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Background: Liver metastases are common in patients with breast cancer, and determining the factors associated with such metastases may improve both their early detection and treatment. Given that liver function protein level changes in these patients have not been determined, the aim of our study was to investigate liver function protein level changes over time, spanning 6 months before the detection of liver metastasis to 12 months after. Methods: We retrospectively studied 104 patients with hepatic metastasis from breast cancer who were treated at the Departments of Internal Medicine I and the Department of Obstetrics and Gynecology at the Medical University of Vienna between 1980 and 2019. Data were extracted from patient records. Results: Aspartate aminotransferase, alanine aminotransferase, gamma-glutamyltransferase, lactate dehydrogenase and alkaline phosphatase were significantly elevated when compared to normal range 6 months before the detection of liver metastases (p<0.001) Albumin was decreased (p<0.001). The values of aspartate aminotransferase, gamma-glutamyltransferase, and lactate dehydrogenase were significantly increased at the time of diagnosis compared to 6 months prior (p<0.001). Patient- and tumor-specific parameters had no influence on these liver function indicators. Elevated aspartate aminotransferase (p = 0.002) and reduced albumin (p = 0.002) levels at the time of diagnosis were associated with shorter overall survival. Conclusion: Liver function protein levels should be considered as potential indicators when screening for liver metastasis in patients with breast cancer. With the new treatment options available, it could lead to prolonged life. [ABSTRACT FROM AUTHOR]

So far, no cost-efficient, widely-used biomarkers have been established to facilitate the objectivization of Alzheimer's disease (AD) diagnosis and monitoring. Research suggests that event-related potentials (ERPs) reflect neurodegenerative processes in AD and might qualify as neurophysiological AD markers. First, to examine which ERP component correlates the most with AD severity, as measured by the Mini-Mental State Examination (MMSE). Then, to analyze the temporal change of this component as AD progresses. Sixty-three subjects (31 with possible, 32 with probable AD diagnosis) were recruited as part of the cohort study Prospective Dementia Registry Austria (PRODEM). For a maximum of 18 months patients revisited every 6 months for follow-up assessments. ERPs were elicited using an auditory oddball paradigm. P300 and N200 latency was determined with regard to target as well as difference wave ERPs, whereas P50 amplitude was measured from standard stimuli waveforms. P300 latency exhibited the strongest association with AD severity (e.g., r = –0.512, p < 0.01 at Pz for target stimuli in probable AD subjects). Further, there were significant Pearson correlations for N200 latency (e.g., r = –0.407, p = 0.026 at Cz for difference waves in probable AD subjects). P50 amplitude, as measured by different detection methods and at various scalp sites, did not significantly correlate with disease severity – neither in probable AD, possible AD, nor in both subgroups of patients combined. ERP markers for the group of possible AD patients did not show any significant correlations with MMSE scores. Post-hoc pairwise comparisons between baseline and 18-months follow-up assessment revealed significant P300 latency differences (e.g., p < 0.001 at Cz for difference waves in probable AD subjects). However, there were no significant correlations between the change rates of P300 latency and MMSE score. P300 and N200 latency significantly correlated with disease severity in probable AD, whereas P50 amplitude did not. P300 latency, which showed the highest correlation coefficients with MMSE, significantly increased over the course of the 18 months study period in probable AD patients. The magnitude of the observed prolongation is in line with other longitudinal AD studies and substantially higher than in normal ageing, as reported in previous trials (no healthy controls were included in our study). • Highest number of AD subjects (N = 63) for correlation analysis between ERP markers and AD severity in a prospective study. • ERP study with the highest number of AD patients longitudinally followed (N = 29), considering study periods above 6 months. • First study to investigate correlation coefficients between P50 amplitude and AD severity. • P300 and N200 latency significantly correlate with disease severity in probable AD patients, while P50 amplitude does not. • P300 latency significantly increases over 18 months in probable AD subjects. [ABSTRACT FROM AUTHOR]

The interaction between the T cell receptor and the major histocompatibility complex is one of the most important events in adaptive immunology. Although several different models for the activation process of the T cell via the T cell receptor have been proposed, it could not be shown that a structural mechanism, which discriminates between peptides of different immunogenicity levels, exists within the T cell receptor. In this study, we performed systematic molecular dynamics simulations of 172 closely related altered peptide ligands in the same T cell receptor/major histocompatibility complex system. Statistical evaluations yielded significant differences in the initial relaxation process between sets of peptides at four different immunogenicity levels. [ABSTRACT FROM AUTHOR]

Provides information on a study that presented a semi-nonparametric approach to risk-neutral density extraction from option prices which is based on an extension of the concept of mixture density networks. Option pricing models; Estimation and evaluation of models; Comparison of risk-neutral densities.

Neurosciences clinical trials continue to have notoriously high failure rates. Appropriate outcomes selection in early clinical trials is key to maximizing the likelihood of identifying new treatments in psychiatry and neurology. The field lacks good standards for designing outcome strategies, therefore The Outcomes Research Group was formed to develop and promote good practices in outcome selection. This article describes the first published guidance on the standardization of the process for clinical outcomes in neuroscience. A minimal step process is defined starting as early as possible, covering key activities for evidence generation in support of content validity, patient-centricity, validity requirements and considerations for regulatory acceptance. Feedback from expert members is provided, regarding the risks of shortening the process and examples supporting the recommended process are summarized. This methodology is now available to researchers in industry, academia or clinics aiming to implement consensus-based standard practices for clinical outcome selection, contributing to maximizing the efficiency of clinical research. [ABSTRACT FROM AUTHOR]

Objectives. The retinoblastoma (RB) pathway is crucial in the development and progression of many cancers. To better understand the biology of progressive breast cancer (BC), we examined protein expression of the RB pathway in primary BCs and matched axillary lymph node metastases (LM). Methods. Immunohistochemistry was used to evaluate cyclin D1, CDK4/6, RB, phosphorylated RB (pRB), and E2F1 expression in tissue arrays containing cores of 50 primary BCs and matched LM. The number of positive tumor cells and staining intensity were scored. Results. The proteins were localized in the nucleus, while CDK6 was detected in the cytoplasm and CDK4 was found in both. pRB and E2F1 showed higher expression in matched LM than in primary tumors. Expression of these proteins differed significantly by the percentage of positive tumor cells, while proteins in the proximal portion of the RB pathway showed no significant differences. The main path of alteration consisted of high pRB in primary BC, remaining pRB high in the majority of LM, variations occurring in fewer cases. All matched LM of the few primary tumors that had unaltered RB and pRB expression showed changes in RB or pRB expression. Conclusion. Expression of pRB and E2F1 was significantly higher in LM than in primary BC. A majority of cancers with LM showed altered RB or pRB expression, suggesting that proteins downstream in the RB pathway play a critical role in metastatic BC and disease progression. So looking at the RB pathway could be an option for chemotherapy decisions in patients with only few LM. [ABSTRACT FROM AUTHOR]

