|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | APACHE II、BISAP、Ranson评分和改良CTSI对急性胰腺炎严重程度(基于Atlanta 2012分类)预测价值的比较显示文摘目的:本研究前瞻性比较了急性生理和慢性健康状况评分(APACHEⅡ)、急性胰腺炎严重程度床边指数(BISAP)、Ranson评分与改良CT严重度指数(CTSI)这四种方法,评估印度北部一家三甲医院急性胰腺炎患者疾病严重程度(基于Atlanta 2012分类)的准确性。方法:前瞻性纳入2015年3月至2016年9月间我院收治的50例急性胰腺炎患者,对所有入组病例进行APACHEⅡ、BISAP和Ranson评分,并基于胰腺对比增加CT进行改良CTSI评估。基于受试者工作特征(ROC)曲线评估这些评分系统预测急性胰腺炎严重程度的最佳阈值和曲线下面积(AUC),并对这些评分系统的预测价值进行前瞻性比较。结果:50例急性胰腺炎患者中14例被评估为重症急性胰腺炎。15例患者出现胰腺坏死,14例出现持续器官功能衰竭,14例需要收治重症监护室(ICU)。改良CTSI对重症急性胰腺炎、胰腺坏死、器官衰竭、入住ICU的预测价值均为最高,其AUC分别为0.919、0.993、0.893和0.993。APACHEⅡ对重症急性胰腺炎和器官衰竭的预测价值均仅次于改良CTSI,其AUC分别为0.934和0.931。APACHEⅡ对胰腺坏死、器官衰竭、入住ICU的预测具有较高的敏感性(分别为93.33%、92.86%和92.31%)和阴性预测值(分别为96.15%、96.15%和95.83%)。结论:APACHEⅡ是预测急性胰腺炎严重程度的一项有效的评估系统,有助于筛选出病情危重、需要早期复苏和尽快转诊上级医院的急性胰腺炎患者,尤其在一些医疗资源匮乏的发展中国家。 | Anubhav Harshit Kumar Mahavir Singh Griwan | 2018 | Gastroenterology Report2018，6，2： | 27 |
| 2 | Efficacy and safety of intravenous recombinant tissue plasminogen activator in mild ischaemic stroke:a meta-analysis显示文摘The benefits and safety of intravenous recombinant tissue plasminogen activator(IV-tPA)for patients with mild ischaemic stroke(MIS)are still unclear.The objective of this meta-analysis was to evaluate the efficacy and safety of IV-tPA as treatment for patients with MIS.We performed a systematic literature search across MEDLINE,Embase,Central,Global Health and Cumulative Index to Nursing and Allied Health Literature(CINAHL),from inception to 10 November 2016,to identify all related studies.Where possible,data were pooled for meta-analysis with odds ratio(OR)and corresponding 95%confidence interval(CI)using the fixed-effects model.MIS was defined as having National Institutes of Health Stroke Scale score of≤6.We included seven studies with a total of 1591 patients based on the prespecified inclusion and exclusion criteria.The meta-analysis indicated a high odds of excellent functional outcome based on the modified Rankin Scale or Oxfordshire Handicap Score 0-1(OR=1.43;95%CI 1.14 to 1.79;P=0.002,I2=35%)in patients treated with IV-tPA compared with those not treated with IV-tPA(74.8%vs 67.6%).There was a high risk of symptomatic intracranial haemorrhage(sICH)with IV-tPA treatment(OR=10.13;95%CI 1.93 to 53.02;P=0.006,I2=0%)(1.9%vs 0.0%)but not mortality(OR=0.78;95%CI 0.43 to 1.43;P=0.43,I2=0%)(2.4%vs 2.9%).Treatment with IV-tPA was associated with better functional outcome but not mortality among patients with MIS,although there was an increased risk of sICH.Randomised trials are warranted to confirm these findings. | Shoujiang You Anubhav Saxena Xia Wang WeeYong Tan Qiao Han Yongjun Cao Chun-Feng Liu | 2018 | Stroke & Vascular Neurology2018，3，1： | 24 |
| 3 | Effective mass and Fermi surface complexity factor from ab initio band structure calculations显示文摘The effective mass is a convenient descriptor of the electronic band structure used to characterize the density of states and electron transport based on a free electron model.While effective mass is an excellent first-order descriptor in real systems,the exact value can have several definitions,each of which describe a different aspect of electron transport.Here we use Boltzmann transport calculations applied to ab initio band structures to extract a density-of-states effective mass from the Seebeck Coefficient and an inertial mass from the electrical conductivity to characterize the band structure irrespective of the exact scattering mechanism.We identify a Fermi Surface Complexity Factor:N_(v)^(*)K^(*) from the ratio of these two masses,which in simple cases depends on the number of Fermi surface pockets eN_(v)^(*) T and their anisotropy K^(*),both of which are beneficial to high thermoelectric performance as exemplified by the high values found in PbTe.The Fermi Surface Complexity factor can be used in high-throughput search of promising thermoelectric materials. | Zachary M.Gibbs Francesco Ricci Guodong Li Hong Zhu Kristin Persson Gerbrand Ceder Geoffroy Hautier Anubhav Jain G.Jeffrey Snyder | 2017 | npj Computational Materials2017，，1： | 11 |
