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A refined mathematical model for interpretation of the data on fluorescein kinetics across the blood-vitreous barrier is presented. This model is better adapted to the geometry of the human eye and thus yields superior results.
OBJECTIVE: Verbal autopsy (VA) -- the interviewing of family members or caregivers about the circumstances of a death after the event -- is an established tool in areas where routine death registration is non-existent or inadequate. We assessed the performance of a probabilistic model (InterVA) for interpreting community-based VA interviews, in order to investigate patterns of cause-specific mortality in a rural Ethiopian community. We compared results with those obtained after review of the VA by local physicians, with a view to validating the model as a community-based tool. METHODS: Two-hundred and eighty-nine VA interviews were successfully completed; these included most deaths occurring in a defined community over a 1-year period. The VA interviews were interpreted by physicians and by the model, and cause-specific mortality fractions were derived for the whole community and for particular age groups using both approaches. FINDINGS: The results of the two approaches to interpretation correlated well in this example from Ethiopia. Four major cause groups accounted for over 60% of all mortality, and patterns within specific age groups were consistent with expectations for an underdeveloped high-mortality community in sub-Saharan Africa. CONCLUSION: Compared with interpretation by physicians, the InterVA model is much less labour intensive and offers 100% consistency. It is a valuable new tool for characterizing patterns of cause-specific mortality in communities without death registration and for comparing patterns of mortality in different populations.
We present a method to measure the relative importance of the descriptors present in a QSAR model developed with a computational neural network (CNN). The approach is based on a sensitivity analysis of the descriptors. We tested the method on three published data sets for which linear and CNN models were previously built. The original work reported interpretations for the linear models, and we compare the results of the new method to the importance of descriptors in the linear models as described by a PLS technique. The results indicate that the proposed method is able to rank descriptors such that important descriptors in the CNN model correspond to the important descriptors in the linear model.
Views about suicide prevention are based on underlying beliefs about the origins of problems and basic concepts about humankind. These implicit theories have an effect on prevention practices. Making these views explicit is one of the keys for the further development of suicide prevention. In this study a paradigm for analyzing suicide prevention of means of a coding frame and interpreting findings by means of theoretical models of prevention was elaborated. The analysis was based on empirical data consisting of definitions of prevention given by psychologists (N = 34) participating in the national suicide prevention project in Finland. The study demonstrates that suicide prevention can be differentiated at the operational level by means of the analysis method generated. Moreover, the findings can be interpreted according to theoretical criteria. Views expressed by the psychologists seemed to correspond largely to central features of current prevention models. Furthermore, the data can be seen to serve as an empirical validation of these models. Suicide prevention proved to be a multifactorial concept manifesting mainly process theory and interactional explanations of suicidality, and prevention practices fell into a simple typology of four categories.
OBJECTIVES: To show that Markov chain modelling can be applied to data on geriatric patients and use these models to assess the effects of covariates. METHODS: Phase-type distributions were fitted by maximum likelihood to data on times spent by the patients in hospital and in community-based care. Data on the different events that ended the patients' periods of care were used to estimate the dependence of the probabilities of these events on the phase from which the time in care ended. The age of the patients at admission to care and the year of admission were also included as covariates. RESULTS: Differential effects of these covariates were shown on the various parameters of the fitted model, and interpretations of these effects made. CONCLUSIONS: Models based on phase-type distributions were appropriate for describing times spent in care, as the ordered phases had an interpretable structure corresponding to increasing amounts of care being given.
Multiwavelength ultraviolet/visible (UV-Vis) spectra of microorganisms and cell suspensions contain quantitative information on properties such as number, size, shape, chemical composition, and internal structure of the suspended particles. These properties are essential for the identification and classification of microorganisms and cells. The complexity of microorganisms in terms of their chemical composition and internal structure make the interpretation of their spectral signature a difficult task. In this paper, a model is proposed for the quantitative interpretation of spectral patterns resulting from transmission measurements of prokaryotic microorganism suspensions. It is also demonstrated that different organisms give rise to spectral differences that may be used for their identification and classification. The proposed interpretation model is based on light scattering theory, spectral deconvolution techniques, and on the approximation of the frequency dependent optical properties of the basic constituents of living organisms. The quantitative deconvolution in terms of the interpretation model yields critical information necessary for the detection and identification of microorganisms, such as size, dry mass, dipicolinic acid concentration, nucleotide concentration, and an average representation of the internal scattering elements of the organisms. E. coli, P. agglomerans, B. subtilis spores, and vegetative cells and spores of Bacillus globigii are used as case studies. It is concluded that spectroscopy techniques coupled with effective interpretation models are applicable to a wide range of cell types found in diverse environments.
