Australian Journal of Basic and Applied Sciences
        
   

Sep-Oct 2024

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An Updated Assessment of Safety Strategies for Platelet Blood Concentrates (PCs): A Short Review Article


Ali Hadadi, Sulaiman Alatawi, Munirah Almulhim, Maryam M. Almousa, Adel K. Alkhathami, Jawaher Alnahyan, Safia I. Aljabr, Maryam K. Almuhaysh, Abdullah Alanazi, Amani M. Marawan

ABSTRACT:: Background: Platelet concentrates (PCs) are one of the most used blood components in blood banks due to their essential role in treating thrombocytopenia, and while viral transmission has been largely addressed, bacterial contamination of platelet concentrates (PCs) are still regarded as a crucial safety concern. Objective: This short review aimed to consolidate current knowledge on the strategies followed in blood banks to maximize the quality and shelf life of platelet concentrates (PCs). Different methods of each strategy were considered, focusing on outcomes related to approved and effective outcomes. Methods: The current study is based on PubMed, Wiley, and Web of Science databases to assess donor selection, bacterial screening, and pathogen reduction technologies utilized for PCs quality. All articles were collected until April 2024. Studies utilizing approved interventions and relevant hospital outcomes were inclusive. However, repetitive data and unapproved methods were among the exclusive criteria. Collected published manuscripts were reviewed. Results: The studies reviewed and referenced in this article consensus that pathogen reduction technologies (PRTs) utilizing Ultraviolet rays (UV) was found to be effective in eliminating various bacterial contamination. Furthermore, the need for donor and bacterial screening is still essential and could not be overlooked. Pathogen reduction technologies (PRTs) remain important and valid despite studies suggesting the reduced platelet concentrates (PCs) quality and the need for frequent transmission of PCs. Some studies even foresee pathogen reduction technologies (PRTs) as replacing the cultural bacterial detection methods. Conclusion:We conclude that preventing bacterial contamination in platelet concentrates (PCs) requires a multifaceted approach. However, future research should focus on optimizing these strategies while minimizing their potential adverse effects on platelet function and safety.

[ FULL TEXT PDF 1-7 ] DOI: 10.22587/ajbas.2024.18.5.1

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Agreement methods between models: a simulation study and application

George Lucas Santana de Moura, Thaynara Aparecida de Souza Neto, Paulo Roberto Cecon, Luciano Gonçalves Batista, Samantha Gouvêa Oliveira, Jhennifer dos Santos Nascimento, Alice dos Santos Ribeiro

ABSTRACT: In practice, many phenomena do not fit a linear regression model, requiring the use of nonlinear models, with growth models being one of the most well-known types. These models are widely used in biology to study the growth of plants and organisms over time or age. Therefore, the aim of this study was to conduct a simulation to evaluate the methods of agreement between regression models, using indices such as Willmott’s and its respective variations, applied to the growth of total dry mass of garlic plants over time. In this way, the Gompertz and Logistic models were fitted to the garlic dry matter database from the Vegetable Germplasm Bank of the Federal University of Viçosa (BGH/UFV). After fitting the models, a simulation was performed to study the behavior of Willmott’s indices, considering sample pair sizes of 10, 20, 50, and 100, with each pair repeated 100 times. Thus, through model fitting, it was found that the Logistic model had the smallest residual but converged more slowly than the Gompertz model. Furthermore, through the simulation of Willmott’s indices and their variations, it was observed that the original Willmott index always had higher values compared to the modified and refined indices, as expected. Therefore, it can be concluded that both Logistic and Gompertz models are consistent according to all variations of Willmott’s indices, and the simulation study showed divergence between agreement indices when exposed to low data correlation, and agreement between indices when exposed to high correlation.

[ FULL TEXT PDF 8-14 ] DOI: 10.22587/ajbas.2024.18.5.2

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