Publications by Year: 2023

2023

Bomfim, Gisele F., Fernanda Priviero, Emma Poole, Rita C. Tostes, John H. Sinclair, Dimitrios Stamou, Mark J. Uline, Mark R. Wills, and Clinton Webb. (2023) 2023. “Cytomegalovirus and Cardiovascular Disease: A Hypothetical Role for Viral G-Protein-Coupled Receptors in Hypertension”. American Journal of Hypertension 36 (9): 471-80.

Cytomegalovirus (CMV) is a member of the β-herpesviruses and is ubiquitous, infecting 50%–99% of the human population depending on ethnic and socioeconomic conditions. CMV establishes lifelong, latent infections in their host. Spontaneous reactivation of CMV is usually asymptomatic, but reactivation events in immunocompromised or immunosuppressed individuals can lead to severe morbidity and mortality. Moreover, herpesvirus infections have been associated with several cardiovascular and post-transplant diseases (stroke, atherosclerosis, post-transplant vasculopathy, and hypertension). Herpesviruses, including CMV, encode viral G-protein-coupled receptors (vGPCRs) that alter the host cell by hijacking signaling pathways that play important roles in the viral life cycle and these cardiovascular diseases. In this brief review, we discuss the pharmacology and signaling properties of these vGPCRs, and their contribution to hypertension. Overall, these vGPCRs can be considered attractive targets moving forward in the development of novel hypertensive therapies.

Corti, David S., Donya Ohadi, Ricardo Fariello, and Mark J. Uline. (2023) 2023. “Microcanonical Thermodynamics of Small Ideal Gas Systems”. The Journal of Physical Chemistry B 127 (15): 3431-42.

We consider the thermal, mechanical, and chemical contact of two subsystems composed of ideal gases, both of which are not in the thermodynamic limit. After contact, the combined system is isolated, and the entropy is determined through the use of its standard connection to the phase space density (PSD), where only those microstates at a given energy value are counted. The various intensive properties of these small systems that follow from a derivative of the PSD, such as the temperature, pressure, and chemical potential (evaluated via a backward difference), while equal when the two subsystems are in equilibrium are nevertheless found not to behave in accordance with what is expected from macroscopic thermodynamics. Instead, it is the entropy, defined from its connection to the PSD, that still controls the behavior of these small (nonextensive) systems. We also analyze the contact of these two subsystems utilizing an alternative entropy definition, through its proposed connection to the phase space volume (PSV), where all microstates at or below a given energy value are counted. We show that certain key properties of these small systems obtained with the PSV either do not become equal or do not consistently describe the two subsystems when in contact, suggesting that the PSV should not be used for analyzing the behavior of small isolated systems.

Moss, Melissa A., Mihyun L. Waugh, Nicholas D. Boltin, Lauren Wolf, Ronnie Horner, Matthew Hermes, Thomas L Wheeler II, Richard L. Goodwin, and Richard Michael Gower. (2023) 2023. Levels of immune response markers as adverse outcome predictors following biomaterial implant surgery. 17960462, issued 2023.

 

In general, the present disclosure is directed to systems and methods of evaluating a subject's risk of one or more complications associated with pelvic organ prolapse surgery. The method comprising: obtaining, by a computing system comprising one or more computing devices, sample data associated with the subject; inputting, by the computing system, the sample data into a machine-learned immune response model; receiving, by the computing system as an output of the machine-learned immune response model, one or more predictions of post-surgical complications of mesh exposure through the vaginal wall associated with the subject; and performing a pelvic organ prolapse repair surgery on the subject, wherein the surgery is performed based at least in part on the one or more predictions of post-surgical complications by the machine-learned immune response model associated with a likelihood of success.

Waugh, Mihyun L., Nicholas D. Boltin, Lauren Wolf, Jane Goodwin, Patti Parker, Ronnie Horner, Matthew Hermes, Thomas L Wheeler II, Richard L. Goodwin, and Melissa A. Moss. (2025) 2023. “Prediction of Pelvic Organ Prolapse Postsurgical Outcome Using Biomaterial-Induced Blood Cytokine Levels: Machine Learning Approach”. JMIR Formative Research 9.

Background: Pelvic organ prolapse (POP) refers to symptomatic descent of the vaginal wall. To reduce surgical failure rates, surgical correction can be augmented with the insertion of polypropylene mesh. This benefit is offset by the risk of mesh complication, predominantly mesh exposure through the vaginal wall. If mesh placement is under consideration as part of prolapse repair, patient selection and counseling would benefit from the prediction of mesh exposure; yet, no such reliable preoperative method currently exists. Past studies indicate that inflammation and associated cytokine release is correlated with mesh complication. While some degree of mesh-induced cytokine response accompanies implantation, excessive or persistent cytokine responses may elicit inflammation and implant rejection.

Objective: Here, we explore the levels of biomaterial-induced blood cytokines from patients who have undergone POP repair surgery to (1) identify correlations among cytokine expression and (2) predict postsurgical mesh exposure through the vaginal wall.

Methods: Blood samples from 20 female patients who previously underwent surgical intervention with transvaginal placement of polypropylene mesh to correct POP were collected for the study. These included 10 who experienced postsurgical mesh exposure through the vaginal wall and 10 who did not. Blood samples incubated with inflammatory agent lipopolysaccharide, with sterile polypropylene mesh, or alone were analyzed for plasma levels of 13 proinflammatory and anti-inflammatory cytokines using multiplex assay. Data were analyzed by principal component analysis (PCA) to uncover associations among cytokines and identify cytokine patterns that correlate with postsurgical mesh exposure through the vaginal wall. Supervised machine learning models were created to predict the presence or absence of mesh exposure and probe the number of cytokine measurements required for effective predictions.

Results: PCA revealed that proinflammatory cytokines interferon gamma, interleukin 12p70, and interleukin 2 are the largest contributors to the variance explained in PC 1, while anti-inflammatory cytokines interleukins 10, 4, and 6 are the largest contributors to the variance explained in PC 2. Additionally, PCA distinguished cytokine correlations that implicate prospective therapies to improve postsurgical outcomes. Among machine learning models trained with all 13 cytokines, the artificial neural network, the highest performing model, predicted POP surgical outcomes with 83% (15/18) accuracy; the same model predicted POP surgical outcomes with 78% (14/18) accuracy when trained with just 7 cytokines, demonstrating retention of predictive capability using a smaller cytokine group.

Conclusions: This preliminary study, incorporating a sample size of just 20 participants, identified correlations among cytokines and demonstrated the potential of this novel approach to predict mesh exposure through the vaginal wall following transvaginal POP repair surgery. Further study with a larger sample size will be pursued to confirm these results. If corroborated, this method could provide a personalized medicine approach to assist surgeons in their recommendation of POP repair surgeries with minimal potential for adverse outcomes.

JMIR Perioper Med 2023;6:e40402