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  1. AU="Ren, Zhiyun"
  2. AU="Sabari, Benjamin R"
  3. AU="Sellal, Nabila"
  4. AU="Kamei, Yoshiki"
  5. AU="Htun Nyunt, Oo"
  6. AU="Lalonde, Donald H"
  7. AU=Olliaro Piero L AU=Olliaro Piero L
  8. AU="Fortney, J J"

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  1. Artikel: M

    Peng, Bo / Ren, Zhiyun / Parthasarathy, Srinivasan / Ning, Xia

    IEEE transactions on knowledge and data engineering

    2022  Band 35, Heft 4, Seite(n) 4033–4046

    Abstract: Next-basket recommendation considers the problem of recommending a set of items into the next basket that users will purchase as a whole. In this paper, we develop a novel mixed model with preferences, popularities and transitions ( ... ...

    Abstract Next-basket recommendation considers the problem of recommending a set of items into the next basket that users will purchase as a whole. In this paper, we develop a novel mixed model with preferences, popularities and transitions (M
    Sprache Englisch
    Erscheinungsdatum 2022-01-13
    Erscheinungsland United States
    Dokumenttyp Journal Article
    ISSN 1041-4347
    ISSN 1041-4347
    DOI 10.1109/tkde.2022.3142773
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  2. Artikel: HAM: Hybrid Associations Models for Sequential Recommendation.

    Peng, Bo / Ren, Zhiyun / Parthasarathy, Srinivasan / Ning, Xia

    IEEE transactions on knowledge and data engineering

    2021  Band 34, Heft 10, Seite(n) 4838–4853

    Abstract: Sequential recommendation aims to identify and recommend the next few items for a user that the user is most likely to purchase/review, given the user's purchase/rating trajectories. It becomes an effective tool to help users select favorite items from a ...

    Abstract Sequential recommendation aims to identify and recommend the next few items for a user that the user is most likely to purchase/review, given the user's purchase/rating trajectories. It becomes an effective tool to help users select favorite items from a variety of options. In this manuscript, we developed hybrid associations models (HAM) to generate sequential recommendations. using three factors: 1) users' long-term preferences, 2) sequential, high-order and low-order association patterns in the users' most recent purchases/ratings, and 3) synergies among those items. HAM uses simplistic pooling to represent a set of items in the associations, and element-wise product to represent item synergies of arbitrary orders. We compared HAM models with the most recent, state-of-the-art methods on six public benchmark datasets in three different experimental settings. Our experimental results demonstrate that HAM models significantly outperform the state of the art in all the experimental settings. with an improvement as much as 46.6%. In addition, our run-time performance comparison in testing demonstrates that HAM models are much more efficient than the state-of-the-art methods. and are able to achieve significant speedup as much as 139.7 folds.
    Sprache Englisch
    Erscheinungsdatum 2021-01-06
    Erscheinungsland United States
    Dokumenttyp Journal Article
    ISSN 1041-4347
    ISSN 1041-4347
    DOI 10.1109/tkde.2021.3049692
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  3. Artikel ; Online: Improving information retrieval from electronic health records using dynamic and multi-collaborative filtering.

    Ning, Xia / Fan, Ziwei / Burgun, Evan / Ren, Zhiyun / Schleyer, Titus

    PloS one

    2021  Band 16, Heft 8, Seite(n) e0255467

    Abstract: Due to the rapid growth of information available about individual patients, most physicians suffer from information overload and inefficiencies when they review patient information in health information technology systems. In this paper, we present a ... ...

    Abstract Due to the rapid growth of information available about individual patients, most physicians suffer from information overload and inefficiencies when they review patient information in health information technology systems. In this paper, we present a novel hybrid dynamic and multi-collaborative filtering method to improve information retrieval from electronic health records. This method recommends relevant information from electronic health records to physicians during patient visits. It models information search dynamics using a Markov model. It also leverages the key idea of collaborative filtering, originating from Recommender Systems, for prioritizing information based on various similarities among physicians, patients and information items. We tested this new method using electronic health record data from the Indiana Network for Patient Care, a large, inter-organizational clinical data repository maintained by the Indiana Health Information Exchange. Our experimental results demonstrated that, for top-5 recommendations, our method was able to correctly predict the information in which physicians were interested in 46.7% of all test cases. For top-1 recommendations, the corresponding figure was 24.7%. In addition, the new method was 22.3% better than the conventional Markov model for top-1 recommendations.
    Mesh-Begriff(e) Algorithms ; Electronic Health Records ; Indiana ; Information Storage and Retrieval
    Sprache Englisch
    Erscheinungsdatum 2021-08-05
    Erscheinungsland United States
    Dokumenttyp Journal Article ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, Non-P.H.S.
    ZDB-ID 2267670-3
    ISSN 1932-6203 ; 1932-6203
    ISSN (online) 1932-6203
    ISSN 1932-6203
    DOI 10.1371/journal.pone.0255467
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  4. Artikel: DOPAMINE D4 RECEPTOR DOWN-REGULATES RENAL SODIUM CHLORIDE COTRANSPORTER VIA UBIQUITINATION-ASSOCIATED LYSOSOME DEGRADATION.

