Deep Learning Versus Neurologists: Functional Outcome Prediction in LVO Stroke Patients Undergoing Mechanical Thrombectomy.

BACKGROUND: Despite evolving treatments, functional recovery in patients with large vessel occlusion stroke remains variable and outcome prediction challenging. Can we improve estimation of functional outcome with interpretable deep learning models using clinical and magnetic resonance imaging data? METHODS: In this observational study, we collected data of 222 patients with middle cerebral artery M1 segment occlusion who received mechanical thrombectomy. In a 5-fold cross validation, we evaluated interpretable deep learning models for predicting functional outcome in terms of modified Rankin scale at 3 months using clinical variables, diffusion weighted imaging and perfusion weighted imaging, and a combination thereof. Based on 50 test patients, we compared model performances to those of 5 experienced stroke neurologists. Prediction performance for ordinal (modified Rankin scale score, 0-6) and binary (modified Rankin scale score, 0-2 versus 3-6) functional outcome was assessed using discrimination and calibration measures like area under the receiver operating characteristic curve and accuracy (percentage of correctly classified patients). RESULTS: In the cross validation, the model based on clinical variables and diffusion weighted imaging achieved the highest binary prediction performance (area under the receiver operating characteristic curve, 0.766 [0.727-0.803]). Performance of models using clinical variables or diffusion weighted imaging only was lower. Adding perfusion weighted imaging did not improve outcome prediction. On the test set of 50 patients, binary prediction performance between model (accuracy, 60% [55.4%-64.4%]) and neurologists (accuracy, 60% [55.8%-64.21%]) was similar when using clinical data. However, models significantly outperformed neurologists when imaging data were provided, alone or in combination with clinical variables (accuracy, 72% [67.8%-76%] versus 64% [59.8%-68.4%] with clinical and imaging data). Prediction performance of neurologists with comparable experience varied strongly. CONCLUSIONS: We hypothesize that early prediction of functional outcome in large vessel occlusion stroke patients may be significantly improved if neurologists are supported by interpretable deep learning models.

Deep transformation models for functional outcome prediction after acute ischemic stroke.

In many medical applications, interpretable models with high prediction performance are sought. Often, those models are required to handle semistructured data like tabular and image data. We show how to apply deep transformation models (DTMs) for distributional regression that fulfill these requirements. DTMs allow the data analyst to specify (deep) neural networks for different input modalities making them applicable to various research questions. Like statistical models, DTMs can provide interpretable effect estimates while achieving the state-of-the-art prediction performance of deep neural networks. In addition, the construction of ensembles of DTMs that retain model structure and interpretability allows quantifying epistemic and aleatoric uncertainty. In this study, we compare several DTMs, including baseline-adjusted models, trained on a semistructured data set of 407 stroke patients with the aim to predict ordinal functional outcome three months after stroke. We follow statistical principles of model-building to achieve an adequate trade-off between interpretability and flexibility while assessing the relative importance of the involved data modalities. We evaluate the models for an ordinal and dichotomized version of the outcome as used in clinical practice. We show that both tabular clinical and brain imaging data are useful for functional outcome prediction, whereas models based on tabular data only outperform those based on imaging data only. There is no substantial evidence for improved prediction when combining both data modalities. Overall, we highlight that DTMs provide a powerful, interpretable approach to analyzing semistructured data and that they have the potential to support clinical decision-making.

Distributional anchor regression.

Prediction models often fail if train and test data do not stem from the same distribution. Out-of-distribution (OOD) generalization to unseen, perturbed test data is a desirable but difficult-to-achieve property for prediction models and in general requires strong assumptions on the data generating process (DGP). In a causally inspired perspective on OOD generalization, the test data arise from a specific class of interventions on exogenous random variables of the DGP, called anchors. Anchor regression models, introduced by Rothenhäusler et al. (J R Stat Soc Ser B 83(2):215-246, 2021. 10.1111/rssb.12398), protect against distributional shifts in the test data by employing causal regularization. However, so far anchor regression has only been used with a squared-error loss which is inapplicable to common responses such as censored continuous or ordinal data. Here, we propose a distributional version of anchor regression which generalizes the method to potentially censored responses with at least an ordered sample space. To this end, we combine a flexible class of parametric transformation models for distributional regression with an appropriate causal regularizer under a more general notion of residuals. In an exemplary application and several simulation scenarios we demonstrate the extent to which OOD generalization is possible.