• A deep bidirectional language model is used to capture the context of sentences. • The shared content between sentences is quantified using contextualized embeddings. • A hierarchical clustering algorithm is utilized to identify the most relevant sentences. • The summarizer improves the performance of biomedical text summarization. • Contextualized embeddings can effectively capture the context in biomedical summarization. Capturing the context of text is a challenging task in biomedical text summarization. The objective of this research is to show how contextualized embeddings produced by a deep bidirectional language model can be utilized to quantify the informative content of sentences in biomedical text summarization. We propose a novel summarization method that utilizes contextualized embeddings generated by the Bidirectional Encoder Representations from Transformers (BERT) model, a deep learning model that recently demonstrated state-of-the-art results in several natural language processing tasks. We combine different versions of BERT with a clustering method to identify the most relevant and informative sentences of input documents. Using the ROUGE toolkit, we evaluate the summarizer against several methods previously described in literature. The summarizer obtains state-of-the-art results and significantly improves the performance of biomedical text summarization in comparison to a set of domain-specific and domain-independent methods. The largest language model not specifically pretrained on biomedical text outperformed other models. However, among language models of the same size, the one further pretrained on biomedical text obtained best results. We demonstrate that a hybrid system combining a deep bidirectional language model and a clustering method yields state-of-the-art results without requiring labor-intensive creation of annotated features or knowledge bases or computationally demanding domain-specific pretraining. This study provides a starting point towards investigating deep contextualized language models for biomedical text summarization. [ABSTRACT FROM AUTHOR]

• 1:4 frequency relationship between heart rate and breathing frequency both during wakefulness and sleep. • 1:8 frequency relationship between heart rate and alpha frequency during task performance but not during eyes-closed rest. • 1:32 frequency relationship between breathing frequency and alpha frequency during task performance but not during eyes-closed rest. • Irrational frequency relationship between spindle frequency and heart rate as well as spindle frequency and breathing frequency during sleep – suggesting decoupling. Cross frequency coupling is used to study the cross talk between brain oscillations. In this paper we focus on a special type of frequency coupling between brain and body oscillations, which is reflected by the numerical ratio (r) between two frequencies (m and n; n > m). This approach is motivated by theoretical considerations, indicating that during alert wakefulness brain-body oscillations form a coupled hierarchy of frequencies with integer relationships that are binary multiples (r = n:m = 1:2, 1:4, 1:8.....). During sleep we expect an irrational relationship (r = n/m = irrational number) between brain and body oscillations that reflects decoupling. We analyzed alpha frequency, heart rate, breathing frequency during performance of a memory tasks and in addition spindle frequency from data collected by the SIESTA sleep research group. As predicted, our results show a binary multiple frequency relationship between alpha, heart rate and breathing frequency during task performance but an irrational relationship between spindle frequency, heart rate and breathing frequency during sleep. [ABSTRACT FROM AUTHOR]

Germline variations in the BRCA‐1 and BRCA‐2 genes are associated with an increased risk of breast cancer. These variants are found in 5% of all breast cancer cases. Prophylactic mastectomy is the most effective risk‐reducing method and shows high rates of patient satisfaction and acceptance. We established a registry of Austrian BRCA‐1 and BRCA‐2 mutation carriers who had undergone mastectomy for oncologic or prophylactic reasons. Data were collected on the type of operation, complications, and type of reconstructive surgery for patients between 2014 and 2017. The complication rate in patients with nipple‐sparing mastectomy was significantly lower (23.1%) than in those with other types of mastectomies (60.7%; P =.005). In patients with implant‐based breast reconstruction, subpectoral placement was associated with a significantly higher rate of complications than prepectoral placement (P =.025). Median implant volume was 350 cc (range: 155‐650 cc), and a 100‐cc increase was associated with doubling of the odds of a complication (regression coefficient = 0.007); based on this finding, some surgeons may decide on using smaller implants. In summary, we identified significant associations between the risk of complications and surgical characteristics, and found host factors like diabetes, BMI, and smoking among Austrian patients with BRCA‐1 and BRCA‐2 variants. [ABSTRACT FROM AUTHOR]

Comments on the proposal made by Schyns et al for flexible features in categorization in relation to the three areas not explicitly discussed by the authors, namely, connectionist models of categorization, computational learning theory and constructivist theories of the mind. Two divisions of connectionist models; Relevance of computational learning to the theories of the authors.

Evidence theory by Dempster-Shafer for determination of hormone receptor status in breast cancer samples was introduced in our previous paper. One major topic pointed out here is the link between pieces of evidence found from different origins. In this paper the challenge of selecting appropriate ways of fusing evidence, depending on the type and quality of data involved is addressed. A parameterized family of evidence combination rules, covering the full range of potential needs, from emphasizing discrepancies in the measurements to aspiring accordance, is covered. The consequences for real patient samples are shown by modeling different decision strategies. [ABSTRACT FROM AUTHOR]

Digitalisation is changing all areas of our daily life. This changing environment requires new competences from physicians in all specialities. This study systematically surveyed the knowledge, attitude, and interests of medical students. These results will help further develop the medical curriculum, as well as increase our understanding of future physicians by other healthcare market players. A web-based survey consisting of four sections was developed: Section one queried demographic data, section two assessed the current digital health knowledge of medical students, section three queried their attitudes about the future impact of digital health in medicine and section four assessed the recommendations medical students have for the medical curriculum in terms of digital health. This survey was distributed to all (11,978) student at all public Austrian medical schools. A total of 8.4% of the medical student population started the survey. At the knowledge self-assessment section, the medical students reached mean of 11.74 points (SD 4.42) out of a possible maximum of 32 (female mean 10.66/ SD 3.87, male mean 13.34/SD 4.50). The attitude section showed that students see digitalisation as a threat, especially with respect to the patient–physician relationship. The curriculum recommendation section showed a high interest for topics related to AI, a per study year increasing interest in impact of digital health in communication, as well as a decreasing interest in robotic related topics. The attitude towards digital health can be described as sceptical. To ensure that future physicians keep pace with this development and fulfil their responsibility towards the society, medical schools need to be more proactive to foster the understanding of medical students that digital health will persistently alter the medical practice. [ABSTRACT FROM AUTHOR]

