Objective The present study aims to understand to what extent obesity is related to adverse maternal, obstetrical, and neonatal outcomes in a Portuguese obstetrical population. Methods A retrospective case-control study was conducted at the Department of Obstetrics of a differentiated perinatal care facility. The study compared 1,183 obese pregnant women with 5,399 normal or underweight pregnant women for the occurrence of gestational diabetes, hypertensive pregnancy disorders, and preterm birth. Mode of delivery, birthweight, and neonatal intensive care unit (ICU) admissions were also evaluated. Mean blood glucose values were evaluated and compared between groups, in the first and second trimesters of pregnancy. Only singleton pregnancies were considered. Results The prevalence of obesity was 13.6%. Obese pregnant women were significantly more likely to have cesarean sections (adjusted odds ratio [aOR] 2.0, p < 0.001), gestational diabetes (aOR 2.14, p < 0.001), hypertensive pregnancy disorders (aOR 3.43, p < 0.001), and large-for-gestational age or macrosomic infants (aOR 2.13, p < 0.001), and less likely to have small-for-gestational age newborns (aOR 0.51, p < 0.009). No significant differences were found in terms of preterm births, fetal/neonatal deaths, low birthweight newborns, and neonatal ICU admissions among cases and controls. Maternal obesity was significantly associated with higher mean blood glucose levels, in the first and second trimesters of pregnancy. Conclusion Obesity is associated with increased risks of adverse pregnancy and neonatal outcomes. These risks seem to increase progressively with increasing body mass index (BMI) class. Female obesity should be considered a major public health issue and has consequences on maternal-fetal health.

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A new era of virus surveillance is emerging based on the real-time monitoring of virus evolution at whole-genome scale (World Health Organization 2021). Although national and international health authorities have strongly recommended this technological transition, especially for influenza and SARS-CoV-2 (World Health Organization 2021, Revez et al. 2017), the implementation of genomic surveillance can be particularly challenging due to the lack of bioinformatics infrastructures and/or expertise to process and interpret next-generation sequencing (NGS) data (Oakeson et al. 2017). We developed and implemented INSaFLU-TELE-Vir platform (https://insaflu.insa.pt/) (Borges et al. 2018), which is an influenza- and SARS-CoV-2-oriented bioinformatics free web-based suite that handles primary NGS data (reads) towards the automatic generation of the main “genetic requests'' for effective and timely laboratory surveillance. By handling NGS data collected from any amplicon-based schema (making it applicable for other pathogens), INSaFLU-TELE-Vir enables any laboratory to perform multi-step and intensive bioinformatics analyses in a user-oriented manner without requiring advanced training. INSaFLU-TELE-Vir handles NGS data collected from distinct sequencing technologies (Illumina, Ion Torrent and Oxford Nanopore Technologies), with the possibility of constructing comparative analyses using different technologies. It gives access to user-restricted sample databases and project management, being a transparent and flexible tool specifically designed to automatically update project outputs as more samples are uploaded. Data integration is thus cumulative and scalable, fitting the need for both routine surveillance and outbreak investigation activities. The bioinformatics pipeline consists of six core steps: read quality analysis and improvement, human betacoronaviruses (including SARS-CoV-2 Pango lineages) and influenza type/subtype classification, mutation detection and consensus generation, coverage analysis, alignment/phylogeny, intra-host minor variant detection (and automatic detection of putative mixed infections). read quality analysis and improvement, human betacoronaviruses (including SARS-CoV-2 Pango lineages) and influenza type/subtype classification, mutation detection and consensus generation, coverage analysis, alignment/phylogeny, intra-host minor variant detection (and automatic detection of putative mixed infections). The multiple outputs are provided in nomenclature-stable and standardized formats that can be visualized and explored in situ or through multiple compatible downstream applications for fine-tuned data analysis. Novel features are being implemented into the INSaFLU-TELE-Vir bioinformatics toolkit as part of the OHEJP TELE-Vir (https://onehealthejp.eu/jrp-tele-vir/) project, including rapid detection of selected genotype-phenotype associations, and enhanced geotemporal data visualization. All the code is available in github (https://github.com/INSaFLU) with the possibility of a local docker installation (https://github.com/INSaFLU/docker). A detailed documentation and tutorial is also available (https://insaflu.readthedocs.io/en/latest/). In summary, INSaFLU supplies public health laboratories and researchers with an open and user-friendly framework, potentiating a strengthened and timely multi-country genome-based virus surveillance.