Machine-learning-based outcome prediction in stroke patients with middle cerebral artery-M1 occlusions and early thrombectomy.

BACKGROUND AND PURPOSE: Clinical outcomes vary substantially among individuals with large vessel occlusion (LVO) stroke. A small infarct core and large imaging mismatch were found to be associated with good recovery. The aim of this study was to investigate whether those imaging variables would improve individual prediction of functional outcome after early (<6 h) endovascular treatment (EVT) in LVO stroke. METHODS: We included 222 patients with acute ischemic stroke due to middle cerebral artery (MCA)-M1 occlusion who received EVT. As predictors, we used clinical variables and region of interest (ROI)-based magnetic resonance imaging features. We developed different machine-learning models and quantified their prediction performance according to the area under the receiver-operating characteristic curves and the Brier score. RESULTS: The rate of successful recanalization was 78%, with 54% patients having a favorable outcome (modified Rankin scale score 0-2). Small infarct core was associated with favorable functional outcome. Outcome prediction improved only slightly when imaging was added to patient variables. Age was the driving factor, with a sharp decrease in likelihood of favorable functional outcome above the age of 78 years. CONCLUSIONS: In patients with MCA-M1 occlusion strokes referred to EVT within 6 h of symptom onset, infarct core volume was associated with outcome. However, ROI-based imaging variables led to no significant improvement in outcome prediction at an individual patient level when added to a set of clinical predictors. Our study is in concordance with current practice, where imaging mismatch or collateral readouts are not recommended as factors for excluding patients with MCA-M1 occlusion for early EVT.

A prospective observational study on trajectories and prognostic factors of mid back pain.

BACKGROUND: Although mid back pain (MBP) is a common condition that causes significant disability, it has received little attention in research and knowledge about trajectories and prognosis of MBP is limited. The purpose of this study was to identify trajectories of MBP and baseline risk factors for an unfavorable outcome in MBP patients undergoing chiropractic treatment. METHODS: This prospective-observational study analyzes outcome data of 90 adult MBP patients (mean age = 37.0 ± 14.6 years; 49 females) during one year (at baseline, after 1 week, 1 month, 3, 6 and 12 months) after start of chiropractic treatment. Patients completed an 11-point (0 to 10) numeric pain rating scale (NRS) at baseline and one week, one month, three, six and twelve months after treatment start and the Patient's Global Impression of Change (PGIC) questionnaire at all time points except baseline. To determine trajectories, clustering with the package kml (software R), a variant of k-means clustering adapted for longitudinal data, was performed using the NRS-data. The identified NRS-clusters and PGIC data after three months were tested for association with baseline variables using univariable logistic regression analyses, conditional inference trees and random forest plots. RESULTS: Two distinct NRS-clusters indicating a favourable (rapid improvement within one month from moderate pain to persistent minor pain or recovery after one year, 80% of patients) and an unfavourable trajectory (persistent moderate to severe pain, 20% of patients) were identified. Chronic (> 3 months) pain duration at baseline significantly predicted that a patient was less likely to follow a favourable trajectory [OR = 0.16, 95% CI = 0.05-0.50, p = 0.002] and to report subjective improvement after twelve months [OR = 0.19, 95% CI = 0.07-0.51, p = 0.001], which was confirmed by the conditional inference tree and the random forest analyses. CONCLUSIONS: This prospective exploratory study identified two distinct MBP trajectories, representing a favourable and an unfavourable outcome over the course of one year after chiropractic treatment. Pain chronicity was the factor that influenced outcome measures using NRS or PGIC.

Integrating uncertainty in deep neural networks for MRI based stroke analysis.

