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| 1 | Ursodeoxycholic acid as a means of preventing atherosclerosis,steatosis and liver fibrosis in patients with nonalcoholic fatty liver disease显示文摘BACKGROUND Atherosclerotic cardiovascular disease(ASCVD)is the leading cause of mortality in patients with nonalcoholic fatty liver disease(NAFLD).Weight loss is a key factor for successful NAFLD and CVD therapy.Ursodeoxycholic acid(UDCA),which is one of the first-line therapeutic agents for treatment of NAFLD,is reported to have a beneficial effect on dyslipidemia and ASCVD risk because of antioxidant properties.AIM To evaluate the effects of 6 mo of UDCA treatment on hepatic function tests,lipid profile,hepatic steatosis and fibrosis,atherogenesis,and ASCVD risk in men and women with NAFLD,as well as to assess the impact of>5%weight reduction on these parameters.METHODS An open-label,multicenter,international noncomparative trial was carried out at primary health care settings and included 174 patients with ultrasound-diagnosed NAFLD who received 15 mg/kg/d UDCA for 6 mo and were prescribed lifestyle modification with diet and exercise.The efficacy criteria were liver enzymes,lipid profile,fatty liver index(FLI),noninvasive liver fibrosis tests(nonalcoholic fatty liver disease fibrosis score and liver fibrosis index),carotid intima-media thickness(CIMT),and ASCVD risk score.To test statistical hypotheses,the Wilcoxon test,paired t-test,Fisher’s exact test,and Pearson's chi-squared test were used.RESULTS The alanine aminotransferase(ALT)level changed by-14.1 U/L(-31.0;-5.3)from baseline to 3 mo and by-6.5 U/L(-14.0;0.1)from 3 to 6 mo.The magnitude of ALT,aspartate transaminase,and glutamyltransferase decrease was greater during the first 3 mo of treatment compared to the subsequent 3 mo(P<0.001,P<0.01,P<0.001,respectively).At 6 mo,in the total sample,we observed a statistically significant decrease in body weight and levels of FLI:84.9±10.4 vs 72.3±17.6,P<0.001,total cholesterol:6.03±1.36 vs 5.76±1.21,Р<0.001,lowdensity lipoprotein:3.86±1.01 vs 3.66±0.91,Р<0.001,and triglyceride:3.18(2.00;4.29)vs 2.04(1.40;3.16),Р<0.001.No effect on nonalcoholic fatty liver disease fibrosis score or liver fibrosis index was found.The CIMT decreased significantly in the total sample(0.985±0.243 vs 0.968±0.237,P=0.013),whereas the highdensity lipoprotein(Р=0.036)and 10-year ASCVD risk(Р=0.003)improved significantly only in women.Fifty-four patients(31%)achieved>5%weight loss.At the end of the study,the FLI decreased significantly in patients with(88.3±10.2 vs 71.4±19.6,P<0.001)and without>5%weight loss(83.5±10.3 vs 72.8±16.7,P<0.001).The changes in ALT,aspartate transaminase,glutamyltransferase,total cholesterol,and low-density lipoprotein levels were similar between the subgroups.CONCLUSION UDCA normalizes liver enzymes greatly within the first 3 mo of treatment,improves lipid profile and hepatic steatosis independent of weight loss,and has a positive effect on CIMT in the total sample and 10-year ASCVD risk in women after 6 mo of treatment. | Maria Nadinskaia Marina Maevskaya Vladimir Ivashkin Khava Kodzoeva Irina Pirogova Evgeny Chesnokov Alexander Nersesov Jamilya Kaibullayeva Akzhan Konysbekova Aigul Raissova Feruza Khamrabaeva Elena Zueva | 2021 | World Journal of Gastroenterology2021，27，10： | 4 |
| 2 | Acute experimentalpancreatitis and NF-icB/Relactivation 显示文摘 | Aigul H Tando Y Schneider G | 2002 | Pancreatology2002，2，6： | 1 |
| 3 | Iron and zinc grain density in common wheat grown in Central Asia显示文摘 | Alexei Morgounov Hugo Ferney Gómez-Becerra Aigul Abugalieva Mira Dzhunusova M. Yessimbekova Hafiz Muminjanov Yu Zelenskiy Levent Ozturk Ismail Cakmak | 2007 | Euphytica (-)2007，，1： | 1 |
