Bidirectional modulation of TCA cycle metabolites and anaplerosis by metformin and its combination with SGLT2i.

BACKGROUND: Metformin and sodium-glucose-cotransporter-2 inhibitors (SGLT2i) are cornerstone therapies for managing hyperglycemia in diabetes. However, their detailed impacts on metabolic processes, particularly within the citric acid (TCA) cycle and its anaplerotic pathways, remain unclear. This study investigates the tissue-specific metabolic effects of metformin, both as a monotherapy and in combination with SGLT2i, on the TCA cycle and associated anaplerotic reactions in both mice and humans. METHODS: Metformin-specific metabolic changes were initially identified by comparing metformin-treated diabetic mice (MET) with vehicle-treated db/db mice (VG). These findings were then assessed in two human cohorts (KORA and QBB) and a longitudinal KORA study of metformin-naïve patients with Type 2 Diabetes (T2D). We also compared MET with db/db mice on combination therapy (SGLT2i + MET). Metabolic profiling analyzed 716 metabolites from plasma, liver, and kidney tissues post-treatment, using linear regression and Bonferroni correction for statistical analysis, complemented by pathway analyses to explore the pathophysiological implications. RESULTS: Metformin monotherapy significantly upregulated TCA cycle intermediates such as malate, fumarate, and α-ketoglutarate (α-KG) in plasma, and anaplerotic substrates including hepatic glutamate and renal 2-hydroxyglutarate (2-HG) in diabetic mice. Downregulated hepatic taurine was also observed. The addition of SGLT2i, however, reversed these effects, such as downregulating circulating malate and α-KG, and hepatic glutamate and renal 2-HG, but upregulated hepatic taurine. In human T2D patients on metformin therapy, significant systemic alterations in metabolites were observed, including increased malate but decreased citrulline. The bidirectional modulation of TCA cycle intermediates in mice influenced key anaplerotic pathways linked to glutaminolysis, tumorigenesis, immune regulation, and antioxidative responses. CONCLUSION: This study elucidates the specific metabolic consequences of metformin and SGLT2i on the TCA cycle, reflecting potential impacts on the immune system. Metformin shows promise for its anti-inflammatory properties, while the addition of SGLT2i may provide liver protection in conditions like metabolic dysfunction-associated steatotic liver disease (MASLD). These observations underscore the importance of personalized treatment strategies.

Prediction of Myocardial Infarction Using a Combined Generative Adversarial Network Model and Feature-Enhanced Loss Function.

Accurate risk prediction for myocardial infarction (MI) is crucial for preventive strategies, given its significant impact on global mortality and morbidity. Here, we propose a novel deep-learning approach to enhance the prediction of incident MI cases by incorporating metabolomics alongside clinical risk factors. We utilized data from the KORA cohort, including the baseline S4 and follow-up F4 studies, consisting of 1454 participants without prior history of MI. The dataset comprised 19 clinical variables and 363 metabolites. Due to the imbalanced nature of the dataset (78 observed MI cases and 1376 non-MI individuals), we employed a generative adversarial network (GAN) model to generate new incident cases, augmenting the dataset and improving feature representation. To predict MI, we further utilized multi-layer perceptron (MLP) models in conjunction with the synthetic minority oversampling technique (SMOTE) and edited nearest neighbor (ENN) methods to address overfitting and underfitting issues, particularly when dealing with imbalanced datasets. To enhance prediction accuracy, we propose a novel GAN for feature-enhanced (GFE) loss function. The GFE loss function resulted in an approximate 2% improvement in prediction accuracy, yielding a final accuracy of 70%. Furthermore, we evaluated the contribution of each clinical variable and metabolite to the predictive model and identified the 10 most significant variables, including glucose tolerance, sex, and physical activity. This is the first study to construct a deep-learning approach for producing 7-year MI predictions using the newly proposed loss function. Our findings demonstrate the promising potential of our technique in identifying novel biomarkers for MI prediction.

A review on the evaluation models and impact factors of greenhouse gas emissions from municipal solid waste management processes.