At present, the majority of the proposed Deep Learning (DL) methods provide point predictions without quantifying the model's uncertainty. However, a quantification of the reliability of automated image analysis is essential, in particular in medicine when physicians rely on the results for making critical treatment decisions. In this work, we provide an entire framework to diagnose ischemic stroke patients incorporating Bayesian uncertainty into the analysis procedure. We present a Bayesian Convolutional Neural Network (CNN) yielding a probability for a stroke lesion on 2D Magnetic Resonance (MR) images with corresponding uncertainty information about the reliability of the prediction. For patient-level diagnoses, different aggregation methods are proposed and evaluated, which combine the individual image-level predictions. Those methods take advantage of the uncertainty in the image predictions and report model uncertainty at the patient-level. In a cohort of 511 patients, our Bayesian CNN achieved an accuracy of 95.33% at the image-level representing a significant improvement of 2% over a non-Bayesian counterpart. The best patient aggregation method yielded 95.89% of accuracy. Integrating uncertainty information about image predictions in aggregation models resulted in higher uncertainty measures to false patient classifications, which enabled to filter critical patient diagnoses that are supposed to be closer examined by a medical doctor. We therefore recommend using Bayesian approaches not only for improved image-level prediction and uncertainty estimation but also for the detection of uncertain aggregations at the patient-level.

Know When You Don't Know: A Robust Deep Learning Approach in the Presence of Unknown Phenotypes.

Deep convolutional neural networks show outstanding performance in image-based phenotype classification given that all existing phenotypes are presented during the training of the network. However, in real-world high-content screening (HCS) experiments, it is often impossible to know all phenotypes in advance. Moreover, novel phenotype discovery itself can be an HCS outcome of interest. This aspect of HCS is not yet covered by classical deep learning approaches. When presenting an image with a novel phenotype to a trained network, it fails to indicate a novelty discovery but assigns the image to a wrong phenotype. To tackle this problem and address the need for novelty detection, we use a recently developed Bayesian approach for deep neural networks called Monte Carlo (MC) dropout to define different uncertainty measures for each phenotype prediction. With real HCS data, we show that these uncertainty measures allow us to identify novel or unclear phenotypes. In addition, we also found that the MC dropout method results in a significant improvement of classification accuracy. The proposed procedure used in our HCS case study can be easily transferred to any existing network architecture and will be beneficial in terms of accuracy and novelty detection.

Developing Deep Learning Applications for Life Science and Pharma Industry.

Deep Learning has boosted artificial intelligence over the past 5 years and is seen now as one of the major technological innovation areas, predicted to replace lots of repetitive, but complex tasks of human labor within the next decade. It is also expected to be 'game changing' for research activities in pharma and life sciences, where large sets of similar yet complex data samples are systematically analyzed. Deep learning is currently conquering formerly expert domains especially in areas requiring perception, previously not amenable to standard machine learning. A typical example is the automated analysis of images which are typically produced en-masse in many domains, e. g., in high-content screening or digital pathology. Deep learning enables to create competitive applications in so-far defined core domains of 'human intelligence'. Applications of artificial intelligence have been enabled in recent years by (i) the massive availability of data samples, collected in pharma driven drug programs (='big data') as well as (ii) deep learning algorithmic advancements and (iii) increase in compute power. Such applications are based on software frameworks with specific strengths and weaknesses. Here, we introduce typical applications and underlying frameworks for deep learning with a set of practical criteria for developing production ready solutions in life science and pharma research. Based on our own experience in successfully developing deep learning applications we provide suggestions and a baseline for selecting the most suited frameworks for a future-proof and cost-effective development.

Patients with a Normal Pressure Hydrocephalus Shunt Have Fewer Complications than Do Patients with Other Shunts.

