Fungal-derived cues promote ocular autoimmunity through a Dectin-2/Card9-mediated mechanism.

Uveitis (intraocular inflammation) is a leading cause of loss of vision. Although its aetiology is largely speculative, it is thought to arise from complex genetic-environmental interactions that break immune tolerance to generate eye-specific autoreactive T cells. Experimental autoimmune uveitis (EAU), induced by immunization with the ocular antigen, interphotoreceptor retinoid binding protein (IRBP), in combination with mycobacteria-containing complete Freund's adjuvant (CFA), has many clinical and histopathological features of human posterior uveitis. Studies in EAU have focused on defining pathogenic CD4+ T cell effector responses, such as those of T helper type 17 (Th17) cells, but the innate receptor pathways precipitating development of autoreactive, eye-specific T cells remain poorly defined. In this study, we found that fungal-derived antigens possess autoimmune uveitis-promoting function akin to CFA in conventional EAU. The capacity of commensal fungi such as Candida albicans or Saccharomyces cerevisae to promote IRBP-triggered EAU was mediated by Card9. Because Card9 is an essential signalling molecule of a subgroup of C-type lectin receptors (CLRs) important in host defence, we evaluated further the proximal Card9-activating CLRs. Using single receptor-deficient mice we identified Dectin-2, but not Mincle or Dectin-1, as a predominant mediator of fungal-promoted uveitis. Conversely, Dectin-2 activation by α-mannan reproduced the uveitic phenotype of EAU sufficiently, in a process mediated by the Card9-coupled signalling axis and interleukin (IL)-17 production. Taken together, this report relates the potential of the Dectin-2/Card9-coupled pathway in ocular autoimmunity. Not only does it contribute to understanding of how innate immune receptors orchestrate T cell-mediated autoimmunity, it also reveals a previously unappreciated ability of fungal-derived signals to promote autoimmunity.

Morphology and texture evolution of nanostructured CaF2 films on amorphous substrates under oblique incidence flux.

The morphology and biaxial texture of vacuum evaporated CaF(2) films on amorphous substrates as a function of vapour incident angle, substrate temperature and film thickness were investigated by scanning electron microscopy, x-ray pole figure and reflection high energy electron diffraction surface pole figure analyses. Results show that an anomalous [220] out-of-plane texture was preferred in CaF(2) films deposited on Si substrates at < 200 °C with normal vapour incidence. With an increase of the vapour incident angle, the out-of-plane orientation changed from [220] to [111] at a substrate temperature of 100 °C. In films deposited with normal vapour incidence, the out-of-plane orientation changed from [220] at 100 °C to [111] at 400 °C. In films deposited with an oblique vapour incidence at 100 °C, the texture changed from random at small thickness (5 nm) to biaxial at larger thickness (20 nm or more). Using first principles density functional theory calculation, it was shown that [220] texture formation is a consequence of energetically favourable adsorption of CaF(2) molecules onto the CaF(2)(110) facet.

Prediction of alcohol-related harm from controlled drinking strategies and alcohol consumption trajectories.

AIMS: To establish predictors of age 21 alcohol-related harm from prior drinking patterns, current levels of alcohol consumption and use of controlled drinking strategies. PARTICIPANTS: One thousand, five hundred and ninety-six students recruited from an initial sample of 3300 during their final year of high school in 1993. DESIGN: Longitudinal follow-up across five waves of data collection. SETTING: Post high school in Victoria, Australia. MEASUREMENTS: Self-administered surveys examining a range of health behaviours, including alcohol consumption patterns and related behaviour. FINDINGS: Drinking behaviours at age 21 were found to be strongly predicted by drinking trajectories established through the transition from high school. Multivariate regression analysis revealed that alcohol-related harms at age 21 were reduced where current levels of alcohol use fell within limits recommended in Australian national guidelines. After controlling for this effect it was found that the range of strategies employed by participants to control alcohol use maintained a small protective influence. Post-high-school drinking trajectories continued to demonstrate a significant effect after controlling for current behaviours. FINDINGS: revealed that over one quarter of males and females drank alcohol, but on a less-than-weekly basis. This pattern of alcohol use demonstrated considerable stability through the post-school transition and was associated with a low level of subsequent harm at age 21. CONCLUSIONS: Future research should investigate whether encouraging more Australian adolescents to drink alcohol on a less-than-weekly basis may be a practical intervention target for reducing alcohol-related harms.

