Utilization of machine learning for prediction of post-traumatic stress: a re-examination of cortisol in the prediction and pathways to non-remitting PTSD.

To date, studies of biological risk factors have revealed inconsistent relationships with subsequent post-traumatic stress disorder (PTSD). The inconsistent signal may reflect the use of data analytic tools that are ill equipped for modeling the complex interactions between biological and environmental factors that underlay post-traumatic psychopathology. Further, using symptom-based diagnostic status as the group outcome overlooks the inherent heterogeneity of PTSD, potentially contributing to failures to replicate. To examine the potential yield of novel analytic tools, we reanalyzed data from a large longitudinal study of individuals identified following trauma in the general emergency room (ER) that failed to find a linear association between cortisol response to traumatic events and subsequent PTSD. First, latent growth mixture modeling empirically identified trajectories of post-traumatic symptoms, which then were used as the study outcome. Next, support vector machines with feature selection identified sets of features with stable predictive accuracy and built robust classifiers of trajectory membership (area under the receiver operator characteristic curve (AUC)=0.82 (95% confidence interval (CI)=0.80-0.85)) that combined clinical, neuroendocrine, psychophysiological and demographic information. Finally, graph induction algorithms revealed a unique path from childhood trauma via lower cortisol during ER admission, to non-remitting PTSD. Traditional general linear modeling methods then confirmed the newly revealed association, thereby delineating a specific target population for early endocrine interventions. Advanced computational approaches offer innovative ways for uncovering clinically significant, non-shared biological signals in heterogeneous samples.

Plasma levels of interleukin-1 receptor antagonist (IL1Ra) predict radiographic progression of symptomatic knee osteoarthritis.

OBJECTIVE: Pro- and anti-inflammatory mediators, such as IL-1ß and IL1Ra, are produced by joint tissues in osteoarthritis (OA), where they may contribute to pathogenesis. We examined whether inflammatory events occurring within joints are reflected in plasma of patients with symptomatic knee osteoarthritis (SKOA). DESIGN: 111 SKOA subjects with medial disease completed a 24-month prospective study of clinical and radiographic progression, with clinical assessment and specimen collection at 6-month intervals. The plasma biochemical marker IL1Ra was assessed at baseline and 18 months; other plasma biochemical markers were assessed only at 18 months, including IL-1ß, TNFα, VEGF, IL-6, IL-6Rα, IL-17A, IL-17A/F, IL-17F, CRP, sTNF-RII, and MMP-2. RESULTS: In cross-sectional studies, WOMAC (total, pain, function) and plasma IL1Ra were modestly associated with radiographic severity after adjustment for age, gender and body mass index (BMI). In addition, elevation of plasma IL1Ra predicted joint space narrowing (JSN) at 24 months. BMI did associate with progression in some but not all analyses. Causal graph analysis indicated a positive association of IL1Ra with JSN; an interaction between IL1Ra and BMI suggested either that BMI influences IL1Ra or that a hidden confounder influences both BMI and IL1Ra. Other protein biomarkers examined in this study did not associate with radiographic progression or severity. CONCLUSIONS: Plasma levels of IL1Ra were modestly associated with the severity and progression of SKOA in a causal fashion, independent of other risk factors. The findings may be useful in the search for prognostic biomarkers and development of disease-modifying OA drugs.

Trends and developments in bioinformatics in 2010: prospects and perspectives.

OBJECTIVES: To survey major developments and trends in the field of Bioinformatics in 2010 and their relationships to those of previous years, with emphasis on long-term trends, on best practices, on quality of the science of informatics, and on quality of science as a function of informatics. METHODS: A critical review of articles in the literature of Bioinformatics over the past year. RESULTS: Our main results suggest that Bioinformatics continues to be a major catalyst for progress in Biology and Translational Medicine, as a consequence of new assaying technologies, most pre-dominantly Next Generation Sequencing, which are changing the landscape of modern biological and medical research. These assays critically depend on bioinformatics and have led to quick growth of corresponding informatics methods development. Clinical-grade molecular signatures are proliferating at a rapid rate. However, a highly publicized incident at a prominent university showed that deficiencies in informatics methods can lead to catastrophic consequences for important scientific projects. Developing evidence-driven protocols and best practices is greatly needed given how serious are the implications for the quality of translational and basic science. CONCLUSIONS: Several exciting new methods have appeared over the past 18 months, that open new roads for progress in bioinformatics methods and their impact in biomedicine. At the same time, the range of open problems of great significance is extensive, ensuring the vitality of the field for many years to come.

HITON: a novel Markov Blanket algorithm for optimal variable selection.

UNLABELLED: We introduce a novel, sound, sample-efficient, and highly-scalable algorithm for variable selection for classification, regression and prediction called HITON. The algorithm works by inducing the Markov Blanket of the variable to be classified or predicted. A wide variety of biomedical tasks with different characteristics were used for an empirical evaluation. Namely, (i) bioactivity prediction for drug discovery, (ii) clinical diagnosis of arrhythmias, (iii) bibliographic text categorization, (iv) lung cancer diagnosis from gene expression array data, and (v) proteomics-based prostate cancer detection. State-of-the-art algorithms for each domain were selected for baseline comparison. RESULTS: (1) HITON reduces the number of variables in the prediction models by three orders of magnitude relative to the original variable set while improving or maintaining accuracy. (2) HITON outperforms the baseline algorithms by selecting more than two orders-of-magnitude smaller variable sets than the baselines, in the selected tasks and datasets.

[X-ray localizer for transthoracic puncture biopsy]. / Rentgenolokalizator dlia transtorakal'noi punktsionnoi biopsii.

A spot-centring appliance whose purpose is to guide an instrument for transthoracic puncture biopsy with maximum precision into the spector of the thoracic cavity chosen during roentgenoscopy. The X-ray locator is made up of a centring device, depth gauge and a spotting device with roentgen-contrast orientation points which ensure all the spotting and centring manipulations. The transthoracic puncture biopsy is performed outside the range of the X-ray action.