 2014) was 5309.6 CNY (US$870.4), and the annual growth rates from 2015 to 2019 (compared with the previous year) were 9.3, 7.7, 21.7, 14.5, and 4.3%, respectively. Surgical expenses accounted for the highest proportion (33.1%) of the total medical expenses followed by examinations expenses (19.7%) and medical consumables expenses (18.7%). The regression coefficient for general anaesthesia was 1804.5 and age was less than 0. CONCLUSION: The average direct medical expenditure for strabismus increases year by year, and the growth rate is rapid. Anesthesia was the most important factor increasing medical cost, and age was negatively correlated with cost.

A study on the interaction between logistics industry and manufacturing industry from the perspective of integration field.

Studying the linkage between manufacturing industry and logistics industry is conducive to explore and improve the efficiency of the common development of them. In order to study the interaction of logistics industry on the development of manufacturing industry and the development of two-industry-linkage, it first calculates the high-quality development level of logistics industry and manufacturing industry, then uses the coupling coordination model to theoretically analyze and empirically test the coupling and coordinated development level of high-quality development of logistics industry and manufacturing industry from three aspects: coupling degree, coordination degree and coupling coordination degree, and based on the perspective of integration field theory, it takes the three basic synthetic fields of logistics integrator, logistics base-nuclear and logistics connection-key as the analysis dimension, PVAR model was introduced for in-depth analysis the impact of logistics industry on manufacturing industry and the level of the two-industry-linkage. It was found that the high-quality development of China's logistics industry and manufacturing industry is close on the whole, and the development trend is consistent, the high-quality development of them is mainly caused by the change of scale, but there is no obvious change in technical efficiency, which also provides a way for the high-quality development of the two-industry-linkage in the future. The two-industry-linkage mostly belongs to the situation of low-level mutual restriction, which has not yet reached a high level of mutual promotion, resulting in the overall coupling coordination degree basically in a state of barely coordination. The development of logistics industry and manufacturing industry need to go through certain practice and running in, when there is an error matching between the two, the logistics industry will inhibit the two-industry-linkage. When the economy develops to a certain extent, the expansion of the logistics system scale to the level of the two-industry-linkage is not necessarily beneficial, blindly exceeding the demand for logistics investment will cause a waste of resources, which is not conducive to the high-quality development of the logistics industry and the coupling and coordinated development of the two industries. In the long run, the change of the logistics basic-nuclear capacity, the logistics integrator scale and logistics connection-key level will have a positive impact on the change of green total factor productivity in manufacturing industry.