Cigarette smoking in teenage girls: exploring the role of peer reputations, self-concept and coping.

Reducing the prevalence of smoking among teenagers is an important goal for health and education professionals. In the present study, self-reported cigarette smoking status was examined among 241 adolescent females from four metropolitan, independent girls' schools in suburban Melbourne, Australia. This study was particularly concerned with the role of peer reputations, coping and self-concept as influences on teenage girls' decision making with respect to smoking. Coping strategies, levels of self-concept and reputation enhancement were assessed using the High School Student Activity Questionnaire. Three separate multivariate analyses of variance (MANOVAs) revealed that six of the 11 reputation variables, three of the four self-concept variables and two of the three coping variables contributed to the main effect of smoking status. Post hoc analyses allowed some profiling of current cigarette smokers as compared to never smokers. This study offers support to reputation enhancement theory with regard to cigarette smoking in adolescent girls, and also provides continued evidence for the importance of particular aspects of self-concept and coping skills with respect to cigarette smoking. Suggestions for further research are discussed and some possible implications of the findings for school-based health education programmes are raised.

Associativity and normative credal probability.

Cox's Theorem is a widely cited motivation for probabilistic models of uncertain belief. The theorem relates the associativity of the logical connectives to that of the arithmetic operations of probability. Recent questions about the correctness of Cox's Theorem have been resolved, but there are new questions about one functional equation used by Cox in 1946. This equation is missing from his later work. Advances in knowledge since 1946 and changes in Cox's research interests explain the equation's disappearance. Other associativity-based motivations avoid functional equations altogether, and so may be more transparently applied to finite domains and discrete beliefs. A discrete counterpart of Cox's Theorem can be assembled from results that have been in the literature since 1959.

Impact of different variables on the outcome of patients with clinically confined prostate carcinoma: prediction of pathologic stage and biochemical failure using an artificial neural network.

BACKGROUND: The advent of advanced computing techniques has provided the opportunity to analyze clinical data using artificial intelligence techniques. This study was designed to determine whether a neural network could be developed using preoperative prognostic indicators to predict the pathologic stage and time of biochemical failure for patients who undergo radical prostatectomy. METHODS: The preoperative information included TNM stage, prostate size, prostate specific antigen (PSA) level, biopsy results (Gleason score and percentage of positive biopsy), as well as patient age. All 309 patients underwent radical prostatectomy at the University of Colorado Health Sciences Center. The data from all patients were used to train a multilayer perceptron artificial neural network. The failure rate was defined as a rise in the PSA level > 0.2 ng/mL. The biochemical failure rate in the data base used was 14.2%. Univariate and multivariate analyses were performed to validate the results. RESULTS: The neural network statistics for the validation set showed a sensitivity and specificity of 79% and 81%, respectively, for the prediction of pathologic stage with an overall accuracy of 80% compared with an overall accuracy of 67% using the multivariate regression analysis. The sensitivity and specificity for the prediction of failure were 67% and 85%, respectively, demonstrating a high confidence in predicting failure. The overall accuracy rates for the artificial neural network and the multivariate analysis were similar. CONCLUSIONS: Neural networks can offer a convenient vehicle for clinicians to assess the preoperative risk of disease progression for patients who are about to undergo radical prostatectomy. Continued investigation of this approach with larger data sets seems warranted.

Evaluation of artificial neural networks for the prediction of pathologic stage in prostate carcinoma.