| 4 | Artificial intelligence-Developments in medicine in the last two years显示文摘Dear Editor , Artificial intelligence (AI) is the theory and development of computer systems that are able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages. There are some knowledge and thinking tasks that humans cannot perform as perfectly as they wish to or should be able to. These tasks are closely related to security and responsibility. A multitude of cognitive distortions have been well explored1 and present opportunities to use AI for powerful assistance in thinking tasks. The core of the Industrial Revolution 4.0 is the adoption of AI methods. This revolution has affected all aspects of human activities and medicine is one example. AI systems can usually include formal algorithms for subtasks that can be solved using logic, for example, a decision tree. The task solution process moves from logic point to logic point similar to a train on a railway. These algorithms are fast and have the ability to explain. | Rezida Maratovna Galimova Igor Vyacheslavovich Buzaev Kireev Ayvar Ramilevich Lev Khadyevich Yuldybaev Aigul Fazirovna Shaykhulova | 2019 | Chronic Diseases and Translational Medicine2019，5，1： | 1 |
| 5 | The role of CXCR2 in systemic neovascularization of the mouse lung显示文摘 | Jesu′s Sa′nchez Aigul Moldobaeva Jessica McClintock | 2007 | J Appl Physiol2007，103，： | 1 |
| 6 | How to detect early left atrial remodelling and dysfunction in mild-to-moderate hypertension显示文摘 | Aigul Baltabaeva Maciej Marciniak Bart Bijnens Chirine Parsai James Moggridge Tarek F Antonios Graham A MacGregor George R Sutherland | 2009 | Journal of Hypertension2009，，10： | 1 |
| 7 | Iron and zinc grain density in common wheat grown in Central Asia显示文摘 | Alexei M Hugo F G Aigul A | 2007 | Euphytica2007，155，： | 1 |
| 8 | Radiobiogeochemical Assessment of the Soil Near the Issyk-kul Region显示文摘 | Djenbaev Bekmamat Kaldybaev Bakit Toktoeva Tamara Kenjebaeva Aigul | 2016 | Journal of Geological Resource and Engineering2016，4，1： | 1 |
| 9 | A Review of Machine Learning Techniques in Cyberbullying Detection显示文摘Automatic identification of cyberbullying is a problem that is gaining traction,especially in the Machine Learning areas.Not only is it complicated,but it has also become a pressing necessity,considering how social media has become an integral part of adolescents’lives and how serious the impacts of cyberbullying and online harassment can be,particularly among teenagers.This paper contains a systematic literature review of modern strategies,machine learning methods,and technical means for detecting cyberbullying and the aggressive command of an individual in the information space of the Internet.We undertake an in-depth review of 13 papers from four scientific databases.The article provides an overview of scientific literature to analyze the problem of cyberbullying detection from the point of view of machine learning and natural language processing.In this review,we consider a cyberbullying detection framework on social media platforms,which includes data collection,data processing,feature selection,feature extraction,and the application ofmachine learning to classify whether texts contain cyberbullying or not.This article seeks to guide future research on this topic toward a more consistent perspective with the phenomenon’s description and depiction,allowing future solutions to be more practical and effective. | Daniyar Sultan Batyrkhan Omarov Zhazira Kozhamkulova Gulnur Kazbekova Laura Alimzhanova Aigul Dautbayeva Yernar Zholdassov Rustam Abdrakhmanov | 2023 | Computers, Materials & Continua2023，，3： | 0 |
| 10 | Responses of a ^(234)U/^(238)U activity ratio in groundwater to earthquakes in the South Baikal Basin,Siberia显示文摘In the western part of the South Baikal Basin,spatial-temporal distribution of earthquake epicenters shows quasi-periodic seismic reactivation.The largest earthquakes that occurred in 1999(M_(W)=6.0)and 2008(M_(W)=6.3)fall within seismic intervals of 1994-2003 and 2003-2012,respectively.In the seismic interval that began in 2013,the ^(234)U/^(238)U activity ratio(AR)in groundwater was monitored assuming its dependence on crack opening/closing that facilitated/prevented water circulation in an active boundary fault of the basin.Transitions from disordered,high-amplitude fluctuations of AR values to consistent,low-amplitude fluctuations in different monitoring sites were found to be sensitive indicators of both small seismic events occurring directly on the observation area,and of a large remote earthquake.The hydroisotopic responses to seismic events were consistent with monitoring data on deformation and temperature variations of rocks.The hydroisotopic effects can be applied for detecting a seismically dangerous state of an active fault and prediction of a large future earthquake. | Sergei Rasskazov Aigul Ilyasova Sergei Bornyakov Irina Chuvashova Eugene Chebykin | 2020 | Frontiers of Earth Science2020，14，4： | 0 |
