X

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
  • 1
    Online Resource
    Online Resource
    Resistance Training Improves Muscle Function and Cardiometabolic Risks But Not Quality of Life in Older People With Type 2 Diabetes Mellitus: A Randomized Controlled Trial
    Ovid Technologies (Wolters Kluwer Health) ; 2018
    In:  Journal of Geriatric Physical Therapy Vol. 41, No. 2 ( 2018-04), p. 65-76
    Details
    In: Journal of Geriatric Physical Therapy, Ovid Technologies (Wolters Kluwer Health), Vol. 41, No. 2 ( 2018-04), p. 65-76
    Abstract: In older people with type 2 diabetes mellitus (T2DM), the effects of aging and T2DM may compromise the function of skeletal muscle, deteriorate metabolic status, and jeopardize physical performance, aerobic capacity, and quality of life (QoL). The purpose of this study was to investigate the effects of 12 weeks of resistance training (RT) on muscle function, physical performance, cardiometabolic risks, and QoL in older people with T2DM. Methods: This study was a randomized controlled trial that employed block randomization, assessor blinding, and the intention-to-treat principle. Thirty people 65 years or older with a diagnosis of T2DM were randomly assigned to either an exercise group or a control group and were further stratified by gender. The exercise group performed 8 RT exercises in 3 sets of 8 to 12 repetitions at 75% 1-repetition maximum (1-RM) 3 times per week for 12 weeks. The control group received usual care and maintained their daily activities and lifestyle. Muscle function (1-RM and muscle oxygenation responses), physical performance (5-repetition sit-to-stand test and Timed Up and Go test), cardiometabolic risks (aerobic capacity, blood pressure, body composition, glycemic control, lipids levels, and high-sensitivity C-reactive protein levels), and QoL (Audit of Diabetes-Dependent Quality of Life 19) were assessed at baseline (week 0) and after the 12-week interventions (week 12). Results: The 1-RM chest-press and leg-press strength and physical performance in 5-repetition sit-to-stand test were significantly improved in the exercise group compared with the controls after the interventions. The exercise group had significantly lower resting systolic blood pressure (by −12.1 mm Hg, P = 0.036) than did the controls after 12 weeks of RT, without any significant within-group change in either group after intervention. The waist circumference, fasting glucose levels, and peak diastolic blood pressure tended to favor RT over usual care after the interventions. Conclusion: Twelve weeks of RT increased the maximal strength in chest-press and leg-press tests, and improved 5-repetition sit-to-stand performance in older people with T2DM. Our study demonstrated that supervised, structured RT was able to promote muscle function and alleviate cardiometabolic risks in people with T2DM 65 years or older.
    Type of Medium: Online Resource
    ISSN: 1539-8412
    URL: Article
    DOI: 10.1519/JPT.0000000000000107
    Language: English
    Publisher: Ovid Technologies (Wolters Kluwer Health)
    Publication Date: 2018
    detail.hit.zdb_id: 2159678-5
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    Open Access Request
    Availability (electronic / print)
  • 2
    Online Resource
    Online Resource
    Exploring possible mechanisms of action for the nanotoxicity and protein binding of decorated nanotubes: interpretation of physicochemical properties from optimal QSAR models
    Elsevier BV ; 2015
    In:  Toxicology and Applied Pharmacology Vol. 288, No. 1 ( 2015-10), p. 52-62
    Details
    In: Toxicology and Applied Pharmacology, Elsevier BV, Vol. 288, No. 1 ( 2015-10), p. 52-62
    Type of Medium: Online Resource
    ISSN: 0041-008X
    URL: Article
    DOI: 10.1016/j.taap.2015.07.008
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2015
    detail.hit.zdb_id: 1471923-X
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    Open Access Request
    Availability (electronic / print)
  • 3
    Online Resource
    Online Resource
    LipidPedia: a comprehensive lipid knowledgebase
    Oxford University Press (OUP) ; 2018
    In:  Bioinformatics Vol. 34, No. 17 ( 2018-09-01), p. 2982-2987
    Details
    In: Bioinformatics, Oxford University Press (OUP), Vol. 34, No. 17 ( 2018-09-01), p. 2982-2987