| 4 | Recent advances and applications of deep learning methods in materials science显示文摘Deep learning(DL)is one of the fastest-growing topics in materials data science,with rapidly emerging applications spanning atomistic,image-based,spectral,and textual data modalities.DL allows analysis of unstructured data and automated identification of features.The recent development of large materials databases has fueled the application of DL methods in atomistic prediction in particular.In contrast,advances in image and spectral data have largely leveraged synthetic data enabled by high-quality forward models as well as by generative unsupervised DL methods.In this article,we present a high-level overview of deep learning methods followed by a detailed discussion of recent developments of deep learning in atomistic simulation,materials imaging,spectral analysis,and natural language processing.For each modality we discuss applications involving both theoretical and experimental data,typical modeling approaches with their strengths and limitations,and relevant publicly available software and datasets.We conclude the review with a discussion of recent cross-cutting work related to uncertainty quantification in this field and a brief perspective on limitations,challenges,and potential growth areas for DL methods in materials science. | Kamal Choudhary Brian DeCost Chi Chen Anubhav Jain Francesca Tavazza Ryan Cohn Cheol Woo Park Alok Choudhary Ankit Agrawal Simon J.L.Billinge Elizabeth Holm Shyue Ping Ong Chris Wolverton | 2022 | npj Computational Materials2022，，1： | 9 |
| 5 | Benchmarking materials property prediction methods:the Matbench test set and Automatminer reference algorithm显示文摘We present a benchmark test suite and an automated machine learning procedure for evaluating supervised machine learning(ML)models for predicting properties of inorganic bulk materials.The test suite,Matbench,is a set of 13 ML tasks that range in size from 312 to 132k samples and contain data from 10 density functional theory-derived and experimental sources. | Alexander Dunn Qi Wang Alex Ganose Daniel Dopp Anubhav Jain | 2020 | npj Computational Materials2020，，1： | 7 |
| 6 | Author Correction:Benchmarking materials property prediction methods:the Matbench test set and Automatminer reference algorithm显示文摘The original version of the Article contained an error in Fig.3,in which the label at the top of the first column of Fig.3 originally incorrectly read‘Yield Strength(GPa)’,rather than the correct‘Yield Strength(MPa)’.This has been corrected in both the PDF and HTML versions of the Article. | Alexander Dunn Qi Wang Alex Ganose Daniel Dopp Anubhav Jain | 2020 | npj Computational Materials2020，，1： | 7 |
| 7 | A critical examination of compound stability predictions from machine-learned formation energies显示文摘Machine learning has emerged as a novel tool for the efficient prediction of material properties,and claims have been made that machine-learned models for the formation energy of compounds can approach the accuracy of Density Functional Theory(DFT).The models tested in this work include five recently published compositional models,a baseline model using stoichiometry alone,and a structural model.By testing seven machine learning models for formation energy on stability predictions using the Materials Project database of DFT calculations for 85,014 unique chemical compositions,we show that while formation energies can indeed be predicted well,all compositional models perform poorly on predicting the stability of compounds,making them considerably less useful than DFT for the discovery and design of new solids.Most critically,in sparse chemical spaces where few stoichiometries have stable compounds,only the structural model is capable of efficiently detecting which materials