    Zhang, Mingzhuo / Liu, Mingda / Ren, Zhiyun / Wang, Weiwan / Upadhyay, Kiran / Asico, Laureano Asico / Armando, Ines / Jia, Yutao / Wang, Ping / Xue, Ying / Wang, Xiaoyan

    bioRxiv : the preprint server for biology

    2024  

    Abstract: Background: The thiazide-sensitive sodium chloride cotransporter (NCC) is the major apical sodium transporter located in the mammalian renal distal convoluted tubule (DCT). The amount of sodium reabsorbed in the DCT through NCC plays an important role ... ...

    Abstract Background: The thiazide-sensitive sodium chloride cotransporter (NCC) is the major apical sodium transporter located in the mammalian renal distal convoluted tubule (DCT). The amount of sodium reabsorbed in the DCT through NCC plays an important role in the regulation of extracellular fluid volume and blood pressure. Dopamine and its receptors constitute a renal antihypertensive system in mammals. The disruption of Drd4 in mice causes kidney-related hypertension. However, the pathogenesis of D4R-deficiency associated hypertension is not well documented.
    Method: We assessed the effects of D4R on NCC protein abundances and activities of DCT in mice with renal or global Drd4-deficiencies and expressing human D4.7 variant and in cultured mouse DCT cells, and explored the molecular mechanism.
    Results: NCC inhibitor hydrochlorothiazide enhanced the natriuresis in Drd4-/- mice. Renal NCC protein was greater while ubiquitination of NCC was less in Drd4-/- than Drd4+/+ mice. Silencing of D4R in cultured mouse DCT cells increased NCC protein but decreased NCC ubiquitination. D4R agonist had opposite effects that were blocked by the antagonist. In mouse kidneys and DCT cells D4R and NCC colocalized and co-immunoprecipitated. Moreover, D4R-agonist promoted the binding between the two proteins demonstrated by fluorescence resonance energy transfer. D4R agonism internalized NCC, decreased NCC in the plasma membrane, increased NCC in lysosomes and reduced NCC-dependent-intracellular-sodium transport. The lysosomal inhibitor chloroquine prevented the D4R-induced NCC-reduction. A shortened NCC half-life was suggested by its decay under cycloheximide-chase. Ubiquitin-specific-protease 48 (USP48, a deubiquitinating enzyme) was increased in the kidneys and cells with Drd4-deficiency while D4R stimulation decreased it in vitro and reduction of USP48 with siRNA decreased NCC expression. The mice carrying human D4.7 variant or with renal supcapsular-Drd4-siRNA-delivery developed hypertension with increased NCC.
    Conclusion: Our data demonstrates that D4R downregulates NCC by promoting USP48-associated deubiquitination and subsequent internalization, lysosome relocation and degradation.
    Sprache Englisch
    Erscheinungsdatum 2024-02-16
    Erscheinungsland United States
    Dokumenttyp Preprint
    DOI 10.1101/2024.02.14.580405
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  5. Artikel ; Online: Candesartan upregulates angiotensin-converting enzyme 2 in kidneys of male animals by decreased ubiquitination.

    Wang, Ping / Ren, Zhiyun / Wang, Weiwan / Liu, Mingda / Jia, Yutao / Zhang, Mingzhuo / Xue, Ying / Zhang, Chenyang / Xu, Jianteng / Wang, Cheng / Wang, Xiaoyan

    FASEB journal : official publication of the Federation of American Societies for Experimental Biology

    2024  Band 38, Heft 6, Seite(n) e23537

    Abstract: Candesartan is a common angiotensin-II receptor-1 blocker used for patients with cardiovascular and renal diseases. Angiotensin-converting enzyme 2 (ACE2) is a negative regulator of blood pressure (BP), and also a major receptor for coronaviruses. To ... ...