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Representation of clinical practice guidelines in a computer-interpretable format is a critical issue for guideline development, implementation, and evaluation. We studied 11 types of guideline representation models that can be used to encode guidelines in computer-interpretable formats. We have consistently found in all reviewed models that primitives for representation of actions and decisions are necessary components of a guideline representation model. Patient states and execution states are important concepts that closely relate to each other. Scheduling constraints on representation primitives can be modeled as sequences, concurrences, alternatives, and loops in a guideline's application process. Nesting of guidelines provides multiple views to a guideline with different granularities. Integration of guidelines with electronic medical records can be facilitated by the introduction of a formal model for patient data. Data collection, decision, patient state, and intervention constitute four basic types of primitives in a guideline's logic flow. Decisions clarify our understanding on a patient's clinical state, while interventions lead to the change from one patient state to another.
Atherosclerosis has often been defined as a multifactoral disease; however, a common risk factor associated with accelerated vascular disease in man or animals is an elevated plasma cholesterol level. Even though there is no one perfect animal model that completely replicates the stages of human atherosclerosis, cholesterol feeding and mechanical endothelial injury are two common features shared by most models of atherosclerosis. The models may differ with respect to degree of dietary cholesterol supplementation, length of hypercholesterolemia, dietary regimen and type, duration and degree of mechanical endothelial injury. With the advent of genetic engineering, transgenic mouse models have supplemented the classical dietary cholesterol induced disease models such as the cholesterol-fed hamster, rabbit, pig and monkey. The desire to limit the progression of atherosclerosis has spawned numerous drug intervention studies. Biochemical as well as morphologic and morphometric changes in the extent, structure and composition of atherosclerotic lesions following drug intervention have become major endpoints of in vivo drug intervention studies. Interpretations of alterations in vascular pathology following drug administration are often confounded by associated changes in plasma lipids and lipoproteins, limitation of the animal models and additional properties of compounds unrelated to their primary mode of action. Thus, the current review will summarize the pathology of atherosclerosis, describe various animal models of vascular disease and provide a critical review of the methods utilized and conclusions drawn when evaluating pharmacologic agents in animals.
Accurate prediction of peptide binding to human leukocyte antigen class I (HLA-I) molecules is critical for advancing immunological research, particularly in vaccine design and immunotherapy. However, limitations in model performance, interpretability, and dataset quality impede the widespread adoption of existing predictive tools. Here, we present a comprehensive evaluation of 17 HLA-I peptide binding prediction models, utilizing a meticulously curated dataset comprising over 290,000 peptides spanning 44 HLA-I alleles. We assessed model accuracy, robustness, and interpretability, employing explainability techniques such as SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) to elucidate underlying prediction mechanisms. Our results reveal substantial performance disparities, with self-attention-based models, including STMHCpan and BigMHC, exhibiting superior accuracy. Notably, the capsule network model CapsNet-MHC_AN demonstrated robust performance. Models trained on eluted ligand datasets outperformed those relying on binding affinity data, underscoring the critical role of high-quality training data. Ensemble and multi-algorithm approaches further improved prediction reliability. These findings highlight the need for ongoing innovation in model architecture, integration of diverse and high-quality datasets, and incorporation of structural predictors to develop more accurate, interpretable, and clinically applicable HLA-I peptide binding prediction tools.
This article described three techniques used to study phase transitions in phospholipid bilayers. The complementarity of the three techniques in characterizing the thermotropic and structural properties of phospholipid bilayers has been demonstrated by describing their use to characterize a series of mixed-chain-length PCs. It has been shown that an understanding of the energetics that govern the packing of phospholipid chains in the gel phase can be used to construct a model to interpret thermodynamic data of the PCs. This model, in turn, provided a framework for designing and interpreting the Raman spectroscopic and X-ray diffraction experiments on this series of phospholipids. The result was a complete description of the phase transitions and gel phase packing properties of the mixed-chain-length PCs. The phase diagram of Fig. 5B has been expanded to include the mixed-chain-length PC series C18C18PC through C18C0PC. Furthermore, the phase diagram and the chain inequivalence parameter have been shown to describe the behavior of any mixed-chain-length PC, irrespective of the lengths of the hydrocarbon chains or the position of the chains on the glycerol backbone. This is demonstrated by the additional mixed-chain-length PCs plotted in Fig. 5B. With minor modifications, the phase diagram also accurately describes the behavior of mixed-chain-length phosphatidylethanolamines, sphingomyelins, and unsaturated PCs. Finally, it has been demonstrated that a correlation exists between the thermodynamic and the Raman spectroscopic parameters determined for the phase transition of phospholipid bilayers. This correlation is based on the common chain energetics being measured by these two techniques.
BACKGROUND: Secondary structure prediction is a useful first step toward 3D structure prediction. A number of successful secondary structure prediction methods use neural networks, but unfortunately, neural networks are not intuitively interpretable. On the contrary, hidden Markov models are graphical interpretable models. Moreover, they have been successfully used in many bioinformatic applications. Because they offer a strong statistical background and allow model interpretation, we propose a method based on hidden Markov models. RESULTS: Our HMM is designed without prior knowledge. It is chosen within a collection of models of increasing size, using statistical and accuracy criteria. The resulting model has 36 hidden states: 15 that model alpha-helices, 12 that model coil and 9 that model beta-strands. Connections between hidden states and state emission probabilities reflect the organization of protein structures into secondary structure segments. We start by analyzing the model features and see how it offers a new vision of local structures. We then use it for secondary structure prediction. Our model appears to be very efficient on single sequences, with a Q3 score of 68.8%, more than one point above PSIPRED prediction on single sequences. A straightforward extension of the method allows the use of multiple sequence alignments, rising the Q3 score to 75.5%. CONCLUSION: The hidden Markov model presented here achieves valuable prediction results using only a limited number of parameters. It provides an interpretable framework for protein secondary structure architecture. Furthermore, it can be used as a tool for generating protein sequences with a given secondary structure content.
