Tissue engineering scaffolds are designed to mimic physical, chemical, and biological features of the extracellular matrix, thereby providing a constant support that is crucial to improved regenerative medicine outcomes. Beyond mechanical and structural support, the next generation of these materials must also consider the more dynamic presentation and delivery of drugs or growth factors to guide new and regenerating tissue development. These two aspects are explored expansively separately, but they must interact synergistically to achieve optimal regeneration. This review explores common tissue engineering materials types, electrospun polymers and hydrogels, and strategies used for incorporating drug delivery systems into these scaffolds.

The Plasmodium falciparum var multigene family encodes the cytoadhesive, variant antigen PfEMP1. P. falciparum antigenic variation and cytoadhesion specificity are controlled by epigenetic switching between the single, or few, simultaneously expressed var genes. Most var genes are maintained in perinuclear clusters of heterochromatic telomeres. The active var gene(s) occupy a single, perinuclear var expression site. It is unresolved whether the var expression site forms in situ at a telomeric cluster or whether it is an extant compartment to which single chromosomes travel, thus controlling var switching. Here we show that transcription of a var gene did not require decreased colocalisation with clusters of telomeres, supporting var expression site formation in situ. However following recombination within adjacent subtelomeric sequences, the same var gene was persistently activated and did colocalise less with telomeric clusters. Thus, participation in stable, heterochromatic, telomere clusters and var switching are independent but are both affected by subtelomeric sequences. The var expression site colocalised with the euchromatic mark H3K27ac to a greater extent than it did with heterochromatic H3K9me3. H3K27ac was enriched within the active var gene promoter even when the var gene was transiently repressed in mature parasites and thus H3K27ac may contribute to var gene epigenetic memory.

BACKGROUND: There are limited data regarding noninvasive prenatal testing (NIPT) in low-risk populations, and the ideal aneuploidy screening model for a pregnant population has yet to be established. AIMS: To assess the implementation of NIPT into clinical practice utilising both first- and second-line screening models. MATERIALS AND METHODS: Three private practices specialising in obstetric ultrasound and prenatal diagnosis in Australia offered NIPT as a first-line test, ideally followed by combined first-trimester screening (cFTS), or as a second-line test following cFTS, particularly in those with a calculated risk between 1:50 and 1:1000. RESULTS: NIPT screening was performed in 5267 women and as a first-line screening method in 3359 (63.8%). Trisomies 21 and 13 detection was 100% and 88% for trisomy 18. Of cases with known karyotypes, the positive predictive value (PPV) of the test was highest for trisomy 21 (97.7%) and lowest for monosomy X (25%). Ultrasound detection of fetal structural abnormality resulted in the detection of five additional chromosome abnormalities, two of which had high-risk cFTS results. For all chromosomal abnormalities, NIPT alone detected 93.4%, a contingent model detected 81.8% (P = 0.097), and cFTS alone detected 65.9% (P < 0.005). CONCLUSIONS: NIPT achieved 100% T21 detection and had a higher DR of all aneuploidy when used as a first-line test. Given the false-positive rate for all aneuploidies, NIPT is an advanced screening test, rather than a diagnostic test. The benefit of additional cFTS was the detection of fetal structural abnormalities and some unusual chromosomal abnormalities.

A substantial impediment to widespread Coronavirus disease (COVID-19) vaccination is vaccine hesitancy. Many researchers across scientific disciplines have presented countless studies in favor of COVID-19 vaccination, but misinformation on social media could hinder vaccination efforts and increase vaccine hesitancy. Nevertheless, studying people's perceptions on social media to understand their sentiment presents a powerful medium for researchers to identify the causes of vaccine hesitancy and therefore develop appropriate public health messages and interventions. To the best of the authors' knowledge, previous studies have presented vaccine hesitancy in specific cases or within one scientific discipline (i.e., social, medical, and technological). No previous study has presented findings via sentiment analysis for multiple scientific disciplines as follows: (1) social, (2) medical, public health, and (3) technology sciences. Therefore, this research aimed to review and analyze articles related to different vaccine hesitancy cases in the last 11 years and understand the application of sentiment analysis on the most important literature findings. Articles were systematically searched in Web of Science, Scopus, PubMed, IEEEXplore, ScienceDirect, and Ovid from January 1, 2010, to July 2021. A total of 30 articles were selected on the basis of inclusion and exclusion criteria. These articles were formed into a taxonomy of literature, along with challenges, motivations, and recommendations for social, medical, and public health and technology sciences. Significant patterns were identified, and opportunities were promoted towards the understanding of this phenomenon.

OBJECTIVE: Analysis of functional magnetic resonance imaging (fMRI) data from multiple subjects is at the heart of many medical imaging studies, and approaches based on dictionary learning (DL) are recently noted as promising solutions to the problem. However, the DL-based methods for fMRI analysis proposed to date do not naturally extend to multisubject analysis. In this paper, we propose a DL algorithm for multisubject fMRI data analysis. METHODS: The proposed algorithm [named shared and subject-specific dictionary learning (ShSSDL)] is derived based on a temporal concatenation, which is particularly attractive for the analysis of multisubject task-related fMRI datasets. It differs from existing DL algorithms in both its sparse coding and dictionary update stages and has the advantage of learning a dictionary shared by all subjects as well as a set of subject-specific dictionaries. RESULTS: The performance of the proposed DL algorithm is illustrated using simulated and real fMRI datasets. The results show that it can successfully extract shared as well as subject-specific latent components. CONCLUSION: In addition to offering a new DL approach, when applied on multisubject fMRI data analysis, the proposed algorithm generates a group level as well as a set of subject-specific spatial maps. SIGNIFICANCE: The proposed algorithm has the advantage of learning simultaneously multiple dictionaries providing us with a shared as well discriminative source of information about the analyzed fMRI datasets.