[Characteristics and Source Analysis of VOCs Pollution During Emergency Response in Tianjin].

To elucidate the characteristics of VOCs chemical components during heavy pollution episodes, hourly online VOCs data derived from 11 heavy pollution events in Tianjin from 2019 to 2020 were employed. The positive matrix factorization （PMF） and conditional bivariate probability function （CBPF） were employed to analyze the sources of VOCs during heavy pollution episodes. The results indicated that the average VOCs volume fraction during these episodes was recorded at 35.7×10-9. Furthermore, it was observed that during the winter emergency response period, there was a discernible increase in the volume fraction of VOCs when compared to that during the autumn season. Specifically, there was a notable upswing of 48% in the olefins category, whereas alkanes registered a 4% increase. Additionally, the VOCs component structure changed significantly during the heavy pollution episodes. During the orange warning period, the proportion of alkanes increased by 36%, and the proportion of acetylene decreased by 32%. During the yellow warning period, the proportion of alkanes increased by 14%, and the proportion of acetylene decreased by 5%. During the emergency response period, motor vehicle emission sources, natural gas evaporative sources, and solvent use sources were the main contributors of VOCs in environmental receptors, contributing 17.5%, 15.4%, and 15.2%, respectively. Compared with that during the period antecedent to the emergency response, the contribution of vehicle emission sources and diesel volatile sources to VOCs in environmental receptors decreased by 2.0% to 5.5% and 2.1% to 6.6%, respectively, and the contribution of solvent use sources decreased by 0.2% to 2.4% during the yellow warning period. During the orange warning period, the contribution of motor vehicle emission sources was reduced by 0.1% to 8.3%, and the contribution of solvent use sources was reduced by 0.5% to 6.2%.

Preparation of Graphene Oxide/Polymer Hybrid Microcapsules via Photopolymerization for Double Self-Healing Anticorrosion Coatings.

In this work, graphene oxide (GO)/polymer hybrid microcapsule-loaded self-healing agents were prepared via the combination of the emulsion template method and photopolymerization technology. The incorporation of GO in the microcapsule shell not only improved the impermeability, mechanical property, and solvent resistance property of the microcapsules significantly but also endowed the microcapsules with photothermal conversion property. By incorporating GO/polymer hybrid microcapsules in water-borne epoxy resin, a novel kind of anticorrosion coating with a double self-healing property was successfully fabricated. When the coating was scratched, the linseed oil (LO) encapsulated in the microcapsules could fill the crack, and the photothermal conversion property of GO could promote the molecular chain movement of the damaged area under near-infrared (NIR) irradiation to realize the close of the crack. Based on the filling of LO and photothermal conversion-induced scratch narrowing, the "filling" and "close" double self-healing effect can be realized under temporal NIR irradiation, which could lead to the complete recovery of the scratched coating. The |Z|f=0.1Hz value of the damaged coating with GO/polymer microcapsules after double healing was comparable to that of the intact coating, which was about 4 orders of magnitude higher than that of the scratched blank coating and single self-healing coating. As to the neutral salt spray test, the scratched blank coating failed in protection after 100 h, while the healed composite coating did not show any corrosion after 300 h.

Predicting Secondary Vertebral Compression Fracture After Vertebral Augmentation via CT-Based Machine Learning Radiomics-Clinical Model.

