Electrical stimulation methods and protocols for the treatment of traumatic brain injury: a critical review of preclinical research.

BACKGROUND: Traumatic brain injury (TBI) is a leading cause of disabilities resulting from cognitive and neurological deficits, as well as psychological disorders. Only recently, preclinical research on electrical stimulation methods as a potential treatment of TBI sequelae has gained more traction. However, the underlying mechanisms of the anticipated improvements induced by these methods are still not fully understood. It remains unclear in which stage after TBI they are best applied to optimize the therapeutic outcome, preferably with persisting effects. Studies with animal models address these questions and investigate beneficial long- and short-term changes mediated by these novel modalities. METHODS: In this review, we present the state-of-the-art in preclinical research on electrical stimulation methods used to treat TBI sequelae. We analyze publications on the most commonly used electrical stimulation methods, namely transcranial magnetic stimulation (TMS), transcranial direct current stimulation (tDCS), deep brain stimulation (DBS) and vagus nerve stimulation (VNS), that aim to treat disabilities caused by TBI. We discuss applied stimulation parameters, such as the amplitude, frequency, and length of stimulation, as well as stimulation time frames, specifically the onset of stimulation, how often stimulation sessions were repeated and the total length of the treatment. These parameters are then analyzed in the context of injury severity, the disability under investigation and the stimulated location, and the resulting therapeutic effects are compared. We provide a comprehensive and critical review and discuss directions for future research. RESULTS AND CONCLUSION: We find that the parameters used in studies on each of these stimulation methods vary widely, making it difficult to draw direct comparisons between stimulation protocols and therapeutic outcome. Persisting beneficial effects and adverse consequences of electrical simulation are rarely investigated, leaving many questions about their suitability for clinical applications. Nevertheless, we conclude that the stimulation methods discussed here show promising results that could be further supported by additional research in this field.

Apparent prevalence and risk factors for udder skin diseases and udder edema in Bavarian dairy herds.

The objective of this cross-sectional study was to assess the prevalence and risk factors for teat warts, udder edema, udder thigh dermatitis, and udder cleft dermatitis on Bavarian dairy farms. Udder health and hygiene scores of lactating cows were recorded on 152 farms in Bavaria, Germany. Management practices (e.g., housing, milking systems, and feeding regimens) were assessed with a comprehensive questionnaire. Adjusted prevalence estimates were determined using regression analysis with herd as the random effect. Mann-Whitney U or Fisher's exact on herd level and regression analyses on cow level were performed to determine risk factors. Of the 6,208 cows examined, 4.0% had teat warts, 1.1% udder edema, 0.2% udder thigh dermatitis, and 0.3% udder cleft dermatitis. The apparent median within-herd prevalence was less than 4% for all 4 diseases. Herd-level factors that were associated with the presence of teat warts on a farm were the proportion of cows with poor teat ends as well as conventional milking systems compared with milking robots. At a cow level, teat warts were associated with high somatic cell counts. Herds with poor depth (<5 cm) of bedding material and cows with days in milk less than 60 d had increased odds for udder edema. First-lactating cows had higher odds for udder thigh dermatitis. Freestall housing and comfort rubber mats were identified as risk factors for udder cleft dermatitis on a herd level. In conclusion, although most nonmastitis udder diseases were rarely observed in this study, some herd management practices and cow factors were associated with their presence on a farm or cow level. Future studies are needed to further investigate risk factors for each disease in more detail.

Identifying poor metabolic adaptation during early lactation in dairy cows using cluster analysis.