BACKGROUND: Currently, the standard for predicting pathologic stage from information available at the time of prostate biopsy is the "Partin nomograms" that were derived using logistic regression analysis. The authors retrospectively reviewed a large series of men with clinically localized prostate carcinoma who underwent staging pelvic lymphadenectomy and radical retropubic prostatectomy. They then utilized pathologic and clinical data at the time of prostate biopsy to develop and test an artificial neural network (ANN) to predict the final pathologic stage for this group of men. They then compared the results of ANN with the previous nomograms. METHODS: Five thousand seven hundred forty-four men were treated at the authors' institution from 1985 to 1998. An ANN was developed using two randomly selected training and validation sets for predicting pathologic stage. Input variables included age, preoperative serum prostate specific antigen level, clinical TNM (tumor, lymph node, and metastasis) classification, and Gleason score from the biopsy specimen. Outcomes included organ confinement and lymph node involvement status. RESULTS: The ANN was slightly superior to the nomograms in predicting pathologic stage, such as organ confinement and lymph node involvement status. CONCLUSIONS: In predicting organ confinement and lymph node involvement status, ANN was more accurate and had a larger area under ROC than the nomograms based on the logistic regression method. Artificial neural network models can be developed and used to better predict final pathologic stage when preoperative pathologic and clinical features are known.

The problem of cutoff levels in a screened population: appropriateness of informing screenees about their risk of having prostate carcinoma.

BACKGROUND: Transrectal prostate biopsy decisions often have been based on absolute cutoff values for total and free prostate-specific antigen (PSA). The authors decided that it would be more appropriate to develop risk profiles for the individual patient to allow him to decide whether to undergo a prostate biopsy. METHODS: To develop risk profiles, the authors first used multivariate logistic regression analysis to analyze 2054 males who were part of the Tyrol (Austria) PSA Screening Project. Second, artificial neural network (ANN) analyses were performed using data from 3474 males who also were part of the Tyrol PSA Screening Project and who had undergone prostate biopsy. These analyses were compared with standard cutoff levels of specificity for the detection of prostate carcinoma. RESULTS: To the authors' knowledge, this was the first time that multivariate logistic regression analysis was used to decide whether to perform prostate biopsies based on risk profiles rather than on single cutoff levels. For the detection of prostate carcinoma, at sensitivity levels of 90--95%, the ANN was 150--200% more specific than the standard cutoff points. For screened volunteers with total PSA levels below 4 ng/mL, ANN showed a lower cancer predictive ability in comparison with volunteers with total PSA levels above 4 ng/mL. However, the ANN was approximately 150--200% more specific than the standard cutoff levels in both groups. CONCLUSIONS: At high sensitivity levels, ANN increased the specificity for prostate carcinoma detection in a PSA-based screened population. The improvement in specificity between standard cutoff levels and ANN ranged between 150--200% and was not affected by the presence of benign prostatic hyperplasia or prostatitis.

Neural network and regression predictions of 5-year survival after colon carcinoma treatment.

BACKGROUND: The Commission on Cancer data from the National Cancer Data Base (NCDB) for patients with colon carcinoma was used to develop several artificial neural network and regression-based models. These models were designed to predict the likelihood of 5-year survival after primary treatment for colon carcinoma. METHODS: Two modeling methods were used in the study. Artificial neural networks were used to select the more important variables from the NCDB database and model 5-year survival. A standard parametric logistic regression also was used to model survival and the two methods compared on a prospective set of patients not used in model development. RESULTS: The neural network yielded a receiver operating characteristic (ROC) area of 87.6%. At a sensitivity to mortality of 95% the specificity was 41%. The logistic regression yielded a ROC area of 82% and at a sensitivity to mortality of 95% gave a specificity of 27%. CONCLUSIONS: The neural network found a strong pattern in the database predictive of 5-year survival status. The logistic regression produced somewhat less accurate, but good, results.

Crystal structure of the ectodomain of Methuselah, a Drosophila G protein-coupled receptor associated with extended lifespan.