    Abstract Candesartan is a common angiotensin-II receptor-1 blocker used for patients with cardiovascular and renal diseases. Angiotensin-converting enzyme 2 (ACE2) is a negative regulator of blood pressure (BP), and also a major receptor for coronaviruses. To determine whether and how candesartan upregulates ACE2, we examined BP and ACE2 in multi-organs from male and female C57BL/6J mice treated with candesartan (1 mg/kg, i.p.) for 7 days. Relative to the vehicle, candesartan lowered BP more in males than females; ACE2 protein abundances were increased in kidneys, not lungs, hearts, aorta, liver, spleen, brain, or serum, only from males. Ace2-mRNA was similar in kidneys. Candesartan also decreased BP in normal, hypertensive, and nephrotic male rats. The renal ACE2 was increased by the drug in normal and nephrotic male rats but not spontaneously hypertensive ones. In male mouse kidneys, ACE2 was distributed at sodium-hydrogen-exchanger-3 positive proximal-convoluted-tubules; ACE2-ubiquitination was decreased by candesartan, accompanied with increased ubiquitin-specific-protease-48 (USP48). In candesartan-treated mouse renal proximal-convoluted-tubule cells, ACE2 abundances and activities were increased while ACE2-ubiquitination and colocalization with lysosomal and proteosomal markers were decreased. The silence of USP48 by siRNA caused a reduction of ACE2 in the cells. Thus, the sex-differential ACE2 upregulation by candesartan in kidney from males may be due to the decreased ACE2-ubiquitination, associated with USP48, and consequent degradation in lysosomes and proteosomes. This is a novel mechanism and may shed light on candesartan-like-drug choice in men and women prone to coronavirus infections.
    Mesh-Begriff(e) Humans ; Female ; Male ; Rats ; Mice ; Animals ; Angiotensin-Converting Enzyme 2/metabolism ; Mice, Inbred C57BL ; Kidney/metabolism ; Hypertension/metabolism ; Tetrazoles/pharmacology ; Ubiquitination ; Benzimidazoles ; Biphenyl Compounds
    Chemische Substanzen Angiotensin-Converting Enzyme 2 (EC 3.4.17.23) ; candesartan (S8Q36MD2XX) ; Tetrazoles ; Benzimidazoles ; Biphenyl Compounds
    Sprache Englisch
    Erscheinungsdatum 2024-03-18
    Erscheinungsland United States
    Dokumenttyp Journal Article
    ZDB-ID 639186-2
    ISSN 1530-6860 ; 0892-6638
    ISSN (online) 1530-6860
    ISSN 0892-6638
    DOI 10.1096/fj.202302707R
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.A 2266: Hefte anzeigen Standort:
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    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)
    Zs.MO 296: Hefte anzeigen

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  6. Artikel ; Online: Hybrid collaborative filtering methods for recommending search terms to clinicians.

    Ren, Zhiyun / Peng, Bo / Schleyer, Titus K / Ning, Xia

    Journal of biomedical informatics

    2020  Band 113, Seite(n) 103635

    Abstract: With increasing and extensive use of electronic health records (EHR), clinicians are often challenged in retrieving relevant patient information efficiently and effectively to arrive at a diagnosis. While using the search function built into an EHR can ... ...

    Abstract With increasing and extensive use of electronic health records (EHR), clinicians are often challenged in retrieving relevant patient information efficiently and effectively to arrive at a diagnosis. While using the search function built into an EHR can be more useful than browsing in a voluminous patient record, it is cumbersome and repetitive to search for the same or similar information on similar patients. To address this challenge, there is a critical need to build effective recommender systems that can recommend search terms to clinicians accurately. In this study, we developed a hybrid collaborative filtering model to recommend search terms for a specific patient to a clinician. The model draws on information from patients' clinical encounters and the searches that were performed during them. To generate recommendations, the model uses search terms which are (1) frequently co-occurring with the ICD codes recorded for the patient and (2) highly relevant to the most recent search terms. In one variation of the model (Hybrid Collaborative Filtering Method for Healthcare, or HCFMH), we use only the most recent ICD codes assigned to the patient, and in the other (Co-occurrence Pattern based HCFMH, or cpHCFMH), all ICD codes. We have conducted comprehensive experiments to evaluate the proposed model. These experiments demonstrate that our model outperforms state-of-the-art baseline methods for top-N search term recommendation on different data sets.
    Mesh-Begriff(e) Electronic Health Records ; Humans
    Sprache Englisch
    Erscheinungsdatum 2020-12-09
    Erscheinungsland United States
    Dokumenttyp Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
    ZDB-ID 2057141-0
    ISSN 1532-0480 ; 1532-0464
    ISSN (online) 1532-0480
    ISSN 1532-0464
    DOI 10.1016/j.jbi.2020.103635
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  7. Buch ; Online: HAM