Currently, cows with poor metabolic adaptation during early lactation, or poor metabolic adaptation syndrome (PMAS), are often identified based on detection of hyperketonemia. Unfortunately, elevated blood ketones do not manifest consistently with indications of PMAS. Expected indicators of PMAS include elevated liver enzymes and bilirubin, decreased rumen fill, reduced rumen contractions, and a decrease in milk production. Cows with PMAS typically are higher producing, older cows that are earlier in lactation and have greater body condition score at the start of lactation. It was our aim to evaluate commonly used measures of metabolic health (input variables) that were available [i.e., blood ß-hydroxybutyrate acid, milk fat:protein ratio, blood nonesterified fatty acids (NEFA)] to characterize PMAS. Bavarian farms (n = 26) with robotic milking systems were enrolled for weekly visits for an average of 6.7 wk. Physical examinations of the cows (5-50 d in milk) were performed by veterinarians during each visit, and blood and milk samples were collected. Resulting data included 790 observations from 312 cows (309 Simmental, 1 Red Holstein, 2 Holstein). Principal component analysis was conducted on the 3 input variables, followed by K-means cluster analysis of the first 2 orthogonal components. The 5 resulting clusters were then ascribed to low, intermediate, or high PMAS classes based on their degree of agreement with expected PMAS indicators and characteristics in comparison with other clusters. Results revealed that PMAS classes were most significantly associated with blood NEFA levels. Next, we evaluated NEFA values that classify observations into appropriate PMAS classes in this data set, which we called separation values. Our resulting NEFA separation values [<0.39 mmol/L (95% confidence limits = 0.360-0.410) to identify low PMAS observations and ≥0.7 mmol/L (95% confidence limits = 0.650-0.775) to identify high PMAS observations] were similar to values determined for Holsteins in conventional milking settings diagnosed with hyperketonemia and clinical symptoms such as anorexia and a reduction in milk yield, as reported in the literature. Future studies evaluating additional clinical and laboratory data, breeds, and milking systems are needed to validate these finding. The aim of future studies would be to build a PMAS prediction model to alert producers of cows needing attention and help evaluate on-farm metabolic health management at the herd level.

Anti-contactin-associated protein-2 encephalitis: relevance of antibody titres, presentation and outcome.

BACKGROUND AND PURPOSE: To clarify the relevance of titres of IgG antibodies against contactin-associated protein-2 (CASPR2) in diagnosing anti-CASPR2 encephalitis and to describe features and outcomes. METHODS: This was a retrospective analysis of 64 patients with CASPR2 antibodies, categorized independently as 'autoimmune encephalitis' or 'other disease'. Logistic regression methods were performed to identify potential predictors of 'autoimmune encephalitis' in addition to CASPR2 antibodies. RESULTS: An upfront CASPR2 antibody serum titre cut-off at ≥1:200 had a diagnostic sensitivity of 85% and a specificity of 81%. Logistic regression analyses indicated that, in addition to titre, encephalitic magnetic resonance imaging (MRI) was a significant predictor of 'autoimmune encephalitis' (Nagelkerke's R2 = 0.81, P < 0.001) with high sensitivity (84%) and very high specificity (100%). Patients with CASPR2 antibodies and an estimated probability of >70% of having anti-CASPR2 encephalitis (n = 22) had limbic encephalitis (n = 18, one patient plus ataxia), Morvan syndrome (n = 2) or a hyperkinetic movement disorder (n = 2). Median modified Rankin score (mRS) at diagnosis was 3 (range 1-4). Twenty patients were male; median age was 64 (range 54-75) years; 5/15 patients with cerebrospinal fluid data had intrathecal CASPR2 antibody synthesis, and 12/19 with follow-ups >3 months (median 12 months, range 4-43 months) improved by ≥1 mRS point resulting in a median mRS of 2 (range 0-6; one death; all but one having received immunotherapy); and 2/15 patients with follow-up MRI developed hippocampal atrophy. CONCLUSIONS: Only higher CASPR2 serum antibody titres indicate anti-CASPR2 encephalitis, and diagnostic accuracy increases if MRI findings are considered. Anti-CASPR2 encephalitis has characteristic features and a favourable outcome with immunotherapy.

Prediction of rhythmic and periodic EEG patterns and seizures on continuous EEG with early epileptiform discharges.