Computational sleep scoring from multimodal neurophysiological time-series (polysomnography PSG) has achieved impressive clinical success. Models that use only a single electroencephalographic (EEG) channel from PSG have not yet received the same clinical recognition, since they lack Rapid Eye Movement (REM) scoring quality. The question whether this lack can be remedied at all remains an important one. We conjecture that predominant Long Short-Term Memory (LSTM) models do not adequately represent distant REM EEG segments (termed epochs), since LSTMs compress these to a fixed-size vector from separate past and future sequences. To this end, we introduce the EEG representation model ENGELBERT (electro En cephalo G raphic E poch L ocal B idirectional E ncoder R epresentations from T ransformer). It jointly attends to multiple EEG epochs from both past and future. Compared to typical token sequences in language, for which attention models have originally been conceived, overnight EEG sequences easily span more than 1000 30 s epochs. Local attention on overlapping windows reduces the critical quadratic computational complexity to linear, enabling versatile sub-one-hour to all-day scoring. ENGELBERT is at least one order of magnitude smaller than established LSTM models and is easy to train from scratch in a single phase. It surpassed state-of-the-art macro F1-scores in 3 single-EEG sleep scoring experiments. REM F1-scores were pushed to at least 86%. ENGELBERT virtually closed the gap to PSG-based methods from 4–5 percentage points (pp) to less than 1 pp F1-score. [ABSTRACT FROM AUTHOR]

Highlights: [•] We search for objective sleep quality measures. [•] We model sleep as a continuous process represented by a set of sleep microstates. [•] We correlate parameters of the sleep model with latent factors of daytime behavioral and life-quality measures. [•] We report a set of sleep parameters significantly correlating with daytime factors. [ABSTRACT FROM AUTHOR]

Background: While machine learning (ML) algorithms may predict cardiovascular outcomes more accurately than statistical models, their result is usually not representable by a transparent formula. Hence, it is often unclear how specific values of predictors lead to the predictions. We aimed to demonstrate with graphical tools how predictor-risk relations in cardiovascular risk prediction models fitted by ML algorithms and by statistical approaches may differ, and how sample size affects the stability of the estimated relations.Methods: We reanalyzed data from a large registry of 1.5 million participants in a national health screening program. Three data analysts developed analytical strategies to predict cardiovascular events within 1 year from health screening. This was done for the full data set and with gradually reduced sample sizes, and each data analyst followed their favorite modeling approach. Predictor-risk relations were visualized by partial dependence and individual conditional expectation plots.Results: When comparing the modeling algorithms, we found some similarities between these visualizations but also occasional divergence. The smaller the sample size, the more the predictor-risk relation depended on the modeling algorithm used, and also sampling variability played an increased role. Predictive performance was similar if the models were derived on the full data set, whereas smaller sample sizes favored simpler models.Conclusion: Predictor-risk relations from ML models may differ from those obtained by statistical models, even with large sample sizes. Hence, predictors may assume different roles in risk prediction models. As long as sample size is sufficient, predictive accuracy is not largely affected by the choice of algorithm. [ABSTRACT FROM AUTHOR]

Abstract: We are introducing and validating an EEG data-based model of the sleep process with an arbitrary number of different sleep stages and a high time resolution allowing modeling of sleep microstructure. In contrast to the standard practice of sleep staging, defined by scoring rules, we describe sleep via posterior probabilities of a finite number of states, not necessarily reflecting the traditional sleep stages. To test the proposed probabilistic sleep model (PSM) for validity, we correlate statistics derived from the state posteriors with the results of psychometric tests, physiological variables and questionnaires collected before and after sleep. Considering short, in this study 3s long, data window the PSM allows describing the sleep process on finer time scale in comparison to the traditional sleep staging based on 20 or 30s long data segments visual inspection. By combining sleep states and using two measures derived from the posterior curves we show that the average absolute correlations between the measures and subjective and objective sleep quality measures are considerably higher when compared with the analogous measures derived from hypnograms based on sleep staging. In most cases these differences are significant. The results obtained with the PSM support its wider use in sleep process modeling research and these results also suggest that EEG signals contain more information about sleep than what sleep profiles based on discrete stages can reveal. Therefore the standardized scoring of sleep may not be sufficient to reveal important sleep changes related to subjective and objective sleep quality indexes. The proposed PSM represents a promising alternative. [Copyright &y& Elsevier]

The objectives of this study were to investigate the multiple-dose tolerability, safety, pharmacokinetics, and pharmacodynamics of the dual orexin receptor antagonist almorexant. Healthy subjects received daily doses of almorexant (100, 200, 400 or 1000 mg) or placebo in the morning for four days followed by two days with evening administration (Days 5–6). Each dose level was investigated in a new group of 10 subjects (eight active, two placebo, 1:1 sex). Dose-dependent increases in frequency and intensity were observed for somnolence and other adverse events. Pharmacokinetics at steady state showed rapid absorption, low concentrations eight hours post-dose, and minimal accumulation. Following evening, administration absorption was delayed and Cmax decreased. Almorexant at 400 and 1000 mg administered in the morning reduced vigilance, alertness, visuomotor coordination, and motor coordination assessed in a psychometric test battery. Polysomnography recordings following evening administration showed a trend towards shorter latency to sleep stages 3 and 4, and shorter latency to, and longer time in, rapid-eye-movement sleep at higher doses when compared to placebo. Whether these findings in healthy subjects translate into relevant sleep-enabling effects in insomnia patients needs to be investigated in future studies. [ABSTRACT FROM AUTHOR]

We present a novel hybrid technique for improving the predictive performance of an online machine learning system: Combining advantages from both memory-based and concept-based procedures selective relearning tackles the problem of learning in gradually changing domains with delayed feedback. The idea is based on training and retraining the model only on the subsegment of the historical dataset which has been identified as the one most similar to the current conditions. We exemplify the effectiveness of our approach by evaluation in a well-known artificial dataset and show that selective relearning is rather insensitive to noise. Additionally, we present preliminary experimental results for a complex synthetic dataset resembling an online diagnostic system for the tile manufacturing industry and show that the procedure for selecting the best segment yields favorable training results in terms of the mean-squared error of the predictions. [ABSTRACT FROM AUTHOR]

We use neural networks (NN) as a tool for a nonlinear autoregression to predict the second moment of the conditional density of return series. The NN models are compared to the popular econometric GARCH(1,1) model. We estimate the models in a Bayesian framework using Markov chain Monte Carlo posterior simulations. The interlinked aspects of the proposed Bayesian methodology are identification of NN hidden units and treatment ofNNcomplexity based onmodel evidence. The empirical study includes the application of the designed strategy to market data, where we found a strong support for a nonlinear multilayer perceptron model with two hidden units. [ABSTRACT FROM AUTHOR]