The total amount of global municipal solid waste (MSW) will reach 3.5 billion tons by 2050, thereby bringing tremendous environmental pressure, especially global warming. Large amounts of greenhouse gases (GHGs) have been released during MSW management (MSWM). Accounting for GHG emissions is a prerequisite for providing recommendations on appropriate treatment options to mitigate emissions from MSWM systems. There are many methods involved in estimating emissions. This paper summarizes the computing models commonly used in each process of the integrated MSWM system and emphasizes the influence of parameters and other factors. Compared with other disposal methods, landfilling has the highest emissions, commonly estimated using first-order decay (FOD) methods. Emission reduction can be realized through waste to energy (WtE) and resource recovery measures. IPCC is commonly used for calculating direct emissions, while LCA-based models can calculate emissions including upstream and downstream processes, whose results depend on assumptions and system boundaries. The estimation results of models vary greatly and are difficult to compare with each other. Besides, large gaps exist between the default emission factors (EFs) provided by models and those F measured in specific facilities. These findings provide a systematic view for a bettering understanding of MSW emissions as well as the estimating methods and also reveal the key points that need be developed in the future.

Metabolic effects of SGLT2i and metformin on 3-hydroxybutyric acid and lactate in db/db mice.

Combining a Sodium-Glucose-Cotransporter-2-inhibitor (SGLT2i) with metformin is recommended for managing hyperglycemia in patients with type 2 diabetes (T2D) who have cardio-renal complications. Our study aimed to investigate the metabolic effects of SGLT2i and metformin, both individually and synergistically. We treated leptin receptor-deficient (db/db) mice with these drugs for two weeks and conducted metabolite profiling, identifying 861 metabolites across kidney, liver, muscle, fat, and plasma. Using linear regression and mixed-effects models, we identified two SGLT2i-specific metabolites, X-12465 and 3-hydroxybutyric acid (3HBA), a ketone body, across all examined tissues. The levels of 3HBA were significantly higher under SGLT2i monotherapy compared to controls and were attenuated when combined with metformin. We observed similar modulatory effects on metabolites involved in protein catabolism (e.g., branched-chain amino acids) and gluconeogenesis. Moreover, combination therapy significantly raised pipecolate levels, which may enhance mTOR1 activity, while modulating GSK3, a common target of SGLT2i and 3HBA inhibition. The combination therapy also led to significant reductions in body weight and lactate levels, contrasted with monotherapies. Our findings advocate for the combined approach to better manage muscle loss, and the risks of DKA and lactic acidosis, presenting a more effective strategy for T2D treatment.

Impaired cerebral microvascular endothelial cells integrity due to elevated dopamine in myasthenic model.

Myasthenia gravis is an autoimmune disease characterized by pathogenic antibodies that target structures of the neuromuscular junction. However, some patients also experience autonomic dysfunction, anxiety, depression, and other neurological symptoms, suggesting the complex nature of the neurological manifestations. With the aim of explaining the symptoms related to the central nervous system, we utilized a rat model to investigate the impact of dopamine signaling in the central nervous and peripheral circulation. We adopted several screening methods, including western blot, quantitative PCR, mass spectrum technique, immunohistochemistry, immunofluorescence staining, and flow cytometry. In this study, we observed increased and activated dopamine signaling in both the central nervous system and peripheral circulation of myasthenia gravis rats. Furthermore, changes in the expression of two key molecules, Claudin5 and CD31, in endothelial cells of the blood-brain barrier were also examined in these rats. We also confirmed that dopamine incubation reduced the expression of ZO1, Claudin5, and CD31 in endothelial cells by inhibiting the Wnt/ß-catenin signaling pathway. Overall, this study provides novel evidence suggesting that pathologically elevated dopamine in both the central nervous and peripheral circulation of myasthenia gravis rats impair brain-blood barrier integrity by inhibiting junction protein expression in brain microvascular endothelial cells through the Wnt/ß-catenin pathway.

