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Institution

Sichuan University

EducationChengdu, China
About: Sichuan University is a education organization based out in Chengdu, China. It is known for research contribution in the topics: Population & Catalysis. The organization has 107623 authors who have published 102844 publications receiving 1612131 citations. The organization is also known as: Sìchuān Dàxué.


Papers
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Journal ArticleDOI
TL;DR: The DHHFL-MULTIMOORA method is applied to deal with a practical case about selecting the optimal city in China by evaluating the implementation status of haze controlling measures and some comparisons are provided to show the advantages of the proposed method.

247 citations

Journal ArticleDOI
TL;DR: Neither the virus nor potential risk factors for seizures seem to be significant risks for the occurrence of acute symptomatic seizures in people with COVID‐19.
Abstract: Our aim was to clarify the incidence and risk of acute symptomatic seizures in people with coronavirus disease 2019 (COVID-19). This multicenter retrospective study enrolled people with COVID-19 from January 18 to February 18, 2020 at 42 government-designated hospitals in Hubei province, the epicenter of the epidemic in China; Sichuan province; and Chongqing municipality. Data were collected from medical records by 11 neurologists using a standard case report form. A total of 304 people were enrolled, of whom 108 had a severe condition. None in this cohort had a known history of epilepsy. Neither acute symptomatic seizures nor status epilepticus was observed. Two people had seizurelike symptoms during hospitalization due to acute stress reaction and hypocalcemia, and 84 (27%) had brain insults or metabolic imbalances during the disease course known to increase the risk of seizures. There was no evidence suggesting an additional risk of acute symptomatic seizures in people with COVID-19. Neither the virus nor potential risk factors for seizures seem to be significant risks for the occurrence of acute symptomatic seizures in COVID-19.

246 citations

Journal ArticleDOI
Xuepin Liao1, Bi Shi1
TL;DR: The investigation on desorption indicated that this adsorbent is easily regenerated by use of dilute NaOH solution, implying that the mechanism of chemical adsorption might be involved in the adsorptive process of fluoride on the absorbent and that fluorides are adsorbed in the form of monolayer coverage on the surface of the adsorbents.
Abstract: A novel adsorbent, zirconium(IV)-impregnated collagen fiber, was prepared. Zr(IV) was uniformly dispersed in collagen fiber, mainly through chemical bonds, and was able to withstand the extraction of water. This adsorbent is effective for the removal of fluoride from aqueous solutions. The adsorption capacity was 2.29 mmol/g at pH = 5.5 when 5.00 mmol/L fluoride solution was adsorbed by use of 0.100 g of adsorbent, and the extent of removal was 97.4% when the adsorbent dose was 0.300 g. The adsorption isotherms were well fitted by the Langmuir equation, and the maximum adsorption capacities calculated by the Langmuir equation were close to those determined by experiment. The adsorption capacity increased with rising temperature. These facts imply that the mechanism of chemical adsorption might be involved in the adsorption process of fluoride on the absorbent and that fluorides are adsorbed in the form of monolayer coverage on the surface of the adsorbent. The adsorption kinetics of fluoride onto Zr(IV)-impregnated collagen fiber could be described by Lagergren's pseudo-first-order rate mode. The investigation on desorption indicated that this adsorbent is easily regenerated by use of dilute NaOH solution.

246 citations

Journal ArticleDOI
Xia Yuan1, Xiangxian Zhang1, Lu Sun1, Yuquan Wei1, Xiawei Wei1 
TL;DR: A review of the latest research findings on the toxicological profiles of carbon-based nanomaterials, highlighting both the cellular toxicities and immunological effects of carbon nanmaterials.
Abstract: Carbon nanomaterials are a growing family of materials featuring unique physicochemical properties, and their widespread application is accompanied by increasing human exposure. Considerable efforts have been made to characterize the potential toxicity of carbon nanomaterials in vitro and in vivo. Many studies have reported various toxicology profiles of carbon nanomaterials. The different results of the cytotoxicity of the carbon-based materials might be related to the differences in the physicochemical properties or structures of carbon nanomaterials, types of target cells and methods of particle dispersion, etc. The reported cytotoxicity effects mainly included reactive oxygen species generation, DNA damage, lysosomal damage, mitochondrial dysfunction and eventual cell death via apoptosis or necrosis. Despite the cellular toxicity, the immunological effects of the carbon-based nanomaterials, such as the pulmonary macrophage activation and inflammation induced by carbon nanomaterials, have been thoroughly studied. The roles of carbon nanomaterials in activating different immune cells or inducing immunosuppression have also been addressed. Conclusion: Here, we provide a review of the latest research findings on the toxicological profiles of carbon-based nanomaterials, highlighting both the cellular toxicities and immunological effects of carbon nanomaterials. This review provides information on the overall status, trends, and research needs for toxicological studies of carbon nanomaterials.

