Search

Since the emergence of the highly pathogenic avian influenza (HPAI) A(H5N1) virus (A/chicken/ Scotland/59) in Scotland, UK, several outbreaks of H5Nx viruses have been reported in poultry worldwide (1). In [...]

Background: Medical students and professionals often struggle to understand medical test results, which can lead to poor medical decisions. Natural frequency tree-based training (NF-TT) has been suggested to help people correctly estimate the predictive value of medical tests. We aimed to compare the effectiveness of NF-TT with conventional conditional probability formula-based training (CP-FT) and investigate student variables that may influence NF-TT's effectiveness. Methods: We conducted a parallel group randomized controlled trial of NF-TT vs. CP-FT in two medical schools in South Korea (a 1:1 allocation ratio). Participants were randomly assigned to watch either NF-TT or CP-FT video at individual computer stations. NF-TT video showed how to translate relevant probabilistic information into natural frequencies using a tree structure to estimate the predictive values of screening tests. CP-FT video showed how to plug the same information into a mathematical formula to calculate predictive values. Both videos were 15 min long. The primary outcome was the accuracy in estimating the predictive value of screening tests assessed using multiple-choice questions at baseline, post-intervention (i.e., immediately after training), and one-month follow-up. The secondary outcome was the accuracy of conditional probabilistic reasoning in non-medical contexts, also assessed using multiple-choice questions, but only at follow-up as a measure of transfer of learning. 231 medical students completed their participation. Results: Overall, NF-TT was not more effective than CP-FT in improving the predictive value estimation accuracy at post-intervention (NF-TT: 87.13%, CP-FT: 86.03%, p =.86) and follow-up (NF-TT: 72.39%, CP-FT: 68.10%, p =.40) and facilitating transfer of training (NF-TT: 75.54%, CP-FT: 71.43%, p =.41). However, for participants without relevant prior training, NF-TT was more effective than CP-FT in improving estimation accuracy at follow-up (NF-TT: 74.86%, CP-FT: 58.71%, p =.02) and facilitating transfer of learning (NF-TT: 82.86%, CP-FT: 66.13%, p =.04). Conclusions: Introducing NF-TT early in the medical school curriculum, before students are exposed to a pervasive conditional probability formula-based approach, would offer the greatest benefit. Trial registration: Korea Disease Control and Prevention Agency Clinical Research Information Service KCT0004246 (the date of first trial registration: 27/08/2019). The full trial protocol can be accessed at https://cris.nih.go.kr/cris/search/detailSearch.do?seq=15616&search_page=L. [ABSTRACT FROM AUTHOR]

Piezoelectric fiber yarns produced by electrospinning offer a versatile platform for intelligent devices, demonstrating mechanical durability and the ability to convert mechanical strain into electric signals. While conventional methods involve twisting a single poly(vinylidene fluoride‐co‐trifluoroethylene)(P(VDF‐TrFE)) fiber mat to create yarns, by limiting control over the mechanical properties, an approach inspired by composite laminate design principles is proposed for strengthening. By stacking multiple electrospun mats in various sequences and twisting them into yarns, the mechanical properties of P(VDF‐TrFE) yarn structures are efficiently optimized. By leveraging a multi‐objective Bayesian optimization‐based machine learning algorithm without imposing specific stacking restrictions, an optimal stacking sequence is determined that simultaneously enhances the ultimate tensile strength (UTS) and failure strain by considering the orientation angles of each aligned fiber mat as discrete design variables. The conditions on the Pareto front that achieve a balanced improvement in both the UTS and failure strain are identified. Additionally, applying corona poling induces extra dipole polarization in the yarn state, successfully fabricating mechanically robust and high‐performance piezoelectric P(VDF‐TrFE) yarns. Ultimately, the mechanically strengthened piezoelectric yarns demonstrate superior capabilities in self‐powered sensing applications, particularly in challenging environments and sports scenarios, substantiating their potential for real‐time signal detection. [ABSTRACT FROM AUTHOR]

Introduction: Cell population data (CPD) parameters may be putative biomarkers for the screening of various diseases including some infections and myelodysplastic syndrome. This study aimed to establish the age‐ and sex‐specific reference intervals (RIs) for the CPD parameters in the Korean population. Methods: The reference population for the RIs of CPD parameters comprised 124 856 subjects aged 20–99 years. CPD parameters were obtained from Sysmex XN‐2000 (Kobe, Japan) datasets from 17 health promotion centers in 13 South Korean cities. We determined significant partitions for age and sex, and calculated RIs according to Clinical and Laboratory Standards Institute C28‐A3 guidelines. Results: The side scattered light intensity in the neutrophil area and the lymphocyte area did not require sex‐related partitioning except in those over the age of 50, among whom the lower limit (LL) and upper limit (UL) were lower in females. However, the side scattered light distribution width in the lymphocyte area required age‐ and sex‐related partitioning, in which LL and UL were higher in females. The LL and UL of the fluorescent light distribution width were higher in males in the neutrophil area and higher in females in the lymphocyte area, but age‐related partitioning was not required. The forward scattered light intensity in the neutrophil area, lymphocyte area, and monocyte area did not require age‐related partitioning in males. Conclusion: This study has determined comprehensive age‐ and sex‐specific RIs for CPD parameters, which could help to prove the clinical significance of these parameters in the Sysmex XN‐2000. [ABSTRACT FROM AUTHOR]