RATIONALE AND OBJECTIVES: Secondary vertebral compression fractures (SVCF) are very common in patients after vertebral augmentation (VA). The aim of this study was to establish a radiomic-based model to predict SVCF and specify appropriate treatment strategies. MATERIALS AND METHODS: Patients diagnosed with osteoporotic vertebral compression fracture (OVCF) and undergoing VA surgery at our center between 2017 and 2021 were subject to a retrospective analysis. Radiological features of the T6-L5 vertebrae were derived from CT images. Clustering analysis, t-test, and LASSO (least absolute shrinkage and selection operator) regression were used to identify the optimization characteristics. A radiological signature model was constructed through the best combination of 13 machine learning algorithms. Radiomics signature was integrated with clinical characteristics into a nomogram for clinical applications. The model reliability was assessed by receiver operating characteristic (ROC) curve, calibration curve, clinical decision analysis (DCA), log-rank test, and confusion matrix. RESULTS: A total of 470 eligible patients (81 with SVCF and 389 without) were identified in the clinical cohort. Eight radiomics features were identified and incorporated into machine learning, and "XGBoost" model showed the best performance. Final logistic nomogram included radiomics signature (P < 0.001), bone cement volume (P = 0.034), and T-scores of L1-L4 (P = 0.001), and showed satisfactory prediction capability in training set (0.986, 95%CI 0.969-1.000) and verification set (0.884, 95%CI 0.823-0.946). CONCLUSION: Our radiomics-clinical model based on machine learning showed potential to prospectively predict SVCF after VA and provide precise treatment strategies.

Application of Short T1 Inversion Recovery Sequence in Increased Signal Intensity Following Cervical Spondylotic Myelopathy.

OBJECTIVES: To compare magnetic resonance (MR) short T1 inversion recovery (STIR) sequence with MR T2-weighted (T2W) sequence for detecting increased signal intensity (ISI) and assessing outcomes of ISI in cervical spondylotic myelopathy (CSM). METHODS: Data of patients with CSM who showed ISI on MR imaging and had undergone cervical spine surgery were retrospectively reviewed. STIR and T2W images were examined to assess signal intensity ratio (SIR), length and grading of the ISI, maximal spinal cord compression, canal narrowing ratio, and ligamentum flavum hypertrophy. The patients were divided into good and poor groups based on their outcomes. χ2 tests and variance analysis were used to assess intergroup differences. Univariate and multivariate logistic regression analyses were performed to identify risk factors for poor outcomes, and receiver operating characteristic curves were plotted to detect prognostic effects. RESULTS: SIR and ISI lengths were significantly different between the STIR and T2 images. In the univariate logistic regression analysis, age, diabetes, SIRT2, SIRSTIR, and ISISTIR grading were significant factors. Accordingly, in the multivariate logistic regression analysis, age, diabetes, SIRT2, and SIRSTIR were included in the model. Among patients with diabetes, we observed a significant difference between SIRT2 and SIRSTIR. CONCLUSIONS: The STIR sequence demonstrated superior capability to the T2W sequence in detecting ISI; however, there was no obvious difference in predicted outcomes. STIR sequence has a better prognostic value than T2W sequence in patients with diabetes who have CSM. ISI grading based on the STIR sequence may be a clinically valuable indicator.

Effects of dapagliflozin on body weight in patients with type 2 diabetes mellitus: Evidence­based practice.

The dose-dependent pharmacological response to dapagliflozin in patients with type 2 diabetes mellitus (T2DM) with regard to weight loss remain unknown. The aim of the present study was to investigate the effects of dapagliflozin on weight loss in patients with T2DM. A total of 8,545 patients with T2DM from 24 randomized controlled trials reported in the literature were selected for inclusion in the study. Data from these trials were analyzed using maximal effect (Emax) models with nonlinear mixed effects modeling; the evaluation index was the body weight change rate from baseline values. Patients treated with 2.5 mg/day dapagliflozin exhibited an Emax of -3.04%, and the time taken for therapy to reach half of the Emax (ET50) was estimated to be 30.8 weeks for patients treated with this dose. Patients treated with 5, 10 and 20 mg/day dapagliflozin exhibited Emax values of -6.57, -4.12 and -3.23%, respectively, and their ET50 values were estimated to be 27.3, 20.4 and 4.23 weeks, respectively. The data indicated ideal linear relationships between individual predictions and observations, suggesting the optimal fitting of the final models. The present study is the first systematic analysis of the effect of dapagliflozin on weight loss in patients with T2DM. The application of dapagliflozin at 5 mg/day exhibited a greater weight loss effect compared with the other doses used, and the weight loss onset time shortened as the dose of dapagliflozin increased.