Mitigating heavy metal volatilization during thermal treatment of MSWI fly ash by using iron(III) sulfate as a chlorine depleting agent.

In the thermal treatment of municipal solid waste incineration fly ash (FA), the presence of chlorides leads to the pronounced volatilization of heavy metals at high temperature, making heavy metals stabilization challenging. Conventional washing processes struggle to remove chlorides completely, and even minor residual chlorides can lead to significant heavy metal volatilization. This study innovatively applied iron(III) sulfate as a chlorine depleting agent, which can form FeCl3 (boiling point 316 °C) and volatilize to remove the residual chlorides at below 500 °C, thus preventing the chlorination and volatilization of heavy metals at 600-1000 °C. Using water-washed FA to produce lightweight aggregate (LWA) preparation, after adding iron(III) sulfate, the volatilization rates of Pb and Cd at 1140 °C decreased to 5.4% and 9.3%, respectively, a reduction of 82.8% and 84.1% compared to before its addition. The LWA met standard requirements in both performance and heavy metal leaching toxicity. The mechanism was further studied through thermodynamic equilibrium calculations and heating experiments of pure chemicals. This study presents novel approaches and insights for suppressing the volatilization of heavy metals in FA at high temperature, thereby promoting the advancement of thermal treatment techniques and the safe, resourceful disposal of FA.

Impacts of seasonal variation on volatile fatty acids production of food waste anaerobic fermentation.

This study aims to investigate the influence of seasonal variations on Volatile fatty acids (VFAs) production from food waste (FW) and to quantify their impact. Results of batch experiments with external pH control indicated that the properties of FW exhibited significant seasonal variations and were markedly different from kitchen waste (KW). The spring group demonstrated the highest VFA concentration and VFA/SCOD, at 31.24 g COD/L and 92.20 % respectively, which were 1.22 and 1.27 times higher than those observed in the summer season. The combined proportion of acetic acid and butyric acid accounted for 81.10 % of the total VFAs in spring, suggesting the highest applicability to the carbon source. The VFA content of all seasonal groups in descending order was butyric acid, propionic acid and acetic acid. Carbohydrates, along with spicy and citrusy substances, promoted the conversion of total VFA and butyric acid, while proteins and lipids favored the production of acetic acid and propionic acid.

Recent Advances on Pulsed Laser Deposition of Large-Scale Thin Films.

2D thin films, possessing atomically thin thickness, are emerging as promising candidates for next-generation electronic devices, due to their novel properties and high performance. In the early years, a wide variety of 2D materials are prepared using several methods (mechanical/liquid exfoliation, chemical vapor deposition, etc.). However, the limited size of 2D flakes hinders their fundamental research and device applications, and hence the effective large-scale preparation of 2D films is still challenging. Recently, pulsed laser deposition (PLD) has appeared to be an impactful method for wafer-scale growth of 2D films, owing to target-maintained stoichiometry, high growth rate, and efficiency. In this review, the recent advances on the PLD preparation of 2D films are summarized, including the growth mechanisms, strategies, and materials classification. First, efficacious strategies of PLD growth are highlighted. Then, the growth, characterization, and device applications of various 2D films are presented, such as graphene, h-BN, MoS2 , BP, oxide, perovskite, semi-metal, etc. Finally, the potential challenges and further research directions of PLD technique is envisioned.

High-Throughput Computational Design of 2D Ternary Chalcogenides for Sustainable Energy.

Two-dimensional materials are considered to be promising for next-generation electronic and energy devices. However, the limited availability of 2D materials hinders their applications. Herein, we employed high-throughput computation to discover new 2D materials by cleaving the bulk and to investigate their electronic, thermoelectric, and optoelectronic properties. Using our database containing 810 structures of chalcogenides ABX3 (A or B = Al, Ga, In, Si, Ge, Sn, P, As, Sb, and Bi; X = S, Se, and Te), we identified 204 new 2D compounds promising for experimental preparation according to the exfoliation energy. Notably, 96 of them are more easily exfoliated than graphene, 52 compounds show higher Seebeck coefficients than Bi2Te3 at 300 K, and 20 compounds have power factors beyond 2 × 10-3 Wm-1 K-2 at 900 K. Also, 6 new compounds exhibit high theoretical photovoltaic efficiency exceeding 30%. Our findings expand the 2D materials family and provide new 2D compounds for sustainable thermoelectric and optoelectronic energy applications.

