Browsing by Author "Sun, Y"

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    ItemOpen Access
    Analysis of main risk factors causing stroke in Shanxi Province based on machine learning models
    (Informatics in Medicine Unlocked, 2021-01-01) Liu, J; Sun, Y; Ma, J; Tu, J; Deng, Y; He, P; Li, R; Hu, F; Huang, H; Zhou, X; Xu, S
    Background: In China, stroke has been the first leading cause of death in recent years. It is a major cause of long-term physical and cognitive impairment, which bring great pressure on the National Public Health System. On the other hand, China is a big country, evaluation of the risk of getting stroke is important for the prevention and treatment of stroke in China. Methods: A data set with 2000 hospitalized stroke patients in 2018 and 27583 residents during the year 2017 to 2020 is analyzed in this study. With the cleaned data, three models on stroke risk levels are built by using machine learning methods. The importance of “8+2” factors from China National Stroke Prevention Project (CSPP) is evaluated via decision tree and random forest models. The importance of more detailed features and their SHAP values are evaluated and ranked via random forest model. Furthermore, a logistic regression model is applied to evaluate the probability of getting stroke for different risk levels. Results: Among all “8+2” risk factors of getting stroke, the decision tree model reveals that top three factors are Hypertension (0.4995), Physical Inactivity (0.08486) and Diabetes Mellitus (0.07889), and the random forest model shows that top three factors are Hypertension (0.3966), Hyperlipidemia (0.1229) and Physical Inactivity (0.1146). In addition to “8+2” factors the importance of features for lifestyle information, demographic information and medical measurement are evaluated via random forest model. It shows that top five features are Systolic Blood Pressure (SBP) (0.3670), Diastolic Blood Pressure (DBP) (0.1541), Physical Inactivity (0.0904), Body Mass Index (BMI) (0.0721) and Fasting Blood Glucose (FBG)(0.0531). SHAP values show that DBP, Physical Inactivity, SBP, BMI, Smoking, FBG, and Triglyceride(TG) are positively correlated to the risk of getting stroke. High-density Lipoprotein (HDL) is negatively correlated to the risk of getting stroke. Combining with the data of 2000 hospitalized stroke patients, the logistic regression model shows that the average probabilities of getting stroke are 7.20%±0.55% for the low-risk level patients, 19.02%±0.94% for the medium-risk level patients and 83.89%±0.97% for the high-risk level patients. Conclusion: Based on the census data from Shanxi Province, we investigate stroke risk factors and their ranking. It shows that Hypertension, Physical Inactivity, and Overweight are ranked as the top three high stroke risk factors in Shanxi. The probability of getting a stroke is also estimated through our interpretable machine learning methods.
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    ItemOpen Access
    Correction to: AI is a viable alternative to high throughput screening: a 318-target study (Scientific Reports, (2024), 14, 1, (7526), 10.1038/s41598-024-54655-z)
    (Scientific Reports, 2024-12-01) Giles, E; Heifets, A; Artía, Z; Inde, Z; Liu, Z; Zhang, Z; Wang, Z; Su, Z; Chung, Z; Frangos, ZJ; Li, Y; Yen, Y; Sidorova, YA; Tse-Dinh, YC; He, Y; Tang, Y; Li, Y; Pérez-Pertejo, Y; Gupta, YK; Zhu, Y; Sun, Y; Li, Y; Chen, Y; Aldhamen, YA; Hu, Y; Zhang, YJ; Zhang, X; Yuan, X; Wang, X; Qin, X; Yu, X; Xu, X; Qi, X; Lu, X; Wu, X; Blanchet, X; Foong, WE; Bradshaw, WJ; Gerwick, WH; Kerr, WG; Hahn, WC; Donaldson, WA; Van Voorhis, WC; Zhang, W; Tang, W; Li, W; Houry, WA; Lowther, WT; Clayton, WB; Van Hung Le, V; Ronchi, VP; Woods, VA; Scoffone, VC; Maltarollo, VG; Dolce, V; Maranda, V; Segers, VFM; Namasivayam, V; Gunasekharan, V; Robinson, VL; Banerji, V; Tandon, V; Thai, VC; Pai, VP; Desai, UR; Baumann, U; Chou, TF; Chou, T; O’Mara, TA; Banjo, T; Su, T; Lan, T; Ogunwa, TH; Hermle, T; Corson, TW; O’Meara, TR; Kotzé, TJ; Herdendorf, TJ; Richardson, TI; Kampourakis, T; Gillingwater, TH; Jayasinghe, TD; Teixeira, TR; Ikegami, T; Moreda, TL; Haikarainen, T; Akopian, T; Abaffy, T; Swart, T; Mehlman, T; Teramoto, T; Azeem, SM; Dallman, S; Brady-Kalnay, SM; Sarilla, S; Van Doren, SR; Marx, SO; Olson, SH; Poirier, S; Waggoner, SN
