Mining tissue microarray data to uncover combinations of biomarker expression patterns that improve intermediate staging and grading of clear cell renal cell cancer

PURPOSE: Tumor stage and nuclear grade are the most important prognostic parameters of clear cell renal cell carcinoma (ccRCC). The progression risk of ccRCC remains difficult to predict particularly for tumors with organ-confined stage and intermediate differentiation grade. Elucidating molecular pathways deregulated in ccRCC may point to novel prognostic parameters that facilitate planning of therapeutic approaches. EXPERIMENTAL DESIGN: Using tissue microarrays, expression patterns of 15 different proteins were evaluated in over 800 ccRCC patients to analyze pathways reported to be physiologically controlled by the tumor suppressors von Hippel-Lindau protein and phosphatase and tensin homologue (PTEN). Tumor staging and grading were improved by performing variable selection using Cox regression and a recursive bootstrap elimination scheme. RESULTS: Patients with pT2 and pT3 tumors that were p27 and CAIX positive had a better outcome than those with all remaining marker combinations. A prolonged survival among patients with intermediate grade (grade 2) correlated with both nuclear p27 and cytoplasmic PTEN expression, as well as with inactive, nonphosphorylated ribosomal protein S6. By applying graphical log-linear modeling for over 700 ccRCC for which the molecular parameters were available, only a weak conditional dependence existed between the expression of p27, PTEN, CAIX, and p-S6, suggesting that the dysregulation of several independent pathways are crucial for tumor progression. CONCLUSIONS: The use of recursive bootstrap elimination, as well as graphical log-linear modeling for comprehensive tissue microarray (TMA) data analysis allows the unraveling of complex molecular contexts and may improve predictive evaluations for patients with advanced renal cancer.

Periodontitis: a future risk of acute coronary syndrome? A follow-up study over 3 years

BACKGROUND: Periodontitis has been associated with cardiovascular disease. We assess if the recurrence of acute coronary syndrome (ACS) could be predicted by preceding medical and periodontal conditions. METHODS: A total of 165 consecutive subjects with ACS and 159 medically healthy, matched control subjects were examined and followed for 3 years. Periodontitis was defined by alveolar bone loss. Subgingival microbial samples were studied by the checkerboard DNA-DNA hybridization method. RESULTS: The recurrence of ACS was found in 66 of 165 (40.0%) subjects, and a first ACS event was found in seven of 159 (4.4%) subjects among baseline control subjects. Subjects who later had a second ACS event were older (P <0.001). Significantly higher serum levels of high-density lipoprotein (P <0.05), creatinine (P <0.01), and white blood cell (WBC) counts (P <0.001) were found in subjects with future ACS. Periodontitis was associated with a first event of ACS (crude odds ratio [OR]: 10.3:1; 95% confidence interval [CI]: 6.1 to 17.4; P <0.001) and the recurrence of ACS (crude OR: 3.6:1; 95% CI: 2.0 to 6.6; P <0.001). General linear modeling multivariate analysis, controlling for age and the prediction of a future ACS event, identified that WBC counts (F = 20.6; P <0.001), periodontitis (F = 17.6; P <0.001), and serum creatinine counts (F = 4.5; P <0.05) were explanatory of a future ACS event. CONCLUSIONS: The results of this study indicate that recurrent ACS events are predicted by serum WBC counts, serum creatinine levels, and a diagnosis of periodontitis. Significantly higher counts of putative pathogens are found in subjects with ACS, but these counts do not predict future ACS events.

Longitudinal platelet reactivity to acute psychological stress among older men and women

Platelet reactivity to acute stress is associated with increased cardiovascular disease risk; however, little research exists to provide systematic methodological foundations needed to generate strong longitudinal research designs. Study objectives were: 1) to evaluate whether markers of platelet function increase in response to an acute psychological stress test among older adults, 2) to establish whether reactivity remains robust upon repeated administration (i.e. three occasions approximately 1 year apart), and 3) to evaluate whether two different acute speech stress tasks elicit similar platelet responses. The 149 subjects (mean age 71 years) gave a brief impromptu speech on one of two randomly assigned topics involving interpersonal conflict. Blood samples drawn at baseline and post-speech were assayed using flow cytometry for platelet responses on three outcomes (% aggregates, % P-selectin expression, and % fibrinogen receptor expression). Three-level hierarchical linear modeling analyses revealed significant stress-induced increases in platelet activation on all outcomes (p < 0.001). No significant habituation on any measure was found. Additional reactivity differences were associated with male gender, history of myocardial infarction, and use of aspirin, statins, and antidepressants. The results demonstrate that laboratory acute stress tests continued to produce robust platelet reactivity on three activation markers among older adults over 3 years.