In this paper, we address methods of multivariate regression. We discuss the value of regression compared to matched pairs analysis, methods of coding variables, basic concepts of the Cox model and interpretation of results of the Cox model. We present methods of handling variables whose effect changes with time. We present methods to check the assumptions of the Cox regression. Finally, and perhaps most importantly, we provide suggestions for presenting the results in clear and thorough tables and graphs.
Air Force laser safety standards are developed from laser-exposure data obtained in studies using experimental animals and from biomathematical modeling procedures. Interpretation of research data and predictive modeling calculations are enhanced by knowledge of the temperature values of the tissue absorption sites. Corneal temperature values for the normal, the anesthetized, and the laser-injured Dutch belted rabbit are presented and compared with values obtained in other studies. The corneal temperatures were measured by infrared radiometry.
The purpose of this study is to show an approach to making an intelligent support system for understanding and modifying a large circulatory system model using techniques of system analysis. Structural analysis makes it possible to visualize hierarchies of Coleman's circulatory model Human. Two techniques are successively applied for structural analysis, model reduction and graph analysis by interpretative structural modeling (ISM). First, the analysis for model reduction removes input-output relations with an input-output gain less than a given threshold, and second, the ISM technique applied to the reduced model of Human provides hierarchical directed graphs. The proposed approach: 1) enables visualization of a hierarchy graph of cause and effect relations of the large circulatory model, 2) suggests control and diagnostic information to the model by tracing back a path in the hierarchy, and 3) allows the user to modify the circulatory model. The efficiency and performance of the proposed approach demonstrates technical indications of success in analyzing and justifying experimental evidences with the online help of the system.
Qualitative research methods are increasingly utilised by health researchers. Along with this the criteria for assessing the quality of qualitative research are changing from a natural science model to an interpretative social science model. This is a product of the realisation by health researchers that qualitative methods utilise a different epistemology to statistical methods. I demonstrate that a recent article in the Australian and New Zealand Journal of Public Health draws on a now outdated natural science methodology of assessing bias in focus groups. Drawing on interpretativist social science theory and recent work in the British Medical Joumal I argue for the importance of examining the social contexts through which qualitative data is produced.
A dynamic, stochastic, and mechanistic Monte Carlo model, simulating a dairy herd with focus on the feeding-health-production complex is presented. By specifying biological parameters at cow level and a management strategy at herd level, the model can simulate the technical and economic consequences of scenarios at herd level. The representation of the feeding-health-production complex is aimed to be sufficiently detailed, to include relationships likely to cause significant herd effects, and to be sufficiently simple to enable a feasible parameterization of the model and interpretation of the results from the model. Consequently, diseases are defined as four disease types: two metabolic disease types, an udder disease type, and a reproductive disease type. Risk factors for the diseases were defined as parity, yield capacity, disease recurrence, disease interrelationships, lactation stage, and season. Direct effects of the diseases were defined according to milk yield, feed intake, feed utilization, conception, culling, involuntary removal, and death. Scenarios differing in base risks of milk fever and ketosis, heat detection rate, and culling strategy were simulated for describing the model behavior. Annual milk yield per cow was decreased by increased risk of ketosis and by increased risk of milk fever, even though no direct effect of milk fever on milk yield was modeled at the cow level. The indirect effect from milk fever is a consequence of increased replacement rate (relatively lower milk yield from younger cows). By ignoring the history of milk fever in insemination and replacement decisions, a significantly reduced net income per cow was found in some herds. We concluded that important benefits from using such a herd model are the capability of accounting for herd management factors and the advantage of avoiding to double count the indirect effects from disease, such as increased risk of other diseases, poorer reproduction results, and increased risk of culling and death.
In order to better define management policies we attempted to construct risk models for rebleeding on initial conservative management and mortality after emergency surgery for failure of medical therapy in 387 patients with bleeding gastric ulcer. Several different models were constructed using logistic regression analysis with validation by the 'leaving-one-out' method. However, despite large patient numbers, modelling in this way is difficult because of inherent wide variation between patients. Suitable models for rebleeding were regarded as rather unsatisfactory, for although overall accuracy was 86%, sensitivity was only 54%. More promising was a model for mortality after emergency surgery which had an accuracy of 93% and a sensitivity of 80%. Such mortality models incorporating age, history of previous malignant disease or dyspepsia, the presence or absence of ascites and total transfusion requirements may well prove to be of value in surgical practice. This paper seeks to examine the process of modelling rebleeding and mortality and of interpreting the models produced.