BACKGROUND: Continuous EEG (cEEG) is necessary to document nonconvulsive seizures (NCS), nonconvulsive status epilepticus (NCSE), as well as rhythmic and periodic EEG patterns of 'ictal-interictal uncertainty' (RPPIIU) including periodic discharges, rhythmic delta activity, and spike-and-wave complexes in neurological intensive care patients. However, cEEG is associated with significant recording and analysis efforts. Therefore, predictors from short-term routine EEG with a reasonably high yield are urgently needed in order to select patients for evaluation with cEEG. OBJECTIVE: The aim of this study was to assess the prognostic significance of early epileptiform discharges (i.e., within the first 30 min of EEG recording) on the following: (1) incidence of ictal EEG patterns and RPPIIU on subsequent cEEG, (2) occurrence of acute convulsive seizures during the ICU stay, and (3) functional outcome after 6 months of follow-up. METHODS: We conducted a separate analysis of the first 30 min and the remaining segments of prospective cEEG recordings according to the ACNS Standardized Critical Care EEG Terminology as well as NCS criteria and review of clinical data of 32 neurological critical care patients. RESULTS: In 17 patients with epileptiform discharges within the first 30 min of EEG (group 1), electrographic seizures were observed in 23.5% (n = 4), rhythmic or periodic EEG patterns of 'ictal-interictal uncertainty' in 64.7% (n = 11), and neither electrographic seizures nor RPPIIU in 11.8% (n = 2). In 15 patients with no epileptiform discharges in the first 30 min of EEG (group 2), no electrographic seizures were recorded on subsequent cEEG, RPPIIU were seen in 26.7% (n = 4), and neither electrographic seizures nor RPPIIU in 73.3% (n = 11). The incidence of EEG patterns on cEEG was significantly different between the two groups (p = 0.008). Patients with early epileptiform discharges developed acute seizures more frequently than patients without early epileptiform discharges (p = 0.009). Finally, functional outcome six months after discharge was significantly worse in patients with early epileptiform discharges (p=0.01). CONCLUSIONS: Epileptiform discharges within the first 30 min of EEG recording are predictive for the occurrence of ictal EEG patterns and for RPPIIU on subsequent cEEG, for acute convulsive seizures during the ICU stay, and for a worse functional outcome after 6 months of follow-up. This article is part of a Special Issue entitled Status Epilepticus.

Prospective assessment and validation of rhythmic and periodic pattern detection in NeuroTrend: A new approach for screening continuous EEG in the intensive care unit.

BACKGROUND: NeuroTrend is a computational method that analyzes long-term scalp EEGs in the ICU according to ACNS standardized critical care EEG terminology (CCET) including electrographic seizures. At present, it attempts to become a screening aid for continuous EEG (cEEG) recordings in the ICU to facilitate the review process and optimize resources. METHODS: A prospective multicenter study was performed in two neurological ICUs including 68 patients who were subjected to video-cEEG. Two reviewers independently annotated the first minute of each hour in the cEEG according to CCET. These segments were also screened for faster patterns with frequencies higher than 4 Hz. The matching annotations (2911 segments) were then used as gold standard condition to test sensitivity and specificity of the rhythmic and periodic pattern detection of NeuroTrend. RESULTS: Interrater agreement showed substantial agreement for localization (main term 1) and pattern type (main term 2) of the CCET. The overall detection sensitivity of NeuroTrend was 94% with high detection rates for periodic discharges (PD = 80%) and rhythmic delta activity (RDA = 82%). Overall specificity was moderate (67%) mainly because of false positive detections of RDA in cases of general slowing. In contrast, a detection specificity of 88% for PDs was reached. Localization revealed only a slight agreement between reviewers and NeuroTrend. CONCLUSIONS: NeuroTrend might be a suitable screening tool for cEEG in the ICU and has the potential to raise efficiency of long-term EEG monitoring in the ICU. At this stage, pattern localization and differentiation between RDA and general slowing need improvement. This article is part of a Special Issue entitled "Status Epilepticus".

Alterations of field potentials in isotropic cardiomyocyte cell layers induced by multiple endogenous pacemakers under normal and hypothermal conditions.

The use of autonomous contracting randomly grown cardiomyocyte monolayers cultivated on microelectrode arrays (MEAs) represents an accepted experimental setting for preclinical experimental research in the field of cardiac electrophysiology. A dominant pacemaker forces a monolayer to adhere to a regular and synchronized contraction. Randomly distributed multiple pacemakers interfere with this dominant center, resulting in more or less frequent changes of propagation direction. This study aims to characterize the impact of changing propagation directions at single electrodes of the MEA on the four intrinsic parameters of registered field potentials (FPs) FPrise, FPMIN, FPpre, and FPdur and conduction velocity (CV) under normal and hypothermal conditions. Primary cultures of chicken cardiomyocytes (n = 18) were plated directly onto MEAs and FPs were recorded in a temperature range between 37 and 29°C. The number and spatiotemporal distribution of biological and artificial pacemakers of each cell layer inside and outside of the MEA registration area were evaluated using an algorithm developed in-house. In almost every second myocardial cell layer, interfering autonomous pacemakers were detected at stable temperatures, showing random spatial distributions with similar beating rates. Additionally, a temperature-dependent change of the dominant pacemaker center was observed in n = 16 experiments. A significant spread-direction-dependent variation of CV, FPrise, FPMIN, and FPpre up to 14% could be measured between different endogenous pacemakers. In conclusion, based on our results, disregarding the spatial origin of excitation may lead to misinterpretations and erroneous conclusions of FP parameters in the verification of research hypotheses in cellular electrocardiology.