Polyaniline Microspheres with Corrosion Inhibition, Corrosion Sensing, and Photothermal Self-Healing Properties toward Intelligent Coating.

A smart coating integrating functions of corrosion inhibition, self-healing, and corrosion-sensing was developed based on a polyaniline (PANI) microsphere-loading corrosion sensing probe (8-hydroxyquinone, 8-HQ). The PANI microsphere was prepared in a facile one-pot process via the combination of photopolymerization and an emulsion template. The 8-HQ-loaded PANI microsphere achieved three synergetic effects simultaneously: corrosion inhibition, corrosion sensing, and photothermal self-healing abilities. Benefiting from the corrosion inhibition effect of PANI, the coating with the PANI microsphere exhibited significantly enhanced anticorrosion behavior. After soaking in NaCl solution for 35 days, its impedance was maintained at 1.26 × 109 Ω·cm2, nearly 3 orders of magnitude higher than that of pure resin coating. Meanwhile, the encapsulated 8-HQ exhibited pH-responsive release behavior thanks to the pH-responsive characteristics of PANI, which could chelate with Al3+ ions to form 8-HQ-Al3+ coordinates with a conspicuous fluorescence, achieving a real-time corrosion diagnosing function. Moreover, benefiting from the photothermal property of PANI, the coating with the PANI microsphere displayed rapid crack closure behavior under NIR light irradiation, and the healing efficiency could reach 83.56% under near-infrared irradiation. This work presents an innovative strategy for fabricating an intelligent self-healing, self-reporting, and anticorrosion coating, which provides a new vision to prolong the lifetime of metals.

[Source Apportionment of Ambient VOCs in Qingdao Based on Photochemical Loss Correction].

Ozone was one of the major pollutants affecting the environmental air quality in China. The accurate apportionment of key sources and their contributions of ambient ozone and its precursor VOCs played an important role in the effective prevention and control of ozone pollution. Therefore, this study utilized the photochemical-age-based parameterization method to estimate the initial concentrations of ambient VOCs data collected from January 1 to February 28, 2021 in Jiaozhou, Qingdao and corrected the photochemical losses of ambient VOC species. The positive matrix factorization(PMF) and ozone formation potential(OFP) models were used to conduct source apportionment of ambient VOCs and their OFPs so as to provide data support for the prevention and control of ozone pollution in Qingdao. The results showed that the average values of ambient ρ(TVOCs) and OFP in Qingdao during the study period were 65.9 µg·m-3 and 176.7 µg·m-3, respectively. Propane had the highest concentration(12.4 µg·m-3) and percentage(18.9%), whereas m/p-xylene had the highest OFP(24.6 µg·m-3) and percentage(13.9%). The mean initial concentration of TVOCs during the study was 153.1 µg·m-3, and its photochemical loss rate reached 63.8%. Alkenes were the VOC species with the highest photochemical loss rate(92.1%), and the photochemical loss rate of isoprene reached 98.6%, which was substantially higher than that of other VOC species. According to the source apportionment results of initial concentrations(IC-PMF), liquefied petroleum gas(24.2%), solvent use(17.8%), natural gas and petrochemical-related enterprises(16.6%), gasoline volatilization(13.2%), combustion and gasoline vehicle emissions(12.2%), biogenic emissions(8.6%), and diesel vehicle emissions(7.4%) were the main contributing sources of the ambient VOCs in Jiaozhou. Compared with the apportioned results of IC-PMF, the contribution of biogenic emissions was underestimated by 38.9% in the apportioned results based on observed concentrations(OC-PMF), and the contribution of natural gas and petrochemical-related enterprises was underestimated by 28.5%, and the underestimations of their contributions were substantially higher than those of other sources. Compared with that before the Spring Festival, the contribution of gasoline volatilization to ambient VOCs increased markedly during the Spring Festival, whereas the contributions of solvent use, combustion, and gasoline vehicle emissions to ambient VOCs increased most significantly after the Spring Festival. The main contributing sources of ambient ozone during the study period were solvent use(31.3%), natural gas and petrochemical-related enterprises(16.1%), biogenic emissions(14.5%), and combustion and gasoline vehicle emissions(13.2%). The primary contributors of ambient ozone in different Spring Festival periods showed substantial differences. Before the Spring Festival, solvent use had the highest contribution(71.1 µg·m-3), and gasoline volatilization was the highest contributor during the Spring Festival(34.4 µg·m-3), whereas biogenic emissions after the Spring Festival were the highest contributor(39.1 µg·m-3).