Analysis of enrichment, correlation, and leaching patterns of rare earth elements in coal fly ash assisted by statistical measures.

Coal fly ash (CFA) is a typical industrial solid waste, which has recently been reported to contain rare earth elements (REEs). REEs are important materials in many industrial fields. Therefore, extracting REEs from CFA becomes a win-win strategy to both make full use of CFA and reclaim REEs. However, the stable crystalline structure of CFA is hard to break, which limits the extraction of REEs. The inter-correlation and the leaching patterns of the REEs in CFA also remain unclear. In this work, REEs were enriched by desilication, and the correlation and the influences of multiple acids of the leached REEs were investigated. It was found that desilication could increase the leachable amount of REEs from 137.37 ppm to 346.12 ppm. The light rare earth elements (LREEs) were less inter-correlated than heavy rare earth elements (HREEs) and desilication enhanced the leaching of LREEs more than that of HREEs. The ratio and type of the leaching acids both influenced the extraction of REEs from CFA: HCl and HF played important roles in the extraction from the untreated CFA while HNO3 and HF were more decisive for the desilicated CFA. In addition, we used statistical analysis to quantificationally confirm that desilication and acids both significantly influenced the extraction of REEs. This work provides evidence for the enrichment of REEs in CFA and acid choosing when leaching REEs from CFA.

Prognostic Biomarkers Based on Proteomic Technology in COPD: A Recent Review.

Chronic obstructive pulmonary disease (COPD) is a common heterogeneous respiratory disease which is characterized by persistent and incompletely reversible airflow limitation. Due to the heterogeneity and phenotypic complexity of COPD, traditional diagnostic methods provide limited information and pose a great challenge to clinical management. In recent years, with the development of omics technologies, proteomics, metabolomics, transcriptomics, etc., have been widely used in the study of COPD, providing great help to discover new biomarkers and elucidate the complex mechanisms of COPD. In this review, we summarize the prognostic biomarkers of COPD based on proteomic studies in recent years and evaluate their association with COPD prognosis. Finally, we present the prospects and challenges of COPD prognostic-related studies. This review is expected to provide cutting-edge evidence in prognostic evaluation of clinical patients with COPD and to inform future proteomic studies on prognostic biomarkers of COPD.

Identification of candidate metabolite biomarkers for metabolic syndrome and its five components in population-based human cohorts.

BACKGROUND: Metabolic Syndrome (MetS) is characterized by risk factors such as abdominal obesity, hypertriglyceridemia, low high-density lipoprotein cholesterol (HDL-C), hypertension, and hyperglycemia, which contribute to the development of cardiovascular disease and type 2 diabetes. Here, we aim to identify candidate metabolite biomarkers of MetS and its associated risk factors to better understand the complex interplay of underlying signaling pathways. METHODS: We quantified serum samples of the KORA F4 study participants (N = 2815) and analyzed 121 metabolites. Multiple regression models adjusted for clinical and lifestyle covariates were used to identify metabolites that were Bonferroni significantly associated with MetS. These findings were replicated in the SHIP-TREND-0 study (N = 988) and further analyzed for the association of replicated metabolites with the five components of MetS. Database-driven networks of the identified metabolites and their interacting enzymes were also constructed. RESULTS: We identified and replicated 56 MetS-specific metabolites: 13 were positively associated (e.g., Val, Leu/Ile, Phe, and Tyr), and 43 were negatively associated (e.g., Gly, Ser, and 40 lipids). Moreover, the majority (89%) and minority (23%) of MetS-specific metabolites were associated with low HDL-C and hypertension, respectively. One lipid, lysoPC a C18:2, was negatively associated with MetS and all of its five components, indicating that individuals with MetS and each of the risk factors had lower concentrations of lysoPC a C18:2 compared to corresponding controls. Our metabolic networks elucidated these observations by revealing impaired catabolism of branched-chain and aromatic amino acids, as well as accelerated Gly catabolism. CONCLUSION: Our identified candidate metabolite biomarkers are associated with the pathophysiology of MetS and its risk factors. They could facilitate the development of therapeutic strategies to prevent type 2 diabetes and cardiovascular disease. For instance, elevated levels of lysoPC a C18:2 may protect MetS and its five risk components. More in-depth studies are necessary to determine the mechanism of key metabolites in the MetS pathophysiology.