are stable.The nonincremental improvement of structural models compared with compositional models is noteworthy and encourages the use of structural models for materials discovery,with the constraint that for any new composition,the ground-state structure is not known a priori.This work demonstrates that accurate predictions of formation energy do not imply accurate predictions of stability,emphasizing the importance of assessing model performance on stability predictions,for which we provide a set of publicly available tests. | Christopher J.Bartel Amalie Trewartha Qi Wang Alexander Dunn Anubhav Jain Gerbrand Ceder | 2020 | npj Computational Materials2020，，1： | 7 |
| 8 | Does combined therapy of curcumin and epigallocatechin gallate have a synergistic neuroprotective effect against spinal cord injury?显示文摘Systematic inflammatory response after spinal cord injury(SCI) is one of the factors leading to lesion development and a profound degree of functional loss. Anti-inflammatory compounds, such as curcumin and epigallocatechin gallate(EGCG) are known for their neuroprotective effects. In this study, we investigated the effect of combined therapy of curcumin and EGCG in a rat model of acute SCI induced by balloon compression. Immediately after SCI, rats received curcumin, EGCG, curcumin + EGCG or saline [daily intraperitoneal doses(curcumin, 6 mg/kg; EGCG 17 mg/kg)] and weekly intramuscular doses(curcumin, 60 mg/kg; EGCG 17 mg/kg)] for 28 days. Rats were evaluated using behavioral tests(the Basso, Beattie, and Bresnahan(BBB) open-field locomotor test, flat beam test). Spinal cord tissue was analyzed using histological methods(Luxol Blue-cresyl violet staining) and immunohistochemistry(anti-glial fibrillary acidic protein, anti-growth associated protein 43). Cytokine levels(interleukin-1β, interleukin-4, interleukin-2, interleukin-6, macrophage inflammatory protein 1-alpha, and RANTES) were measured using Luminex assay. Quantitative polymerase chain reaction was performed to determine the relative expression of genes(Sort1, Fgf2, Irf5, Mrc1, Olig2, Casp3, Gap43, Gfap, Vegf, NfκB, Cntf) related to regenerative processes in injured spinal cord. We found that all treatments displayed significant behavioral recovery, with no obvious synergistic effect after combined therapy of curcumin and ECGC. Curcumin and EGCG alone or in combination increased axonal sprouting, decreased glial scar formation, and altered the levels of macrophage inflammatory protein 1-alpha, interleukin-1β, interleukin-4 and interleukin-6 cytokines. These results imply that although the expected synergistic response of this combined therapy was less obvious, aspects of tissue regeneration and immune responses in severe SCI were evident. | Jiri Ruzicka Lucia Machova Urdzikova Barbora Svobodova Anubhav G. Amin Kristyna Karova Jana Dubisova Kristyna Zaviskova Sarka Kubinova Meic Schmidt Meena Jhanwar-Uniyal Pavla Jendelova | 2018 | Neural Regeneration Research2018，13，1： | 4 |
| 9 | Cardiac Troponins I and T: Molecular Markers for Early Diagnosis, Prognosis, and Accurate Triaging of Patients with Acute Myocardial Infarction显示文摘 | Ram P. Tiwari Anubhav Jain Zakir Khan Veena Kohli R. N. Bharmal S. Kartikeyan Prakash S. Bisen | 2012 | Molecular Diagnosis & Therapy2012，，6： | 3 |
| 10 | Evaluation of the role of preoperative Double-J ureteral stenting in retroperitoneal laparoscopic pyelolithotomy显示文摘 | Jagdish Chander Anuj Deep Dangi Nikhil Gupta Anubhav Vindal Pawanindra Lal V. K. Ramteke | 2010 | Surgical Endoscopy2010，，7： | 2 |
| 11 | Infection potpourri: Are we watching?显示文摘 | Rama Chaudhry Anubhav Pandey Anupam Das Shobha Broor | 2009 | Indian Journal of Pathology and Microbiology2009，，1： | 2 |
