Alzheimer’s disease (AD) is a complex progressive neurodegenerative disorder commonly characterized by short term memory loss. Presently no effective therapeutic treatments exist that can completely cure this disease. The cause of Alzheimer’s is still unclear, however one of the other major factors involved in AD pathogenesis are the genetic factors and around 70 % risk of the disease is assumed to be due to the large number of genes involved. Although genetic association studies have revealed a number of potential AD susceptibility genes, there still exists a need for identification of unidentified AD-associated genes and therapeutic targets to have better understanding of the disease-causing mechanisms of Alzheimer’s towards development of effective AD therapeutics.In the present study, we have used machine learning approach to identify candidate AD associated genes by integrating topological properties of the genes from the protein-protein interaction networks, sequence features and functional annotations. We also used molecular docking approach and screened already known anti-Alzheimer drugs against the novel predicted probable targets of AD and observed that an investigational drug, AL-108, had high affinity for majority of the possible therapeutic targets. Furthermore, we performed molecular dynamics simulations and MM/GBSA calculations on the docked complexes to validate our preliminary findings.To the best of our knowledge, this is the first comprehensive study of its kind for identification of putative Alzheimer-associated genes using machine learning approaches and we propose that such computational studies can improve our understanding on the core etiology of AD which could lead to the development of effective anti-Alzheimer drugs.The online version of this article (doi:10.1186/s12864-016-3108-1) contains supplementary material, which is available to authorized users. Alzheimer’s disease (AD) is the most common neurological disease, accounting for 60–70 % of total dementia cases, affecting masses of people across the globe . The groA wide range of population surveys, genetic linkage studies and genome-wide association studies (GWAS) have been conducted to identify AD-associated genes and genetic mutations that alter with the expression of the genes in the brain. Apolipoprotein E (ApoE), Presenilin-1 (PSEN1) and Presenilin-2 (PSEN2), amyloid precursor protein (APP) and the linked mutations are some of the strongest risk factors that were observed to be associated with the brain disorder, Alzheimer’s . ResearcSince the already discovered AD-associated genes do not cover a significant portion of the human genome, there can be an innumerable number of disease genes still left to be discovered. Thus, in spite of the discovery of many genes responsible for AD, identification of disease-associated genes in humans still remains a huge problem to be addressed. Additionally due to the fact that no cure for AD exists, the identification of novel AD genes can disclose novel effective therapeutic targets which could advance the discovery of drugs for the disease . Lately,In the present study, we have used machine learning approaches to generate highly accurate predictive classifiers which could predict the probable Alzheimer-associated genes from a large pool of the total genes available on the Entrez gene database. We have investigated the interaction patterns of the genes from their network properties using PPI datasets, and the sequence features and the functional annotations of the genes and employed these properties to classify disease and non-disease genes. We have used eleven machine learning algorithms and trained the classifiers using Alzheimer (Alz) and non-Alzheimer (NonAlz) genes and examined the relevance of the features in the classification task and studied their behavior for both the classes of the genes. Finally, to identify candidate drugs for the predicted novel genes we have used molecular docking approach and screened the already known approved and investigational Alzheimer specific drugs against the novel targets. To validate our initial findings and to further evaluate the affinity of the drugs against the predicted novel targets we have carried out molecular dynamics (MD) simulations and MM/GBSA calculations on the ligand-bound protein complexes. Using the computational approach presented in the current study, we have identified 13 novel potential Alz-associated genes which could prove beneficial for the development of drugs and improve our understanding of the AD pathogenesis.Homo sapiens species were obtained from the Entrez Gene [A total of 56405 genes belonging to rez Gene  databaseTo compute topological features of the Alz and NonAlz genes, human protein-protein interaction (PPI) datasets were retrieved from Online Predicted Human Interaction Database (OPID) , STRING UniProtKB  , a freelp-value <0.001 and fold-enrichment >1.5 and the final 62 functional features were retrieved for the Alz and