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Changes Over Time
Published in Tom Walker, Ethics and Chronic Illness, 2019
None of this may be apparent, however, to the patient faced with a decision about whether to switch to a new treatment. What she needs to do is not likely to look difficult at the time she makes that decision, particularly where this is done in the cool of her doctor’s office. The circumstances in which a decision to change is made are likely to be very different from those in which it must be implemented. If, in this situation, the patient attempts to decide what to do by using simulation—imagining herself using the treatment—she is unlikely to do so well (simply because she is unlikely to take adequate account of how hard it is to implement changes in the midst of a busy life). As such, the decision about whether to switch to a new treatment is similar to those cases (discussed in chapter three) where other people have been through a change the patient has not been through. Indeed, in many cases other people will have done exactly this. As we saw in that earlier chapter this is a situation where a different method of deciding what is best for the patient—surrogation (basing the decision on what others have experienced in the patient’s situation)—can be more accurate. In that case, however, healthcare professionals have no good reason to think that their patients are the best judges of their own interests.
Surrogation
Published in Nicholas Stergiou, Nonlinear Analysis for Human Movement Variability, 2018
Specifically, knee angle kinematic time series from healthy subjects were evaluated using the PPS algorithm, and Theiler et al. algorithm 0. The average time lag for the series was 9.833 and the average embedding dimension was 6.333. The noise radii that maximized the number of short segments that are the same for the original time series and the surrogate was 3.351. The paper demonstrated that Theiler et al. algorithm 0 destroyed the intracycle dynamics of the gait time series by changing the overall shape, which resulted in a false rejection of the null hypothesis. The PPS algorithm did not alter the intracycle dynamics of the original time series, which made it more appropriate to explore the presence of underlying processes within these dynamics. Example Box 5.1 shows the general surrogation procedure using the PPS algorithm. The data for a knee flexion and extension angle, along with one surrogate generated using the PPS algorithm are included in Appendix 5.A. The SampEn values of the original and surrogate series, along with the parameters used are included in Example Box 5.1.
Development, optimisation, and evaluation of nanoencapsulated diacerein emulgel for potential use in osteoarthritis
Published in Journal of Microencapsulation, 2020
Bazla Siddiqui, Asim.ur. Rehman, Ihsan-Ul Haq, Nasir M. Ahmad, Naveed Ahmed
For performance of permeation analysis, rat skin was taken due to the presence of easy availability and structural similarity to that of the human skin with the presence of slight variations of lipid or water content in its structure and can be used as a better surrogation model for permeation analysis (Abd et al. 2016). So, the freshly excised skin was taken from male albino Sprague–Dawley rats (200–250 g) after the approval of animal handling protocols from the ethical committee of Quaid-i-Azam University, Islamabad, Pakistan (BEC-FBS-QAU-2019–171). Rat skin was mounted in between the donor and receiver compartments of Franz diffusion apparatus with 0.77 cm2 total area for diffusion; filled with phosphate buffer solution (5.2 ml) at pH 7.4 in the receiver compartment maintained at 32 ± 0.5 °C (Skin temperature). DCR-nanoemugel, DCR-nanogel, and control gel containing 2 mg of total DCR were placed in donor compartments with replacement of the same buffer media at specified time intervals from 0.5 to 24 h. Analysis was then performed at 258 nm through UV–Vis spectrophotometer (Dynamica, Halo DB-20, Livingston, UK) and the cumulative amount of DCR (Qt) was calculated using the following formula: Cn = Current concentration in receiver medium at nth sample.Vr = Volume of medium in the receiver compartment.Vs = Withdrawn sample volume.