[How do Swiss family physicians treat subclinical hypothyroidism?]. / Comment tes médecins de famille prennent-ils en charge l'hypothyroïdie infraclinique?

Subclinical hypothyroidism is a common condition, and its prevalence increases with age. Currently, guidelines regarding the screening and treatment of subclinical hypothyroidism are controversial. An international survey of general practitioners (GPs), to which Swiss GPs also contributed, showed large inter-country variations in treatment strategies for subclinical hypothyroidism. These differences are mainly explained by the lack of strong evidence for the management of this condition. The European randomized-controlled clinical trial TRUST should help clarify recommendations for screening and thyroxin replacement for the elderly with subclinical hypothyroidism. Working in close collaboration with GPs in Switzerland for the recruitment of patients will ensure that the findings from this study will be applicable to primary care settings.

Postictal psychosis in temporal lobe epilepsy: a case-control study.

BACKGROUND AND PURPOSE: To investigate the prevalence of postictal psychosis (PP) in patients with temporal lobe epilepsy (TLE) and to estimate the predictive value of various variables for the development of PP. METHODS: By retrospectively reviewing the charts of all patients evaluated with video-electroencephalogram (EEG)-monitoring at our unit between January 1995 and February 2012, we identified 684 patients with TLE, of which 48 patients had a history of PP. Patients with TLE and PP were compared with 200 controls (patients with TLE without a psychotic history) on demographic, clinical, EEG and magnetic resonance imaging (MRI) variables. RESULTS: The prevalence of PP in our TLE sample was 7.0%. Aggressive behaviour during PP was present in 22.9% of the sample. Univariate analysis revealed that PP was significantly associated with early age at epilepsy onset (P = 0.007), longer duration of epilepsy (P = 0.002), presence of ictal fear (P = 0.005), impaired intellectual function (P = 0.045), and bilateral ictal and interictal epileptiform activity (both P < 0.0001). Using logistic regression analysis, ictal fear [odds ratio (OR) 2.88; P = 0.015] and bilateral interictal EEG activity (OR 6.40; P < 0.0001) were predictive of PP development. No association of PP with MRI pathology or epilepsy-relevant aetiological factors was found. CONCLUSIONS: PP is a frequent and potentially dangerous complication within the course of TLE. Bilateral or widespread functional central nervous system disturbances rather than distinct structural brain alterations or certain predisposing aetiologies of epilepsy appear to be a risk factor for the development of PP. Ictal fear may be a predictive clinical variable of PP in TLE.

A novel method for the identification of synchronization effects in multichannel ECoG with an application to epilepsy.

In this paper, we present a novel method for the identification of synchronization effects in multichannel electrocorticograms (ECoG). Based on autoregressive modeling, we define a dependency measure termed extrinsic-to-intrinsic power ratio (EIPR) which quantifies directed coupling effects in the time domain. Hereby, a dynamic input channel selection algorithm assures the estimation of the model parameters despite the strong spatial correlation among the high number of involved ECoG channels. We compare EIPR to the partial directed coherence, show its ability to indicate Granger causality and successfully validate a signal model. Applying EIPR to ictal ECoG data of patients suffering from temporal lobe epilepsy allows us to identify the electrodes of the seizure onset zone. The results obtained by the proposed method are in good accordance with the clinical findings.

Sign reversal of the Josephson inductance magnetochiral anisotropy and 0-π-like transitions in supercurrent diodes.

The recent discovery of the intrinsic supercurrent diode effect, and its prompt observation in a rich variety of systems, has shown that non-reciprocal supercurrents naturally emerge when both space-inversion and time-inversion symmetries are broken. In Josephson junctions, non-reciprocal supercurrent can be conveniently described in terms of spin-split Andreev states. Here we demonstrate a sign reversal of the Josephson inductance magnetochiral anisotropy, a manifestation of the supercurrent diode effect. The asymmetry of the Josephson inductance as a function of the supercurrent allows us to probe the current-phase relation near equilibrium, and to probe jumps in the junction ground state. Using a minimal theoretical model, we can then link the sign reversal of the inductance magnetochiral anisotropy to the so-called 0-π-like transition, a predicted but still elusive feature of multichannel junctions. Our results demonstrate the potential of inductance measurements as sensitive probes of the fundamental properties of unconventional Josephson junctions.