246 citations

Journal ArticleDOI
TL;DR: A large meta-analysis of 36 studies examining the association of type 2 diabetes mellitus (T2DM) with polymorphisms in the TCF7L2 gene indicates a multiplicative genetic model for all the four polymorphisms, as well as suggests the TCFs involved in near 1/5 of all T2MD.
Abstract: Transcription factor 7-like 2 (TCF7L2) has been shown to be associated with type 2 diabetes mellitus (T2MD) in multiple ethnic groups in the past two years, but, contradictory results were reported for Chinese and Pima Indian populations. The authors then performed a large meta-analysis of 36 studies examining the association of type 2 diabetes mellitus (T2DM) with polymorphisms in the TCF7L2 gene in various ethnicities, containing rs7903146 C-to-T (IVS3C>T), rs7901695 T-to-C (IVS3T>C), a rs12255372 G-to-T (IVS4G>T), and rs11196205 G-to-C (IVS4G>C) polymorphisms and to evaluate the size of gene effect and the possible genetic mode of action. Literature-based searching was conducted to collect data and three methods, that is, fixed-effects, random-effects and Bayesian multivariate mete-analysis, were performed to pool the odds ratio (OR). Publication bias and study-between heterogeneity were also examined. The studies included 35,843 cases of T2DM and 39,123 controls, using mainly primary data. For T2DM and IVS3C>T polymorphism, the Bayesian OR for TT homozygotes and TC heterozygotes versus CC homozygote was 1.968 (95% credible interval (CrI): 1.790, 2.157), 1.406 (95% CrI: 1.341, 1.476), respectively, and the population attributable risk (PAR) for the TT/TC genotypes of this variant is 16.9% for overall. For T2DM and IVS4G>T polymorphism, TT homozygotes and TG heterozygotes versus GG homozygote was 1.885 (95%CrI: 1.698, 2.088), 1.360 (95% CrI: 1.291, 1.433), respectively. Four ORs among these two polymorphisms all yielded significant between-study heterogeneity (P 0.10). Pooled ORs fit a codominant, multiplicative genetic model for all the four polymorphisms of TCF7L2 gene, and this model was also confirmed in different ethnic populations when stratification of IVS3C>T and IVS4G>T polymorphisms except for Africans, where a dominant, additive genetic mode is suggested for IVS3C>T polymorphism. This meta-analysis demonstrates that four variants of TCF7L2 gene are all associated with T2DM, and indicates a multiplicative genetic model for all the four polymorphisms, as well as suggests the TCF7L2 gene involved in near 1/5 of all T2MD. Potential gene-gene and gene-environmental interactions by which common variants in the TCF7L2 gene influence the risk of T2MD need further exploration.

246 citations


Authors

Showing all 108474 results

NameH-indexPapersCitations
Jie Zhang1784857221720
Robin M. Murray1711539116362
Xiang Zhang1541733117576
Rui Zhang1512625107917
Xiaoyuan Chen14999489870
Yi Yang143245692268
Xinliang Feng13472173033
Chuan He13058466438
Lei Zhang130231286950
Jian Zhou128300791402
Shaobin Wang12687252463
Yi Xie12674562970
Pak C. Sham124866100601
Wei Chen122194689460
Bo Wang119290584863
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023339
20221,712
202113,846
202011,702
20199,714
20187,906