Objective: We developed a probabilistic continuous sleep stager based on Hidden Markov models using only a single EEG signal. It offers the advantage of being objective by not relying on human scorers, having much finer temporal resolution (1 s instead of 30 s), and being based on solid probabilistic principles rather than a predefined set of rules (Rechtschaffen & Kales) Methods and material: Sixty-eight whole night sleep recordings from two different sleep labs are analysed using Gaussian observation Hidden Markov models. Results: Our unsupervised approach detects the cornerstones of human sleep (wakefulness, deep and rem sleep) with around 80% accuracy based on data from a single EEG channel. There are some difficulties in generalizing results across sleep labs. Conclusion: Using data from a single electrode is sufficient for reliable continuous sleep staging. Sleep recordings from different sleep labs are not directly comparable. Training of separate models for the sleep labs is necessary. [Copyright &y& Elsevier]

Purpose: To compare the performance of neural networks with that of linear regression to predict the postoperative effective lens position (ELP) from preoperative biometry measurements.Setting: Departments of Ophthalmology, Medical Cybernetics and Artificial Intelligence, and Medical Physics, Medical University of Vienna, Vienna, Austria.Methods: The neural-network-type multilayer perceptron (MLP) and a linear regression technique were used to predict ELP. Suitable MLP models and variable input combinations were selected by extended-feature subset selection. Apart from the usual preoperative biometric variables, anterior chamber depth and lens thickness were measured with partial coherence interferometry and white-to-white measurements were used as input variables.Results: Prediction of ELP could be improved from a correlation coefficient (Pearson) of 0.54 for linear regression to a coefficient of 0.68 for the MLP; however, this difference was not statistically significant.Conclusion: The prediction of postoperative ACD with the MLP was not significantly better than the prediction using linear regression. [Copyright &y& Elsevier]

Presents a study on recurrent neural networks. Trading of options; Predictive models; Elman recurrent neural network; Discussion on data, volatility measures and trading strategy.

Methods of suicide have received considerable attention in suicide research. The common approach to differentiate methods of suicide is the classification into "violent" versus "non-violent" method. Interestingly, since the proposition of this dichotomous differentiation, no further efforts have been made to question the validity of such a classification of suicides. This study aimed to challenge the traditional separation into "violent" and "non-violent" suicides by generating a cluster analysis with a data-driven, machine learning approach. In a retrospective analysis, data on all officially confirmed suicides (N = 77,894) in Austria between 1970 and 2016 were assessed. Based on a defined distance metric between distributions of suicides over age group and month of the year, a standard hierarchical clustering method was performed with the five most frequent suicide methods. In cluster analysis, poisoning emerged as distinct from all other methods – both in the entire sample as well as in the male subsample. Violent suicides could be further divided into sub-clusters: hanging, shooting, and drowning on the one hand and jumping on the other hand. In the female sample, two different clusters were revealed – hanging and drowning on the one hand and jumping, poisoning, and shooting on the other. Our data-driven results in this large epidemiological study confirmed the traditional dichotomization of suicide methods into "violent" and "non-violent" methods, but on closer inspection "violent methods" can be further divided into sub-clusters and a different cluster pattern could be identified for women, requiring further research to support these refined suicide phenotypes. [ABSTRACT FROM AUTHOR]

Background: Previous research suggests significant increases in suicide mortality rates in European countries following the economic crisis of 2008. However, the relationship between national differences in availability and use of mental health services and suicide rates has not been extensively examined yet.Materials and Methods: Data on mental health services and socioeconomic variables were derived from Eurostat for the years 2000-2013 for ten European countries. The national health care variables consisted of suicide mortality rate (SMR), average length of hospital stay and number of hospitalizations due to affective disorder or any psychiatric disorder, number of psychiatric beds and number of prescribed antidepressants. Economic variables included the gross domestic product (GDP), the gross domestic product per 1000 inhabitants (Real GDP), the rate of unemployment and the GINI-coefficient as a measurement for the equality of wealth distribution. Mixed models were used to investigate the potential influence of the onset of the economic crisis in 2008 on each of the psychiatric variables. Multivariable regression analyses were used to assess the influence on suicide mortality rates.Results: In this study, a significant change in slope starting from 2008 was revealed for the number of psychiatric beds, hospitalizations due to affective disorder or any psychiatric disorder and for prescribed antidepressants. Furthermore, a significant step change for hospitalizations due to affective disorder was observed in 2008. SMR exhibited a significant step change in 2008 for males and females as well as a significant change in slope from 2008 onwards for males only. Contrary to our hypothesis, most variables showed no statistically significant influence on SMR. Only a higher number of available psychiatric beds was significantly associated with higher suicide mortality rates. This effect, however, was only significant for females and did not remain significant after correcting for economic variables. Less than 10% of suicide mortality rate variability could be explained by a model including all variables, further corroborating the multifactorial etiology of suicide.Limitations: Since administrative registry data was used, the results should be interpreted with caution. Results might not be applicable to countries not included.Conclusion: While significant changes in the psychiatric variables, as well as SMR, were observed, no statistically significant influence on SMR remained after correcting for country, time and economic variables. Our study suggests the necessity of a more comprehensive international data gathering effort. Further research is needed to identify populations at risk of suicide. [ABSTRACT FROM AUTHOR]

Background: Seltorexant is a potent and selective antagonist of the orexin-2 receptor that is being developed for the treatment of insomnia and major depressive disorder.Aims: The primary objective was to investigate the effect of seltorexant on sleep efficiency after single and multiple dose administration in subjects with insomnia disorder without psychiatric comorbidity. Secondary objectives included evaluation of total sleep time, latency to persistent sleep, and wake after sleep onset. Subjects received 40 mg of seltorexant for five days during Period 1 and placebo during Period 2 or vice versa in this randomized, two-way crossover study. Objective sleep parameters were evaluated by polysomnography over 8 h on Day 1/2 (single dose) and on Day 5/6 (multiple doses). Subjective sleep parameters were assessed by questionnaires.Results: Twenty-seven subjects completed the study. The mean changes in sleep efficiency (% (SD)) of seltorexant from placebo at Day 1/2 were 5.8 (9.2), and 7.9 (9.8) at Day 5/6 ( p < 0.001 at both time points); in total sleep time (min (SD)) 27.7 (44.3) and 37.9 (47.1), respectively; in latency to persistent sleep (min (SD)) -18.8 (21.3) and -29.9 (27.7), respectively; and in wake after sleep onset (min (SD)) -11.1 (36.4) and -11.3 (46.5). The most common adverse events were headache and somnolence.Conclusions: Sleep efficiency was increased with seltorexant treatment compared with placebo. Treatment with seltorexant resulted in a prolonged total sleep time, shorter latency to persistent sleep and wake after sleep onset. There were no unexpected safety findings. [ABSTRACT FROM AUTHOR]