Characteristics and Admission Preferences of Pediatric Emergency Patients and Their Waiting Time Prediction Using Electronic Medical Record Data: Retrospective Comparative Analysis.

BACKGROUND: The growing number of patients visiting pediatric emergency departments could have a detrimental impact on the care provided to children who are triaged as needing urgent attention. Therefore, it has become essential to continuously monitor and analyze the admissions and waiting times of pediatric emergency patients. Despite the significant challenge posed by the shortage of pediatric medical resources in China's health care system, there have been few large-scale studies conducted to analyze visits to the pediatric emergency room. OBJECTIVE: This study seeks to examine the characteristics and admission patterns of patients in the pediatric emergency department using electronic medical record (EMR) data. Additionally, it aims to develop and assess machine learning models for predicting waiting times for pediatric emergency department visits. METHODS: This retrospective analysis involved patients who were admitted to the emergency department of Children's Hospital Capital Institute of Pediatrics from January 1, 2021, to December 31, 2021. Clinical data from these admissions were extracted from the electronic medical records, encompassing various variables of interest such as patient demographics, clinical diagnoses, and time stamps of clinical visits. These indicators were collected and compared. Furthermore, we developed and evaluated several computational models for predicting waiting times. RESULTS: In total, 183,024 eligible admissions from 127,368 pediatric patients were included. During the 12-month study period, pediatric emergency department visits were most frequent among children aged less than 5 years, accounting for 71.26% (130,423/183,024) of the total visits. Additionally, there was a higher proportion of male patients (104,147/183,024, 56.90%) compared with female patients (78,877/183,024, 43.10%). Fever (50,715/183,024, 27.71%), respiratory infection (43,269/183,024, 23.64%), celialgia (9560/183,024, 5.22%), and emesis (6898/183,024, 3.77%) were the leading causes of pediatric emergency room visits. The average daily number of admissions was 501.44, and 18.76% (34,339/183,204) of pediatric emergency department visits resulted in discharge without a prescription or further tests. The median waiting time from registration to seeing a doctor was 27.53 minutes. Prolonged waiting times were observed from April to July, coinciding with an increased number of arrivals, primarily for respiratory diseases. In terms of waiting time prediction, machine learning models, specifically random forest, LightGBM, and XGBoost, outperformed regression methods. On average, these models reduced the root-mean-square error by approximately 17.73% (8.951/50.481) and increased the R2 by approximately 29.33% (0.154/0.525). The SHAP method analysis highlighted that the features "wait.green" and "department" had the most significant influence on waiting times. CONCLUSIONS: This study offers a contemporary exploration of pediatric emergency room visits, revealing significant variations in admission rates across different periods and uncovering certain admission patterns. The machine learning models, particularly ensemble methods, delivered more dependable waiting time predictions. Patient volume awaiting consultation or treatment and the triage status emerged as crucial factors contributing to prolonged waiting times. Therefore, strategies such as patient diversion to alleviate congestion in emergency departments and optimizing triage systems to reduce average waiting times remain effective approaches to enhance the quality of pediatric health care services in China.

Source apportionment of consumed volatile organic compounds in the atmosphere.