| 11 | Effective Approaches to Wheat Improvement in Kazakhstan: Breeding and Conservation Agriculture显示文摘 | Muratbek Karabayev Alexei Morgounov Hans-Joachim Braun Patrick Wall Kenneth Sayre Yuriy Zelenskiy Rauan Zhapayev Aigul Akhmetova Valentin Dvurechenskii Kulyash Iskandarova Theodor Friedrich Turi Fileccia Maurizio Guadagni | 2014 | Journal of Agricultural Science and Technology(B)2014，4，10： | 0 |
| 12 | One Dimensional Conv-BiLSTM Network with Attention Mechanism for IoT Intrusion Detection显示文摘In the face of escalating intricacy and heterogeneity within Internet of Things(IoT)network landscapes,the imperative for adept intrusion detection techniques has never been more pressing.This paper delineates a pioneering deep learning-based intrusion detection model:the One Dimensional Convolutional Neural Networks(1D-CNN)and Bidirectional Long Short-Term Memory(BiLSTM)Network(Conv-BiLSTM)augmented with an Attention Mechanism.The primary objective of this research is to engineer a sophisticated model proficient in discerning the nuanced patterns and temporal dependencies quintessential to IoT network traffic data,thereby facilitating the precise categorization of a myriad of intrusion types.Methodology:The proposed model amal-gamates the potent attributes of 1D convolutional neural networks,bidirectional long short-term memory layers,and attention mechanisms to bolster the efficacy and resilience of IoT intrusion detection systems.A rigorous assessment was executed employing an expansive dataset that mirrors the convolutions and multifariousness characteristic of genuine IoT network settings,encompassing various network traffic paradigms and intrusion archetypes.Findings:The empirical evidence underscores the paramountcy of the One Dimensional Conv-BiLSTM Network with Attention Mechanism,which exhibits a marked superiority over conventional machine learning modalities.Notably,the model registers an exemplary AUC-ROC metric of 0.995,underscoring its precision in typifying a spectrum of intrusions within IoT infrastructures.Conclusion:The presented One Dimensional Conv-BiLSTM Network armed with an Attention Mechanism stands out as a robust and trustworthy vanguard against IoT network breaches.Its prowess in discerning intricate traffic patterns and inherent temporal dependencies transcends that of traditional machine learning frameworks.The commendable diagnostic accuracy manifested in this study advocates for its tangible deployment.This investigation indubitably advances the cybersecurity domain,amplifying the fortification and robustness of IoT frameworks and heralding a new era of bolstered security across pivotal sectors such as residential,medical,and transit systems. | Bauyrzhan Omarov Zhuldyz Sailaukyzy Alfiya Bigaliyeva Adilzhan Kereyev Lyazat Naizabayeva Aigul Dautbayeva | 2023 | Computers, Materials & Continua2023，77，12： | 0 |
| 13 | Optimization of Isolation and Identification Conditions of Glibenclamide Annotation显示文摘 | Ordabayeva Saule Kutymovna Karakulova Aizhan Shirinbekovna Serikbayeva Aigul Djumadullayevna Mirsoatova Mokhinur | 2016 | Journal of Pharmacy and Pharmacology2016，4，8： | 0 |
| 14 | Language and Culture in Foreign Language Teaching显示文摘 | Aigul Kadyskyzy | 2013 | Sino-US English Teaching2013，10，1： | 0 |
| 15 | Sweet Sorghum Genotypes Testing in the High Latitude Rainfed Steppes of the Northern Kazakhstan (for Feed and Biofuel)显示文摘 | Rauan Zhapayev Kulyash Iskandarova Kristina Toderich Irina Paramonova Abdullah Al-Dakheel Shoaib Ismail Srinivasa Rao Pinnamaneni Aiman Omarova Nina Nekrasova Darhan Balpanov Oleg Ten Erlan Ramanculov Yuriy Zelenskiy Aigul Akhmetova Muratbek Karabayev | 2015 | Journal of Environmental Science and Engineering(B)2015，4，1： | 0 |
