Eliciting cytotoxic T-lymphocyte responses from synthetic vectors containing one or two epitopes in a C57BL/6 mouse model using peptide-containing biodegradable microspheres and adjuvants.

We describe a vaccine delivery mechanism consisting of a synthetic, non-living vector of large d,l poly(lactic-co-glycolic) acid (PLGA) microspheres that carry specific cytotoxic T lymphocyte (CTL) epitopes. We demonstrate in mice that it can be used to elicit substantial interferon gamma ELISPOT responses to more than one specific epitope in the same individual. Our data suggest that a superior adjuvant configuration for the formulation is to place a TLR-9 agonist CpG inside the microsphere and a TLR-4 agonist MPLA in the injectate solution. This finding contrasts with the observations of others. Our approach provides a means to elicit immune responses efficiently to select epitopes, which may be important for an effective vaccine against HIV.

Toward normative expert systems: Part II. Probability-based representations for efficient knowledge acquisition and inference.

We address practical issues concerning the construction and use of decision-theoretic or normative expert systems for diagnosis. In particular, we examine Pathfinder, a normative expert system that assists surgical pathologists with the diagnosis of lymph-node diseases, and discuss the representation of dependencies among pieces of evidence within this system. We describe the belief network, a graphical representation of probabilistic dependencies. We see how Pathfinder uses a belief network to construct differential diagnosis efficiently, even when there are dependencies among pieces of evidence. In addition, we introduce an extension of the belief-network representation called a similarity network, a tool for constructing large and complex belief networks. The representation allows a user to construct independent belief networks for subsets of a given domain. A valid belief network for the entire domain can then be constructed from the individual belief networks. We also introduce the partition, a graphical representation that facilitates the assessment of probabilities associated with a belief network. We show that the similarity-network and partition representations made practical the construction of Pathfinder.

Toward normative expert systems: Part I. The Pathfinder project.

Pathfinder is an expert system that assists surgical pathologists with the diagnosis of lymph-node diseases. The program is one of a growing number of normative expert systems that use probability and decision theory to acquire, represent, manipulate, and explain uncertain medical knowledge. In this article, we describe Pathfinder and our research in uncertain-reasoning paradigms that was stimulated by the development of the program. We discuss limitations with early decision-theoretic methods for reasoning under uncertainty and our initial attempts to use non-decision-theoretic methods. Then, we describe experimental and theoretical results that directed us to return to reasoning methods based in probability and decision theory.

An evaluation of the diagnostic accuracy of Pathfinder.

We present an evaluation of the diagnostic accuracy of Pathfinder, an expert system that assists pathologists with the diagnosis of lymph node diseases. We evaluate two versions of the system using both informal and decision-theoretic metrics of performance. In one version of Pathfinder, we assume incorrectly that all observations are conditionally independent. In the other version, we use a belief network to represent accurately the probabilistic dependencies among the observations. In both versions, we make the assumption--reasonable for this domain--that diseases are mutually exclusive and exhaustive. The results of the study show that (1) it is cost effective to represent probabilistic dependencies among observations in the lymph node domain, and (2) the diagnostic accuracy of the more complex version of Pathfinder is at least as good as that of the Pathfinder expert. In addition, the study illustrates how informal and decision-theoretic metrics for performance complement one another.

Probabilistic diagnosis using a reformulation of the INTERNIST-1/QMR knowledge base. I. The probabilistic model and inference algorithms.

In Part I of this two-part series, we report the design of a probabilistic reformulation of the Quick Medical Reference (QMR) diagnostic decision-support tool. We describe a two-level multiply connected belief-network representation of the QMR knowledge base of internal medicine. In the belief-network representation of the QMR knowledge base, we use probabilities derived from the QMR disease profiles, from QMR imports of findings, and from National Center for Health Statistics hospital-discharge statistics. We use a stochastic simulation algorithm for inference on the belief network. This algorithm computes estimates of the posterior marginal probabilities of diseases given a set of findings. In Part II of the series, we compare the performance of QMR to that of our probabilistic system on cases abstracted from continuing medical education materials from Scientific American Medicine. In addition, we analyze empirically several components of the probabilistic model and simulation algorithm.

Probabilistic diagnosis using a reformulation of the INTERNIST-1/QMR knowledge base. II. Evaluation of diagnostic performance.

We have developed a probabilistic reformulation of the Quick Medical Reference (QMR) system. In Part I of this two-part series, we described a two-level, multiply connected belief-network representation of the QMR knowledge base and a simulation algorithm to perform probabilistic inference on the reformulated knowledge base. In Part II of this series, we report on an evaluation of the probabilistic QMR, in which we compare the performance of QMR to that of our probabilistic system on cases abstracted from continuing medical education materials from Scientific American Medicine. In addition, we analyze empirically several components of the probabilistic model and simulation algorithm.

Integrated expert systems and videodisc in surgical pathology: an overview.

We present an overview of our 6-year experience in the design of expert systems for anatomic pathology. Our practical goal is to help practicing pathologists with learning, teaching, and the task of diagnosis by providing them with dynamic expert knowledge by means of a personal computer. This project could only be undertaken by first addressing a scientific goal: to characterize the problem-solving strategies that expert pathologists use in making a diagnosis and to state them in the logical terms of computer science. Our approach has been to build systems first for experimentation and then for use. The result of our work is an integrated computer-based approach that handles expert knowledge as formal relationships and morphologic images and that uses a number of logical strategies to provide multiple perspectives on diagnostic tasks. Configured as a pathologist's workstation, this approach can be expected to enhance the performance of trained general pathologists and pathologists in training. Lymph node pathology has been used as the prototype domain for this research, but care has been taken to seek a generalized authoring and inference structure that can be applied to other areas of pathology by changing the contents but not the structure itself. Excursions into various surgical pathology specialties suggest that the ways the system is constructed and exercised is fundamentally robust. Such computer-based expert systems can be expected to generate a new standard in the practice of pathology--based on the "gold standard" of classical morphology, but including the coordinated use of new methods from immunology and molecular biology in a multidisciplinary approach to diagnosis when these techniques are relevant. The benefits from this technology can be expected to be widespread with the evolution, refinement, and diffusion of these systems.