Abstract: Event‐related potentials (ERPs) are commonly used in Neuroscience research, particularly the P3 waveform because it is associated with cognitive brain functions and is easily elicited by auditory or sensory inputs. ERPs are affected by drugs such as lorazepam, which increase the latency and decrease the amplitude of the P3 wave. In this study, auditory‐evoked ERPs were generated in 13 older healthy volunteers using an oddball tone paradigm, after administration of single 0.5 and 2 mg doses of lorazepam. Population pharmacokinetics (PK)/pharmacodynamics (PD) models were developed using nonlinear mixed‐effects methods in order to assess the effect of lorazepam on the latency and amplitude of the P3 waveforms. The PK/PD models showed that doses of 0.3 mg of lorazepam achieved approximately half of the maximum effect on the latency of the P3 waveform. For P3 amplitude, half the maximum effect was achieved with a dose of 1.2 mg of lorazepam. The PK/PD models also predicted an efficacious dose range of lorazepam, which was close to the recommended therapeutic range. The use of longitudinal P3 latency data allowed better predictions of the lorazepam efficacious dose range than P3 amplitude or aggregate exposure–response data, suggesting that latency could be a more sensitive parameter for drugs with similar mechanisms of action as lorazepam and that time course rather than single time‐point ERP data should be collected. Overall, the results suggest that P3 ERP waveforms could be used as potential non‐specific biomarkers for functional target engagement for drugs with brain activity, and PK/PD models can aid trial design and choice of doses for development of new drugs with ERP activity. [ABSTRACT FROM AUTHOR]

Background: Seasonal changes and climatic factors like ambient temperature, sunlight duration and rainfall can influence suicidal behavior. Methods: This study analyses the relationship between seasonal changes and climatic variations and suicide attempts in 2131 young patients in Istanbul, Turkey. Results: In our study sample, there was an association between suicide attempts in youths and seasonal changes, as suicide attempts occurred most frequently during summer in females as well as in males. Furthermore, there was a positive correlation between the mean temperature over the past 10 days and temperature at the index day and suicide attempts in females. After seasonality effects were mathematically removed, the mean temperature 10 days before a suicide attempt remained significant in males only, indicating a possible short-term influence of temperature on suicide attempts. Conclusions: This study shows an association between suicide attempts of young people and climatic changes, in particular temperature changes as well as seasonal changes. Therefore, the influence of seasonal changes and climatic factors on young suicide attempters should get more attention in research to understand the biopsychosocial mechanisms playing a role in suicide attempts of young people. As suicide attempts most frequently occur in young people, further research is of considerable clinical importance. [ABSTRACT FROM AUTHOR]

Background: Controlling a myoelectric prosthesis for upper limbs is increasingly challenging for the user as more electrodes and joints become available. Motion classification based on pattern recognition with a multi-electrode array allows multiple joints to be controlled simultaneously. Previous pattern recognition studies are difficult to compare, because individual research groups use their own data sets. To resolve this shortcoming and to facilitate comparisons, open access data sets were analysed using components of BioPatRec and Netlab pattern recognition models. Methods: Performances of the artificial neural networks, linear models, and training program components were compared. Evaluation took place within the BioPatRec environment, a Matlab-based open source platform that provides feature extraction, processing and motion classification algorithms for prosthetic control. The algorithms were applied to myoelectric signals for individual and simultaneous classification of movements, with the aim of finding the best performing algorithm and network model. Evaluation criteria included classification accuracy and training time. Results: Results in both the linear and the artificial neural network models demonstrated that Netlab's implementation using scaled conjugate training algorithm reached significantly higher accuracies than BioPatRec. Conclusions: It is concluded that the best movement classification performance would be achieved through integrating Netlab training algorithms in the BioPatRec environment so that future prosthesis training can be shortened and control made more reliable. Netlab was therefore included into the newest release of BioPatRec (v4.0). [ABSTRACT FROM AUTHOR]

Background Neutropaenic patients are at a high risk of contracting severe infections. In particular, in these patients, parameters with a high negative predictive value are desirable for excluding infection or bacteraemia. This study evaluated sepsis biomarkers in neutropaenic patients suffering from systemic inflammatory response syndrome ( SIRS). Further, the predictive capacities of evaluated biomarkers in neutropaenic SIRS patients were compared to non-neutropaenic SIRS patients. Material and methods In this prospective observational cohort study, patients with clinically suspected sepsis were screened. The predictive capacities of procalcitonin ( PCT), C-reactive protein and lipopolysaccharide-binding protein ( LBP) in neutropaenic SIRS patients were evaluated in terms of their potential to identify infection or bacteraemia and were compared to results for non-neutropaenic SIRS patients. To select an appropriate control cohort, propensity score matching was applied, balancing confounding factors between neutropaenic and non-neutropaenic SIRS patients. Results Of 3370 prospectively screened patients with suspected infection, 51 patients suffered from neutropaenic SIRS. For the identification of infection, none of the assessed biomarkers presented a clinically relevant discriminatory potency. Lipopolysaccharide-binding protein and PCT demonstrated discriminatory capacity to discriminate between nonbacteraemic and bacteraemic SIRS in patients with neutropaenia [receiver-operating characteristics-area under the curves ( ROC- AUCs): 0·860, 0·818]. In neutropaenic SIRS patients, LBP had a significantly better ROC- AUC than in a comparable non-neutropaenic patient cohort for identifying bacteraemia ( P = 0·01). Conclusion In neutropaenic SIRS patients, none of the evaluated biomarkers was able to adequately identify infection. LBP and PCT presented a good performance in identifying bacteraemia. Therefore, these markers could be used for screening purposes to increase the pretest probability of blood culture analysis. [ABSTRACT FROM AUTHOR]

