TB research at UT-Houston--a review of cord factor: new approaches to drugs, vaccines and the pathogenesis of tuberculosis.

Tuberculosis remains a major threat as drug resistance continues to increase. Pulmonary tuberculosis in adults is responsible for 80% of clinical cases and nearly 100% of transmission of infection. Unfortunately, since we have no animal models of adult type pulmonary tuberculosis, the most important type of disease remains largely out of reach of modern science and many fundamental questions remain unanswered. This paper reviews research dating back to the 1950's providing compelling evidence that cord factor (trehalose 6,6 dimycolate [TDM]) is essential for understanding tuberculosis. However, the original papers by Bloch and Noll were too far ahead of their time to have immediate impact. We can now recognize that the physical and biologic properties of cord factor are unprecedented in science, especially its ability to switch between two sets of biologic activities with changes in conformation. While TDM remains on organisms, it protects them from killing within macrophages, reduces antibiotic effectiveness and inhibits the stimulation of protective immune responses. If it comes off organisms and associates with lipid, TDM becomes a driver of tissue damage and necrosis. Studies emanating from cord factor research have produced (1) a rationale for improving vaccines, (2) an approach to new drugs that overcome natural resistance to antibiotics, (3) models of caseating granulomas that reproduce multiple manifestations of human tuberculosis. (4) evidence that TDM is a key T cell antigen in destructive lesions of tuberculosis, and (5) a new understanding of the pathology and pathogenesis of postprimary tuberculosis that can guide more informative studies of long standing mysteries of tuberculosis.

Extended kalman filter for estimation of parameters in nonlinear state-space models of biochemical networks.

It is system dynamics that determines the function of cells, tissues and organisms. To develop mathematical models and estimate their parameters are an essential issue for studying dynamic behaviors of biological systems which include metabolic networks, genetic regulatory networks and signal transduction pathways, under perturbation of external stimuli. In general, biological dynamic systems are partially observed. Therefore, a natural way to model dynamic biological systems is to employ nonlinear state-space equations. Although statistical methods for parameter estimation of linear models in biological dynamic systems have been developed intensively in the recent years, the estimation of both states and parameters of nonlinear dynamic systems remains a challenging task. In this report, we apply extended Kalman Filter (EKF) to the estimation of both states and parameters of nonlinear state-space models. To evaluate the performance of the EKF for parameter estimation, we apply the EKF to a simulation dataset and two real datasets: JAK-STAT signal transduction pathway and Ras/Raf/MEK/ERK signaling transduction pathways datasets. The preliminary results show that EKF can accurately estimate the parameters and predict states in nonlinear state-space equations for modeling dynamic biochemical networks.

Patient classification system: An integrated method for measuring nursing intensity and optimizing resource allocation

A patient classification system was developed integrating a patient acuity instrument with a computerized nursing distribution method based on a linear programming model. The system was designed for real-time measurement of patient acuity (workload) and allocation of nursing personnel to optimize the utilization of resources.^ The acuity instrument was a prototype tool with eight categories of patients defined by patient severity and nursing intensity parameters. From this tool, the demand for nursing care was defined in patient points with one point equal to one hour of RN time. Validity and reliability of the instrument was determined as follows: (1) Content validity by a panel of expert nurses; (2) predictive validity through a paired t-test analysis of preshift and postshift categorization of patients; (3) initial reliability by a one month pilot of the instrument in a practice setting; and (4) interrater reliability by the Kappa statistic.^ The nursing distribution system was a linear programming model using a branch and bound technique for obtaining integer solutions. The objective function was to minimize the total number of nursing personnel used by optimally assigning the staff to meet the acuity needs of the units. A penalty weight was used as a coefficient of the objective function variables to define priorities for allocation of staff.^ The demand constraints were requirements to meet the total acuity points needed for each unit and to have a minimum number of RNs on each unit. Supply constraints were: (1) total availability of each type of staff and the value of that staff member (value was determined relative to that type of staff's ability to perform the job function of an RN (i.e., value for eight hours RN = 8 points, LVN = 6 points); (2) number of personnel available for floating between units.^ The capability of the model to assign staff quantitatively and qualitatively equal to the manual method was established by a thirty day comparison. Sensitivity testing demonstrated appropriate adjustment of the optimal solution to changes in penalty coefficients in the objective function and to acuity totals in the demand constraints.^ Further investigation of the model documented: correct adjustment of assignments in response to staff value changes; and cost minimization by an addition of a dollar coefficient to the objective function. ^