Knowledge and valuation of Andean agroforestry species: the role of sex, age, and migration among members of a rural community in Bolivia

Background Agroforestry is a sustainable land use method with a long tradition in the Bolivian Andes. A better understanding of people’s knowledge and valuation of woody species can help to adjust actor-oriented agroforestry systems. In this case study, carried out in a peasant community of the Bolivian Andes, we aimed at calculating the cultural importance of selected agroforestry species, and at analysing the intracultural variation in the cultural importance and knowledge of plants according to peasants’ sex, age, and migration. Methods Data collection was based on semi-structured interviews and freelisting exercises. Two ethnobotanical indices (Composite Salience, Cultural Importance) were used for calculating the cultural importance of plants. Intracultural variation in the cultural importance and knowledge of plants was detected by using linear and generalised linear (mixed) models. Results and discussion The culturally most important woody species were mainly trees and exotic species (e.g. Schinus molle, Prosopis laevigata, Eucalyptus globulus). We found that knowledge and valuation of plants increased with age but that they were lower for migrants; sex, by contrast, played a minor role. The age effects possibly result from decreasing ecological apparency of valuable native species, and their substitution by exotic marketable trees, loss of traditional plant uses or the use of other materials (e.g. plastic) instead of wood. Decreasing dedication to traditional farming may have led to successive abandonment of traditional tool uses, and the overall transformation of woody plant use is possibly related to diminishing medicinal knowledge. Conclusions Age and migration affect how people value woody species and what they know about their uses. For this reason, we recommend paying particular attention to the potential of native species, which could open promising perspectives especially for the young migrating peasant generation and draw their interest in agroforestry. These native species should be ecologically sound and selected on their potential to provide subsistence and promising commercial uses. In addition to offering socio-economic and environmental services, agroforestry initiatives using native trees and shrubs can play a crucial role in recovering elements of the lost ancient landscape that still forms part of local people’s collective identity.

Factors that trigger emergency physicians to contact a poison centre: findings from a Swiss study.

OBJECTIVE Poison centres offer rapid and comprehensive support for emergency physicians managing poisoned patients. This study investigates institutional, case-specific and poisoning-specific factors which influence the decision of emergency physicians to contact a poison centre. METHODS Retrospective, consecutive review of all poisoning-related admissions to the emergency departments (EDs) of a primary care hospital and a university hospital-based tertiary referral centre during 2007. Corresponding poison centre consultations were extracted from the poison centre database. Data were matched and analysed by logistic regression and generalised linear mixed models. RESULTS 545 poisonings were treated in the participating EDs (350 (64.2%) in the tertiary care centre, 195 (35.8%) in the primary care hospital). The poison centre was consulted in 62 (11.4%) cases (38 (61.3%) by the tertiary care centre and 24 (38.7%) by the primary care hospital). Factors significantly associated with poison centre consultation included gender (female vs male) (OR 2.99; 95% CI 1.69 to 5.29; p<0.001), number of ingested substances (>1 vs 1) (OR 2.84; 95% CI 1.65 to 4.9; p<0.001) and situation (accidental vs intentional) (OR 2.76; 95% CI 1.05 to 7.25; p=0.039). In contrast, age, medical history and hospital size did not influence poison centre consultation. Poison centre consultation was significantly higher during the week, and significantly less during night shifts. The poison centre was consulted significantly more when patients were admitted to intensive care units (OR 5.81; 95% CI 3.25 to 10.37; p<0.001). Asymptomatic and severe versus mild cases were associated with more frequent consultation (OR 4.48; 95% CI 1.78 to 11.26; p=0.001 and OR 2.76; 95% CI 1.42 to 5.38; p=0.003). CONCLUSIONS We found low rates of poison centre consultation by emergency physicians. It appears that intensive care unit admission and other factors reflecting either complexity or uncertainty of the clinical situation are the strongest predictors for poison centre consultation. Hospital size did not influence referral behaviour.