NonAlz genes. A list of final 62 functional features associated with the Alz and NonAlz genes has been provided as Additional file Using DAVID  , functioWe employed feature selection techniques, to identify significant features contributing efficiently towards predicting the target class and thus extract the smaller subset of features for classification of Alz and NonAlz genes. Seven feature selection techniques were used that include a gain-ratio based attribute evaluation, oneR algorithm, chi-square based selection, correlation-based selection, information gain-based attribute evaluation and relief-based selection, to select the important attributes. Gain-ratio based attribute selection approach measures the gain ratio regarding the prediction class  while inAdditionally, Principal Component Analysis (PCA) was conducted using FactoMineR  package After the extraction of relevant features, the combined positive and negative datasets were split into 80 % training set and 20 % test set using ‘create Data Partition’ function available from CARET  package Eleven machine learning algorithms were applied to generate classifiers using the training dataset which could predict Alz- and NonAlz-associated genes using the selected network, sequence and functional features . The macTen-fold cross-validation was used for training the classifier models to overcome the problems of overfitting of the generated models and to gain insights into the performance of the models on independent test sets. In cross-validation, say k-fold cross-validation, the training data was split into k subsets or folds and the models were generated using k-1 subsets and the remaining one set was used as previously unseen test set for the generated models. This process was repeated until all the k folds were used as test set at least once. The cross-validation results reported are the averaged over all the generated training classifier models.In order to remove bias in classification of the positive and negative datasets, misclassification costs were applied to the classifiers. Costs were introduced through a 2X2 confusion matrix which was divided into true positives (TP), false positives (FP), true negatives (TN) and false negatives (FN). The costs were applied on FN and a total of 22 classifier models were generated which include 11 models generated using base classifiers and 11 cost-sensitive models , 47.The performance of the generated 11 cost-sensitive classifiers in classifying Alz and NonAlz genes was measured using accuracy, precision, recall, F-measure or F1score and Matthews Correlation Coefficient (MCC). Accuracy (TP + TN/(TP + TN + FP + FN)) is proportion of the correct positive and negative classifications by the classifier models. Precision (TP/(TP + FP)) is the percentage of true positives while recall or sensitivity or TP rate (TP/(TP + FN)) is the proportion of all the positives predicted correctly. F-measure or F1 score is considered as an average of precision and recall and can be calculated as /. MCC is a correlation coefficient between the experimental and the predicted classifications and is computed to introduce a balance in the predictions made by the classifiers in case of classes of varying sizes.A list of 45 already existing approved and investigational drugs specific to Alzheimers was retrieved from the DrugBank  databasePost molecular docking, the docked protein-ligand complexes for the novel targets were subjected to MD simulation studies to evaluate the stability of the ligand and protein in the presence of salt and the solvent . The MD To calculate the relative binding affinities of the ligands with the targets, MM/GBSA calculations were carried out using Schrodinger . MM/GBSAThe protein-ligand complexes obtained after MD simulation analysis were used as input for MM/GBSA calculation.In the present study we have tried to identify potential Alz genes based on the extraction of their network, sequences and functional properties using machine learning approaches. We have carried out feature selection using seven different feature selection techniques along with PCA to extract significant features and used 11 machine learning classifiers to predict candidate Alz genes. To do so, we have obtained a list of known Alz-associated and NonAlz genes from the Entrez Gene database, which made the positive and negative dataset respectively. We also performed a series of docking studies followed by MD and MM/GBSA calculation and screened the already existing approved and investigational anti-Alzheimer drugs to identify drugs against novel candidate genes.P-value = 0.00002) [P-value of 0.00006. The mean values of the shortest path to Alz genes, clustering coefficient, topological coefficient, eccentricity and closeness centrality were similar for the Alz and NonAlz gene datasets. Table p-values between the Alz gene and NonAlz gene sets.A total of nine topological properties were calculated for each gene in the PPI datasets and a comparison of