Deep Learning-Based Image Registration in Dynamic Myocardial Perfusion CT Imaging.

Registration of dynamic CT image sequences is a crucial preprocessing step for clinical evaluation of multiple physiological determinants in the heart such as global and regional myocardial perfusion. In this work, we present a deformable deep learning-based image registration method for quantitative myocardial perfusion CT examinations, which in contrast to previous approaches, takes into account some unique challenges such as low image quality with less accurate anatomical landmarks, dynamic changes of contrast agent concentration in the heart chambers and tissue, and misalignment caused by cardiac stress, respiration, and patient motion. The introduced method uses a recursive cascade network with a ventricle segmentation module, and a novel loss function that accounts for local contrast changes over time. It was trained and validated on a dataset of n = 118 patients with known or suspected coronary artery disease and/or aortic valve insufficiency. Our results demonstrate that the proposed method is capable of registering dynamic cardiac perfusion sequences by reducing local tissue displacements of the left ventricle (LV), whereas contrast changes do not affect the registration and image quality, in particular the absolute CT (HU) values of the entire CT sequence. In addition, the deep learning-based approach presented reveals a short processing time of a few seconds compared to conventional image registration methods, demonstrating its application potential for quantitative CT myocardial perfusion measurements in daily clinical routine.

Achieving elongated lesions employing cardiac cryoablation: a preclinical evaluation study.

Cardiac cryoablation applied for treating cardiac arrhythmias has shown promising results after intervention, particularly for the creation of elongated lesions. A model for simulating and assessing cryoablation interventions was developed, evaluated and validated with animal experiments. We employed two simulations of different freezing outlet settings for a loop shaped cryocatheter, applying Pennes heat equation for cardiac tissue. Our experiments demonstrated that an equidistantly spaced freezing outlet distribution of 5mm led to an improved formation of lesions, i.e., elongated lesions were observed throughout the transmural cardiac volume and on the epicardial structure. A complete transmural frozen lesion was not achieved with a freezing outlet distance of 10mm. These simulation results could be experimentally verified by morphological and histological examinations. Using our simulation model we were able to optimize the intervention procedure by predicting and assessing the freezing process. This should further increase the success rate of cardiac cryoablation in clinical interventions.

AEPUS: a tool for the Automated Extraction of Pennation angles in Ultrasound images with low Signal-to-noise ratio for plane-wave imaging.

The penetrating ability of ultrasound (US) com-bined with its real-time operation make it the perfect tool for investigating muscle contraction mechanics during complex functional tasks, e.g., locomotion. Changes in fascicle lengths and pennation angles of muscle fascicles strongly correlate with the capacity of skeletal muscles to produce forces, thereby represent fundamental parameters to be tracked. While the gold standard for extracting these features from US images is still based on manual annotation, the availability of recording devices capable of generating big data of muscle dynamics makes such manual approach unfeasible, setting the need for automated muscle images annotation tools. Existing approaches, however, are seriously limited, also in view of the continuous developments and technology ad-vancements for ultrafast US and plane-wave imaging. In fact, they rely on conventional (slow) B-mode imaging, make use of point tracking approaches (which often fail due to out-of-plane motion), or can only operate on very high quality images. To overcome all these limitations, we present AEPUS, an automated image labeling tool capable of extracting pennation angles from low quality images using a very small number of plane waves, therefore making it capable of exploiting all the benefits of ultrafast US. Clinical Relevance - Ultrasound is a standard research tool to investigate alterations of spastic muscles in children with Cerebral Palsy. We propose a reliable and time-efficient method to track muscle features in ultrasound images and support clinical biomechanists in their analyses.

CANreduce-SP-adding psychological support to web-based adherence-focused guided self-help for cannabis users: study protocol for a three-arm randomized control trial.

