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End date: 2024 × Start date: 2020 × Publisher: Basel : MDPI × search.filters.applied.f.series: Applied Sciences 11 (2021), Nr. 17 ×

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Now showing 1 - 3 of 3
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    Spinning of Endless Bioactive Silicate Glass Fibres for Fibre Reinforcement Applications
    (Basel : MDPI, 2021) Eichhorn, Julia; Elschner, Cindy; Groß, Martin; Reichenbächer, Rudi; Herrera Martín, Aarón X.; Prates Soares, Ana; Fischer, Heilwig; Kulkova, Julia; Moritz, Niko; Hupa, Leena; Stommel, Markus; Scheffler, Christina; Kilo, Martin
    Bioactive glasses have been used for many years in the human body as bone substitute. Since bioactive glasses are not readily available in the form of endless thin fibres with diameters below 20 µm, their use is limited to mainly non-load-bearing applications in the form of particles or granules. In this study, the spinnability of four bioactive silicate glasses was evaluated in terms of crystallisation behaviour, characteristic processing temperatures and viscosity determined by thermal analysis. The glass melts were drawn into fibres and their mechanical strength was measured by single fibre tensile tests before and after the surface treatment with different silanes. The degradation of the bioactive glasses was observed in simulated body fluid and pure water by recording the changes of the pH value and the ion concentration by inductively coupled plasma optical emission spectrometry; further, the glass degradation process was monitored by scanning electron microscopy. Additionally, first in vitro experiments using murine pre-osteoblast cell line MC3T3E1 were carried out in order to evaluate the interaction with the glass fibre surface. The results achieved in this work show up the potential of the manufacturing of endless bioactive glass fibres with appropriate mechanical strength to be applied as reinforcing fibres in new innovative medical implants.
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    Calibrating mini-mental state examination scores to predict misdiagnosed dementia patients
    (Basel : MDPI, 2021) Vyas, Akhilesh; Aisopos, Fotis; Vidal, Maria-Esther; Garrard, Peter; Paliouras, George
    Mini-Mental State Examination (MMSE) is used as a diagnostic test for dementia to screen a patient’s cognitive assessment and disease severity. However, these examinations are often inaccurate and unreliable either due to human error or due to patients’ physical disability to correctly interpret the questions as well as motor deficit. Erroneous data may lead to a wrong assessment of a specific patient. Therefore, other clinical factors (e.g., gender and comorbidities) existing in electronic health records, can also play a significant role, while reporting her examination results. This work considers various clinical attributes of dementia patients to accurately determine their cognitive status in terms of the Mini-Mental State Examination (MMSE) Score. We employ machine learning models to calibrate MMSE score and classify the correctness of diagnosis among patients, in order to assist clinicians in a better understanding of the progression of cognitive impairment and subsequent treatment. For this purpose, we utilize a curated real-world ageing study data. A random forest prediction model is employed to estimate the Mini-Mental State Examination score, related to the diagnostic classification of patients.This model uses various clinical attributes to provide accurate MMSE predictions, succeeding in correcting an important percentage of cases that contain previously identified miscalculated scores in our dataset. Furthermore, we provide an effective classification mechanism for automatically identifying patient episodes with inaccurate MMSE values with high confidence. These tools can be combined to assist clinicians in automatically finding episodes within patient medical records where the MMSE score is probably miscalculated and estimating what the correct value should be. This provides valuable support in the decision making process for diagnosing potential dementia patients.
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    Uv absorption spectroscopy for the diffusion of plasma-generated reactive species through a skin model
    (Basel : MDPI, 2021) Ki, Se Hoon; Masur, Kai; Baik, Ku Youn; Choi, Eun Ha
    Skin applications of non-thermal atmospheric pressure plasma (NTAPP) have been at-tracting attention from medical and cosmetic aspects. The reactive species generated from plasma sources have been known to play important roles in the skin. For proper applications, it is essential to know how they diffuse into the skin. In this study, the penetration of active species from NTAPP through a skin model was analyzed by UV absorption spectroscopy. The diffusions of hydrogen peroxide, nitrite, and nitrate were quantified through curve fitting. We utilized an agarose gel to mimic epidermis and dermis layers, and we used a lipid film or a pig skin sample to mimic the stratum corneum (SC). The diffusion characteristics of reactive species through this skin model and the limitations of this method were discussed.