Novel Meta-iodobenzylguanidine and Etoposide Complex: Physicochemical Characterization and Mathematical Modeling of Anticancer Activity.

It is hypothesized that meta-iodobenzylguanidine (MIBG) complexation with etoposide (VP-16) will improve drug solubility and specificity towards BE(2)C neuroblastoma (NB) cells, 90% of which are known to be MIBG avid. After MIBG and VP-16 interaction, the dry complex was analyzed for crystalline structure, surface morphology, solubility, and size distribution by X-ray powder diffraction (P-XRD), scanning electron microscopy (SEM), infrared (FTIR) and UV spectroscopy, and dynamic light scattering. After exposure to the complex, the cell viability and decay rates were assessed by the MTS assay and estimated using exponential decay models (EDM). Multi-factorial ANOVA and an independent t-test were used to assess for cell viability and solubility data, respectively. The resulting (1: 3 w/w) VP-16: MIBG complex had a mean diameter and zeta potential of 458.5 nm and 0.951 mV, respectively. It dramatically increased the drug apparent water solubility (~ 12-folds). This was ascribed to the formation of a VP-16/MIBG nanocrystalline state mainly governed by cation-π interactions, evidenced by FTIR, SEM, and P-XRD data following the complexation. The EDM relating percent cell viability to drug concentration yielded an excellent fit (r2 > 0.95) and enabled to estimate the IC50 values of both native drug and its complex: 6.2 µM and 5.23 µM, respectively (indicating a conservation of drug anticancer activity). The statistical results were consistent with those of the exponential decay models, indicating that MIBG does not inhibit the anticancer activity of VP-16. This study indicates that the VP-16/MIBG complexation improves VP-16 solubility without antagonizing its anticancer activity. Moreover, the efficiency of the EDM for drug IC50 estimation provides alternative mathematical method for such in vitro cytotoxicity studies.

Prospective spatial-temporal clusters of COVID-19 in local communities: case study of Kansas City, Missouri, United States.

Kansas City, Missouri, became one of the major United States hotspots for COVID-19 due to an increase in the rate of positive COVID-19 test results. Despite the large numbers of positive cases in Kansas City, MO, the spatial-temporal analysis of data has been less investigated. However, it is critical to detect emerging clusters of COVID-19 and enforce control and preventive policies within those clusters. We conducted a prospective Poisson spatial-temporal analysis of Kansas City, MO data to detect significant space-time clusters of COVID-19 positive cases at the zip code level in Kansas City, MO. The analysis focused on daily infected cases in four equal periods of 3 months. We detected temporal patterns of emerging and re-emerging space-time clusters between March 2020 and February 2021. Three statistically significant clusters emerged in the first period, mainly concentrated in downtown. It increased to seven clusters in the second period, spreading across a broader region in downtown and north of Kansas City. In the third period, nine clusters covered large areas of north and downtown Kansas City, MO. Ten clusters were present in the last period, further extending the infection along the State Line Road. The statistical results were communicated with local health officials and provided the necessary guidance for decision-making and allocating resources (e.g., vaccines and testing sites). As more data become available, statistical clustering can be used as a COVID-19 surveillance tool to measure the effects of vaccination.

Effectiveness of motivational interviewing, health education and brief advice in a population of smokers who are not ready to quit.

BACKGROUND: Motivational Interviewing (MI), Brief Advice (BA) and Health Education (HE) are established smoking cessation induction methods for smokers with low desire to quit. Although randomized controlled trials (RCT's) have been frequently used to assess these interventions the temporal efficacy and effectiveness of these interventions have been poorly elaborated. The present work endeavors to fill the gap by considering the full range of possible motivational outcomes for all of the participants. METHODS: As a two-step process, Markov Chain (MC) and Ordinary Differential Equation (ODE) models were successively employed to examine the temporal efficacy and effectiveness of these interventions by computing the gradual movements of participants from an initial stage of unmotivated smoker to stages of increased motivation to quit and cessation. Specifically, in our re-analysis of data from the RCT we examined the proportion of participants in 4 stages of readiness to quit (unmotivated, undecided, motivated, former smokers) over 6 months, across treatment groups [MI (n = 87), BA (n = 43) and HE (n = 91)]. RESULTS: Although HE had greater efficacy compared to MI and BA (i.e., the highest smoking cessation rates), it had lower effectiveness at certain time points. This was due to the fact that HE had the greatest proportion of motivated smokers who quit smoking but simultaneously a large proportion of the motivated smokers became unmotivated to quit. The effectiveness of HE dropped substantially in weeks 3-12 and remained below the effectiveness of BA from week 12 onward. The 2-year ODE model projections show that the prevalence of motivated smokers in HE group may fall below 5%. The prevalence of HE former smokers can reach an equilibrium of 26%, where the prevalence of both BA and MI former smokers exceeds this equilibrium. CONCLUSIONS: The methodology proposed in this paper strongly benefits from the capabilities of both MC and ODE modeling approaches, in the event of low observations over the time. Particularly, the temporal population sizes are first estimated by the MC model. Then they are used to parametrize the ODE model and predict future values. The methodology enabes us to determine and compare the temporal efficacy and effectiveness of smoking cessation interventions, yielding predictive and analytic insights related to temporal characteristics and capabilities of these interventions during the study period and beyond. TRIAL REGISTRATION: Testing Counseling Styles to Motivate Smokers to Quit, NCT01188018 , (July 4, 2012). This study is registered at www.clinicaltrials.gov NCT01188018.