    Abstract: Lipids are divided into fatty acyls, glycerolipids, glycerophospholipids, sphingolipids, saccharolipids, sterols, prenol lipids and polyketides. Fatty acyls and glycerolipids are commonly used as energy storage, whereas glycerophospholipids, sphingolipids, sterols and saccharolipids are common used as components of cell membranes. Lipids in fatty acyls, glycerophospholipids, sphingolipids and sterols classes play important roles in signaling. Although more than 36 million lipids can be identified or computationally generated, no single lipid database provides comprehensive information on lipids. Furthermore, the complex systematic or common names of lipids make the discovery of related information challenging. Results Here, we present LipidPedia, a comprehensive lipid knowledgebase. The content of this database is derived from integrating annotation data with full-text mining of 3923 lipids and more than 400 000 annotations of associated diseases, pathways, functions and locations that are essential for interpreting lipid functions and mechanisms from over 1 400 000 scientific publications. Each lipid in LipidPedia also has its own entry containing a text summary curated from the most frequently cited diseases, pathways, genes, locations, functions, lipids and experimental models in the biomedical literature. LipidPedia aims to provide an overall synopsis of lipids to summarize lipid annotations and provide a detailed listing of references for understanding complex lipid functions and mechanisms. Availability and implementation LipidPedia is available at http://lipidpedia.cmdm.tw. Supplementary information Supplementary data are available at Bioinformatics online.
    Type of Medium: Online Resource
    ISSN: 1367-4803 , 1367-4811
    URL: Article
    DOI: 10.1093/bioinformatics/bty213
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2018
    detail.hit.zdb_id: 1468345-3
    SSG: 12
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    Open Access Request
    Availability (electronic / print)
  • 4
    Online Resource
    Online Resource
    Exploring molecular moieties on carbonized polymer dots from flavonoid glycosides with activity against enterovirus A71
    Elsevier BV ; 2022
    In:  Carbon Vol. 192 ( 2022-06), p. 285-294
    Details
    In: Carbon, Elsevier BV, Vol. 192 ( 2022-06), p. 285-294
    Type of Medium: Online Resource
    ISSN: 0008-6223
    URL: Article
    DOI: 10.1016/j.carbon.2022.03.009
    RVK:
    VA 1120
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2022
    detail.hit.zdb_id: 2014715-6
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    Open Access Request
    Availability (electronic / print)
  • 5
    Online Resource
    Online Resource
    RSDB: A rare skin disease database to link drugs with potential drug targets for rare skin diseases
    Springer Science and Business Media LLC ; 2022
    In:  Scientific Data Vol. 9, No. 1 ( 2022-08-26)
    Details
    In: Scientific Data, Springer Science and Business Media LLC, Vol. 9, No. 1 ( 2022-08-26)
    Abstract: Rare skin diseases include more than 800 diseases affecting more than 6.8 million patients worldwide. However, only 100 drugs have been developed for treating rare skin diseases in the past 38 years. To investigate potential treatments through drug repurposing for rare skin diseases, it is necessary to have a well-organized database to link all known disease causes, mechanisms, and related information to accelerate the process. Drug repurposing provides less expensive and faster potential options to develop treatments for known diseases. In this work, we designed and constructed a rare skin disease database (RSDB) as a disease-centered information depository to facilitate repurposing drug candidates for rare skin diseases. We collected and integrated associated genes, chemicals, and phenotypes into a network connected by pairwise relationships between different components for rare skin diseases. The RSDB covers 891 rare skin diseases defined by the Orphanet and GARD databases. The organized network for each rare skin disease comprises associated genes, phenotypes, and chemicals with the corresponding connections. The RSDB is available at https://rsdb.cmdm.tw .
