Structural identifiability analyses of candidate models for in vitro Pitavastatin hepatic uptake

In this paper a review of the application of four different techniques (a version of the similarity transformation approach for autonomous uncontrolled systems, a non-differential input/output observable normal form approach, the characteristic set differential algebra and a recent algebraic input/output relationship approach) to determine the structural identifiability of certain in vitro nonlinear pharmacokinetic models is provided. The Organic Anion Transporting Polypeptide (OATP) substrate, Pitavastatin, is used as a probe on freshly isolated animal and human hepatocytes. Candidate pharmacokinetic non-linear compartmental models have been derived to characterise the uptake process of Pitavastatin. As a prerequisite to parameter estimation, structural identifiability analyses are performed to establish that all unknown parameters can be identified from the experimental observations available

Pengaruh Graphical User Interface untuk Industri Medis: Sebuah Tinjauan Sistematis

Human Computer Interaction sangat penting dalam evolusi komputerisasi. Graphical user interface merupakan subset dari HCI yang berupa interaksi antara manusia dengan mesin melalui user interface berbasis grafis. GUI digunakan oleh berbagai bidang teknik, elektronik, ekonomi bahkan kedokteran. Perkembangan perangkat keras grafis memicu berbagai bidang untuk memvisualisasikan berbagai model dalam rangka peningkatan pengetahuan. Tinjauan Sistematik ini bertujuan memberikan informasi yang jelas tentang pengaruh graphical user interface terhadap bidang medis baik untuk prediksi penyakit, pengobatan serta medical record dari pasien melalui bukti penelitian yang telah dilakukan serta pengembangan framework khusus untuk penelitian bidang medis.Penelitian dilakukan melaluipencarian pada digital libraray online, IEEExplorer dan ScienceDirect, dibulan Desember 2014 dengan sitasi dari 2008 danabstrak menggunakan bahasa inggris, memanfaatkan parameter AND atau OR. Literatur yang ditemukan akan dieliminasi berdasarkan publication title, relevansi topik, abstak serta hasil penelitian.Ditemukan 347027 literatur kemudian 346923 dieliminasi berdasarkan judul dan abstract. Diperolah 104 artikel lengkap yang kemudian 91 dieliminasi berdasarkan penyaringan judul, abstrak dan hasil penelitian dari literatur tersebut. Sehingga diperoleh 13 literaturlengkap yang digunakan sebagai primary studi. Penelitianakan dibagi menjadi tiga kategori (1) GUI untuk simulator, (2) GUI untuk memprediksi analisis penyakit, dan (3) perkembangan framework GUI yang baru khusus bidang medis. Berdasarkan analisa 69.2% penelitian yang dilakukan untuk memanfaatkan teknologi open-source dan cross-platform dan 61.2% penelitian dibidang visualisasi 3D. Graphical user interface memiliki pengaruh yang sangat besar dibidang medis, pemodelan visualisasi 3D dapat memudahkan pemahaman tentang model anatomi manusia, pemanfaatan teknologi cross-platform dan open-source belum secara signifikan menjadi prioritas dari peneliti dibidang medis

Keywords given by authors of scientific articles in database descriptors

This paper analyses the keywords given by authors of scientific articles and the descriptors assigned to the articles in order to ascertain the presence of the keywords in the descriptors. 640 INSPEC, CAB abstracts, ISTA and LISA database records were consulted. After detailed comparisons it was found that keywords provided by authors have an important presence in the database descriptors studied, since nearly 25% of all the keywords appeared in exactly the same form as descriptors, with another 21% while normalized, are still detected in the descriptors. This means that almost 46% of keywords appear in the descriptors, either as such or after normalization. Elsewhere, three distinct indexing policies appear, one represented by INSPEC and LISA (indexers seem to have freedom to assign the descriptors they deem necessary); another is represented by CAB (no record has fewer than four descriptors and, in general, a large number of descriptors is employed; in contrast, in ISTA, a certain institutional code towards economy in indexing, since 84% of records contain only four descriptors

Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table