BACKGROUND: Ventriculoperitoneal (VP) shunting is a well-established therapy for hydrocephalus. However, complications are frequent. The incidence of idiopathic normal pressure hydrocephalus (NPH) increases with the aging of the population. We evaluated the functional status of patients and the classification of complications associated with VP shunt procedures in our center. METHODS: We recorded all VP shunt procedures in our prospective patient registry from January 2013 to December 2015. Functional outcome (Karnofsky Performance Status [KPS] and modified Rankin Scale) and complications were compiled from patient records. Any deviation from the normal postoperative course within 3 months after surgery was considered a complication. Complications were classified with the therapy-oriented Clavien-Dindo grading system. We evaluated potential risk factors with a logistic regression model. RESULTS: From 285 procedures in the reporting period, 90 were excluded, resulting in 195 patients. Among those patients, 174 (90%) were shunt implantations and 21 (11%) were shunt revisions. Forty-four shunts (23%) were implanted for NPH. Median KPS improved over the first year after surgery. Although some type of complication was observed in 114 patients (58%), 60 of those complications (31%) did not require surgical treatment (Clavien-Dindo grade <3). In 50 patients (26%), the complication concerned the shunt itself. A high KPS at admission and NPH as underlying indication significantly reduced the odds ratio for a complication. CONCLUSIONS: Although shunt surgery has a high general rate of complications, this rate is significantly lower for patients with NPH. The decision for shunting in patients with NPH should consider the low complication rate specific for the group of patients with NPH.

Sensitivities of in vivo markers of arterial organ damage in patients with peripheral atherosclerosis.

BACKGROUND: Biomarkers of vascular diseases such as ankle-brachial index (ABI), peripheral pulse pressure (pPP), central pulse pressure (cPP), and pulse wave velocity (PWV) allow assessment of arterial organ damage (AOD). However, the utility of markers other than ABI in patients with peripheral arterial disease (PAD), which are also associated with a significant increase of cardiovascular events, remains unclear. PATIENTS AND METHODS: Asymptomatic (n = 21) and symptomatic patients (n = 46) with a positive sonography for PAD or history of lower limb revascularization were included. ABI, pPP, cPP, and PWV were assessed. PWV were performed using a brachial cuff-based method (aortic PWV (aPWV)) and oscillography (carotid-femoral pulse wave velocity (cfPWV)), respectively. The two methods for PWV were compared using Bland Altman analysis. Sensitivities of ABI, pPP, cPP, cfPWV, and aPWV for AOD were calculated. RESULTS: Sixty-seven patients (35.8 % female, mean age 69, range 39-91 years) had a significantly higher aPWV than cfPWV (median 10.5 m/s (IQR: 8.8-12.65 m/s) vs. median 9.0 m/s (IQR: 7.57-10.55 m/s), p = 0.0013). There was no correlation between cfPWV and age (r = 0.311, p = 0.116). Bland Altman analysis revealed a mean difference of -1.04 (-2SD; -6.38 to + 2SD; 4.31). The sensitivities for AOD were 68.7 % for ABI, 61.2 % for aPWV, 40.3 % for cfPWV, 31.3 % for peripheral PP, and 10.4 % for central aortic PP (p < 0.001). CONCLUSIONS: Brachial-derived aPWV differs from the gold standard assessment (cfPWV), which may be underestimated in PAD due to atherosclerotic obstructions along the aorto-iliac segment. The sensitivities of noninvasive in vivo markers of AOD vary widely and tend to underestimate the actual presence of AOD.

Analysis of Prognostic Values of Various PET Metrics in Preoperative 18F-FDG PET for Early-Stage Bronchial Carcinoma for Progression-Free and Overall Survival: Significantly Increased Glycolysis Is a Predictive Factor.

The purpose of this study was to assess various volume-based PET quantification metrics, including metabolic tumor volume and total lesion glycolysis (TLG) with different thresholds, as well as background activity-based PET metrics (background-subtracted lesion activity [BSL] and background-subtracted volume) as prognostic markers for progression-free and overall survival (PFS and OS, respectively) in early-stage I and II non-small cell lung cancer (NSCLC) after resection. Methods: Patients (n = 133) underwent an adequate 18F-FDG PET/CT scan before surgery between January 2003 and December 2010. All PET activity metrics showed a skewed distribution and were log-transformed before calculation of the Pearson correlation coefficients. Survival tree analysis was used to discriminate between high- and low-risk patients and to select the most important prognostic markers. The Akaike information criterion was used to compare 2 univariate models. Results: Within the study time, 36 patients died from NSCLC and 26 patients from other causes. At the end of follow-up, 70 patients were alive, with 67 patients being free of disease. All log-transformed PET metrics showed a strong linear association, with a Pearson correlation coefficient between 0.703 and 0.962. After multiple testing corrections, only 1 prognostic marker contributed a significant split point in the survival tree analysis. Of 10 potential predictors including 7 PET metrics, a BSL greater than 6,852 (P = 0.017) was chosen as split point, assigning 13 patients into a high-risk group. If BSL was removed from the set of predictors, a 42% TLG (TLG42%) of greater than 4,204 (P = 0.023) was chosen as split point. When a dichotomized BSL or TLG42% variable was used for a univariate Cox model, the Akaike information criterion difference of both models was smaller than 2; therefore, the data do not provide evidence that 1 of the 2 prognostic factors is superior. Conclusion: Volume-based PET metrics correlate with PFS and OS and could be used for risk assessment in stage I-II NSCLC. The different PET metrics assessed in this study showed a high correlation; therefore, it is not surprising that there was no significant difference to predict PFS or OS within this study. Overall, patients with large and metabolically active tumors should be considered high risk and might need further treatment after resection. Because all analysis steps were done with the same data, these results should be validated on new patient data.