the properties between Alz and NonAlz genes was performed. Our results showed that the mean value of the degree for the Alz genes was considerably larger than the NonAlz genes which confirmed a previous finding that disease genes have higher degree value (0.00002) , 63. The0.00002) , 64. We P-value = 1.64E-07). The more number of arginine residues in non-disease genes also explains the same. The average number of residues for disease genes (491) and non-disease genes (443) confirmed that disease drug targets are longer than non-disease drug targets. The mean value of molecular weight of the Alz proteins (54349.54 Da), was also higher than NonAlz proteins (49547.60 Da) with a significant P-value of 0.01. The mean value of isoelectric point was lower for Alz proteins as compared to NonAlz proteins with the values being 6.60 and 7.22 respectively and P-value of 3.06E-08 which was due to more number of positively charged amino acids. Table p-values between the Alz proteins and NonAlz proteins sets.A statistical comparison between the sequence properties for Alz and NonAlz genes was also performed which provided us interesting results. The mean value of charge on amino acids was much higher for non-disease genes suggesting that disease genes targets majorly included more hydrophobic and less polar amino acids  related terms, response to endogenous stimulus and organic substance, phosphorylation and its regulation, and metabolic processes and their regulation which clearly states that the AD related genes are largely involved in neuronal death . The NonIn order to detect the features that contribute significantly towards distinguishing between disease genes and non-disease genes, we used seven feature selection techniques on an initial set of 92 features. We identified a final subset of 33 features which were selected by five out of seven selection algorithms and had loadings value >0.1 in PCA, indicating their association with AD  stimulates generation of inositol 1, 4, 5-trisphosphate (IP) further resulting in enhanced Ca2+ concentration [The overexpression of PLCG2 on phosphatidylinositol 4, 5-bisphosphate (PIPntration . Anotherntration .Numerous studies have suggested that the Down syndrome (DS) patients develop multiple conditions, one among which is AD and that the genes overexpressed in case of DS can be considered as novel therapeutic targets against AD . ITSN1 iRhoA, a small GTPase protein known to regulate synaptic strength and plasticity, has also been pointed out as a key therapeutic target in AD pathogenesis through RhoA GTPase/ROCK (Rho-associated protein kinase) pathway . RhoA-RONecroptosis is a significant cell death mechanism which is involved in many neurodegenerative disorders including AD . RIPK3 iA wide range of studies have reported that increased levels of a specific miRNA, miR-26b, may play a vital role in pathogenesis of AD suggesting a connection amid cell cycle entry and tau aggregation , 91. TheRb1 is a tumor-suppressor protein and major target of miR-26B, which controls cell growth by inhibiting transcription factor, E2F required for further transcription of genes. Cdk5 causes hyper-phosphorylation of Rb1 upon which it is unable to bind to E2F and consequently E2F transcriptional targets, that include genes for cell cycle, are highly expressed . Thus itTUBB protein is a principal constituent of microtubules which are formed by polymerization of dimers of α-tubulin and β-tubulin for which α- and β-tubulin bind to Guanosine-5′-triphosphate (GTP). It has been reported that higher levels of β-tubulin can be associated with aberrant hyper-phosphorylated tau aggregates which play a major role in etiology of AD .Using STRING database we generated interaction networks and explored the associations between the already known Alz genes and the 13 novel Alz genes identified in the present study. We found the interactions for all the predicted genes except CDH1, CARD8, RHOA and VCL. F7 was found to be interacting with apolipoprotein B (APOB) which was present in high concentrations in AD patients . ITSN1 iIn order to identify drugs against the predicted novel Alz-associated targets, we employed molecular docking approach and screened a total of 37 already known Alz-specific drugs against the novel target genes. Among the 13 Alz-associated genes identified, the crystal structures were available only for seven and the same were downloaded from PDB. A list of the existing approved and investigational Alz-specific drugs  trajectories and no major structural changes were observed. Figures The MD simulated protein-ligand complexes were used to calculate the binding free energies and we found that the binding of AL-108 was thermodynamically favorable for all the drug targets. The Rb1-AL108 complex had the highest free energy value –13.66 kcal/mol followed by NFKBIA-AL108 with binding energy –12.09 kcal/mol. Table The 11 machine learning classifiers generated were applied to identify the Alz genes from the HuGENet