Examining B-cell dynamics and responsiveness in different inflammatory milieus using an agent-based model.

Introduction: B-cells are essential components of the immune system that neutralize infectious agents through the generation of antigen-specific antibodies and through the phagocytic functions of naïve and memory B-cells. However, the B-cell response can become compromised by a variety of conditions that alter the overall inflammatory milieu, be that due to substantial, acute insults as seen in sepsis, or due to those that produce low-level, smoldering background inflammation such as diabetes, obesity, or advanced age. This B-cell dysfunction, mediated by the inflammatory cytokines Interleukin-6 (IL-6) and Tumor Necrosis Factor-alpha (TNF-α), increases the susceptibility of late-stage sepsis patients to nosocomial infections and increases the incidence or severity of recurrent infections, such as SARS-CoV-2, in those with chronic conditions.

Characterizing the Crosstalk Between Programmed Cell Death Pathways in Cytokine Storm With an Agent-Based Model.

There is increasing recognition of extensive crosstalk between programmed cell death pathways (PCDPs), such as apoptosis, pyroptosis, and necroptosis, resulting in a highly redundant system responsive to a breadth of potential pathogens. However, because pyroptosis and necroptosis propagate inflammation, these redundancies also present challenges for therapeutic control of dysregulated hyperinflammation seen in cytokine storm (CS) generated organ dysfunction. We hypothesize that the conversion of existing knowledge regarding apoptosis, pyroptosis, and necroptosis into a computational model can enhance our understanding of the crosstalk between PCDPs via simulation experiments of microbe interactions and experimental interventions.

Children are small adults (when properly normalized): Transferrable/generalizable sepsis prediction.

Background: Though governed by the same underlying biology, the differential physiology of children causes the temporal evolution from health to a septic/diseased state to follow trajectories that are distinct from adult cases. As pediatric sepsis data sets are less readily available than for adult sepsis, we aim to leverage this shared underlying biology by normalizing pediatric physiological data such that it would be directly comparable to adult data, and then develop machine-learning (ML) based classifiers to predict the onset of sepsis in the pediatric population. We then externally validated the classifiers in an independent adult dataset.

Artificial Intelligence Optical Biopsy for Evaluating the Functional State of Wounds.

Introduction: The clinical characterization of the functional status of active wounds in terms of their driving cellular and molecular biology remains a considerable challenge that currently requires excision via a tissue biopsy. In this pilot study, we use convolutional Siamese neural network (SNN) architecture to predict the functional state of a wound using digital photographs of wounds in a canine model of volumetric muscle loss (VML).

Methods: Digital images of VML injuries and tissue biopsies were obtained in a standardized fashion from an established canine model of VML.

A Virtual Reading Center Model Using Crowdsourcing to Grade Photographs for Trachoma: Validation Study.

Background: As trachoma is eliminated, skilled field graders become less adept at correctly identifying active disease (trachomatous inflammation-follicular [TF]). Deciding if trachoma has been eliminated from a district or if treatment strategies need to be continued or reinstated is of critical public health importance. Telemedicine solutions require both connectivity, which can be poor in the resource-limited regions of the world in which trachoma occurs, and accurate grading of the images.