Conventional source apportionments of ambient volatile organic compounds (VOCs) have been based on observed and initial concentrations after photochemical correction. However, these results have not been related to ozone (O3) and secondary organic aerosol (SOA) formation. Thus, the apportioned contributions could not effectively support secondary pollution control development. Source apportionment of the VOCs consumed in forming O3 and SOA is needed. A consumed VOC source apportionment approach was developed and applied to hourly speciated VOCs data from June to August 2022 measured in Laoshan, Qingdao. Biogenic emissions (56.3%), vehicle emissions (17.2%), and gasoline evaporation (9.37%) were the main sources of consumed VOCs. High consumed VOCs from biogenic emissions mainly occurred during transport from parks to the southwest and northwest of study site. During the O3 pollution period, biogenic emissions (46.3%), vehicle emissions (24.2%), and gasoline evaporation (14.3%) provided the largest contributions to the consumed VOCs. However, biogenic emissions contribution increased to 57.1% during the non-O3 pollution period, and vehicle emissions and gasoline evaporation decreased to 16.5% and 9.01%, respectively. Biogenic emissions and the mixed source of combustion sources and solvent use contributed the most to O3 and SOA formation potentials during the O3 pollution period, respectively.

Brachyury positively regulates extracellular matrix synthesis via directly promoting aggrecan transcription in nucleus pulposus.

Nucleus pulposus (NP) degeneration is characterized by the decreased cellularity of nucleus pulposus cells (NPCs) and diminished content of hydrophilic extracellular matrix (ECM). Overexpression of brachyury has been reported to reverse the degenerated NPCs into healthy phenotypes. However, the direct correlation between brachyury and ECM has not been fully elucidated. This study revealed that brachyury expression decreased in human degenerated NP tissues and Lipopolysaccharide (LPS)-induced degenerated rat NPCs model. In vitro and in vivo experiments further showed that brachyury deficiency suppressed the synthesis of aggrecan and collagen II in NP. Mechanistically, ChIP-qPCR assays demonstrated that brachyury bound to the promoter region of aggrecan in NPCs. Furthermore, luciferase reporter assays revealed that brachyury transcriptionally activated aggrecan expression through binding with a novel specific motif. In rat in vivo model, brachyury overexpression partially reversed the degenerative phenotype. In conclusion, brachyury positively regulated ECM synthesis via directly promoting aggrecan transcription in NPCs. Accordingly, it may be helpful to be developed into a promising therapeutic target for NP degeneration.

Deep-learning-based denoising of X-ray differential phase and dark-field images.

PURPOSE: Statistical photon noise has always been a common problem in X-ray multi-contrast imaging and significantly influenced the quality of retrieved differential phase and dark-field images. We intend to develop a deep learning-based denoising algorithm to reduce the noise of retrieved X-ray differential phase and dark-field images. METHODS: A novel deep learning based image noise suppression algorithm (named DnCNN-P) is presented. We proposed two different denoising modes: Retrieval-Denoising mode (R-D mode) and Denoising-Retrieval mode (D-R mode). While the R-D mode denoises the retrieved images, the D-R mode denoises the raw phase stepping data. The two denoising modes are evaluated under different photon counts and visibilities. RESULTS: Experimental results show that with the algorithm DnCNN-P used, the D-R mode always exhibits a better noise reduction under diverse experimental conditions, even in the case of a low photon count and/or a low visibility. With a detected photon count of 1800 and a visibility of 0.3, compared to the differential phase images without denoising, the standard deviation is reduced by 89.1% and 16.4% in the D-R and R-D modes. Compared to the dark-field images without denoising, the standard deviation is reduced by 83.7% and 12.6% in the D-R and R-D modes, respectively. CONCLUSIONS: The novel supervised DnCNN-P algorithm can significantly reduce the noise in retrieved X-ray differential phase and dark-field images. We believe this novel algorithm can be a promising approach to improve the quality of X-ray differential phase and dark-field images, and therefore dose efficiency in future biomedical applications.

[Chemical Characteristics and Source Apportionment for VOCs During the Ozone Pollution Episodes and Non-ozone Pollution Periods in Qingdao].

