Patient made Long Covid empowers the patient voice

Successful, patient-driven advocacy and research in Long Covid is contributing to change our understanding of SARS-CoV-2 infection, viral-onset diseases, and knowledge building in medicine and beyond. Events and epistemic shifts surrounding the rise of Long Covid represent a massive opportunity for empowering the patient voice. Strategies that have proven key to grassroots Long Covid advocacy in our digital era could be further explored and expanded across different patient communities. It is my hope that patient-centred expertise will be further incorporated into the biomedical community. This would contribute to critical changes in medical awareness of chronic diseases and patient care.

A Partition-Based Group Testing Algorithm for Estimating the Number of Infected Individuals

The dangers of COVID-19 remain ever-present worldwide. The asymptomatic nature of COVID-19 obfuscates the signs policy makers look for when deciding to reopen public areas or further quarantine. In much of the world, testing resources are often scarce, creating a need for testing potentially infected individuals that prioritizes efficiency. This report presents an advancement to Beigel and Kasif's Approximate Counting Algorithm (ACA). ACA estimates the infection rate with a number of tests that is logarithmic in the population size. Our newer version of the algorithm provides an extra level of efficiency: each subject is tested exactly once. A simulation of the algorithm, created for and presented as part of this paper, can be used to find a linear regression of the results with R^2 > 0.999. This allows stakeholders and members of the biomedical community to estimate infection rates for varying population sizes and ranges of infection rates.

Epidemiology of Pregnancy Complications Through the Lens of Immunological Memory

In the fifteen minutes it takes to read this short commentary, more than 400 babies will have been born too early, another 300 expecting mothers will develop preeclampsia, and 75 unborn third trimester fetuses will have died in utero (stillbirth). Given the lack of meaningful progress in understanding the physiological changes that occur to allow a healthy, full term pregnancy, it is perhaps not surprising that effective therapies against these great obstetrical syndromes that include prematurity, preeclampsia, and stillbirth remain elusive. Meanwhile, pregnancy complications remain the leading cause of infant and childhood mortality under age five. Does it have to be this way? What more can we collectively, as a biomedical community, or individually, as clinicians who care for women and newborn babies at high risk for pregnancy complications, do to protect individuals in these extremely vulnerable developmental windows? The problem of pregnancy complications and neonatal mortality is extraordinarily complex, with multiple unique, but complementary perspectives from scientific, epidemiological and public health viewpoints. Herein, we discuss the epidemiology of pregnancy complications, focusing on how the outcome of prior pregnancy impacts the risk of complication in the next pregnancy — and how the fundamental immunological principle of memory may promote this adaptive response.

pmparser and PMDB: resources for large-scale, open studies of the biomedical literature

PubMed is an invaluable resource for the biomedical community. Although PubMed is freely available, the existing API is not designed for large-scale analyses and the XML structure of the underlying data is inconvenient for complex queries. We developed an R package called pmparser to convert the data in PubMed to a relational database. Our implementation of the database, called PMDB, currently contains data on over 31 million PubMed Identifiers (PMIDs) and is updated regularly. Together, pmparser and PMDB can enable large-scale, reproducible, and transparent analyses of the biomedical literature. pmparser is licensed under GPL-2 and available at https://pmparser.hugheylab.org. PMDB is available in both PostgreSQL (DOI 10.5281/zenodo.4008109) and Google BigQuery (https://console.cloud.google.com/bigquery?project=pmdb-bq&d=pmdb).

Alignment of biomedical data repositories with open, FAIR, citable and trust-worthy principles

Increasing attention is being paid to the operation of biomedical data repositories in light of efforts to improve how scientific data is handled and made available for the long term. Simultaneously, groups around the world have been coming together to formalize principles that govern different aspects of open science and data sharing.The most well known are the FAIR data principles. These are joined by principles and practices that govern openness, citation, credit and good stewardship (trustworthiness). Together, these define a framework for data repositories to support Open, FAIR, Citable and Trustworthy (OFCT) data. Here we developed an instrument using the open source PolicyModels toolkit that attempts to operationalize key aspects of OFCT principles and applied the instrument to eight biomedical community repositories listed by the NIDDK Information Network (dkNET.org). The evaluation was performed through inspection of documentation and interaction with the sites. Overall, there was little explicit acknowledgement of any of the OFCT principles, although the majority of repositories provided at least some support for their tenets.

pmparser and PMDB: resources for large-scale, open studies of the biomedical literature

PubMed is an invaluable resource for the biomedical community. Although PubMed is freely available, the existing API is not designed for large-scale analyses and the XML structure of the underlying data is inconvenient for complex queries. We developed an R package called pmparser to convert the data in PubMed to a relational database. Our implementation of the database, called PMDB, currently contains data on over 31 million PubMed Identifiers (PMIDs) and is updated regularly. Together, pmparser and PMDB can enable large-scale, reproducible, and transparent analyses of the biomedical literature. pmparser is licensed under GPL-2 and available at https://pmparser.hugheylab.org. PMDB is stored in PostgreSQL and compressed dumps are available on Zenodo (https://doi.org/10.5281/zenodo.4008109).

Device-Associated Menstrual Toxic Shock Syndrome

SUMMARY In the 1980s, menstrual toxic shock syndrome (mTSS) became a household topic, particularly among mothers and their daughters. The research performed at the time, and for the first time, exposed the American public as well as the biomedical community, in a major way, to understanding disease progression and investigation. Those studies led to the identification of the cause, Staphylococcus aureus and the pyrogenic toxin superantigen TSS toxin 1 (TSST-1), and many of the risk factors, for example, tampon use. Those studies in turn led to TSS warning labels on the outside and inside of tampon boxes and, as important, uniform standards worldwide of tampon absorbency labeling. This review addresses our understanding of the development and conclusions related to mTSS and risk factors. We leave the final message that even though mTSS is not commonly in the news today, cases continue to occur. Additionally, S. aureus strains cycle in human populations in roughly 10-year intervals, possibly dependent on immune status. TSST-1-producing S. aureus bacteria appear to be reemerging, suggesting that physician awareness of this emergence and mTSS history should be heightened.

Remdesivir: A Review of Its Discovery and Development Leading to Human Clinical Trials for Treatment of COVID-19

The global pandemic of SARS-CoV-2, the causative viral pathogen of COVID-19, has driven the biomedical community to action – to uncover and develop anti-viral interventions. One potential therapeutic approach currently being evaluated in numerous clinical trials is the agent remdesivir, which has endured a long and winding developmental path. Remdesivir is a nucleotide analog prodrug that perturbs viral replication, originally evaluated in clinical trials to thwart the Ebola outbreak in 2014. Subsequent evaluation by numerous virology laboratories demonstrated the ability of remdesivir to inhibit coronavirus replication, including SARS-CoV-2. Here, we provide an overview of its mechanism of action, discovery, and the current studies exploring its clinical effectiveness.