    Type of Medium: Online Resource
    ISSN: 2052-4463
    URL: Article
    DOI: 10.1038/s41597-022-01654-2
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
    detail.hit.zdb_id: 2775191-0
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    Open Access Request
    Availability (electronic / print)
  • 6
    Online Resource
    Online Resource
    When Machine Learning and Deep Learning Come to the Big Data in Food Chemistry
    American Chemical Society (ACS) ; 2023
    In:  ACS Omega Vol. 8, No. 18 ( 2023-05-09), p. 15854-15864
    Details
    In: ACS Omega, American Chemical Society (ACS), Vol. 8, No. 18 ( 2023-05-09), p. 15854-15864
    Type of Medium: Online Resource
    ISSN: 2470-1343 , 2470-1343
    URL: Article
    DOI: 10.1021/acsomega.2c07722
    Language: English
    Publisher: American Chemical Society (ACS)
    Publication Date: 2023
    detail.hit.zdb_id: 2861993-6
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    Open Access Request
    Availability (electronic / print)
  • 7
    Online Resource
    Online Resource
    Intelligent pharmaceutical patent search on a near-term gate-based quantum computer
    Springer Science and Business Media LLC ; 2022
    In:  Scientific Reports Vol. 12, No. 1 ( 2022-01-07)
    Details
    In: Scientific Reports, Springer Science and Business Media LLC, Vol. 12, No. 1 ( 2022-01-07)
    Abstract: Pharmaceutical patent analysis is the key to product protection for pharmaceutical companies. In patent claims, a Markush structure is a standard chemical structure drawing with variable substituents. Overlaps between apparently dissimilar Markush structures are nearly unrecognizable when the structures span a broad chemical space. We propose a quantum search-based method which performs an exact comparison between two non-enumerated Markush structures with a constraint satisfaction oracle. The quantum circuit is verified with a quantum simulator and the real effect of noise is estimated using a five-qubit superconductivity-based IBM quantum computer. The possibilities of measuring the correct states can be increased by improving the connectivity of the most computation intensive qubits. Depolarizing error is the most influential error. The quantum method to exactly compares two patents is hard to simulate classically and thus creates a quantum advantage in patent analysis.
    Type of Medium: Online Resource
    ISSN: 2045-2322
    URL: Article
    DOI: 10.1038/s41598-021-04031-y
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
    detail.hit.zdb_id: 2615211-3
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    Open Access Request
    Availability (electronic / print)
  • 8
    Online Resource
    Online Resource
    A general optimization protocol for molecular property prediction using a deep learning network
    Oxford University Press (OUP) ; 2022
    In:  Briefings in Bioinformatics Vol. 23, No. 1 ( 2022-01-17)
    Details
    In: Briefings in Bioinformatics, Oxford University Press (OUP), Vol. 23, No. 1 ( 2022-01-17)
    Abstract: The key to generating the best deep learning model for predicting molecular property is to test and apply various optimization methods. While individual optimization methods from different past works outside the pharmaceutical domain each succeeded in improving the model performance, better improvement may be achieved when specific combinations of these methods and practices are applied. In this work, three high-performance optimization methods in the literature that have been shown to dramatically improve model performance from other fields are used and discussed, eventually resulting in a general procedure for generating optimized CNN models on different properties of molecules. The three techniques are the dynamic batch size strategy for different enumeration ratios of the SMILES representation of compounds, Bayesian optimization for selecting the hyperparameters of a model and feature learning using chemical features obtained by a feedforward neural network, which are concatenated with the learned molecular feature vector. A total of seven different molecular properties (water solubility, lipophilicity, hydration energy, electronic properties, blood–brain barrier permeability and inhibition) are used. We demonstrate how each of the three techniques can affect the model and how the best model can generally benefit from using Bayesian optimization combined with dynamic batch size tuning.