Rapid adaptation to microgravity in mammalian macrophage cells.

Despite the observed severe effects of microgravity on mammalian cells, many astronauts have completed long term stays in space without suffering from severe health problems. This raises questions about the cellular capacity for adaptation to a new gravitational environment. The International Space Station (ISS) experiment TRIPLE LUX A, performed in the BIOLAB laboratory of the ISS COLUMBUS module, allowed for the first time the direct measurement of a cellular function in real time and on orbit. We measured the oxidative burst reaction in mammalian macrophages (NR8383 rat alveolar macrophages) exposed to a centrifuge regime of internal 0 g and 1 g controls and step-wise increase or decrease of the gravitational force in four independent experiments. Surprisingly, we found that these macrophages adapted to microgravity in an ultra-fast manner within seconds, after an immediate inhibitory effect on the oxidative burst reaction. For the first time, we provided direct evidence of cellular sensitivity to gravity, through real-time on orbit measurements and by using an experimental system, in which all factors except gravity were constant. The surprisingly ultra-fast adaptation to microgravity indicates that mammalian macrophages are equipped with a highly efficient adaptation potential to a low gravity environment. This opens new avenues for the exploration of adaptation of mammalian cells to gravitational changes.

Effect of Early Prophylactic High-Dose Recombinant Human Erythropoietin in Very Preterm Infants on Neurodevelopmental Outcome at 2 Years: A Randomized Clinical Trial.

IMPORTANCE: Very preterm infants are at risk of developing encephalopathy of prematurity and long-term neurodevelopmental delay. Erythropoietin treatment is neuroprotective in animal experimental and human clinical studies. OBJECTIVE: To determine whether prophylactic early high-dose recombinant human erythropoietin (rhEPO) in preterm infants improves neurodevelopmental outcome at 2 years' corrected age. DESIGN, SETTING, AND PARTICIPANTS: Preterm infants born between 26 weeks 0 days' and 31 weeks 6 days' gestation were enrolled in a randomized, double-blind, placebo-controlled, multicenter trial in Switzerland between 2005 and 2012. Neurodevelopmental assessments at age 2 years were completed in 2014. INTERVENTIONS: Participants were randomly assigned to receive either rhEPO (3000 IU/kg) or placebo (isotonic saline, 0.9%) intravenously within 3 hours, at 12 to 18 hours, and at 36 to 42 hours after birth. MAIN OUTCOMES AND MEASURES: Primary outcome was cognitive development assessed with the Mental Development Index (MDI; norm, 100 [SD, 15]; higher values indicate better function) of the Bayley Scales of Infant Development, second edition (BSID-II) at 2 years corrected age. The minimal clinically important difference between groups was 5 points (0.3 SD). Secondary outcomes were motor development (assessed with the Psychomotor Development Index), cerebral palsy, hearing or visual impairment, and anthropometric growth parameters. RESULTS: Among 448 preterm infants randomized (mean gestational age, 29.0 [range, 26.0-30.9] weeks; 264 [59%] female; mean birth weight, 1210 [range, 490-2290] g), 228 were randomized to rhEPO and 220 to placebo. Neurodevelopmental outcome data were available for 365 (81%) at a mean age of 23.6 months. In an intention-to-treat analysis, mean MDI was not statistically significantly different between the rhEPO group (93.5 [SD, 16.0] [95% CI, 91.2 to 95.8]) and the placebo group (94.5 [SD, 17.8] [95% CI, 90.8 to 98.5]) (difference, -1.0 [95% CI, -4.5 to 2.5]; P = .56). No differences were found between groups in the secondary outcomes. CONCLUSIONS AND RELEVANCE: Among very preterm infants who received prophylactic early high-dose rhEPO for neuroprotection, compared with infants who received placebo, there were no statistically significant differences in neurodevelopmental outcomes at 2 years. Follow-up for cognitive and physical problems that may not become evident until later in life is required. TRIAL REGISTRATION: clinicaltrials.gov Identifier: NCT00413946.