    Peng, Bo / Ren, Zhiyun / Parthasarathy, Srinivasan / Ning, Xia

    Hybrid Associations Models for Sequential Recommendation

    2020  

    Abstract: Sequential recommendation aims to identify and recommend the next few items for a user that the user is most likely to purchase/review, given the user's purchase/rating trajectories. It becomes an effective tool to help users select favorite items from a ...

    Abstract Sequential recommendation aims to identify and recommend the next few items for a user that the user is most likely to purchase/review, given the user's purchase/rating trajectories. It becomes an effective tool to help users select favorite items from a variety of options. In this manuscript, we developed hybrid associations models (HAM) to generate sequential recommendations using three factors: 1) users' long-term preferences, 2) sequential, high-order and low-order association patterns in the users' most recent purchases/ratings, and 3) synergies among those items. HAM uses simplistic pooling to represent a set of items in the associations, and element-wise product to represent item synergies of arbitrary orders. We compared HAM models with the most recent, state-of-the-art methods on six public benchmark datasets in three different experimental settings. Our experimental results demonstrate that HAM models significantly outperform the state of the art in all the experimental settings, with an improvement as much as 46.6%. In addition, our run-time performance comparison in testing demonstrates that HAM models are much more efficient than the state-of-the-art methods, and are able to achieve significant speedup as much as 139.7 folds.

    Comment: This paper has been accepted by IEEE Transactions on Knowledge and Data Engineering (TKDE)
    Schlagwörter Computer Science - Information Retrieval ; Computer Science - Machine Learning
    Thema/Rubrik (Code) 006
    Erscheinungsdatum 2020-02-26
    Erscheinungsland us
    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  8. Buch ; Online: M2

    Peng, Bo / Ren, Zhiyun / Parthasarathy, Srinivasan / Ning, Xia

    Mixed Models with Preferences, Popularities and Transitions for Next-Basket Recommendation

    2020  

    Abstract: Next-basket recommendation considers the problem of recommending a set of items into the next basket that users will purchase as a whole. In this paper, we develop a novel mixed model with preferences, popularities and transitions (M2) for next-basket ... ...

    Abstract Next-basket recommendation considers the problem of recommending a set of items into the next basket that users will purchase as a whole. In this paper, we develop a novel mixed model with preferences, popularities and transitions (M2) for next-basket recommendation. This method explicitly models three important factors in next-basket generation process: 1) users' general preferences, 2) items' global popularities and 3) transition patterns among items. We also propose a simple encoder-decoder based framework (ed-Trans) to better model the transition patterns among items. We compared M2 with 5 state-of-the-art next-basket recommendation methods on 4 public benchmark datasets. Our experimental results demonstrate that M2 significantly outperforms the state-of-the-art methods on all the datasets, with an improvement as much as 19.0% at recall@5. We also compared M2 with these baseline methods in recommending the second next and third next baskets. Our experimental results demonstrate that M2 could consistently outperform the baseline methods in all these tasks, with an improvement as much as 14.4% at recall@5. In addition, we conducted a comprehensive ablation study to verify the effects of the different factors. The results show that learning all the factors together could significantly improve the recommendation performance compared to learning each of them alone. The results also show that ed-Trans in learning item transitions among baskets could outperform recurrent neural network-based methods on the benchmark datasets, with an improvement as much as 20.4% at recall@5. We also have a thorough discussion on various experimental protocols and evaluation metrics for next-basket recommendation evaluation.
    Schlagwörter Computer Science - Machine Learning ; Computer Science - Information Retrieval ; Statistics - Machine Learning
    Thema/Rubrik (Code) 006
    Erscheinungsdatum 2020-04-03
    Erscheinungsland us
    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  9. Artikel: Assessment of Urinary Exosomal NHE3 as a Biomarker of Acute Kidney Injury.

