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Biomedical subjects

Philip S Crooke

Publications and source records attributed to Philip S Crooke.

6 recordsLinked to original sources
PubMed2006

Estrogens, enzyme variants, and breast cancer: a risk model.

Oxidative metabolites of estrogens have been implicated in the development of breast cancer, yet relatively little is known about the metabolism of estrogens in the normal breast. We developed a mathematical model of mammary estrogen metabolism based on the conversion of 17beta-estradiol (E(2)) by the enzymes cytochrome P450 (CYP) 1A1 and CYP1B1, catechol-O-methyltransferase (COMT), and glutathione S-transferase P1 into eight metabolites [i.e., two catechol estrogens, 2-hydroxyestradiol (2-OHE(2)) and 4-hydroxyestradiol (4-OHE(2)); three methoxyestrogens, 2-methoxyestradiol, 2-hydroxy-3-methoxyestradiol, and 4-methoxyestradiol; and three glutathione (SG)-estrogen conjugates, 2-OHE(2)-1-SG, 2-OHE(2)-4-SG, and 4-OHE(2)-2-SG]. When used with experimentally determined rate constants with purified enzymes, the model provides for a kinetic analysis of the entire metabolic pathway. The predicted concentration of each metabolite during a 30-minute reaction agreed well with the experimentally derived results. The model also enables simulation for the transient quinones, E(2)-2,3-quinone (E(2)-2,3-Q) and E(2)-3,4-quinone (E(2)-3,4-Q), which are not amenable to direct quantitation. Using experimentally derived rate constants for genetic variants of CYP1A1, CYP1B1, and COMT, we used the model to simulate the kinetic effect of enzyme polymorphisms on the pathway and identified those haplotypes generating the largest amounts of catechols and quinones. Application of the model to a breast cancer case-control population identified a subset of women with an increased risk of breast cancer based on their enzyme haplotypes and consequent E(2)-3,4-Q production. This in silico model integrates both kinetic and genomic data to yield a comprehensive view of estrogen metabolomics in the breast. The model offers the opportunity to combine metabolic, genetic, and lifetime exposure data in assessing estrogens as a breast cancer risk factor.

Area Under Curve↗
PubMed2006

Pathogen transmission and clinic scheduling.

We developed a model of pathogen dissemination in the outpatient clinic that incorporates key kinetic aspects of the transmission process, as well as uncertainty regarding whether or not each incident patient is contagious. Assigning appointments late in the day to patients suspected of being infectious should decrease pathogen dissemination.

Ambulatory Care Facilities↗
PubMed2005

An agent-based and spatially explicit model of pathogen dissemination in the intensive care unit.

OBJECTIVE: To develop and disseminate a spatially explicit model of contact transmission of pathogens in the intensive care unit. DESIGN: A model simulating the spread of a pathogen transmitted by direct contact (such as methicillin-resistant Staphylococcus aureus or vancomycin-resistant Enterococcus) was constructed. The modulation of pathogen dissemination attending changes in clinically relevant pathogen- and institution-specific factors was then systematically examined. SETTING AND PATIENTS: The model was configured as a hypothetical 24-bed intensive care unit. The model can be parameterized with different pathogen transmissibilities, durations of caregiver and/or patient contamination, and caregiver allocation and flow patterns. INTERVENTIONS: Pathogen- and institution-specific factors examined included pathogen transmissibility, duration of caregiver contamination, regional cohorting of contaminated or infected patients, delayed detection and isolation of newly contaminated patients, reduction of the number of caregiver visits, and alteration of caregiver allocation among patients. MEASUREMENTS AND MAIN RESULTS: The model predicts the probability that a given fraction of the population will become contaminated or infected with the pathogen of interest under specified spatial, initial prevalence, and dynamic conditions. Per-encounter pathogen acquisition risk and the duration of caregiver pathogen carriage most strongly affect dissemination. Regional cohorting and rapid detection and isolation of contaminated patients each markedly diminish the likelihood of dissemination even absent other interventions. Strategies reducing "crossover" between caregiver domains diminish the likelihood of more widespread dissemination. CONCLUSIONS: Spatially explicit discrete element models, such as the model presented, may prove useful for analyzing the transmission of pathogens within the intensive care unit.

Caregivers↗
PubMed2002

Oscillations and noise: inherent instability of pressure support ventilation?

Pressure support ventilation (PSV) is almost universally employed in the management of actively breathing ventilated patients with acute respiratory failure. In this partial support mode of ventilation, a fixed pressure is applied to the airway opening, and flow delivery is monitored by the ventilator. Inspiration is terminated when measured inspiratory flow falls below a set fraction of the peak flow rate (flow cutoff); the ventilator then cycles to a lower pressure and expiration commences. We used linear and nonlinear mathematical models to investigate the dynamic behavior of pressure support ventilation and confirmed the predicted behavior using a test lung. Our mathematical and laboratory analyses indicate that pressure support ventilation in the setting of airflow obstruction can be accompanied by marked variations in tidal volume and end-expiratory alveolar pressure, even when subject effort is unvarying. Unstable behavior was observed in the simplest plausible linear mathematical model and is an inherent consequence of the underlying dynamics of this mode of ventilation. The mechanism underlying the observed instability is "feed forward" behavior mediated by oscillatory elevation in end-expiratory pressure. In both mathematical and mechanical models, unstable behavior occurred at impedance values and ventilator settings that are clinically realistic.

Adult↗
PubMedDate not supplied

Dynamic analysis of peritoneal dialysis associated peritonitis.

Peritoneal dialysis associated peritonitis (PDAP) has a historical incidence of approximately 0.3 to 0.5 episodes per patient per year; it represents the leading cause for hospitalization in patients on peritoneal dialysis (PD) and imposes a significant burden of morbidity. PDAP is unique in that each dialysis exchange removes a relatively large fraction of the bacteria laden free intraperitoneal fluid. The attendant removal of bacteria existing in the fluid phase (planktonic bacteria) may interact with bacterial growth to modulate the rate at which the peritoneal burden of microorganisms is reduced. We investigated the potential interactions between bacterial growth dynamics, multiphase bacterial kinetics, and mechanical clearance of microorganisms using simple mathematical analyses based upon in vitro data regarding bacterial growth kinetics in peritoneal dialysate. There are strong dynamic interactions predicted between fluid phase bacterial kinetics, dialysis prescription, and the mechanical clearance of planktonic peritoneal bacteria. There are also strong interactions between fluid phase bacterial kinetics and the kinetics of biofilm/sanctuary site formation and clearance. More frequent exchanges might significantly hasten the clearance of intraperitoneal planktonic bacteria in the absence of catheter-associated bacterial biofilm. The formation of bacteria laden biofilm raises the possibility of a "commensal state," in which ongoing mechanical clearance limits the total peritoneal bacterial burden.

Bacteria↗