Single-Cell Phenotype Classification Using Deep Convolutional Neural Networks.

Deep learning methods are currently outperforming traditional state-of-the-art computer vision algorithms in diverse applications and recently even surpassed human performance in object recognition. Here we demonstrate the potential of deep learning methods to high-content screening-based phenotype classification. We trained a deep learning classifier in the form of convolutional neural networks with approximately 40,000 publicly available single-cell images from samples treated with compounds from four classes known to lead to different phenotypes. The input data consisted of multichannel images. The construction of appropriate feature definitions was part of the training and carried out by the convolutional network, without the need for expert knowledge or handcrafted features. We compare our results against the recent state-of-the-art pipeline in which predefined features are extracted from each cell using specialized software and then fed into various machine learning algorithms (support vector machine, Fisher linear discriminant, random forest) for classification. The performance of all classification approaches is evaluated on an untouched test image set with known phenotype classes. Compared to the best reference machine learning algorithm, the misclassification rate is reduced from 8.9% to 6.6%.

Incidence of metachronous contralateral inguinal hernias in children following unilateral repair - A meta-analysis of prospective studies.

PURPOSE: The objective of this review was to systematically evaluate the incidence of a metachronous contralateral inguinal hernia (MCIH) in children with unilateral inguinal hernia and therefore to propose or to reject routine contralateral groin exploration. METHODS: Electronic searches restricted to prospective studies with a minimal follow-up of 1year included MEDLINE, EMBASE and the Cochrane Central Register of Controlled Trials. RESULTS: Six studies involving 1669 children were included. Overall MCIH was 6% (95% CI from 4% to 8%). The odds for MCIH development were significantly larger in children with an initial left-sided hernia (OR 2.66 with 95% CI from 1.56 to 4.53) and in children with open contralateral processus vaginalis (CPV) (OR 4.17 with 95% CI from 1.25 to 13.9). CONCLUSIONS: The overall incidence of MCIH following unilateral inguinal hernia repair in children is 6%. Initial left-sided hernia (8.5%) and open CPV (13.8%) are risk factors for MCIH development. Female gender (8.2%) and younger age (<1year) (6.9%) non-significantly increase the risk of MCIH.

Ultrasound and Clinical Predictors of Recurrent Ischemia in Symptomatic Internal Carotid Artery Occlusion.

BACKGROUND AND PURPOSE: Occlusion of the internal carotid artery puts patients at risk of recurrent ischemic events because of hemodynamic compromise. Our goal was to characterize clinical and duplex parameters indicating patients at risk of recurrent ischemia. METHODS: We retrospectively identified patients with symptomatic internal carotid artery occlusion. Clinical characteristics and ultrasound parameters, including collateral networks, were analyzed. Predictors for recurrent ipsilateral ischemia were investigated by Cox regression analysis. RESULTS: Of 68 patients, at least 1 recurrent ischemic event within the same vascular territory was observed in 14 patients (20.6%) within 2 to 92 days (median, 29.5 days). The median follow-up period was 6 months. Diabetes mellitus and previous transient ischemic attack were associated with recurrence, as was activation of the maximum number of collateral pathways on transcranial ultrasound (28.6% versus 5.6%; P=0.03). Furthermore, flow in the posterior cerebral arteries was higher in patients with recurrence in ipsilateral and contralateral posterior cerebral artery P2 segments (76 IQR 37.5 versus 59, IQR 22.5 cm/s and 68, IQR 35.6 versus 52, IQR 21 cm/s; P<0.01 and 0.02). CONCLUSIONS: Flow increases in both posterior cerebral artery P2 segments suggest intensified compensatory efforts when other collaterals are insufficient. Together with the presence of diabetes mellitus and a history of transient ischemic attack, this duplex parameter indicates that patients with internal carotid artery are at particular risk of recurrent ischemia.