The enduring effects of psychodynamic treatments vis-à-vis alternative treatments: A multilevel longitudinal meta-analysis

Although evidence suggests that the benefits of psychodynamic treatments are sustained over time, presently it is unclear whether these sustained benefits are superior to non-psychodynamic treatments. Additionally, the extant literature comparing the sustained benefits of psychodynamic treatments compared to alternative treatments is limited with methodological shortcomings. The purpose of the current study was to conduct a rigorous test of the growth of the benefits of psychodynamic treatments relative to alternative treatments across distinct domains of change (i.e., all outcome measures, targeted outcome measures, non-targeted outcome measures, and personality outcome measures). To do so, the study employed strict inclusion criteria to identify randomized clinical trials that directly compared at least one bona fide psychodynamic treatment and one bona fide non-psychodynamic treatment. Hierarchical linear modeling (Raudenbush, Bryk, Cheong, Congdon, & du Toit, 2011) was used to longitudinally model the impact of psychodynamic treatments compared to non-psychodynamic treatments at post-treatment and to compare the growth (i.e., slope) of effects beyond treatment completion. Findings from the present meta-analysis indicated that psychodynamic treatments and non-psychodynamic treatments were equally efficacious at post-treatment and at follow-up for combined outcomes (k=20), targeted outcomes (k=19), non-targeted outcomes (k=17), and personality outcomes (k=6). Clinical implications, directions for future research, and limitations are discussed.

Days spent in acute care hospitals at the end of life of cancer patients in four Swiss cantons: a retrospective database study (SAKK 89/09).

Number of days spent in acute hospitals (DAH) at the end of life is regarded as an important care quality indicator for cancer patients. We analysed DAH during 90 days prior to death in patients from four Swiss cantons. Claims data from an insurance provider with about 20% market share and patient record review identified 2086 patients as dying of cancer. We calculated total DAH per patient. Multivariable generalised linear modelling served to evaluate potential explanatory variables. Mean DAH was 26 days. In the multivariable model, using complementary and alternative medicine (DAH = 33.9; +8.8 days compared to non-users) and canton of residence (for patient receiving anti-cancer therapy, Zürich DAH = 22.8 versus Basel DAH = 31.4; for other patients, Valais DAH = 22.7 versus Ticino DAH = 33.7) had the strongest influence. Age at death and days spent in other institutions were additional significant predictors. DAH during the last 90 days of life of cancer patients from four Swiss cantons is high compared to most other countries. Several factors influence DAH. Resulting differences are likely to have financial impact, as DAH is a major cost driver for end-of-life care. Whether they are supply- or demand-driven and whether patients would prefer fewer days in hospital remains to be established.

Feasibility of resting-state microstates neurofeedback

Despite major progress, currently available treatment options for patients suffering from schizophrenia remain suboptimal. Antipsychotic medication is one such option, and is helpful in acute phases of the disease. However, antipsychotics cause significant side-effects that often require additional medication, and can even trigger the discontinuation of treatment. Taken together, along with the fact that 20-30% of patients are medication-resistant, it is clear that new medical care options should be developed for patients with schizophrenia. Besides medication, an emerging option to treat psychiatric symptoms is through the use of neurofeedback. This technique has proven efficacy for other disorders and, more importantly, has also proven to be feasible in patients with schizophrenia. One of the major advantages of this approach is that it allows for the influence of brain states that otherwise would be inaccessible; i.e. the physiological markers underlying psychotic symptoms. EEG resting-state microstates are a very interesting electrophysiological marker of schizophrenia symptoms. Precisely, a specific class of resting-state microstates, namely microstate class D, has consistently been found to show a temporal shortening in patients with schizophrenia compared to controls, and this shortening is correlated with the presence positive psychotic symptoms. Under the scope of biological psychiatry, appropriate treatment of psychotic symptoms can be expected to modify the underlying physiological markers accompanying behavioral manifestations of a disease. We reason that if abnormal temporal parameters of resting-state microstates seem to be related to positive symptoms in schizophrenia, regulating this EEG feature might be helpful as a treatment for patients. The goal of this thesis was to prove the feasibility of microstate class D contribution self-regulation via neurofeedback. Given that no other study has attempted to regulate microstates via neurofeedback, we first tested its feasibility in a population of healthy subjects. In the first paper we describe the methodological characteristics of the neurofeedback protocol and its implementation. Neurofeedback performance was assessed by means of linear mixed effects modeling, which provided a complete profile of the neurofeedback’s training response within and between-subjects. The protocol included 20 training sessions, and each session contained three conditions: baseline (resting-state) and two active conditions: training (auditory feedback upon self-regulation performance) and transfer (self-regulation with no feedback). With linear modeling we obtained performance indices for each of them as follows: baseline carryover (baseline increments time-dependent) and learning and aptitude for each of the active conditions. Learning refers to the increase/decrease of the microstate class D contribution, time-dependent during each active condition, and aptitude refers to the constant difference of the microstate class D contribution between each active condition and baseline independent of time. The indices provided are discussed in terms of tailoring neurofeedback treatment to individual profiles so that it can be applied in future studies or clinical practice. In our sample of participants, neurofeedback proved feasible, as all participants at least showed positive results in one of the aforementioned learning indices. Furthermore, between-subjects we observed that the contribution of microstate class D across-sessions increased by 0.42% during baseline, 1.93% during training trials, and 1.83% during transfer. This range is expected to be effective in treating psychotic symptoms in patients. In the second paper presented in this thesis, we explored the possible predictors of neurofeedback success among psychological variables measured with questionnaires. An interesting finding was the negative correlation between “motivational incongruence” and some of the neurofeedback performance indices. Even though this finding requires replication, we discuss it in terms of the interfering effects of incompatible psychological processes with neurofeedback training requirements. In the third paper, we present a meta-analysis on all available studies that have related resting-state microstate abnormalities and schizophrenia. We obtained medium effect sizes for two microstate classes, namely C and D. Combining the meta-analysis results with the fact that microstate class D abnormalities are correlated with the presence of positive symptoms in patients with schizophrenia, these results add further support for the training of this precise microstate. Overall, the results obtained in this study encourage the implementation of this protocol in a population of patients with schizophrenia. However, future studies will have to show whether patients will be able to successfully self-regulate the contribution of microstate class D and, if so, whether this regulation will have an impact on symptomatology.