repository. A total of 1686 Alz-associated genes were obtained among which 1304 genes were found to be the part of the training and testing set used for model systems generation and validation respectively. The resulting 382 genes, which were not the part of disease and non-disease gene lists, were used to calculate the network, sequence and functional features. Further, 39 genes were given as input to the 11 trained classifiers and a majority of the models gave around 60 % correct predictions among which the SVM classifier was 97.4 % accurate. Additional file Alzheimer’s, a highly complex neurological disorder, has become the cause of serious global concern owing to the rapidly increasing number of cases and the socioeconomic burden associated with it. The pathogenesis of the disease is still not clear and thus no effective treatments to cure the disease exist so far. However, a plethora of studies have stated genetic factors as the major cause of the disease in light of which identification of novel Alz genes will be of great significance to understand disease etiology and in order to develop effective therapeutics. The computational predictive models generated in the present study successfully identified 13 novel candidate genes that could have a potential role in AD pathology. We incorporated various properties of the genes, network properties from the signaling pathways, sequence properties from the corresponding protein sequences and functional annotations and employed eleven machine learning algorithms to train the model systems. Additionally, we used a molecular docking approach followed by MD simulations and performed a screening of already available anti-Alzheimer drugs against the novel predicted Alz drug targets. Finally, MMMGBSA calculations were performed and the obtained binding free energy values showed that AL-108, an investigational AD-specific drug, had strong binding affinity majorly for all the novel drug targets. The investigational drug, AL-108 can be considered as a probable lead compound having inhibitory properties against the novel drug targets identified in the present study. The computational protocol used in the current study can be successfully applied for the prediction of disease associated genes and have insights into the disease mechanisms for the development of better and effective therapeutic agents.


In mammals, acoustic communication plays an important role during social behaviors. Despite their ethological relevance, the mechanisms by which the auditory cortex represents different communication call properties remain elusive. Recent studies have pointed out that communication-sound encoding could be based on discharge patterns of neuronal populations. Following this idea, we investigated whether the activity of local neuronal networks, such as those occurring within individual cortical columns, is sufficient for distinguishing between sounds that differed in their spectro-temporal properties. To accomplish this aim, we analyzed simple pure-tone and complex communication call elicited multi-unit activity (MUA) as well as local field potentials (LFP), and current source density (CSD) waveforms at the single-layer and columnar level from the primary auditory cortex of anesthetized Mongolian gerbils. Multi-dimensional scaling analysis was used to evaluate the degree of “call-specificity” in the evoked activity. The results showed that whole laminar profiles segregated 1.8-2.6 times better across calls than single-layer activity. Also, laminar LFP and CSD profiles segregated better than MUA profiles. Significant differences between CSD profiles evoked by different sounds were more pronounced at mid and late latencies in the granular and infragranular layers and these differences were based on the absence and/or presence of current sinks and on sink timing. The stimulus-specific activity patterns observed within cortical columns suggests that the joint activity of local cortical populations  could indeed be important for encoding sounds that differ in their acoustic attributes. Saimiri sciureus , the single-layer MUA waveforms  segregatApplying the MDS on whole laminar profiles resulted in an increase of segregation performance in all three types of neuronal activity by factor 2.3 (MUA), 2.6 (LFP), or 1.8 (CSD), measured as the mean Euclidean distances between waveforms/profiles . The cluIn laminar (as opposed to single layer) LFP profiles the stress was equal to 0.014, indicating consistent dissimilarities in laminar LFP data . LFP proThe two-dimensional configuration of Euclidean distances (stress = 0.003) calculated for the CSD data  was qualOverall, the relatively large Euclidean distances between LFP and CSD profiles obtained with different stimuli rendered the LFP and CSD signals more suitable for stimulus-specific segregation see . The betThe results of the Kolmogorov complexity analysis are shown for the stimulus-specific laminar profiles  and stimThe high values found at the level