A green and multi-win strategy for coal fly ash disposal by CO2 fixation and mesoporous silica synthesis.

Coal combustion provides plenty of energy, along with enormous coal fly ash (CFA) and CO2 emission. CFA could be recycled for mesoporous silica synthesis, but expensive templates are usually needed. In this work, we proposed a multi-win strategy using CO2 as the precipitator and template. Mesoporous silica powders, with a maximum specific surface area of 355.45 m2/g, a pore volume of 0.73 cm3/g, and an average pore size of around 7.67 nm, were synthesized. The influences of silicon concentration, CO2 flow rate, and ultrasound were investigated. In addition, the Na2CO3 by-product was produced with a purity of over 92 %. By averagely calculating, 1 ton CFA could generate 285 kg mesoporous silica and 1.02 t crude Na2CO3. Around 433 kg of CO2 could be absorbed. Therefore, multi-goals of CFA disposal, CO2 storage, and valuable silica materials production were realized, and the study could pave the way for large-scale industrial applications.

Comprehensive study of recycling municipal solid waste incineration fly ash in lightweight aggregate with bloating agent: Effects of water washing and bloating mechanism.

Recycling into lightweight aggregate (LWA) by sintering is a promising technology for disposal of municipal solid waste incineration fly ash (FA). In this study, FA and washed FA (WFA) were combined with bentonite and SiC (bloating agent) to make LWA. The performance was comprehensively studied by hot-stage microscopy and laboratory preparation experiments. Water washing and increased FA/WFA improved LWA bloating extent, while shorten the bloating temperature range. Water washing also increased the 1 h-water absorption rate of LWA, making it harder to meet the standard. Excessive FA /WFA usage (70 wt%) will prevent LWA from bloating. For the goal of recycling more FA, mixture with 50 wt% WFA could prepare LWA that meet standard GB/T 17431 at 1140-1160 °C. After water washing, the ratio of Pb, Cd, Zn, and Cu stabilized in LWA increased by 279 %, 410 %, 458 %, and 109 % for 30 wt% FA/WFA addition, and 364 %, 554 %, 717 %, and 697 % for 50 wt% FA/WFA addition, respectively. The change of liquid phase content and viscosity at high temperature were determined using the thermodynamic calculations and chemical compositions. The bloating mechanism was further investigated by integrating these two properties. To obtain accurate results of the bloat viscosity range (2.75-4.44 log Pa·s) for high CaO systems, the composition of the liquid phase should be taken into account. The liquid phase viscosity required for bloating start was proportional to the liquid phase content. With temperature increasing, bloating would end when viscosity drops to 2.75 log Pa·s or liquid phase content reach 95 %. These findings provided further understanding of the heavy metal stabilization during LWA production and the bloating mechanism of high CaO content systems, and could contribute to the feasibility and sustainability of recycling FA and other CaO-rich solid wastes into LWA.

The Aldose Reductase Inhibitor Epalrestat Maintains Blood-Brain Barrier Integrity by Enhancing Endothelial Cell Function during Cerebral Ischemia.