BACKGROUND: Cannabis is the most-frequently used illicit drug in Europe. Over the last few years in Spain, treatment demand has increased, yet most cannabis users do not seek treatment despite the related problems. A web-based self-help tool, like CANreduce 2.0, could help these users to control their consumption. METHODS: This study protocol describes a three-arm randomized controlled trial (RCT) comparing the effectiveness of three approaches, in terms of reducing cannabis use among problematic cannabis users, the first two treatment arms including the Spanish version of CANreduce 2.0 (an adherence-focused, guidance-enhanced, web-based self-help tool) (1) with and (2) without psychological support; and the third group (3) treatment as usual (TAU). Study hypotheses will be tested concerning the primary outcome: change in the number of days of cannabis use over the previous week, comparing assessments at 6 weeks and 3 and 6 months follow-up between groups and against baseline. Secondary outcomes related to cannabis use will be tested similarly. Mental disorders will be explored as predictors of adherence and outcomes. Analyses will be performed on an intention-to-treat basis, then verified by complete case analyses. DISCUSSION: This study will test how effective the Spanish version of CANreduce 2.0 (CANreduce-SP) is at reducing both the frequency and quantity of cannabis use in problematic users and whether adding psychological support increases its effectiveness. TRIAL REGISTRATION: This trial is registered with the Clinical Trials Protocol Registration and Results System (PRS) number: NCT04517474 . Registered 18 August 2020, (Archived by archive.is https://archive.is/N1Y64 ). The project commenced in November 2020 and recruitment is anticipated to end by November 2022.

Effect of Rashba and Dresselhaus spin-orbit coupling on supercurrent rectification and magnetochiral anisotropy of ballistic Josephson junctions.

Simultaneous breaking of inversion- and time-reversal symmetry in Josephson junction (JJ) leads to a possible violation of theI(φ) = -I(-φ) equality for the current-phase relation. This is known as anomalous Josephson effect and it produces a phase shiftφ0in sinusoidal current-phase relations. In ballistic JJs with non-sinusoidal current phase relation the observed phenomenology is much richer, including the supercurrent diode effect and the magnetochiral anisotropy (MCA) of Josephson inductance. In this work, we present measurements of both effects on arrays of JJs defined on epitaxial Al/InAs heterostructures. We show that the orientation of the current with respect to the lattice affects the MCA, possibly as the result of a finite Dresselhaus component. In addition, we show that the two-fold symmetry of the Josephson inductance reflects in the activation energy for phase slips.

Costs of Epilepsy in Austria: Unemployment as a primary driving factor.

PURPOSE: Epilepsy is one of the most common chronic neurological disorders, and long-term treatment with antiseizure medication is often central to its management. The costs of antiseizure medication are more evident than other disease-related costs; thus, we assessed the direct and indirect costs of epilepsy focusing on both drug expenditure and other cost-driving factors. METHODS: Outpatient records and questionnaires applied in a tertiary epilepsy centre in Vienna were used in this bottom-up cost-of-illness study to evaluate disease duration, age at onset, epilepsy syndrome, seizure frequency, sex, healthcare utilisation, diagnostic evaluations, antiseizure medication, and occupation. Cost data were clustered in a histogram-based data analysis, and multivariate regressions were performed to identify cost drivers. RESULTS: The average annual costs of 273 patients amounted to €9,256 ($10,459): €4,486 ($5,069) direct costs and €4,770 ($5,390) indirect costs. A histogram of semi-annual costs revealed distinct groups with low costs (< €2,500 = $2,825) and high costs (> €2,500 = $2,825). Seizure-free patients were clustered in the group with low costs; patients with ongoing seizures appeared more frequently in the group with high costs. Working patients were more often found in the group with low costs, whereas unemployed patients were more prevalent in the group with high costs. The regression analysis confirmed worklessness as the main cost driver. CONCLUSION: Non-productivity and poorly controlled disease with ongoing seizures are associated with higher costs in epilepsy. Providing high-level care and optimal drug treatment that enables patients to remain in work may help reduce the economic burden of epilepsy.

Full model selection using regression trees for numeric predictions of biomarkers for metabolic challenges in dairy cows.