| 12 | Optimal band structure for thermoelectrics with realistic scattering and bands显示文摘Understanding how to optimize electronic band structures for thermoelectrics is a topic of long-standing interest in the community.Prior models have been limited to simplified bands and/or scattering models.In this study,we apply more rigorous scattering treatments to more realistic model band structures—upward-parabolic bands that inflect to an inverted-parabolic behavior—including cases of multiple bands.In contrast to common descriptors(e.g.,quality factor and complexity factor),the degree to which multiple pockets improve thermoelectric performance is bounded by interband scattering and the relative shapes of the bands.We establish that extremely anisotropic“flat-and-dispersive”bands,although best-performing in theory,may not represent a promising design strategy in practice.Critically,we determine optimum bandwidth,dependent on temperature and lattice thermal conductivity,from perfect transport cutoffs that can in theory significantly boost zT beyond the values attainable through intrinsic band structures alone.Our analysis should be widely useful as the thermoelectric research community eyes zT>3. | Junsoo Park Yi Xia Vidvuds Ozoliņš Anubhav Jain | 2021 | npj Computational Materials2021，，1： | 2 |
| 13 | Machine Learning Chemical Guidelines for Engineering Electronic Structures in Half-Heusler Thermoelectric Materials显示文摘Half-Heusler materials are strong candidates for thermoelectric applications due to their high weighted mobilities and power factors,which is known to be correlated to valley degeneracy in the electronic band structure.However,there are over 50 known semiconducting half-Heusler phases,and it is not clear how the chemical composition affects the electronic structure.While all the n-type electronic structures have their conduction band minimum at either theΓ-or X-point,there is more diversity in the p-type electronic structures,and the valence band maximum can be at either theΓ-,L-,or W-point.Here,we use high throughput computation and machine learning to compare the valence bands of known half-Heusler compounds and discover new chemical guidelines for promoting the highly degenerate W-point to the valence band maximum.We do this by constructing an“orbital phase diagram”to cluster the variety of electronic structures expressed by these phases into groups,based on the atomic orbitals that contribute most to their valence bands.Then,with the aid of machine learning,we develop new chemical rules that predict the location of the valence band maximum in each of the phases.These rules can be used to engineer band structures with band convergence and high valley degeneracy. | Maxwell TDylla Alexander Dunn Shashwat Anand Anubhav Jain G.Jeffrey Snyder | 2020 | Research2020，，1： | 2 |
| 14 | Fluid Therapy in Acute Pancreatitis: Anybody?s Guess显示文摘 | Matthew D. Haydock Anubhav Mittal Heath R. Wilms Anthony Phillips Maxim S. Petrov John A. Windsor | 2013 | Annals of Surgery2013，，2： | 2 |
| 15 | Intrinsic viscosity of polymers and biopolymers measured by microchip 显示文摘 | Jinkee L Anubhav Tripathi | 2005 | Analytical Chemistry2005，77，22： | 1 |
| 16 | Modeling of soil–woven geotextile interface behavior from direct shear test results显示文摘 | Anubhav P.K. Basudhar | 2009 | Geotextiles and Geomembranes2009，，4： | 1 |
| 17 | A systematic review of methods to palliate malignant gastric outlet obstruction显示文摘 | Jasen Ly Gregory O’Grady Anubhav Mittal Lindsay Plank John A. Windsor | 2010 | Surgical Endoscopy2010，，2： | 1 |
| 18 | A systematic review of methods to palliate malignant gastric outlet obstruction显示文摘 | Jasen Ly Gregory O’Grady Anubhav Mittal Lindsay Plank John A. Windsor | 2010 | Surgical Endoscopy2010，，2： | 1 |
| 19 | Commentary: the materials project: a materials genome approach to accelerating materials innovation显示文摘 | ANUBHAV J SHYUE P O | 2013 | Applied Physics Letters Materials2013，1，： | 1 |
| 20 | Modeling of Soil-woven Geotextile Interface Behavior from Direct Shear Test Re- sults显示文摘 | ANUBHAV BASUDHAR P K | 2010 | Geotextiles and Geomembranes2010，28，： | 1 |
您所在的机构暂未开通此部分的功能，请联系管理员