Excessive activation of aldose reductase (AR) in the brain is a risk factor for aggravating cerebral ischemia injury. Epalrestat is the only AR inhibitor with proven safety and efficacy, which is used in the clinical treatment of diabetic neuropathy. However, the molecular mechanisms underlying the neuroprotection of epalrestat remain unknown in the ischemic brain. Recent studies have found that blood-brain barrier (BBB) damage was mainly caused by increased apoptosis and autophagy of brain microvascular endothelial cells (BMVECs) and decreased expression of tight junction proteins. Thus, we hypothesized that the protective effect of epalrestat is mainly related to regulating the survival of BMVECs and tight junction protein levels after cerebral ischemia. To test this hypothesis, a mouse model of cerebral ischemia was established by permanent middle cerebral artery ligation (pMCAL), and the mice were treated with epalrestat or saline as a control. Epalrestat reduced the ischemic volume, enhanced BBB function, and improved the neurobehavior after cerebral ischemia. In vitro studies revealed that epalrestat increased the expression of tight junction proteins, and reduced the levels of cleaved-caspase3 and LC3 proteins in mouse BMVECs (bEnd.3 cells) exposed to oxygen-glucose deprivation (OGD). In addition, bicalutamide (an AKT inhibitor) and rapamycin (an mTOR inhibitor) increased the epalrestat-induced reduction in apoptosis and autophagy related protein levels in bEnd.3 cells with OGD treatment. Our findings suggest that epalrestat improves BBB function, which may be accomplished by reducing AR activation, promoting tight junction proteins expression, and upregulating AKT/mTOR signaling pathway to inhibit apoptosis and autophagy in BMVECs.

A multi-scale approach to detecting standing dead trees in UAV RGB images based on improved faster R-CNN.

The health of the trees in the forest affects the ecological environment, so timely detection of Standing Dead Trees (SDTs) plays an important role in forest management. However, due to the large spatial scope of forests, it is difficult to find SDTs through conventional approaches such as field inventories. In recent years, the development of deep learning and Unmanned Aerial Vehicle (UAV) has provided technical support for low-cost real-time monitoring of SDTs, but the inability to fully utilize global features and the difficulty of small-scale SDTs detection have brought challenges to the detection of SDTs in visible light images. Therefore, this paper proposes a multi-scale attention mechanism detection method for identifying SDTs in UAV RGB images. This method takes Faster-RCNN as the basic framework and uses Swin-Transformer as the backbone network for feature extraction, which can effectively obtain global information. Then, features of different scales are extracted through the feature pyramid structure and feature balance enhancement module. Finally, dynamic training is used to improve the quality of the model. The experimental results show that the algorithm proposed in this paper can effectively identify the SDTs in the visible light image of the UAV with an accuracy of 95.9%. This method of SDTs identification can not only improve the efficiency of SDTs exploration, but also help relevant departments to explore other forest species in the future.

Characteristics, application and modeling of solid amine adsorbents for CO2 capture: A review.

In recent years, global warming has become an important topic of public concern. As one of the most promising carbon capture technologies, solid amine adsorbents have received a lot of attention because of their high adsorption capacity, excellent selectivity, and low energy cost, which is committed to sustainable development. The preparation methods and support materials can influence the thermal stability and adsorption capacity of solid amine adsorbents. As a supporting material, it needs to meet the requirements of high pore volume and abundant hydroxyl groups. Industrial and biomass waste are expected to be a novel and cheap raw material source, contributing both carbon dioxide capture and waste recycling. The applied range of solid amine adsorbents has been widened from flue gas to biogas and ambient air, which require different research focuses, including strengthening the selectivity of CO2 to CH4 or separating CO2 under the condition of the dilute concentration. Several kinetic or isotherm models have been adopted to describe the adsorption process of solid amine adsorbents, which select the pseudo-first order model, pseudo-second order model, and Langmuir isotherm model most commonly. Besides searching for novel materials from solid waste and widening the applicable gases, developing the dynamic adsorption and three-dimensional models can also be a promising direction to accelerate the development of this technology. The review has combed through the recent development and covered the shortages of previous review papers, expected to promote the industrial application of solid amine adsorbents.

LION: an integrated R package for effective prediction of ncRNA-protein interaction.