Unsupervised assessment of microarray data quality using a Gaussian mixture model.

BACKGROUND: Quality assessment of microarray data is an important and often challenging aspect of gene expression analysis. This task frequently involves the examination of a variety of summary statistics and diagnostic plots. The interpretation of these diagnostics is often subjective, and generally requires careful expert scrutiny. RESULTS: We show how an unsupervised classification technique based on the Expectation-Maximization (EM) algorithm and the naïve Bayes model can be used to automate microarray quality assessment. The method is flexible and can be easily adapted to accommodate alternate quality statistics and platforms. We evaluate our approach using Affymetrix 3' gene expression and exon arrays and compare the performance of this method to a similar supervised approach. CONCLUSION: This research illustrates the efficacy of an unsupervised classification approach for the purpose of automated microarray data quality assessment. Since our approach requires only unannotated training data, it is easy to customize and to keep up-to-date as technology evolves. In contrast to other "black box" classification systems, this method also allows for intuitive explanations.

Dysregulation of gene expression in primary neuron models of Huntington's disease shows that polyglutamine-related effects on the striatal transcriptome may not be dependent on brain circuitry.

Gene expression changes are a hallmark of the neuropathology of Huntington's disease (HD), but the exact molecular mechanisms of this effect remain uncertain. Here, we report that in vitro models of disease comprised of primary striatal neurons expressing N-terminal fragments of mutant huntingtin (via lentiviral gene delivery) faithfully reproduce the gene expression changes seen in human HD. Neither viral infection nor unrelated (enhanced green fluorescent protein) transgene expression had a major effect on resultant RNA profiles. Expression of a wild-type fragment of huntingtin [htt171-18Q] also caused only a small number of RNA changes. The disease-related signal in htt171-82Q versus htt171-18Q comparisons was far greater, resulting in the differential detection of 20% of all mRNA probe sets. Transcriptomic effects of mutated htt171 are time- and polyglutamine-length dependent and occur in parallel with other manifestations of polyglutamine toxicity over 4-8 weeks. Specific RNA changes in htt171-82Q-expressing striatal cells accurately recapitulated those observed in human HD caudate and included decreases in PENK (proenkephalin), RGS4 (regulator of G-protein signaling 4), dopamine D(1) receptor (DRD1), DRD2, CNR1 (cannabinoid CB(1) receptor), and DARPP-32 (dopamine- and cAMP-regulated phosphoprotein-32; also known as PPP1R1B) mRNAs. HD-related transcriptomic changes were also observed in primary neurons expressing a longer fragment of mutant huntingtin (htt853-82Q). The gene expression changes observed in cultured striatal neurons are not secondary to abnormalities of neuronal firing or glutamatergic, dopaminergic, or brain-derived neurotrophic factor signaling, thereby demonstrating that HD-induced dysregulation of the striatal transcriptome might be attributed to intrinsic effects of mutant huntingtin.

Quality assessment of Affymetrix GeneChip data.

Affymetrix GeneChips are one of the best established microarray platforms. This powerful technique allows users to measure the expression of thousands of genes simultaneously. However, a microarray experiment is a sophisticated and time consuming endeavor with many potential sources of unwanted variation that could compromise the results if left uncontrolled. Increasing data volume and data complexity have triggered growing concern and awareness of the importance of assessing the quality of generated microarray data. In this review, we give an overview of current methods and software tools for quality assessment of Affymetrix GeneChip data. We focus on quality metrics, diagnostic plots, probe-level methods, pseudo-images, and classification methods to identify corrupted chips. We also describe RNA quality assessment methods which play an important role in challenging RNA sources like formalin embedded biopsies, laser-micro dissected samples, or single cells. No wet-lab methods are discussed in this paper.