    Type of Medium: Online Resource
    ISSN: 1467-5463 , 1477-4054
    URL: Article
    DOI: 10.1093/bib/bbab367
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
    detail.hit.zdb_id: 2036055-1
    SSG: 12
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    Open Access Request
    Availability (electronic / print)
  • 9
    Online Resource
    Online Resource
    A novel thromboxane receptor antagonist, nstpbp5185, inhibits platelet aggregation and thrombus formation in animal models
    Georg Thieme Verlag KG ; 2016
    In:  Thrombosis and Haemostasis Vol. 116, No. 08 ( 2016-03), p. 285-299
    Details
    In: Thrombosis and Haemostasis, Georg Thieme Verlag KG, Vol. 116, No. 08 ( 2016-03), p. 285-299
    Abstract: A novel benzimidazole derivative, nstpbp5185, was discovered through in vitro and in vivo evaluations for antiplatelet activity. Thro-maboxane receptor (TP) is important in vascular physiology, haemostasis and pathophysiological thrombosis. Nstpbp5185 concentration-dependently inhibited human platelet aggregation caused by collagen, arachidonic acid and U46619. Nstpbp5185 caused a right-shift of the concentration-response curve of U46619 and competitively inhibited the binding of 3H-SQ-29548 to TP receptor expressed on HEK-293 cells, with an IC50 of 0.1 μM, indicating that nstpbp5185 is a TP antagonist. In murine thrombosis models, nstpbp5185 significantly prolonged the latent period in triggering platelet plug formation in mesenteric and FeCl3-induced thrombi formation, and increased the survival rate in pulmonary embolism model with less bleeding than aspirin. This study suggests nstpbp5185, an orally selective antithrombotic agent, acting through blockade of TXA2 receptor, may be efficacious for prevention or treatment of pathologic thrombosis. Supplementary Material to this article is available online at www.thrombosis-online.com.
    Type of Medium: Online Resource
    ISSN: 0340-6245 , 2567-689X
    URL: Article
    DOI: 10.1160/TH15-12-0993
    Language: English
    Publisher: Georg Thieme Verlag KG
    Publication Date: 2016
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    Open Access Request
    Availability (electronic / print)
  • 10
    Online Resource
    Online Resource
    Ensemble modeling with machine learning and deep learning to provide interpretable generalized rules for classifying CNS drugs with high prediction power
    Oxford University Press (OUP) ; 2022
    In:  Briefings in Bioinformatics Vol. 23, No. 1 ( 2022-01-17)
    Details
    In: Briefings in Bioinformatics, Oxford University Press (OUP), Vol. 23, No. 1 ( 2022-01-17)
    Abstract: The trade-off between a machine learning (ML) and deep learning (DL) model’s predictability and its interpretability has been a rising concern in central nervous system-related quantitative structure–activity relationship (CNS-QSAR) analysis. Many state-of-the-art predictive modeling failed to provide structural insights due to their black box-like nature. Lack of interpretability and further to provide easy simple rules would be challenging for CNS-QSAR models. To address these issues, we develop a protocol to combine the power of ML and DL to generate a set of simple rules that are easy to interpret with high prediction power. A data set of 940 market drugs (315 CNS-active, 625 CNS-inactive) with support vector machine and graph convolutional network algorithms were used. Individual ML/DL modeling methods were also constructed for comparison. The performance of these models was evaluated using an additional external dataset of 117 market drugs (42 CNS-active, 75 CNS-inactive). Fingerprint-split validation was adopted to ensure model stringency and generalizability. The resulting novel hybrid ensemble model outperformed other constituent traditional QSAR models with an accuracy of 0.96 and an F1 score of 0.95. With the power of the interpretability provided with this protocol, our model laid down a set of simple physicochemical rules to determine whether a compound can be a CNS drug using six sub-structural features. These rules displayed higher classification ability than classical guidelines, with higher specificity and more mechanistic insights than just for blood–brain barrier permeability. This hybrid protocol can potentially be used for other drug property predictions.
    Type of Medium: Online Resource
    ISSN: 1467-5463 , 1477-4054
    URL: Article
    DOI: 10.1093/bib/bbab377
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
    detail.hit.zdb_id: 2036055-1
    SSG: 12
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    Open Access Request
    Availability (electronic / print)
Nothing or not found what you are looking for? Please check your search query or use the Interlibrary Loan Search.
Close ⊗
This website uses cookies and the analysis tool Matomo. Further information can be found on the KOBV privacy pages