The ambient concentration of ozone is high in Qingdao, and ozone pollution episodes occur frequently in summer. The refined source apportionment of ambient volatile organic compounds (VOCs) and their ozone formation potential (OFP) during ozone pollution episodes and non-ozone pollution periods can play an important role in effectively reducing air ozone pollution in coastal cities and continuously improving ambient air quality. Therefore, this study applied the online VOCs monitoring data with hourly resolution in summer (from June to August) in 2020 in Qingdao to analyze the chemical characteristics of ambient VOCs during the ozone pollution episodes and non-ozone pollution periods and conducted the refined source apportionment of ambient VOCs and their OFP using a positive matrix factorization (PMF) model. The results showed that the average mass concentration of ambient VOCs in Qingdao in summer was 93.8 µg·m-3, and compared with that during the non-ozone pollution period, the mass concentration of ambient VOCs during the ozone pollution episodes increased by 49.3%, and the mass concentration of aromatic hydrocarbons increased by 59.7%. The total OFP of ambient VOCs in summer was 246.3 µg·m-3. Compared with that in the non-ozone pollution period, the total OFP of ambient VOCs in the ozone pollution episodes increased by 43.1%; that of alkanes increased the most, reaching 58.8%. M-ethyltoluene and 2,3-dimethylpentane were the species with the largest increase in OFP and its proportion during the ozone pollution episodes. The main contributors of ambient VOCs in Qingdao in summer were diesel vehicles (11.2%), solvent use (4.7%), liquefied petroleum gas and natural gas (LPG/NG) (27.5%), gasoline vehicles (8.9%), gasoline volatilization (26.6%), emissions of combustion- and petrochemical-related enterprises (16.4%), and plant emissions (4.8%). Compared with that in the non-ozone pollution period, the contribution concentration of LPG/NG in the ozone pollution episodes increased by 16.4 µg·m-3, which was the source category with the largest increase. The contribution concentration of plant emissions increased by 88.6% in the ozone pollution episodes, which was the source category with the highest increase rate. In addition, emissions from combustion- and petrochemical-related enterprises were the largest contributor to the OFP of ambient VOCs in summer in Qingdao, with its OFP and contribution proportion being 38.0 µg·m-3and 24.5%, respectively, followed by that of LPG/NG and gasoline volatilization. Compared with the non-ozone pollution period, the total contributions of LPG/NG, gasoline volatilization, and solvent use to the increase in OFP for ambient VOCs in the ozone pollution episodes were 74.1%, which were the main contribution source categories.

CO2-switchable emulsions with controllable size and viscosity.

CO2-responsive emulsions have attracted considerable attention in recent years because of their biocompatibility and easy removal of CO2. However, most CO2-responsive emulsions are only used in stabilization and demulsification processes. In this paper, we report CO2-switchable oil-in-dispersion (OID) emulsions co-stabilized with silica nanoparticles and anionic NCOONa, in which the required concentrations of NCOONa and silica particles were as low as 0.01 mM and 0.0001 wt%, respectively. Besides reversible emulsification/demulsification, the aqueous phase containing the emulsifiers was recycled and reused with the CO2/N2 trigger. More importantly, the properties of the emulsions, such as droplet sizes (40-1020 µm) and viscosities (6-2190 Pa s), were intelligently controlled by the CO2/N2 trigger, and meanwhile reversible conversion between OID emulsions and Pickering emulsions was achieved. The present method offers a green and sustainable way to regulate the emulsion states, which enables smart control of emulsions and widens their potential applications.

A facile one-pot strategy for the preparation of porous polymeric microspheres via UV irradiation-induced polymerization in emulsions.

