Mild heat pasteurization, high pressure processing (HP) and pulsed electric field (PEF) processing of freshly squeezed orange juice were comparatively evaluated examining their impact on microbial load and quality parameters immediately after processing and during two months of storage. Microbial counts for treated juices were reduced beyond detectable levels immediately after processing and up to 2 months of refrigerated storage. Quality parameters such as pH, dry matter content and brix were not significantly different when comparing juices immediately after treatment and were, for all treatments, constant during storage time. Quality parameters related to pectinmethylesterase (PME) inactivation, like cloud stability and viscosity, were dependent on the specific treatments that were applied. Mild heat pasteurization was found to result in the most stable orange juice. Results for HP are nearly comparable to PEF except on cloud degradation, where a lower degradation rate was found for HP. For PEF, residual enzyme activity was clearly responsible for changes in viscosity and cloud stability during storage. Industrial relevance: Development of mild processing technologies with a minimal impact on fruit juice can be considered as a true alternative of fresh fruit. The present work presents a fair comparison of mild heat treated, high pressure (HP) and pulsed electric field (PEF) processed orange juice as an alternative for thermal pasteurization. Orange juices were monitored during two months of storage.
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BackgroundThe aim of this study was to describe barriers and facilitators for shared decision making (SDM) as experienced by older patients with multiple chronic conditions (MCCs), informal caregivers and health professionals.MethodsA structured literature search was conducted with 5 databases. Two reviewers independently assessed studies for eligibility and performed a quality assessment. The results from the included studies were summarized using a predefined taxonomy.ResultsOur search yielded 3838 articles. Twenty-eight studies, listing 149 perceived barriers and 67 perceived facilitators for SDM, were included. Due to poor health and cognitive and/or physical impairments, older patients with MCCs participate less in SDM. Poor interpersonal skills of health professionals are perceived as hampering SDM, as do organizational barriers, such as pressure for time and high turnover of patients. However, among older patients with MCCs, SDM could be facilitated when patients share information about personal values, priorities and preferences, as well as information about quality of life and functional status. Informal caregivers may facilitate SDM by assisting patients with decision support, although informal caregivers can also complicate the SDM process, for example, when they have different views on treatment or the patient’s capability to be involved. Coordination of care when multiple health professionals are involved is perceived as important.ConclusionsAlthough poor health is perceived as a barrier to participate in SDM, the personal experience of living with MCCs is considered valuable input in SDM. An explicit invitation to participate in SDM is important to older adults. Health professionals need a supporting organizational context and good communication skills to devise an individualized approach for patient care.
MULTIFILE
Fingermarks are highly relevant in criminal investigations for individualization purposes. In some cases, the question in court changes from ‘Who is the source of the fingermarks?’ to ‘How did the fingermark end up on the surface?’. In this paper, we explore evaluation of fingermarks given activity level propositions by using Bayesian networks. The variables that provide information on activity level questions for fingermarks are identified and their current state of knowledge with regards to fingermarks is discussed. We identified the variables transfer, persistency, recovery, background fingermarks, location of the fingermarks, direction of the fingermarks, the area of friction ridge skin that left the mark and pressure distortions as variables that may provide information on how a fingermark ended up on a surface. Using three case examples, we show how Bayesian networks can be used for the evaluation of fingermarks given activity level propositions.
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