Cluster analysis of hemorrhagic disease in Missouri's white-tailed deer population: 1980-2013.

BACKGROUND: Outbreaks of deer hemorrhagic disease (HD) have been documented in the USA for many decades. In the year 2012, there was a severe HD outbreak in Missouri with mortalities reaching approximately 6.9 per thousand. Moreover, Missouri accounted for more than 43% of all reported epizootic HD cases in captive white-tailed deer. Using the data of suspected HD occurrence in Missouri, the primary goal of this paper was to determine if HD in Missouri's white-tailed deer occurs in spatial clusters. RESULTS: The main results of the cluster analysis are as follows. First, the spatial clusters of years 1980, 1988, 2005-2007, 2010, 2012, and 2013 suggest patterns of outbreaks every 6-8 years, with a potential outbreak in years 2018-2020. Secondly, these spatial clusters were more frequent in the central and southern counties. CONCLUSIONS: The clustering analyses employed in this study have potential applications for improving surveillance programs and designing early warning systems for effective deer population management and potentially reducing the number of HD cases.

Patient flow modeling and simulation to study HAI incidence in an Emergency Department.

Healthcare-associated infections (HAIs), or nosocomial infections, refer to patients getting new infections while getting treatment for an existing condition in a healthcare facility. HAI poses a significant challenge in healthcare delivery that results in higher rates of mortality and morbidity as well as a longer duration of hospital stay. While the real cause of HAI in a hospital varies widely and in most cases untraceable, it is popularly believed that patient flow in a hospital-which hospital units patients visit and where they spend the most time since their admission into the hospital-can trace back to HAI incidence in the hospital. Based on this observation, we, in this paper, model and simulate patient flow in an emergency department of a hospital and then utilize the developed model to study HAI incidence therein. We obtain (a) a flowchart of patient movement (admission to discharge) and (b) anonymous patient data from University Health Medical Center for a duration of 11 months (Aug 2022-June 2023). Based on these data, we develop and validate the patient flow model. Our model captures patient movement in different areas of a typical emergency department, such as triage, waiting room, and minor procedure rooms. We employ the discrete-event simulation (DES) technique to model patient flow and associated HAI infections using the simulation software, Anylogic. Our simulation results show that the rates of HAI incidence are proportional to both the specific areas patients occupy and the duration of their stay. By utilizing our model, hospital administrators and infection control teams can implement targeted strategies to reduce the incidence of HAI and enhance patient safety, ultimately leading to improved healthcare outcomes and more efficient resource allocation.

Incorporating global dynamics to improve the accuracy of disease models: Example of a COVID-19 SIR model.

Mathematical models of infectious diseases exhibit robust dynamics, such as stable endemic, disease-free equilibriums or convergence of the solutions to periodic epidemic waves. The present work shows that the accuracy of such dynamics can be significantly improved by including global effects of host movements in disease models. To demonstrate improved accuracy, we extended a standard Susceptible-Infected-Recovered (SIR) model by incorporating the global dynamics of the COVID-19 pandemic. The extended SIR model assumes three possibilities for susceptible individuals traveling outside of their community: • They can return to the community without any exposure to the infection. • They can be exposed and develop symptoms after returning to the community. • They can be tested positively during the trip and remain quarantined until fully recovered. To examine the predictive accuracy of the extended SIR model, we studied the prevalence of the COVID-19 infection in six randomly selected cities and states in the United States: Kansas City, Saint Louis, San Francisco, Missouri, Illinois, and Arizona. The extended SIR model was parameterized using a two-step model-fitting algorithm. The extended SIR model significantly outperformed the standard SIR model and revealed oscillatory behaviors with an increasing trend of infected individuals. In conclusion, the analytics and predictive accuracy of disease models can be significantly improved by incorporating the global dynamics of the infection.

Assessment of Retrospective COVID-19 Spatial Clusters with Respect to Demographic Factors: Case Study of Kansas City, Missouri, United States.

Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The United States (U.S.) has the highest number of reported COVID-19 infections and related deaths in the world, accounting for 17.8% of total global confirmed cases as of August 2021. As COVID-19 spread throughout communities across the U.S., it became clear that inequities would arise among differing demographics. Several researchers have suggested that certain racial and ethnic minority groups may have been disproportionately impacted by the spread of COVID-19. In the present study, we used the daily data of COVID-19 cases in Kansas City, Missouri, to observe differences in COVID-19 clusters with respect to gender, race, and ethnicity. Specifically, we utilized a retrospective Poisson spatial scan statistic with respect to demographic factors to detect daily clusters of COVID-19 in Kansas City at the zip code level from March to November 2020. Our statistical results indicated that clusters of the male population were more widely scattered than clusters of the female population. Clusters of the Hispanic population had the highest prevalence and were also more widely scattered. This demographic cluster analysis can provide guidance for reducing the social inequalities associated with the COVID-19 pandemic. Moreover, applying stronger preventive and control measures to emerging clusters can reduce the likelihood of another epidemic wave of infection.

Dynamics of notch activity in a model of interacting signaling pathways.

Networks of interacting signaling pathways are formulated with systems of reaction-diffusion (RD) equations. We show that weak interactions between signaling pathways have negligible effects on formation of spatial patterns of signaling molecules. In particular, a weak interaction between Retinoic Acid (RA) and Notch signaling pathways does not change dynamics of Notch activity in the spatial domain. Conversely, large interactions of signaling pathways can influence effects of each signaling pathway. When the RD system is largely perturbed by RA-Notch interactions, new spatial patterns of Notch activity are obtained. Moreover, analysis of the perturbed Homogeneous System (HS) indicates that the system admits bifurcating periodic orbits near a Hopf bifurcation point. Starting from a neighborhood of the Hopf bifurcation, oscillatory standing waves of Notch activity are numerically observed. This is of particular interest since recent laboratory experiments confirm oscillatory dynamics of Notch activity.

A time series modeling approach in risk appraisal of violent and sexual recidivism.

For over half a century, various clinical and actuarial methods have been employed to assess the likelihood of violent recidivism. Yet there is a need for new methods that can improve the accuracy of recidivism predictions. This study proposes a new time series modeling approach that generates high levels of predictive accuracy over short and long periods of time. The proposed approach outperformed two widely used actuarial instruments (i.e., the Violence Risk Appraisal Guide and the Sex Offender Risk Appraisal Guide). Furthermore, analysis of temporal risk variations based on specific time series models can add valuable information into risk assessment and management of violent offenders.

Pediatric multicenter cohort comparison of percutaneous endoscopic and non-endoscopic gastrostomy technique outcomes.

Enteral access is one of the mainstays of nutritional support. Several different modalities for gastrostomy placement are established. In pediatrics, however, there is a limited evidence base supporting the choice of 1 modality over the others. We retrospectively compared elective percutaneous endoscopically placed gastrostomy (PEG) with surgical and interventional radiology-placed gastrostomy outcomes using the Pediatric Hospital Inpatient Sample multicenter administrative database (Pediatric Health Information System). Pediatric patients (<18 years) undergoing planned elective gastrostomy (2010-2015) were included. Coded gastrostomy procedure subtype, patient demographic characteristics, chronic comorbidities and subsequent related outcomes, mortality, readmission, length of stay and total cost of admission were analyzed. Univariate analysis differentiated among gastrostomy techniques. The effect of gastrostomy on mortality and 30-day readmission were determined using a forward, stepwise, binary logistic regression. Generalized linear models were used to estimate the effect of gastrostomy type on length of stay and total cost. During the study period, 11,712 children underwent gastrostomy, including PEG (27%). Patients with chronic comorbidities were more, or as likely to undergo non-PEG procedures. Postoperatively, PEG patients were less likely to require mechanical ventilation and total parenteral nutrition (TPN). Gastrostomy type was not predictive of mortality; predictors included non-White race and need for mechanically assisted ventilation. Readmission following gastrostomy was common (29.5%), and more likely in PEG patients (OR 1.31). Predictors of readmission included earlier TPN (OR 1.39), cardiovascular (OR 1.17) and oncology (OR 4.17) comorbidities. Our study suggests that PEG placement entails similar length of stay and cost as in non-PEG gastrostomy. Patients undergoing PEG were less likely to require mechanical ventilation and TPN postoperatively. Mortality is similar in both groups although more likely with specific comorbidities. Racial background appeared to be associated with choice of gastrostomy, length of stay and mortality.

Study and simulation of reaction-diffusion systems affected by interacting signaling pathways.