Dairy cows suffer poor metabolic adaptation syndrome (PMAS)1 during early post-calving periods caused by negative energy balance. Measurement of blood beta-hydroxy butyric acid (BHBA)2 and blood non-esterified fatty acids (NEFA)3 allow early and accurate detection of negative energy balance. Machine learning prediction of blood BHBA and blood NEFA using milk testing samples represents an opportunity to identify at-risk animals, using less labor than direct blood testing methods. Routine milk testing on modern dairies and computer record keeping provide an immense amount of data which can then be used in machine learning models. Previous research for predicting blood metabolites using Fourier-transform infrared spectroscopy (FTIR)4 milk data has focused mainly on individual models rather than a comparison among the models. Full model selection is the process of comparing different combinations of pre-processing methods, variable selection, and statistical learning algorithms to determine which model results in the lowest prediction error for a given dataset. For this project we used a full model selection approach with regression trees (rtFMS)5 . rtFMS uses the cross-validated performance of different model configurations to feed a regression tree for selecting a final model. A total of 384 possible model configurations (algorithms, predictors and data preprocessing options) for each outcome (blood BHBA and blood NEFA) were considered in the rtFMS technique. rtFMS allows direct comparison of multiple modeling approaches reducing bias due to empirical knowledge, modeling habits, or preferences, identifying the model with minimal root mean squared prediction error (RMSE)6 . An elastic net regression model was selected as the best performing model for both biomarkers. The input data for blood BHBA predictions were FTIR milk spectra, with a second derivative pre-processing, and a filter with 212 wave numbers, obtaining RMSE = 0.354 (0.328-0.392). The best performing model for blood NEFA had input data of FTIR milk spectra, with a second derivative pre-processing, and a filter with 212 wave numbers filter along with the time of milking, obtaining RMSE = 0.601 (0.564-0.654). The comparison of multiple modeling strategies, conducted by rtFMS, present an option for improved FTIR prediction models of blood BHBA and blood NEFA by reducing error due to human bias. The implementation of rtFMS to design future prediction models can guide model inputs and features. Our prediction models have the potential to increase early detection of metabolic disorders in dairy cows during the transition period.

Comorbidity as prognostic variable in MDS: comparative evaluation of the HCT-CI and CCI in a core dataset of 419 patients of the Austrian MDS Study Group.

BACKGROUND: The evaluation of comorbidity is of increasing importance in patients with hematologic disorders. PATIENTS AND METHODS: In the present study, the influence of comorbidity on survival and acute myeloid leukemia (AML) evolution was analyzed retrospectively in 419 patients with de novo myelodysplastic syndromes (MDS) (observation period: 1985-2007). The median age was 71 years (range 24-91 years). Two different scoring systems, the hematopoietic stem-cell transplantation-specific comorbidity index (HCT-CI) and the Charlson comorbidity index (CCI) were applied. RESULTS: The HCT-CI was found to be a significant prognostic factor for overall survival (OS, P < 0.05) as well as event-free survival (EFS, P < 0.05) in our patients, whereas the CCI was of prognostic significance for OS (P < 0.05), but not for EFS. For AML-free survival, neither the HCT-CI nor the CCI were of predictive value. A multivariate analysis including age, lactate dehydrogenase, ferritin, karyotype, number of cytopenias, French-American-British groups, and comorbidity was applied. Comorbidity was found to be an independent prognostic factor in patients with low- or int-1-risk MDS (P < 0.05) regarding OS and EFS. CONCLUSIONS: Together, our data show that comorbidity is an important risk factor for OS and EFS in patients with MDS.

A new ensemble-based algorithm for identifying breath gas marker candidates in liver disease using ion molecule reaction mass spectrometry.

MOTIVATION: Alcoholic fatty liver disease (AFLD) and non-AFLD (NAFLD) can progress to severe liver diseases such as steatohepatitis, cirrhosis and cancer. Thus, the detection of early liver disease is essential; however, minimal invasive diagnostic methods in clinical hepatology still lack specificity. RESULTS: Ion molecule reaction mass spectrometry (IMR-MS) was applied to a total of 126 human breath gas samples comprising 91 cases (AFLD, NAFLD and cirrhosis) and 35 healthy controls. A new feature selection modality termed Stacked Feature Ranking (SFR) was developed to identify potential liver disease marker candidates in breath gas samples, relying on the combination of different entropy- and correlation-based feature ranking methods including statistical hypothesis testing using a two-level architecture with a suggestion and a decision layer. We benchmarked SFR against four single feature selection methods, a wrapper and a recently described ensemble method, indicating a significantly higher discriminatory ability of up to 10-15% for the SFR selected gas compounds expressed by the area under the ROC curve (AUC) of 0.85-0.95. Using this approach, we were able to identify unexpected breath gas marker candidates in liver disease of high predictive value. A literature study further supports top-ranked markers to be associated with liver disease. We propose SFR as a powerful tool for biomarker search in breath gas and other biological samples using mass spectrometry. AVAILABILITY: The algorithm SFR and IMR-MS datasets are available under http://biomed.umit.at/page.cfm?pageid=526.