The Drosophila mutant methuselah (mth) was identified from a screen for single gene mutations that extended average lifespan. Mth mutants have a 35% increase in average lifespan and increased resistance to several forms of stress, including heat, starvation, and oxidative damage. The protein affected by this mutation is related to G protein-coupled receptors of the secretin receptor family. Mth, like secretin receptor family members, has a large N-terminal ectodomain, which may constitute the ligand binding site. Here we report the 2.3-A resolution crystal structure of the Mth extracellular region, revealing a folding topology in which three primarily beta-structure-containing domains meet to form a shallow interdomain groove containing a solvent-exposed tryptophan that may represent a ligand binding site. The Mth structure is analyzed in relation to predicted Mth homologs and potential ligand binding features.

Introduction to artificial neural networks for physicians: taking the lid off the black box.

BACKGROUND: Over the past 5 years, a steady stream of publications has discussed the use of artificial neural networks (ANNs) for urologic and other medical applications. The pace of this research has increased recently, and deployed products based on this technology are now appearing. Before these tools can be widely accepted by clinicians and researchers, a deeper level of understanding of ANNs is necessary. This article attempts to lay some of the groundwork needed to facilitate this familiarity. METHODS: A short discussion of neural network history is included for background. This is followed by an in-depth discussion of how and why ANNs work. This discussion includes the relationship between ANNs and statistical regression. An investigation of issues associated with neural networks follows, applicable to both general and urologic-specific applications. RESULTS: Neural networks are computer models that have been studied extensively for over 50 years, with prostate cancer applications since 1994. From a biological viewpoint, ANNs are artificial analogues of data structures that exist in nervous systems. From a numeric viewpoint, ANNs are matrices of numbers whose values comprise knowledge that is distilled from historic databases. Many types of neural networks are analogous to well-known statistical methods. CONCLUSIONS: ANNs are complex numeric constructs, but no more complex than similar statistical methods. However, several issues associated with neural network derivation demand that developers apply rigorous engineering practices in their studies.

Mutational analysis of the transferrin receptor reveals overlapping HFE and transferrin binding sites.

The transferrin receptor (TfR) binds two proteins critical for iron metabolism: transferrin (Tf) and HFE, the protein mutated in hereditary hemochromatosis. Previous results demonstrated that Tf and HFE compete for binding to TfR, suggesting that Tf and HFE bind to the same or an overlapping site on TfR. TfR is a homodimer that binds one Tf per polypeptide chain (2:2, TfR/Tf stoichiometry), whereas both 2:1 and 2:2 TfR/HFE stoichiometries have been observed. In order to more fully characterize the interaction between HFE and TfR, we determined the binding stoichiometry using equilibrium gel-filtration and analytical ultracentrifugation. Both techniques indicate that a 2:2 TfR/HFE complex can form at submicromolar concentrations in solution, consistent with the hypothesis that HFE competes for Tf binding to TfR by blocking the Tf binding site rather than by exerting an allosteric effect. To determine whether the Tf and HFE binding sites on TfR overlap, residues at the HFE binding site on TfR were identified from the 2.8 A resolution HFE-TfR co-crystal structure, then mutated and tested for their effects on HFE and Tf binding. The binding affinities of soluble TfR mutants for HFE and Tf were determined using a surface plasmon resonance assay. Substitutions of five TfR residues at the HFE binding site (L619A, R629A, Y643A, G647A and F650Q) resulted in significant reductions in Tf binding affinity. The findings that both HFE and Tf form 2:2 complexes with TfR and that mutations at the HFE binding site affect Tf binding support a model in which HFE and Tf compete for overlapping binding sites on TfR.

Neural network analysis of the prediction of cancer recurrence following debulking laparotomy and chemotherapy in stages III and IV ovarian cancer.

An artificial neural network (ANN) has been developed to predict the presence or absence of cancer following debulking laparotomy and chemotherapy in patients with stages III and IV ovarian cancer. The presence or absence of a residual gross tumor or microscopic disease was determined by a second-look laparotomy. The ANN was trained and tested using detailed operative findings and related surgical procedures associated with the debulking surgery. The ANN predictive results were compared with linear and logistic regression. The ANN significantly outperformed both logistic and linear regression analyses, but additional cases are needed to validate the network.