Background: Bacteraemia is a frequent and severe condition with a high mortality rate. Despite profound knowledge about the pre-test probability of bacteraemia, blood culture analysis often results in low rates of pathogen detection and therefore increasing diagnostic costs. To improve the cost-effectiveness of blood culture sampling, we computed a risk prediction model based on highly standardizable variables, with the ultimate goal to identify via an automated decision support tool patients with very low risk for bacteraemia. Methods: In this retrospective hospital-wide cohort study evaluating 15,985 patients with suspected bacteraemia, 51 variables were assessed for their diagnostic potency. A derivation cohort (n = 14.699) was used for feature and model selection as well as for cut-off specification. Models were established using the A2DE classifier, a supervised Bayesian classifier. Two internally validated models were further evaluated by a validation cohort (n = 1,286). Results: The proportion of neutrophile leukocytes in differential blood count was the best individual variable to predict bacteraemia (ROC-AUC: 0.694). Applying the A2DE classifier, two models, model 1 (20 variables) and model 2 (10 variables) were established with an area under the receiver operating characteristic curve (ROC-AUC) of 0.767 and 0.759, respectively. In the validation cohort, ROC-AUCs of 0.800 and 0.786 were achieved. Using predefined cut-off points, 16% and 12% of patients were allocated to the low risk group with a negative predictive value of more than 98.8%. Conclusion: Applying the proposed models, more than ten percent of patients with suspected blood stream infection were identified having minimal risk for bacteraemia. Based on these data the application of this model as an automated decision support tool for physicians is conceivable leading to a potential increase in the cost-effectiveness of blood culture sampling. External prospective validation of the model's generalizability is needed for further appreciation of the usefulness of this tool. [ABSTRACT FROM AUTHOR]

Background: The rationale of a postulated decrease in fertility rate development is still being debated. Among the multiple influencing factors, socioeconomic variables and their complex influence are of particular interest. Methods: Data on socioeconomic and health variables from 1976–2014 of 30 countries within the OECD region were analysed for their respective influence on fertility rates by using mixed-effect regression models. Results: A significant negative influence of the increase in unemployment rate on the following year's changes in fertility rate in Western (−0.00256; p < 0.001) as well as Eastern European (−0.0034; p < 0.001) countries was revealed. The effect of being overweight was significant for Western European (−0.00256; p < 0.001) countries only. When analysing the whole OECD region, an increase in unemployment retained its significant negative influence on the fertility rate (−0.0028; p < 0.001), while being overweight did not. Interestingly, divergent influences of time were revealed and fertility rates increased with time in Eastern Europe while they decreased in Western Europe. Conclusion: Importantly, a significant negative influence of increase in unemployment on the fertility rate was revealed—irrespective of the region and time analysed. Furthermore, an adverse effect of being overweight on the fertility rate in Western European countries was revealed. Interestingly, time was associated with a decreasing fertility rate in Western but not in Eastern Europe. [ABSTRACT FROM AUTHOR]

Aims: We tested the hypothesis that artificial intelligence (AI)-powered algorithms applied to cardiac magnetic resonance (CMR) images could be able to detect the potential patterns of cardiac amyloidosis (CA). Readers in CMR centers with a low volume of referrals for the detection of myocardial storage diseases or a low volume of CMRs, in general, may overlook CA. In light of the growing prevalence of the disease and emerging therapeutic options, there is an urgent need to avoid misdiagnoses. Methods and Results: Using CMR data from 502 patients (CA: n = 82), we trained convolutional neural networks (CNNs) to automatically diagnose patients with CA. We compared the diagnostic accuracy of different state-of-the-art deep learning techniques on common CMR imaging protocols in detecting imaging patterns associated with CA. As a result of a 10-fold cross-validated evaluation, the best-performing fine-tuned CNN achieved an average ROC AUC score of 0.96, resulting in a diagnostic accuracy of 94% sensitivity and 90% specificity. Conclusions: Applying AI to CMR to diagnose CA may set a remarkable milestone in an attempt to establish a fully computational diagnostic path for the diagnosis of CA, in order to support the complex diagnostic work-up requiring a profound knowledge of experts from different disciplines. [ABSTRACT FROM AUTHOR]

Background: In 2007, the AASM Manual for the Scoring of Sleep and Associated Events was published by the American Academy of Sleep Medicine (AASM). Concerning the visual classification of sleep stages, these new rules are intended to replace the rules by Rechtschaffen and Kales (R&K). Methods: We adapted the automatic R&K sleep scoring system Somnolyzer 24 × 7 to comply with the AASM rules and subsequently performed a validation study based on 72 polysomnographies from the Siesta database (56 healthy subjects, 16 patients, 38 females, 34 males, aged 21-86 years). Scorings according to the AASM rules were performed manually by experienced sleep scorers and semi-automatically by the AASM version of the Somnolyzer. Manual scorings and Somnolyzer reviews were performed independently by at least 2 out of 8 experts from 4 sleep centers. Results: In the quality control process, sleep experts corrected 4.8 and 3.7% of the automatically assigned epochs, resulting in a reliability between 2 Somnolyzer-assisted scorings of 99% (Cohen's kappa: 0.99). In contrast, the reliability between the 2 manual scorings was 82% (kappa: 0.76). The agreement between the 2 Somnolyzer-assisted and the 2 visual scorings was between 81% (kappa: 0.75) and 82% (kappa: 0.76). Conclusion: The AASM version of the Somnolyzer revealed an agreement between semi-automated and human expert scoring comparable to that published for the R&K version with a validity comparable to that of human experts, but with a reliability close to 1, thereby reducing interrater variability as well as scoring time to a minimum. Copyright © 2010 S. Karger AG, Basel [ABSTRACT FROM AUTHOR]

Abstract: One of the standard applications of Independent Component Analysis (ICA) to EEG is removal of artifacts due to movements of the eye bulbs. Short blinks as well as slower saccadic movements are removed by subtracting respective independent components (ICs). EEG recorded from blind subjects poses special problems, since it shows a higher quantity of eye movements, which are also more irregular and very different across subjects. It is demonstrated that ICA can still be of use by comparing results from four blind subjects with results from one subject without eye bulbs who therefore does not show eye movement artifacts at all. [Copyright &y& Elsevier]

To date, the only standard for the classification of sleep-EEG recordings that has found worldwide acceptance are the rules published in 1968 by Rechtschaffen and Kales. Even though several attempts have been made to automate the classification process, so far no method has been published that has proven its validity in a study including a sufficiently large number of controls and patients of all adult age ranges. The present paper describes the development and optimization of an automatic classification system that is based on one central EEG channel, two EOG channels and one chin EMG channel. It adheres to the decision rules for visual scoring as closely as possible and includes a structured quality control procedure by a human expert. The final system (Somnolyzer 24 × 7™) consists of a raw data quality check, a feature extraction algorithm (density and intensity of sleep/wake-related patterns such as sleep spindles, delta waves, SEMs and REMs), a feature matrix plausibility check, a classifier designed as an expert system, a rule-based smoothing procedure for the start and the end of stages REM, and finally a statistical comparison to age- and sex-matched normal healthy controls (Siesta Spot Report™). The expert system considers different prior probabilities of stage changes depending on the preceding sleep stage, the occurrence of a movement arousal and the position of the epoch within the NREM/REM sleep cycles. Moreover, results obtained with and without using the chin EMG signal are combined. The Siesta polysomnographic database (590 recordings in both normal healthy subjects aged 20–95 years and patients suffering from organic or nonorganic sleep disorders) was split into two halves, which were randomly assigned to a training and a validation set, respectively. The final validation revealed an overall epoch-by-epoch agreement of 80% (Cohen’s kappa: 0.72) between the Somnolyzer 24 × 7 and the human expert scoring, as compared with an inter-rater reliability of 77% (Cohen’s kappa: 0.68) between two human experts scoring the same dataset. Two Somnolyzer 24 × 7 analyses (including a structured quality control by two human experts) revealed an inter-rater reliability close to 1 (Cohen’s kappa: 0.991), which confirmed that the variability induced by the quality control procedure, whereby approximately 1% of the epochs (in 9.5% of the recordings) are changed, can definitely be neglected. Thus, the validation study proved the high reliability and validity of the Somnolyzer 24 × 7 and demonstrated its applicability in clinical routine and sleep studies. Copyright © 2005 S. Karger AG, Basel [ABSTRACT FROM AUTHOR]