In this study, a facile one-pot strategy was developed to prepare porous polymeric microspheres via photopolymerization, where organic solvents functioned as porogens. In this strategy, an oil phase containing organic solvents and photopolymerizable materials was stabilized in water to form a stable oil-in-water emulsion. Upon UV irradiation, the photopolymerizable materials (photosensitive monomers/photosensitive prepolymers) underwent polymerization to form microspheres and the subsequent removal of organic solvents left pores in microspheres, leading to the generation of porous polymeric microspheres with high yielding. The effects of organic solvents and the chemical structure and concentration of photopolymerizable materials on the microsphere structure were systematically explored. It was found that the polarity of the organic solvents played a decisive role in the preparation of porous microspheres. In addition, the increases in the solvent content and functionalities of photopolymerizable materials were more favorable for the generation of porous microspheres. This strategy could be applicable for a wide selection of photopolymerizable materials, which endowed this strategy with good applicability. The preparation of porous microspheres by this method was facile and easy to handle, enabling the scalable preparation of porous microspheres. In addition, the whole process can be completed within a few minutes at ambient temperature, which was time-saving and energy-saving.

Comparison of Mortality Predictive Models of Sepsis Patients Based on Machine Learning.

Objective To compare the performance of five machine learning models and SAPS II score in predicting the 30-day mortality amongst patients with sepsis. Methods The sepsis patient-related data were extracted from the MIMIC-IV database. Clinical features were generated and selected by mutual information and grid search. Logistic regression, Random forest, LightGBM, XGBoost, and other machine learning models were constructed to predict the mortality probability. Five measurements including accuracy, precision, recall, F1 score, and area under curve (AUC) were acquired for model evaluation. An external validation was implemented to avoid conclusion bias. Results LightGBM outperformed other methods, achieving the highest AUC (0.900), accuracy (0.808), and precision (0.559). All machine learning models performed better than SAPS II score (AUC=0.748). LightGBM achieved 0.883 in AUC in the external data validation. Conclusions The machine learning models are more effective in predicting the 30-day mortality of patients with sepsis than the traditional SAPS II score.

Phytochemical constituents of Camellia osmantha fruit cores with antithrombotic activity.

Camellia osmantha is a new species of the genus Camellia and is an economically important ornamental plant. Its activity and ingredients are less studied than other Camellia plants. This study investigated the antithrombotic effect and chemical components of C. osmantha fruit cores using platelet aggregation assays and coagulation function tests. The cores of C. osmantha fruits were extracted with ethanol to obtain a crude extract. The extract was dissolved in water and further eluted with different concentrations of methanol on an MCI resin column to obtain three fractions. These samples were used for antithrombotic activity tests and phytochemical analysis. The results showed that the extract and its fractions of C. osmantha have strong antithrombotic activity, significantly reducing the platelet aggregation rate and prolonging the thrombin time (TT). The total saponins, flavonoids, and polyphenols in the active fractions may be responsible for the antithrombotic activity. The chemical constituents were analyzed by ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-QTOF/MS). Twenty-three compounds were identified rapidly and accurately. Among them, ellagic acid, naringenin, and quercetin 3-O-glucuronide may be important antithrombotic constituents. Furthermore, interactions between these compounds and the P2Y1 receptor were investigated via molecular modeling, because the P2Y1 receptor is a key drug target of antiplatelet aggregative activity. The molecular docking results suggested that these compounds could combine tightly with the P2Y1R protein. Our results showed that C. osmantha fruit cores are rich in polyphenols, flavonoids, and saponins, which can be developed into a promising antithrombotic functional beverage for the prevention and treatment of cardiovascular and cerebrovascular diseases.

Water-in-Oil Emulsion Gels Stabilized by a Low-Molecular Weight Organogelator Derived from Dehydroabietic Acid.

High concentrations of surfactants or gelators are usually necessary to prepare emulsions gels with unusual physicochemical properties. This situation may be improved by innovating the aggregate morphology in systems. Herein, a rosin-based molecule is designed and synthesized using dehydroabietic acid as the starting material (denoted as R-Lys-R). The molecule acts as an effective organogelator and can gelate several hydrocarbon compounds with a minimum gelation concentration of 0.2% (w/v). Analysis using atomic force microscopy (AFM) and circular dichroism (CD) reveals that in n-decane, R-Lys-R forms left-handed helical fibers with a cross-sectional diameter of approximately 15 nm. The directional hydrogen bonding of the amide group is helpful to the formation of aggregates. At concentrations of R-Lys-R above 2%, water-in-oil emulsions are transformed into emulsion gels owing to the aptitude of R-Lys-R in gelating the oil phase. The concentrations of the emulsifier can be adjusted to obtain emulsion gels with different formulations. This work reveals the potential of rosin derivatives for the formation of small molecular weight organogels and provides a novel method for the utilization of natural resources in soft materials and home care products.

pH-Responsive Liquid Marbles Based on Dihydroxystearic Acid.