Possible effects of interaction (cross-talk) between signaling pathways is studied in a system of Reaction-Diffusion (RD) equations. Furthermore, the relevance of spontaneous neurite symmetry breaking and Turing instability has been examined through numerical simulations. The interaction between Retinoic Acid (RA) and Notch signaling pathways is considered as a perturbation to RD system of axon-forming potential for N2a neuroblastoma cells. The present work suggests that large increases to the level of RA-Notch interaction can possibly have substantial impacts on neurite outgrowth and on the process of axon formation. This can be observed by the numerical study of the homogeneous system showing that in the absence of RA-Notch interaction the unperturbed homogeneous system may exhibit different saddle-node bifurcations that are robust under small perturbations by low levels of RA-Notch interactions, while large increases in the level of RA-Notch interaction result in a number of transitions of saddle-node bifurcations into Hopf bifurcations. It is speculated that near a Hopf bifurcation, the regulations between the positive and negative feedbacks change in such a way that spontaneous symmetry breaking takes place only when transport of activated Notch protein takes place at a faster rate.

A nested compartmental model to assess the efficacy of paratuberculosis control measures on U.S. dairy farms.

Paratuberculosis, also known as Johne's disease (JD), is a chronic contagious disease, caused by Mycobacterium avium subsp. paratuberculosis (MAP). The disease is incurable, fatal and causes economic losses estimated to exceed 200 million dollars to the U.S. dairy industry annually. Several preventive and control measures have been recommended; however, only a few of these measures have been validated empirically. Using a nested compartmental (NC) modeling approach, the main objective of this research was to identify the best combination of control and preventive measures that minimizes the prevalence and incidence of JD and the risk of MAP occurrence in a dairy herd. The NC model employs both MAP transmission estimates and data on pen movement of cattle on a dairy to quantify the effectiveness of control and preventive measures. To obtain reasonable ranges of parameter values for between-pen movements, the NC model was fitted to the movement data of four typical California dairy farms. Using the estimated ranges of the movement parameters and those of JD from previous research, the basic reproduction number was calculated to measure the risk of MAP occurrence in each pen environment as well as the entire dairy. Although the interventions evaluated by the NC model were shown to reduce the infection, no single measure alone was capable of eradicating the infection. The numerical simulations suggest that a combination of test and cull with more frequent manure removal is the most effective method in reducing incidence, prevalence and the risk of MAP occurrence. Other control measures such as limiting calf-adult cow contacts, raising calves in a disease-free herd or colostrum management were less effective.

Reproduction numbers for infections with free-living pathogens growing in the environment.

The basic reproduction number ℛ(0) for a compartmental disease model is often calculated by the next generation matrix (NGM) approach. When the interactions within and between disease compartments are interpreted differently, the NGM approach may lead to different ℛ(0) expressions. This is demonstrated by considering a susceptible-infectious-recovered-susceptible model with free-living pathogen (FLP) growing in the environment. Although the environment could play different roles in the disease transmission process, leading to different ℛ(0) expressions, there is a unique type reproduction number when control strategies are applied to the host population. All ℛ(0) expressions agree on the threshold value 1 and preserve their order of magnitude. However, using data for salmonellosis and cholera, it is shown that the estimated ℛ(0) values are substantially different. This study highlights the utility and limitations of reproduction numbers to accurately quantify the effects of control strategies for infections with FLPs growing in the environment.

Modeling the effect of seasonal variation in ambient temperature on the transmission dynamics of a pathogen with a free-living stage: example of Escherichia coli O157:H7 in a dairy herd.

To explore the potential role of ambient temperature on infection transmission dynamics for pathogens, we used Escherichia coli O157:H7 in a dairy herd and the surrounding farm environment as a model system. For this system, we developed a mathematical model in which a Susceptible-Infectious-Susceptible (SIS) model of infection spread through the host population is coupled with a metapopulation model of E. coli O157:H7 free-living stage in the environment allowing bacterial growth to be influenced by ambient temperature. Model results indicate that seasonal variation in ambient temperature could have a considerable impact on pathogen populations in the environment, specifically on barn surfaces and in water troughs, and consequently on the prevalence of infection in the host population. Based on model assumptions, contaminated drinking water was the most important pathway of E. coli O157:H7 transmission to cattle. Sensitivity analysis indicated that water-borne transmission is amplified during the warmer months if the amount of standing drinking water available to the cattle herd is high. This is because warmer ambient temperature favors faster pathogen replication which when combined with slower water replacement-rate due to high amount of available standing water leads to a greater pathogen load in drinking water. These results offer a possible explanation of the seasonal variation in E. coli O157:H7 prevalence in cattle and suggest that improved drinking-water management could be used for control of this infection in cattle. Our study demonstrates how consideration of ambient temperature in transmission cycles of pathogens able to survive and grow in the environment outside the host could offer novel perspectives on the spread and control of infections caused by such pathogens.