    Yu, Yanting / Ren, Zhiyun / Xie, Anni / Jia, Yutao / Xue, Ying / Wang, Ping / Ji, Daxi / Wang, Xiaoyan

    Diagnostics (Basel, Switzerland)

    2022  Band 12, Heft 11

    Abstract: The diagnosis of acute kidney injury (AKI) traditionally depends on the serum creatinine (Scr) and urine output, which lack sufficient sensitivity and specificity. Using urinary exosomes as a biomarker has unique advantages. To assess whether urinary ... ...

    Abstract The diagnosis of acute kidney injury (AKI) traditionally depends on the serum creatinine (Scr) and urine output, which lack sufficient sensitivity and specificity. Using urinary exosomes as a biomarker has unique advantages. To assess whether urinary exosomal Na
    Sprache Englisch
    Erscheinungsdatum 2022-10-30
    Erscheinungsland Switzerland
    Dokumenttyp Journal Article
    ZDB-ID 2662336-5
    ISSN 2075-4418
    ISSN 2075-4418
    DOI 10.3390/diagnostics12112634
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  10. Artikel ; Online: A low-salt diet with candesartan administration is associated with acute kidney injury in nephritis by increasing nitric oxide.

    Yu, Yanting / Wang, Ping / Ren, Zhiyun / Xue, Ying / Jia, Yutao / Wang, Weiwan / Liu, Mingda / Pan, Kueiching / Xiao, Leijuan / Ji, Daxi / Wang, Xiaoyan

    Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie

    2023  Band 161, Seite(n) 114484

    Abstract: A low-salt diet may activate the renin-angiotensin-aldosterone system (RAAS) and is often applied simultaneously with RAAS inhibitors, especially for treatment of proteinuric nephritis. To explore the effect of a low-salt diet combined with angiotensin ... ...

    Abstract A low-salt diet may activate the renin-angiotensin-aldosterone system (RAAS) and is often applied simultaneously with RAAS inhibitors, especially for treatment of proteinuric nephritis. To explore the effect of a low-salt diet combined with angiotensin receptor blockers (ARB) on kidney function, the proteinuric nephritis model was induced by single intravenous injection of doxorubicin, and then the SD rats were administrated with candesartan intraperitoneal injection and fed with different salt diets. Rats with low-salt plus candesartan, not either alone, experienced acute kidney injury (AKI) at day 7 and could not self-restore when extending the experiment time from 7 days to 21 days, unless switching low-salt to normal-salt. Among three nitric oxide synthetases (NOS), endothelial NOS (eNOS) was obviously elevated and PI3K-Akt-eNOS signal pathway was activated. N
    Mesh-Begriff(e) Rats ; Animals ; Kidney ; NG-Nitroarginine Methyl Ester/pharmacology ; Diet, Sodium-Restricted ; Nitric Oxide/metabolism ; Angiotensin Receptor Antagonists/pharmacology ; Phosphatidylinositol 3-Kinases/metabolism ; bcl-2-Associated X Protein/metabolism ; Rats, Sprague-Dawley ; Angiotensin-Converting Enzyme Inhibitors/pharmacology ; Blood Pressure ; Nitric Oxide Synthase/metabolism ; Sodium Chloride ; Acute Kidney Injury/chemically induced ; Acute Kidney Injury/metabolism ; Nephritis/metabolism
    Chemische Substanzen candesartan (S8Q36MD2XX) ; NG-Nitroarginine Methyl Ester (V55S2QJN2X) ; Nitric Oxide (31C4KY9ESH) ; Angiotensin Receptor Antagonists ; Phosphatidylinositol 3-Kinases (EC 2.7.1.-) ; bcl-2-Associated X Protein ; Angiotensin-Converting Enzyme Inhibitors ; Nitric Oxide Synthase (EC 1.14.13.39) ; Sodium Chloride (451W47IQ8X)
    Sprache Englisch
    Erscheinungsdatum 2023-03-13
    Erscheinungsland France
    Dokumenttyp Journal Article
    ZDB-ID 392415-4
    ISSN 1950-6007 ; 0753-3322 ; 0300-0893
    ISSN (online) 1950-6007
    ISSN 0753-3322 ; 0300-0893
    DOI 10.1016/j.biopha.2023.114484
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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    Ud II Zs.198: Hefte anzeigen Standort:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular
    ab Jg. 2022: Lesesaal (EG)

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