Gut Microbiome Signatures During Acute Infection Predict Long COVID.

Long COVID (LC), manifests in 10-30% of non-hospitalized individuals post-SARS-CoV-2 infection leading to significant morbidity. The predictive role of gut microbiome composition during acute infection in the development of LC is not well understood, partly due to the heterogeneous nature of disease. We conducted a longitudinal study of 799 outpatients tested for SARS-CoV-2 (380 positive, 419 negative) and found that individuals who later developed LC harbored distinct gut microbiome compositions during acute infection, compared with both SARS-CoV-2-positive individuals who did not develop LC and negative controls with similar symptomatology.

Predicting blood loss volume in a canine model of hemorrhagic shock using arterial waveform machine learning analysis.

Objective: To determine if the compensatory reserve algorithm validated in humans can be applied to canines. Our secondary objective was to determine if a simpler waveform analysis could predict the percentage of blood loss volume.

Methods: 6 purpose-bred, anesthetized dogs underwent 5 rounds of controlled hemorrhage and resuscitation while continuously recording invasive arterial blood pressure waveforms in this prospective, experimental study.

Machine Learning Models for Tracking Blood Loss and Resuscitation in a Hemorrhagic Shock Swine Injury Model.

Hemorrhage leading to life-threatening shock is a common and critical problem in both civilian and military medicine. Due to complex physiological compensatory mechanisms, traditional vital signs may fail to detect patients' impending hemorrhagic shock in a timely manner when life-saving interventions are still viable. To address this shortcoming of traditional vital signs in detecting hemorrhagic shock, we have attempted to identify metrics that can predict blood loss.

Two scientific perspectives on nerve signal propagation: how incompatible approaches jointly promote progress in explanatory understanding.

We present a case study of two scientific perspectives on the phenomenon of nerve signal propagation, a bio-electric and a thermodynamic perspective, and compare this case with two accounts of scientific perspectivism: those of Michela Massimi and Juha Saatsi, respectively. We demonstrate that the interaction between the bio-electric perspective and the thermodynamic perspective can be captured in Saatsi's terms of progress in explanatory understanding. Using insights from our case study, we argue that both the epistemic and pragmatic dimensions of scientific understanding are important for increasing explanatory understanding of phenomena.