Modeling of red blood cell life-spans in hematologically normal populations

Despite the impact of red blood cell (RBC) Life-spans in some disease areas such as diabetes or anemia of chronic kidney disease, there is no consensus on how to quantitatively best describe the process. Several models have been proposed to explain the elimination process of RBCs: random destruction process, homogeneous life-span model, or a series of 4-transit compartment model. The aim of this work was to explore the different models that have been proposed in literature, and modifications to those. The impact of choosing the right model on future outcomes prediction--in the above mentioned areas--was also investigated. Both data from indirect (clinical data) and direct life-span measurement (biotin-labeled data) methods were analyzed using non-linear mixed effects models. Analysis showed that: (1) predictions from non-steady state data will depend on the RBC model chosen; (2) the transit compartment model, which considers variation in life-span in the RBC population, better describes RBC survival data than the random destruction or homogenous life-span models; and (3) the additional incorporation of random destruction patterns, although improving the description of the RBC survival data, does not appear to provide a marked improvement when describing clinical data.

Quantitative 1H-magnetic resonance spectroscopy of human brain: Influence of composition and parameterization of the basis set in linear combination model-fitting

Localized short-echo-time (1)H-MR spectra of human brain contain contributions of many low-molecular-weight metabolites and baseline contributions of macromolecules. Two approaches to model such spectra are compared and the data acquisition sequence, optimized for reproducibility, is presented. Modeling relies on prior knowledge constraints and linear combination of metabolite spectra. Investigated was what can be gained by basis parameterization, i.e., description of basis spectra as sums of parametric lineshapes. Effects of basis composition and addition of experimentally measured macromolecular baselines were investigated also. Both fitting methods yielded quantitatively similar values, model deviations, error estimates, and reproducibility in the evaluation of 64 spectra of human gray and white matter from 40 subjects. Major advantages of parameterized basis functions are the possibilities to evaluate fitting parameters separately, to treat subgroup spectra as independent moieties, and to incorporate deviations from straightforward metabolite models. It was found that most of the 22 basis metabolites used may provide meaningful data when comparing patient cohorts. In individual spectra, sums of closely related metabolites are often more meaningful. Inclusion of a macromolecular basis component leads to relatively small, but significantly different tissue content for most metabolites. It provides a means to quantitate baseline contributions that may contain crucial clinical information.

Modeling absolute differences in life expectancy with a censored skew-normal regression approach.

Parameter estimates from commonly used multivariable parametric survival regression models do not directly quantify differences in years of life expectancy. Gaussian linear regression models give results in terms of absolute mean differences, but are not appropriate in modeling life expectancy, because in many situations time to death has a negative skewed distribution. A regression approach using a skew-normal distribution would be an alternative to parametric survival models in the modeling of life expectancy, because parameter estimates can be interpreted in terms of survival time differences while allowing for skewness of the distribution. In this paper we show how to use the skew-normal regression so that censored and left-truncated observations are accounted for. With this we model differences in life expectancy using data from the Swiss National Cohort Study and from official life expectancy estimates and compare the results with those derived from commonly used survival regression models. We conclude that a censored skew-normal survival regression approach for left-truncated observations can be used to model differences in life expectancy across covariates of interest.