    Correction to: Scientific Reportshttps://doi.org/10.1038/s41598-024-54655-z, published online 02 April 2024 The original version of this Article contained errors. In the original version of this article, Ellie Giles was omitted from the Author list. Additionally, the following Affiliation information has been updated: 1. Affiliation 25 was incorrect. Affiliation 25 ‘Queensland University of Technology, Brisbane, USA.’ now reads, ‘Queensland University of Technology, Brisbane, Australia.’ 2. Marta Giorgis was incorrectly affiliated with the ‘University of Aberdeen, Aberdeen, UK.’ The correct Affiliation is listed below: ‘University of Turin, Turin, Italy.’ 3. Affiliations 52, 125 and 261 were duplicated. As a result, the correct Affiliation for Andrew B. Herr, Benjamin Liou, David A. Hildeman, Joseph J. Maciag, Ying Sun, Durga Krishnamurthy, and Stephen N. Waggoner is: ‘Cincinnati Children’s Hospital Medical Center, Cincinnati, USA.’ Furthermore, an outdated version of Figure 1 was typeset. The original Figure 1 and accompanying legend appear below. (Figure presented.) Pairs of representative compounds extracted from AI patents (right) and corresponding prior patents (left) for clinical-stage programs (CDK792,93, A2Ar-antagonist94,95, MALT196,97, QPCTL98,99, USP1100,101, and 3CLpro102,103). The identical atoms between the chemical structures are highlighted in red. Lastly, The Acknowledgements section contained an error. “See Supplementary section S1.” now reads, “See Supplementary section S2.” The original Article has been corrected.
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    ItemOpen Access
    Ghrelin.
    (Mol Metab, 2015-06) Müller, TD; Nogueiras, R; Andermann, ML; Andrews, ZB; Anker, SD; Argente, J; Batterham, RL; Benoit, SC; Bowers, CY; Broglio, F; Casanueva, FF; D'Alessio, D; Depoortere, I; Geliebter, A; Ghigo, E; Cole, PA; Cowley, M; Cummings, DE; Dagher, A; Diano, S; Dickson, SL; Diéguez, C; Granata, R; Grill, HJ; Grove, K; Habegger, KM; Heppner, K; Heiman, ML; Holsen, L; Holst, B; Inui, A; Jansson, JO; Kirchner, H; Korbonits, M; Laferrère, B; LeRoux, CW; Lopez, M; Morin, S; Nakazato, M; Nass, R; Perez-Tilve, D; Pfluger, PT; Schwartz, TW; Seeley, RJ; Sleeman, M; Sun, Y; Sussel, L; Tong, J; Thorner, MO; van der Lely, AJ; van der Ploeg, LHT; Zigman, JM; Kojima, M; Kangawa, K; Smith, RG; Horvath, T; Tschöp, MH
    BACKGROUND: The gastrointestinal peptide hormone ghrelin was discovered in 1999 as the endogenous ligand of the growth hormone secretagogue receptor. Increasing evidence supports more complicated and nuanced roles for the hormone, which go beyond the regulation of systemic energy metabolism. SCOPE OF REVIEW: In this review, we discuss the diverse biological functions of ghrelin, the regulation of its secretion, and address questions that still remain 15 years after its discovery. MAJOR CONCLUSIONS: In recent years, ghrelin has been found to have a plethora of central and peripheral actions in distinct areas including learning and memory, gut motility and gastric acid secretion, sleep/wake rhythm, reward seeking behavior, taste sensation and glucose metabolism.
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    Glucose oxidase triggers gelation of N-hydroxyimide-heparin conjugates to form enzyme-responsive hydrogels for cell-specific drug delivery
    (Chemical Science, 2014-11-01) Su, T; Tang, Z; He, H; Li, W; Wang, X; Liao, C; Sun, Y; Wang, Q
    A new strategy for creating enzyme-responsive hydrogels by employing an N-hydroxyimide-heparin conjugate, designed to act as both an enzyme-mediated radical initiator and an enzyme-sensitive therapeutic carrier, is described. A novel enzyme-mediated redox initiation system involving glucose oxidase (GOx), an N-hydroxyimide-heparin conjugate and glucose is reported. The GOx-mediated radical polymerization reaction allows quick formation of hydrogels under mild conditions, with excellent flexibility in the modulation of the physical and chemical characteristics. The heparin-specific enzymatic cleavage reaction enables the delivery of cargo from the hydrogel in amounts that are controlled by the environmental levels of heparanase, which is frequently associated with tumor angiogenesis and metastasis. The formed hydrogels can realize cell-specific drug delivery by targeting cancer cells that are characterized by heparanase overexpression, whilst showing little toxicity towards normal cells. This journal is