| 16 | Cyberbullying-related Hate Speech Detection Using Shallow-to-deep Learning显示文摘Communication in society had developed within cultural and geographical boundaries prior to the invention of digital technology.The latest advancements in communication technology have significantly surpassed the conventional constraints for communication with regards to time and location.These new platforms have ushered in a new age of user-generated content,online chats,social network and comprehensive data on individual behavior.However,the abuse of communication software such as social media websites,online communities,and chats has resulted in a new kind of online hostility and aggressive actions.Due to widespread use of the social networking platforms and technological gadgets,conventional bullying has migrated from physical form to online,where it is termed as Cyberbullying.However,recently the digital technologies as machine learning and deep learning have been showing their efficiency in identifying linguistic patterns used by cyberbullies and cyberbullying detection problem.In this research paper,we aimed to evaluate shallow machine learning and deep learning methods in cyberbullying detection problem.We deployed three deep and six shallow learning algorithms for cyberbullying detection problems.The results show that bidirectional long-short-term memory is the most efficient method for cyberbullying detection,in terms of accuracy and recall. | Daniyar Sultan Aigerim Toktarova Ainur Zhumadillayeva Sapargali Aldeshov Shynar Mussiraliyeva Gulbakhram Beissenova Abay Tursynbayev Gulmira Baenova Aigul Imanbayeva | 2023 | Computers, Materials & Continua2023，，1： | 0 |
| 17 | Assessment of molecular markers and marker-assisted selection for drought tolerance in barley(Hordeum vulgare L.)显示文摘This review updates the present status of the field of molecular markers and marker-assisted selection(MAS),using the example of drought tolerance in barley.The accuracy of selected quantitative trait loci(QTLs),candidate genes and suggested markers was assessed in the barley genome cv.Morex.Six common strategies are described for molecular marker development,candidate gene identification and verification,and their possible applications in MAS to improve the grain yield and yield components in barley under drought stress.These strategies are based on the following five principles:(1)Molecular markers are designated as genomic‘tags’,and their‘prediction’is strongly dependent on their distance from a candidate gene on genetic or physical maps;(2)plants react differently under favourable and stressful conditions or depending on their stage of development;(3)each candidate gene must be verified by confirming its expression in the relevant conditions,e.g.,drought;(4)the molecular marker identified must be validated for MAS for tolerance to drought stress and improved grain yield;and(5)the small number of molecular markers realized for MAS in breeding,from among the many studies targeting candidate genes,can be explained by the complex nature of drought stress,and multiple stress-responsive genes in each barley genotype that are expressed differentially depending on many other factors. | Akmaral Baidyussen Gulmira Khassanova Maral Utebayev Satyvaldy Jatayev Rystay Kushanova Sholpan Khalbayeva Aigul Amangeldiyeva Raushan Yerzhebayeva KulpashBulatova Carly Schramm Peter Anderson Colin L.D.Jenkins Kathleen LSoole Yuri Shavrukov | 2024 | Journal of Integrative Agriculture2024，23，1： | 0 |
| 18 | Aggregation Processes Modeling in Physical-Chemical Systems显示文摘 | Naukenova Aigul Sagindykovna Bekaulova Aliya Amankulovna Mamitova Aigul Dzhanabaevna SadykovZhenis Abzhanovich Kerimbekova Zaurekul Maidanbekovna Tolegen Marzhankul Erhozhakyzy RamatullaevaLazzat Immamadinovna Tursynbekova Elmira Nurlankyzy | 2013 | 材料科学与工程(中英文B版)2013，3，12： | 0 |
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