Conceptual and methodological challenges in discourse assessment with TBI speakers: towards an understanding.

The primary objective of this paper is to review theoretical and methodological literature pertaining to the clinical evaluation of discourse abilities in speakers who have sustained traumatic brain injury (TBI). A brief history of the study of discourse impairment in this population is followed by consideration of the following issues: (1) sampling (genres selected, the physical setting in which sampling takes place, the relationship between speakers, elicitation techniques, presence of recording devices, the number of samples required, and transcription); (2) measurement; (3) the relationship between sampling and measurement; (4) other approaches to discourse assessment (self and close other report); (5) consideration of the criterion of 'normal' which clinicians should employ; (6) the relationship between discourse impairment and measures of executive function; and (7) the relationship between discourse impairment and seventy of injury. Recommendations arising from a critical review of these domains are made for both clinical practice and research.

The prediction of coronary atherosclerosis employing artificial neural networks.

BACKGROUND: Atherosclerosis is a complex histopathologic process that is analogous to chronic inflammatory conditions. Several factors have been shown to correlate with the extent of atherosclerosis. Whereas hypertension, obesity, hyperlipidemia, diabetes, smoking, and family history are all well documented, recent literature points to additional associated factors. Thus, antibodies to oxidized low-density lipoprotein (oxLDL), cytomegalovirus (CMV), Chlamydia pneumonia, Helicobacter pylori, as well as homocysteine and C-reactive protein (CRP) levels have all been implicated as independent markers of accelerated atherosclerosis. HYPOTHESIS: In the current study we attempted to formulate a system by which to predict the extent of coronary atherosclerosis as assessed by angiographic vessel occlusion. METHODS: The 81 patients were categorized as having single-, double-, triple-, or no vessel involvement. The clinical data concerning the "classic" risk factors were obtained from clinical records, and sera were drawn from the patients for determination of the various parameters that are thought to be associated with atherosclerosis. RESULTS: Using four artificial neural networks, we have found the most effective parameters predictive of coronary vessel involvement were (in decreasing order of importance) antibodies to oxLDL, to cardiolipin, to CMV, to Chlamydia pneumonia, and to beta 2-glycoprotein I (beta 2GPI). Although important in the prediction of vessel occlusion, hyperlipidemia, hypertension, CRP levels, and diabetes were less accurate. CONCLUSION: The results of the current study, if reproduced in a larger population, may establish an integrated system based on the creation of artificial neural networks by which to predict the extent of atherosclerosis in a given subject fairly and noninvasively.

The use of artificial intelligence technology to predict lymph node spread in men with clinically localized prostate carcinoma.

BACKGROUND: The current study assesses artificial intelligence methods to identify prostate carcinoma patients at low risk for lymph node spread. If patients can be assigned accurately to a low risk group, unnecessary lymph node dissections can be avoided, thereby reducing morbidity and costs. METHODS: A rule-derivation technology for simple decision-tree analysis was trained and validated using patient data from a large database (4,133 patients) to derive low risk cutoff values for Gleason sum and prostate specific antigen (PSA) level. An empiric analysis was used to derive a low risk cutoff value for clinical TNM stage. These cutoff values then were applied to 2 additional, smaller databases (227 and 330 patients, respectively) from separate institutions. RESULTS: The decision-tree protocol derived cutoff values of < or = 6 for Gleason sum and < or = 10.6 ng/mL for PSA. The empiric analysis yielded a clinical TNM stage low risk cutoff value of < or = T2a. When these cutoff values were applied to the larger database, 44% of patients were classified as being at low risk for lymph node metastases (0.8% false-negative rate). When the same cutoff values were applied to the smaller databases, between 11 and 43% of patients were classified as low risk with a false-negative rate of between 0.0 and 0.7%. CONCLUSIONS: The results of the current study indicate that a population of prostate carcinoma patients at low risk for lymph node metastases can be identified accurately using a simple decision algorithm that considers preoperative PSA, Gleason sum, and clinical TNM stage. The risk of lymph node metastases in these patients is < or = 1%; therefore, pelvic lymph node dissection may be avoided safely. The implications of these findings in surgical and nonsurgical treatment are significant.

