Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic management is limited by great uncertainty, for both health systems and citizens. Facing this information gap requires a paradigm shift from traditional approaches to healthcare to the participatory model of improving health. This work describes the design and function of the Doing Risk sElf-assessment and Social health Support for COVID (Dress-COV) system. It aims to establish a lasting link between the user and the tool; thus, enabling modeling of the data to assess individual risk of infection, or developing complications, to improve the individual’s self-empowerment. The system uses bot technology of the Telegram application. The risk assessment includes the collection of user responses and the modeling of data by machine learning models, with increasing appropriateness based on the number of users who join the system. The main results reflect: (a) the individual’s compliance with the tool; (b) the security and versatility of the architecture; (c) support and promotion of self-management of behavior to accommodate surveillance system delays; (d) the potential to support territorial health providers, e.g., the daily efforts of general practitioners (during this pandemic, as well as in their routine practices). These results are unique to Dress-COV and distinguish our system from classical surveillance applications.

The analytical representation and simulation of complex molecular pathways can contribute to understanding and evaluating physiological as well as pathological processes. We are interested in modeling the processes of menopause to stratify women in terms of the genotypic and environmental components and their implications for development of individualized risk of postmenopausal disorders, e.g., breast and ovarian cancer, cardiovascular disease, and osteoporosis. We have initiated this study using the UltraSAN package to analyze the pathway associated with estrogen production. This model incorporates detailed information about the hormone factors affecting estrogen production, and the simulations carried out are based on published experimental data corresponding to hormone levels during the course of the normal female reproductive cycle. The agreement between the experimental data and the simulation is typically less than 2 ng/ml or 2 pg/ml respectively for progesterone and estradiol output. This approach further permits inclusion of information about an SNP observed in the gene coding for the enzyme aromatase as a model to study the impact of reduced enzymatic activity on hormone levels.

Nonclinical tests are considered crucial for understanding the safety of investigational medicines. However, the effective translation from nonclinical to human application is limited and must be improved. Drug development stakeholders are working to advance human-based in vitro and in silico methods that may be more predictive of human efficacy and safety in vivo because they enable scientists to model the direct interaction of drugs with human cells, tissues, and biological processes. Here, we recommend test-neutral regulations; increased funding for development and integration of human-based approaches; support for existing initiatives that advance human-based approaches; evaluation of new approaches using human data; establishment of guidelines for procuring human cells and tissues for research; and additional training and educational opportunities in human-based approaches.

We developed a quality assurance (QA) tool, namely microarray outlier filter (MOF), and have applied it to our microarray datasets for the identification of problematic arrays. Our approach is based on the comparison of the arrays using the correlation coefficient and the number of outlier spots generated on each array to reveal outlier arrays. For a human universal reference (HUR) dataset, which is used as a technical control in our standard hybridization procedure, 3 outlier arrays were identified out of 35 experiments. For a human blood dataset, 12 outlier arrays were identified from 185 experiments. In general, arrays from human blood samples displayed greater variation in their gene expression profiles than arrays from HUR samples. As a result, MOF identified two distinct patterns in the occurrence of outlier arrays. These results demonstrate that this methodology is a valuable QA practice to identify questionable microarray data prior to downstream analysis.