The aim of this study was to evaluate the technical performance of the implantation of the Corinthian IQ stent into the femoropopliteal arteries using 6-F introducer sheaths. Thirty-three lesions (24 stenoses, 9 occlusions) in 32 patients (mean age 67 years) were stented. Selective stent implantation was performed after unsatisfactory percutaneous transluminal angioplasty result with an antegrade access in 21 patients and through a crossover sheath in 11 patients. A total of 36 stents were implanted. Stent delivery was successful in all cases (stent diameter 5 or 6 mm, stent length 11-38 mm). In none of the cases was stent dislodgement from the balloon observed. Stent placement was optimal without residual stenosis in 30 cases. In 1 case the stent was chosen too short requiring implantation of a second stent. In 1 patient distal embolization was observed which was treated with catheter embolectomy. Nine-month primary and secondary patency rates were 79+/-8.7 and 86+/-7.7%, respectively. Implantation of the Corinthian IQ stent into the femoropopliteal arteries using 6-F introducer sheaths is safe in antegrade and crossover procedures. [ABSTRACT FROM AUTHOR]

Nuclei instance segmentation can be considered as a key point in the computer-mediated analysis of histological fluorescence-stained (FS) images. Many computer-assisted approaches have been proposed for this task, and among them, supervised deep learning (DL) methods deliver the best performances. An important criterion that can affect the DL-based nuclei instance segmentation performance of FS images is the utilised image bit depth, but to our knowledge, no study has been conducted so far to investigate this impact. In this work, we released a fully annotated FS histological image dataset of nuclei at different image magnifications and from five different mouse organs. Moreover, by different pre-processing techniques and using one of the state-of-the-art DL-based methods, we investigated the impact of image bit depth (i.e., eight bits vs. sixteen bits) on the nuclei instance segmentation performance. The results obtained from our dataset and another publicly available dataset showed very competitive nuclei instance segmentation performances for the models trained with 8 bit and 16 bit images. This suggested that processing 8 bit images is sufficient for nuclei instance segmentation of FS images in most cases. The dataset including the raw image patches, as well as the corresponding segmentation masks is publicly available in the published GitHub repository. [ABSTRACT FROM AUTHOR]

Quantitative analysis of sleep EEG data can provide valuable additional information in sleep research. However, analysis of data contaminated by artifacts can lead to spurious results. Thus, the first step in realizing an automatic sleep analysis system is the implementation of a reliable and valid artifact processing strategy. This strategy should include: (1) high-quality recording techniques in order to minimize the occurrence of avoidable artifacts (e.g. technical artifacts); (2) artifact minimization procedures in order to minimize the loss of data by estimating the contribution of different artifacts in the EEG recordings, thus allowing the calculation of the ‘corrected’ EEG (e.g. ocular and ECG interference), and finally (3) artifact identification procedures in order to define epochs contaminated by remaining artifacts (e.g. movement and muscle artifacts). Therefore, after a short description of the types of artifacts in the sleep EEG and some typical examples obtained in different sleep stages, artifact minimization and identification procedures will be reviewed. [ABSTRACT FROM AUTHOR]

• Investigating the effect of image downsampling and cropping on skin lesion classification performance. • Proposing a three-level fusion approach using multiple fine-tuned deep networks and cropped multi-scale skin lesion images. • Achieving excellent classification performance on the ISIC 2018 challenge test dataset. Background and objective: Skin cancer is among the most common cancer types in the white population and consequently computer aided methods for skin lesion classification based on dermoscopic images are of great interest. A promising approach for this uses transfer learning to adapt pre-trained convolutional neural networks (CNNs) for skin lesion diagnosis. Since pre-training commonly occurs with natural images of a fixed image resolution and these training images are usually significantly smaller than dermoscopic images, downsampling or cropping of skin lesion images is required. This however may result in a loss of useful medical information, while the ideal resizing or cropping factor of dermoscopic images for the fine-tuning process remains unknown. Methods: We investigate the effect of image size for skin lesion classification based on pre-trained CNNs and transfer learning. Dermoscopic images from the International Skin Imaging Collaboration (ISIC) skin lesion classification challenge datasets are either resized to or cropped at six different sizes ranging from 224 × 224 to 450 × 450. The resulting classification performance of three well established CNNs, namely EfficientNetB0, EfficientNetB1 and SeReNeXt-50 is explored. We also propose and evaluate a multi-scale multi-CNN (MSM-CNN) fusion approach based on a three-level ensemble strategy that utilises the three network architectures trained on cropped dermoscopic images of various scales. Results: Our results show that image cropping is a better strategy compared to image resizing delivering superior classification performance at all explored image scales. Moreover, fusing the results of all three fine-tuned networks using cropped images at all six scales in the proposed MSM-CNN approach boosts the classification performance compared to a single network or a single image scale. On the ISIC 2018 skin lesion classification challenge test set, our MSM-CNN algorithm yields a balanced multi-class accuracy of 86.2% making it the currently second ranked algorithm on the live leaderboard. Conclusions: We confirm that the image size has an effect on skin lesion classification performance when employing transfer learning of CNNs. We also show that image cropping results in better performance compared to image resizing. Finally, a straightforward ensembling approach that fuses the results from images cropped at six scales and three fine-tuned CNNs is shown to lead to the best classification performance. [ABSTRACT FROM AUTHOR]