Understanding ncRNA-protein interaction is of critical importance to unveil ncRNAs' functions. Here, we propose an integrated package LION which comprises a new method for predicting ncRNA/lncRNA-protein interaction as well as a comprehensive strategy to meet the requirement of customisable prediction. Experimental results demonstrate that our method outperforms its competitors on multiple benchmark datasets. LION can also improve the performance of some widely used tools and build adaptable models for species- and tissue-specific prediction. We expect that LION will be a powerful and efficient tool for the prediction and analysis of ncRNA/lncRNA-protein interaction. The R Package LION is available on GitHub at https://github.com/HAN-Siyu/LION/.

[Characteristics of air-conducted and bone-conducted vibration cervical vestibular-evoked myogenic potential in preschool children].

Objective: To investigate the characteristics of air-conducted sound cervical vestibular evoked myogenic potential （ACS-cVEMP） and bone-conducted vibration cervical vestibular evoked myogenic potential （BCV-cVEMP） in healthy preschool children, and to provide the normal reference range of VEMP for preschool children in China. Methods:Forty-four normal-hearing children （88 ears） aged 3-7 years were recruited to undergo ACS-cVEMP and BCV-cVEMP determination. These children were divided into two groups according to age: 3-4 years old group （ 17 cases, 34 ears） and 5-7 years old group （ 27 cases, 54 ears）. The response rates and waveform parameters were recorded and analyzed statistically using SPSS 18.0 software. Results:The response rates of ACS-cVEMP, BCV-cVEMP in 44 normal-hearing children （88 ears） were 96.59％（85/88） and 97.73％（86/88） respectively, and there was no significant difference between the two groups （P > 0.05）. The response rates of ACS-cVEMP in 3-4 year old group and 5-7 year old group were 94.12% （32/34） and 98.15% （53/54） respectively, and there was no significant difference between the two groups（P>0.05）; The response rates of BCV-cVEMP in 3-4 year old group and 5-7 year old group were 94.12% （32/34） and 100.00%（54/54） respectively, and there was no significant difference between the two groups （P>0.05）. Compared with the 5-7 year old group, the latency of p1 and n1 in the 3-4 year old group was shorter （P<0.05） and the amplitude was higher （P<0.05）, and there was no significant difference in other parameters （P>0.05）. There was no statistical difference in threshold, n1, p1 latency, n1-p1 wave interval, n1-p1 amplitude, and amplitude asymmetry ratio of BCV-cVEMP between the two groups （P>0.05）. Conclusion:ACS-cVEMP and BCV-cVEMP can be elicited in most preschool children, and cVEMP is a feasible method to detect vestibular function in children.

Recycling municipal solid waste incineration fly ash in super-lightweight aggregates by sintering with clay and using SiC as bloating agent.

Municipal solid waste incineration (MSWI) fly ash is classified as hazardous waste and requires proper treatments. Sintering of MSWI fly ash for the production of lightweight aggregate (LWA) is a promising treatment technology, while the dependence on natural bloating clay to produce high quality LWA has limited its wide application. In this study, by using SiC as a bloating agent, normal clay could be used to produce super-lightweight aggregate (bulk density <500 kg/m3) with MSWI fly ash. Effects of SiC addition amount, sintering temperature and duration on LWA performance were studied. The results showed that LWA with SiC addition of 0.1-0.5 wt% had significant expansion at sintering temperature of 1120 °C-1160 °C. The optimal conditions were 0.3 wt% SiC addition and sintering at 1120 °C for 30 min, and the bulk density could reach 212 kg/m3 with other properties meeting the LWA standard (GB/T 17431.1-2010). Further, the heavy metal leaching toxicity was significantly decreased after sintering and met the MSWI fly ash utilization standard (HJ 1134-2020). The X-ray diffraction results revealed the formation of a complex diopside-based phase after sintering. This study provides a new approach for recycling MSWI fly ash in LWA without dependence on specific clay resources, and makes this technology wider applicability.