A neural network predicts progression for men with gleason score 3+4 versus 4+3 tumors after radical prostatectomy.

OBJECTIVES: To determine the significance of Gleason scores 3+4 (GS3+4) versus 4+3 (GS4+3) with respect to biochemical recurrence in a retrospective review of a series of men with clinically localized prostate cancer who underwent radical retropubic prostatectomy (RRP) and to develop and test an artificial neural network (ANN) to predict the biochemical recurrence after surgery for this group of men using the pathologic and clinical data. METHODS: From 1982 to 1998, 600 men had pathologic Gleason score 7 disease without lymph node or seminal vesicle involvement. We analyzed the freedom from biochemical (prostate-specific antigen) progression after RRP on 564 of these men on the basis of their GS3+4 versus GS4+3 (Gleason 7) status. The Cox proportional hazards model was used to determine the importance of Gleason 7 status as an independent predictor of progression. In addition, an ANN was developed using randomly selected training and validation sets for predicting biochemical recurrence at 3 or 5 years. Different input variable subsets, with or without Gleason 7 status, were compared for the ability of the ANN to maximize the prediction of progression. Standard logistic regression was used concurrently on the same random patient population sets to calculate progression risk. RESULTS: A significant recurrence-free survival advantage was found in men who underwent RRP for GS3+4 compared with those with GS4+3 disease (P <0.0001). The ANN, logistic regression, and proportion hazard models demonstrated the importance of Gleason 7 status in predicting patient outcome. The ANN was better than logistic regression in predicting patient outcome, in terms of prostate-specific antigen progression, at 3 and 5 years. CONCLUSIONS: A simple modification of the Gleason scoring system for men with Gleason 7 disease revealed a difference in the patient outcome after RRP. ANN models can be developed and used to better predict patient outcome when pathologic and clinical features are known.

Evaluation of prostate cancer patients receiving multiple staging tests, including ProstaScint scintiscans.

BACKGROUND: Multiple serum tests were performed on archival samples from patients who participated in trials to assess the ProstaScint scan staging ability. Traditional statistical analysis as well as artificial neural network (ANN) analysis were employed to evaluate individual patients and the group as a whole. The results were evaluated so that each factor was tested for prognostic value. METHODS: Data obtained from serum tests, bone scans, and ProstaScint scans were evaluated by traditional statistical methods and ANN to determine the individual value in clinical staging of prostate cancer. RESULTS: Two hundred seventy-five patients (180 postprostatectomy, 95 intact prostate) with prostate cancer (14 with distant metastases) were available for analysis. Data available included: clinical state (remission or progression), most recent clinical TNM stage, bone scan, and ProstaScint scan. Serum was tested for prostate-specific membrane antigen(PSMA), prostate-specific antigen(PSA), free PSA (fPSA), and complexed PSA (cPSA). Additional calculations included percent free PSA, and percent complexed PSA. Spearman individual statistical assessment for traditional group evaluation revealed no significant factors for T-stage. The free PSA and complex PSA had a significant association with node (N)-status. The distant metastases (M) stage correlated well with the bone scan and clinical stage. ANN analysis revealed no significant T-stage factors. N-stage factors showed a 95% sensitivity and 49% specificity. These factors included the presence or absence of a prostate, PSA serum levels, bone scan, and ProstaScint scans as major associated indicators. ANN analysis of the important variables for M-stage included ProstaScint scan score, and PSA levels (total, percent complexed, percent free, and fPSA). These factors were associated with a 95% sensitivity and 15% specificity level. CONCLUSIONS: Two hundred seventy-five patients receiving treatment for prostate cancer were evaluated by ANN and traditional statistical analysis for factors related to stage of disease. ANN revealed that PSA levels, determined by a variety of ways, ProstaScint scan, and bone scan, were significant variables that had prognostic value in determining the likelihood of nodal disease, or distant disease in prostate cancer patients.