Body perceptions, occupations, eating attitudes, and behaviors emerged during the pandemic: An exploratory cluster analysis of eaters profiles.

Introduction: COVID-19 pandemic negatively impacted people's mental and physical health. Three areas have been significantly impacted, among others: eating-related behaviors, occupational balance, and exposure to self-image due to videoconferencing. This study aims to explore and document eaters profiles that were reported during the pandemic in the general Canadian population using a holistic perspective, including body perceptions, attitudes, and eating behaviors (i.e., body image, behaviors, attitudes, and motivations regarding food), and occupations (i.e., physical activity and cooking). Methods: This cross-sectional study was conducted from May to September 2020. Two hundred and seventy-three Canada's residents, French speaking of 18 years of age and older, participated in an online survey on behaviors, attitudes, and motivations regarding food and eating as well as body image and occupations during the COVID-19 pandemic. A hierarchical cluster analysis was used to determine the eaters profiles. One-way ANOVA and Chi-square test were conducted to differentiate occupational characteristics between eaters profiles. Results: Three distinctive profiles were found during the COVID-19 pandemic and could be placed on a continuum: the Congruent-driven eater is at the functional pole of the continuum, whereas the Incongruent-driven eater is at the dysfunctional pole of the eaters continuum. In the middle of the continuum, the Incongruent-perceptual eater is at a critical crossing point. Significant differences were reported between eaters profiles. Discussion: The empirical results based on an eaters continuum conceptualization highlight the importance of understanding how people perceive their body to assess and promote food well-being.

Accelerated phase Ia/b evaluation of the malaria vaccine candidate PfAMA1 DiCo demonstrates broadening of humoral immune responses.

Plasmodium falciparum apical membrane antigen 1 (PfAMA1) is a candidate malaria vaccine antigen expressed on merozoites and sporozoites. PfAMA1's polymorphic nature impacts vaccine-induced protection. To address polymorphism, three Diversity Covering (DiCo) protein sequences were designed and tested in a staggered phase Ia/b trial. A cohort of malaria-naive adults received PfAMA1-DiCo adjuvanted with Alhydrogel® or GLA-SE and a cohort of malaria-exposed adults received placebo or GLA-SE adjuvanted PfAMA1 DiCo at weeks 0, 4 and 26. IgG and GIA levels measured 4 weeks after the third vaccination are similar in malaria-naive volunteers and placebo-immunised malaria-exposed adults, and have a similar breadth. Vaccination of malaria-exposed adults results in significant antibody level increases to the DiCo variants, but not to naturally occurring PfAMA1 variants. Moreover, GIA levels do not increase following vaccination. Future research will need to focus on stronger adjuvants and/or adapted vaccination regimens, to induce potentially protective responses in the target group of the vaccine.

Vaccine Development: From Preclinical Studies to Phase 1/2 Clinical Trials.

Vaccines are biological pharmaceutical products prescribed as prevention of a hypothetical infection. Development of a new vaccine is the result of a long process involving several stages. During all developmental phases, priority is the safety of the new product, which is often used in young infants. The initial research phase lasts 1-5 years and is followed by a clinical and pharmaceutical development phase (preclinical and clinical phases), which can last from 15 to 20 years on average before licensure is obtained. There are, however, exceptions, like the malaria vaccine for which research has been going on for more than 30 years and at least 30 candidate vaccines have been assessed. This chapter summarizes the different phases of vaccine candidate development from preclinical studies to phase 2 vaccine trials.

Robust skull stripping of clinical glioblastoma multiforme data.

Skull stripping is the first step in many neuroimaging analyses and its success is critical to all subsequent processing. Methods exist to skull strip brain images without gross deformities, such as those affected by Alzheimer's and Huntington's disease. However, there are no techniques for extracting brains affected by diseases that significantly disturb normal anatomy. Glioblastoma multiforme (GBM) is such a disease, as afflicted individuals develop large tumors that often require surgical resection. In this paper, we extend the ROBEX skull stripping method to extract brains from GBM images. The proposed method uses a shape model trained on healthy brains to be relatively insensitive to lesions inside the brain. The brain boundary is then searched for potential resection cavities using adaptive thresholding and the Random Walker algorithm corrects for leakage into the ventricles. The results show significant improvement over three popular skull stripping algorithms (BET, BSE and HWA) in a dataset of 48 GBM cases.