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Chronic diabetic foot ulcers (DFUs) are a significant complication of diabetes mellitus, often leading to amputation, increased morbidity, and a substantial financial burden. Even with the advancements in the treatment of DFU, the risk of amputation still exists, and this occurs due to the presence of gangrene and osteomyelitis. Nonhealing in a chronic DFU is due to decreased angiogenesis, granulation tissue formation, and extracellular matrix remodeling in the presence of persistent inflammation. During wound healing, the proliferation and migration of fibroblasts, smooth muscle cells, and keratinocytes play a critical role in extracellular matrix (ECM) remodeling, angiogenesis, and epithelialization. The molecular factors regulating the migration, proliferation, and differentiation of these cells are scarcely discussed in the literature. The literature review identifies the key factors influencing the proliferation, migration, and differentiation of fibroblasts, keratinocytes, and vascular smooth muscle cells (VSMCs), which are critical in wound healing. This is followed by a discussion on the various novel factors regulating the migration, proliferation, and differentiation of these cells but not in the context of wound healing; however, they may play a role. Using a network analysis, we examined the interactions between various factors, and the findings suggest that the novel factors identified may play a significant role in promoting angiogenesis, granulation tissue formation, and extracellular matrix remodeling during wound healing or DFU healing. However, these interactions warrant further investigation to establish their role alone or synergistically.

SARS-CoV-2 (CoV2) infected, asymptomatic individuals are an important contributor to COVID transmission. CoV2-specific immunoglobulin (Ig)-as generated by the immune system following infection or vaccination-has helped limit CoV2 transmission from asymptomatic individuals to susceptible populations (e.g. elderly). Here, we describe the relationships between COVID incidence and CoV2 lineage, viral load, saliva Ig levels (CoV2-specific IgM, IgA and IgG), and ACE2 binding inhibition capacity in asymptomatic individuals between January 2021 and May 2022. These data were generated as part of a large university COVID monitoring program in Ohio, United States of America, and demonstrate that COVID incidence among asymptomatic individuals occurred in waves which mirrored those in surrounding regions, with saliva CoV2 viral loads becoming progressively higher in our community until vaccine mandates were established. Among the unvaccinated, infection with each CoV2 lineage (pre-Omicron) resulted in saliva Spike-specific IgM, IgA, and IgG responses, the latter increasing significantly post-infection and being more pronounced than N-specific IgG responses. Vaccination resulted in significantly higher Spike-specific IgG levels compared to unvaccinated infected individuals, and uninfected vaccinees' saliva was more capable of inhibiting Spike function. Vaccinees with breakthrough Delta infections had Spike-specific IgG levels comparable to those of uninfected vaccinees; however, their ability to inhibit Spike binding was diminished. These data are consistent with COVID vaccines having achieved hoped-for effects in our community, including the generation of mucosal antibodies that inhibit Spike and lower community viral loads, and suggest breakthrough Delta infections were not due to an absence of vaccine-elicited Ig, but instead limited Spike binding activity in the face of high community viral loads.

For many biological systems, a variety of simulation models exist. A new simulation model is rarely developed from scratch, but rather revises and extends an existing one. A key challenge, however, is to decide which model might be an appropriate starting point for a particular problem and why. To answer this question, we need to identify entities and activities that contributed to the development of a simulation model. Therefore, we exploit the provenance data model, PROV-DM, of the World Wide Web Consortium and, building on previous work, continue developing a PROV ontology for simulation studies. Based on a case study of 19 Wnt/β-catenin signaling models, we identify crucial entities and activities as well as useful metadata to both capture the provenance information from individual simulation studies and relate these forming a family of models. The approach is implemented in WebProv, a web application for inserting and querying provenance information. Our specialization of PROV-DM contains the entities Research Question, Assumption, Requirement, Qualitative Model, Simulation Model, Simulation Experiment, Simulation Data, and Wet-lab Data as well as activities referring to building, calibrating, validating, and analyzing a simulation model. We show that most Wnt simulation models are connected to other Wnt models by using (parts of) these models. However, the overlap, especially regarding the Wet-lab Data used for calibration or validation of the models is small. Making these aspects of developing a model explicit and queryable is an important step for assessing and reusing simulation models more effectively. Exposing this information helps to integrate a new simulation model within a family of existing ones and may lead to the development of more robust and valid simulation models. We hope that our approach becomes part of a standardization effort and that modelers adopt the benefits of provenance when considering or creating simulation models.