• A method for identifying neurons in histology following in vivo Ca2+ imaging. • Combining neuronal activity with post hoc molecular expressions of the same cells. • Freely moving in vivo Ca2+ imaging using microendoscopic lenses and miniscopes. • 60 % of in vivo recorded neurons can be matched post hoc in histology. Cognitive neuroscientists aim to understand behavior often based on the underlying activity of individual neurons. Recently developed miniaturized epifluorescence microscopes allow recording of cellular calcium transients, resembling neuronal activity, of individual neurons even in deep brain areas in freely behaving animals. At the same time, molecular markers allow the characterization of diverse neuronal subtypes by post hoc immunohistochemical labeling. Combining both methods would allow researchers to increase insights into how individual neuronal activity and entities contribute to behavior. Here, we present a novel method for identifying the same neurons, recorded with calcium imaging using a miniaturized epifluorescence microscope, post hoc in fixed histological sections. This allows immunohistochemical investigations to detect the molecular signature of in vivo recorded neurons. Our method utilizes the structure of blood vessels for aligning in vivo acquired 2D images with a reconstructed 3D histological model. We automatically matched, 60 % of all in vivo recorded cells post hoc in histology. Across all animals, we successfully matched 43 % to 89 % of the recorded neurons. We provide a measure for the confidence of matched cells and validated our method by multiple simulation studies. To our knowledge, we present the first method for matching cells, recorded with a miniaturized epifluorescence microscope in freely moving animals, post hoc in histological sections. Our method allows a comprehensive analysis of how cortical circuits relate to freely moving animal behavior by combining functional activity of individual neurons with their underlying histological profiles. [ABSTRACT FROM AUTHOR]

(1) Background: Cardiac amyloidosis (CA) is a rare and complex condition with poor prognosis. While novel therapies improve outcomes, many affected individuals remain undiagnosed due to a lack of awareness among clinicians. This study was undertaken to develop an expert-independent machine learning (ML) prediction model for CA relying on routinely determined laboratory parameters. (2) Methods: In a first step, we developed baseline linear models based on logistic regression. In a second step, we used an ML algorithm based on gradient tree boosting to improve our linear prediction model, and to perform non-linear prediction. Then, we compared the performance of all diagnostic algorithms. All prediction models were developed on a training cohort, consisting of patients with proven CA (positive cases, n = 121) and amyloidosis-unrelated heart failure (HF) patients (negative cases, n = 415). Performances of all prediction models were evaluated on a separate prognostic validation cohort with 37 CA-positive and 124 CA-negative patients. (3) Results: Our best model, based on gradient-boosted ensembles of decision trees, achieved an area under the receiver operating characteristic curve (ROC AUC) score of 0.86, with sensitivity and specificity of 89.2% and 78.2%, respectively. The best linear model had an ROC AUC score of 0.75, with sensitivity and specificity of 84.6 and 71.7, respectively. (4) Conclusions: Our work demonstrates that ML makes it possible to utilize basic laboratory parameters to generate a distinct CA-related HF profile compared with CA-unrelated HF patients. This proof-of-concept study opens a potential new avenue in the diagnostic workup of CA and may assist physicians in clinical reasoning. [ABSTRACT FROM AUTHOR]

Summary Neuronal signals in the prefrontal cortex have been reported to predict upcoming decisions. Such activity patterns are often coupled to perceptual cues indicating correct choices or values of different options. How does the prefrontal cortex signal future decisions when no cues are present but when decisions are made based on internal valuations of past experiences with stochastic outcomes? We trained rats to perform a two-arm bandit-task, successfully adjusting choices between certain-small or possible-big rewards with changing long-term advantages. We discovered specialized prefrontal neurons, whose firing during the encounter of no-reward predicted the subsequent choice of animals, even for unlikely or uncertain decisions and several seconds before choice execution. Optogenetic silencing of the prelimbic cortex exclusively timed to encounters of no reward, provoked animals to excessive gambling for large rewards. Firing of prefrontal neurons during outcome evaluation signals subsequent choices during gambling and is essential for dynamically adjusting decisions based on internal valuations. Highlights • Activity of prelimbic neurons predict upcoming decisions during gambling • Future choice –predictive firing occurs during evaluation of current outcome • Time-specific inactivation of prelimbic cortex increases high-risk gambling behavior • Prelimbic neurons contribute to adjusting decisions based on internal valuations Passecker et al. show that specialized neurons in the prelimbic cortex of rats predict the next choice during the outcome evaluation in a gambling task, even for unlikely or uncertain decisions. Disrupting the prelimbic cortex led to excessive risk taking. [ABSTRACT FROM AUTHOR]

Bacteraemia is a life-threating condition requiring immediate diagnostic and therapeutic actions. Blood culture (BC) analyses often result in a low true positive result rate, indicating its improper usage. A predictive model might assist clinicians in deciding for whom to conduct or to avoid BC analysis in patients having a relevant bacteraemia risk. Predictive models were established by using linear and non-linear machine learning methods. To obtain proper data, a unique data set was collected prior to model estimation in a prospective cohort study, screening 3,370 standard care patients with suspected bacteraemia. Data from 466 patients fulfilling two or more systemic inflammatory response syndrome criteria (bacteraemia rate: 28.8%) were finally used. A 29 parameter panel of clinical data, cytokine expression levels and standard laboratory markers was used for model training. Model tuning was performed in a ten-fold cross validation and tuned models were validated in a test set (80:20 random split). The random forest strategy presented the best result in the test set validation (ROC-AUC: 0.729, 95%CI: 0.679-0.779). However, procalcitonin (PCT), as the best individual variable, yielded a similar ROC-AUC (0.729, 95%CI: 0.679-0.779). Thus, machine learning methods failed to improve the moderate diagnostic accuracy of PCT. [ABSTRACT FROM AUTHOR]

Adequate early empiric antibiotic therapy is pivotal for the outcome of patients with bloodstream infections. In clinical practice the use of surrogate laboratory parameters is frequently proposed to predict underlying bacterial pathogens; however there is no clear evidence for this assumption. In this study, we investigated the discriminatory capacity of predictive models consisting of routinely available laboratory parameters to predict the presence of Gram-positive or Gram-negative bacteremia. Major machine learning algorithms were screened for their capacity to maximize the area under the receiver operating characteristic curve (ROC-AUC) for discriminating between Gram-positive and Gram-negative cases. Data from 23,765 patients with clinically suspected bacteremia were screened and 1,180 bacteremic patients were included in the study. A relative predominance of Gram-negative bacteremia (54.0%), which was more pronounced in females (59.1%), was observed. The final model achieved 0.675 ROC-AUC resulting in 44.57% sensitivity and 79.75% specificity. Various parameters presented a significant difference between both genders. In gender-specific models, the discriminatory potency was slightly improved. The results of this study do not support the use of surrogate laboratory parameters for predicting classes of causative pathogens. In this patient cohort, gender-specific differences in various laboratory parameters were observed, indicating differences in the host response between genders. [ABSTRACT FROM AUTHOR]