Herein, we report pH-responsive liquid marbles stabilized by 9,10-dihydroxystearic acid (DHSA). The particle morphology and the pH-responsive behavior of the liquid marbles were investigated. The rolling time during the preparation of liquid marbles has a great influence on the thickness of powder adsorption and the stability of the marbles. Compared with the liquid marbles stabilized by other fatty acids (e.g., stearic acid and docosoic acid), the liquid marbles prepared by DHSA have a much higher mechanical robustness. The increase in the number of hydroxyl groups on the carbon chain of fatty acids improves the mechanical robustness of the liquid marbles. Such liquid marbles immediately disintegrated on the surface of an alkaline solution or after exposure to NH3 gas, which extends their applications in the NH3 sensor and chemical reactions.

Multiply improved positive matrix factorization for source apportionment of volatile organic compounds during the COVID-19 shutdown in Tianjin, China.

Ambient concentrations of volatile organic compounds (VOCs) vary with emission rates, meteorology, and chemistry. Conventional positive matrix factorization (PMF) loses information because of dilution variations and chemical losses. Multiply improved PMF incorporates the ventilation coefficient, and total solar radiation or oxidants to reduce the effects of dispersion and chemical loss. These methods were applied to hourly speciated VOC data from November 2019 to March 2020 including during the COVID-19 shutdown. Various comparisons were made to assess the influences of these fluctuation drivers by time of day. Dispersion normalized PMF (DN-PMF) reduced the dispersion variations. Dispersion-radiation normalized PMF (DRN-PMF) reduced the impact of chemical loss, especially at night, which was better than Dispersion-Ox normalized PMF (DON-PMF). The conditional bivariate probability function (CBPF) plots of DRN-PMF results were consist with actual source locations. The DN-PMF, DRN-PMF, and DON-PMF results were consistent between 10:00 and 15:00, suggesting dispersion was significantly more influential than photochemical reactions during these times. The DRN-PMF results indicated that the highest VOC contributors during the COVID-19 shutdown were liquefied petroleum gas (LPG) (28.8%), natural gas (25.2%), and pulverized coal boilers emissions (19.6%). Except for petrochemical-related enterprises and LPG, the contribution concentrations of all other sources decreased substantially during the COVID-19 shutdown, by 94.7%, 90.6%, and 86.8% for vehicle emissions, gasoline evaporation, and the mixed source of diesel evaporation and solvent use, respectively. Controlling the use of motor vehicles and related volatilization of diesel fuel and gasoline can be effective in controlling VOCs in the future.

A methodological study on the combination of qualitative and quantitative methods in cognitive interviewing for cross-cultural adaptation.

AIM: The aim of this study is to explore the use of the Questionnaire Appraisal System with a combination of qualitative and quantitative methods in cognitive interviewing for cross-cultural adaptation. DESIGN: This is a descriptive methodological study. METHODS: Using the Mandarin version of the Post-Stroke Checklist as an example, cognitive interviews were conducted with 27 stroke survivors in Guangzhou between November 2020 and February 2021. The Questionnaire Appraisal System was applied as a codebook in focus group discussions to perform quantitative data collections and quantitative content analysis. RESULTS: Thirty-eight problems were proposed in focus group discussions and identified all but four of the 30 questions that emerged in the cognitive interviews. A new item was added to the Questionnaire Appraisal System for better categorization. Four categories and six subcategories of problems in the checklist were revealed.