RESUMO
Driven by recent developments in computational power, algorithms and web-based storage resources, machine learning (ML)-based artificial intelligence (AI) has quickly gained ground as the solution for many technological and societal challenges. AI education has become very popular and is oversubscribed at Dutch universities. Major investments were made in 2018 to develop and build the first AI-driven hospitals to improve patient care and reduce healthcare costs. AI has the potential to greatly enhance traditional statistical analyses in many domains and has been demonstrated to allow the discovery of 'hidden' information in highly complex datasets. As such, AI can also be of significant value in the diagnosis and treatment of cardiovascular disease, and the first applications of AI in the cardiovascular field are promising. However, many professionals in the cardiovascular field involved in patient care, education or science are unaware of the basics behind AI and the existing and expected applications in their field. In this review, we aim to introduce the broad cardiovascular community to the basics of modern ML-based AI and explain several of the commonly used algorithms. We also summarise their initial and future applications relevant to the cardiovascular field.
RESUMO
BACKGROUND: Machine learning (ML) allows the exploration and progressive improvement of very complex high-dimensional data patterns that can be utilised to optimise specific classification and prediction tasks, outperforming traditional statistical approaches. An enormous acceleration of ready-to-use tools and artificial intelligence (AI) applications, shaped by the emergence, refinement, and application of powerful ML algorithms in several areas of knowledge, is ongoing. Although such progress has begun to permeate the medical sciences and clinical medicine, implementation in cardiovascular medicine and research is still in its infancy. OBJECTIVES: To lay out the theoretical framework, purpose, and structure of a novel AI consortium. METHODS: We have established a new Dutch research consortium, the CVON-AI, supported by the Netherlands Heart Foundation, to catalyse and facilitate the development and utilisation of AI solutions for existing and emerging cardiovascular research initiatives and to raise AI awareness in the cardiovascular research community. CVON-AI will connect to previously established CVON consortia and apply a cloud-based AI platform to supplement their planned traditional data-analysis approach. RESULTS: A pilot experiment on the CVON-AI cloud was conducted using cardiac magnetic resonance data. It demonstrated the feasibility of the platform and documented excellent correlation between AI-generated ventricular function estimates as compared to expert manual annotations. The resulting AI solution was then integrated in a web application. CONCLUSION: CVON-AI is a new consortium meant to facilitate the implementation and raise awareness of AI in cardiovascular research in the Netherlands. CVON-AI will create an accessible cloud-based platform for cardiovascular researchers, demonstrate the clinical applicability of AI, optimise the analytical methodology of other ongoing CVON consortia, and promote AI awareness through education and training.
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The unique colloidal properties of cellulose nanofibers (CNF), makes CNF a very interesting new excipient in pharmaceutical formulations, as CNF in combination with some poorly-soluble drugs can create nanofoams with closed cells. Previous nanofoams, created with the model drug indomethacin, demonstrated a prolonged release compared to films, owing to the tortuous diffusion path that the drug needs to take around the intact air-bubbles. However, the nanofoam was only obtained at a relatively low drug content of 21wt% using fixed processing parameters. Herein, the effect of indomethacin content and processing parameters on the foaming properties was analysed. Results demonstrate that a certain amount of dissolved drug is needed to stabilize air-bubbles. At the same time, larger fractions of dissolved drug promote coarsening/collapse of the wet foam. The pendant drop/bubble profile tensiometry was used to verify the wet-foam stability at different pHs. The pH influenced the amount of solubilized drug and the processing-window was very narrow at high drug loadings. The results were compared to real foaming-experiments and solid state analysis of the final cellular solids. The parameters were assembled into a processing chart, highlighting the importance of the right combination of processing parameters (pH and time-point of pH adjustment) in order to successfully prepare cellular solid materials with up to 46 wt% drug loading.
Assuntos
Celulose/química , Excipientes/química , Indometacina/química , Nanofibras/química , Química FarmacêuticaRESUMO
Ellipsometry was used to determine the adsorbed layer thickness (d) and the surface excess (adsorbed amount, Gamma) of a nonionic diblock copolymer, E(106)B(16), of poly(ethylene oxide) (E) and poly(butylene oxide) (B) at the air-water interface. The results were obtained (i) by the conventional ellipsometric evaluation procedure using the change of both ellipsometric angles Psi and Delta and (ii) by using the change of Delta only and assuming values of the layer thickness. It was demonstrated that the calculated surface excesses from the different methods were in close agreement, independent of the evaluation procedure, with a plateau adsorption of about 2.5 mg/m(2) (400 A(2)/molecule). Furthermore, the amount of E(106)B(16) adsorbed at the air-water interface was found to be almost identical to that adsorbed from aqueous solution onto a hydrophobic solid surface. In addition, the possibility to use combined measurements with H(2)O or D(2)O as substrates to calculate values of d and Gamma was investigated and discussed. We also briefly discuss within which limits the Gibbs equation can be used to determine the surface excess of polydisperse block copolymers.