of laminar profiles regarding the CSD data indicate a correlation between structural complexity and segregation performance. However, this correlation could not be found at the single layer level.In this article, we describe MUA, LFP, and CSD responses evoked by complex communication sounds and simple pure-tones at the laminar and columnar level. We show that single-layer LFP and CSD activity characteristically depends on the acoustic stimulus type, in contrast to the MUA, which shows a lower “call-specificity”. The stimulus-specific segregation performance increases significantly (1.8-2.6 times) if whole laminar profiles including all cortical layers are analyzed, instead of single layer profiles. By directly comparing MUA, LFP, and CSD profiles we could show that the LFP and CSD recordings of single columns provide valuable information that could help distinguishing between different spectro-temporal call properties. The increased segregation performance which correlates with a higher structural complexity (only CSD profiles) and the high “call-specificity” in LFP and CSD profiles (higher than that in the MUA) reflecting population synaptic potentials supports the hypothesis that encoding of communication sounds depends not on single neurons but rather on population activity.The data reported in this manuscript was obtained from ketamine-anesthetized animals. To better understand the reported data, possible advantages and disadvantages of studying neural signals under the influence of ketamine need to be addressed. LFPs are influenced by nonspecific factors like ongoing cortical activity (spontaneous or evoked by a preceding stimulus) or the state of excitability , which cStudies regarding the influence of ketamine and xylazine on the complexity of recordings evoked by complex communication sounds are, at present, not available. Studying sound processing under anaesthesia could be useful because it describes the hardwired brain activity that is obtained without considering the influence of active processes such as attention. Such active processes could largely influence the sound discrimination based on activity from single layer and whole laminar profiles.Based on the Euclidean distances in the single-waveform MDS, the MUAs were less suited than LFPs and CSDs for sound discriminability, which is in accordance with previous studies regarding the MUAs and LFPs . HoweverSingle-layer waveforms can be used for distinguishing different call properties at a decent level although they cannot provide all information about the sub- and intracortical processes induced by calls with complex spectro-temporal structures. Laminar profiles including all layers increased the segregation performance by factor 1.8-2.6 in comparison to the single-layer MDS . The LFPIn contradiction to the hypothesis of Medvedev and Kanwal , in prevLFP waveforms, although appearing to be better suited for communication sound segregation, possess apparent similarities between cortical layers  and cannOur data shows that the CSD profiles present a much higher structural complexity when compared to the MUA and LFP profiles . Thus, iThe spectro-temporal cues of communication sounds, which are important for inducing characteristic activity patterns in the AC, are not yet fully understood. Medvedev and Kanwal  could shThe spectral information may be less important as even severe loss  does not prevent AC neurons from correctly classifying individual calls based on the MUAs . In our Several studies have proposed that patterns of synchronized activity within cortical columns represent the acoustic attributes of different vocalizations , 7, 40 aThe LFPs in AI already represent the summed event-driven activity from a wide range of specific cortical loci involving the synaptic activity of several cell assemblies , 81 and Based on the significantly different activity areas in the CSD profiles, obtained when comparing against the response to a control pure tone, the layers whose stimulus-dependent neuronal activity was responsible for most of the characteristic differences could be enclosed to granular (layer IV) and infragranular layers (layers V-VI). This was more obvious in response profiles showing less areas being significantly different to the reference response .The granular layer, which receives inputs from the ventral part of the medial geniculate body , 82 and The synaptic activity associated to initial sinks s1 (layer IIIb/IV) and s2 (layer Vb/VIa), which are believed to reflect depolarizations of terminal portions of thalamic afferents and stellate cells , 88, shoThe call-induced response differences, especially in the mid- and late-synaptic activity within cortical columns, suggest that not only thalamic and intracortical processes but also contralateral and/or higher cortical areas are involved in the call-specific processing that takes place at the cortical level. The structural differences in the laminar CSD profiles in response to calls produced by the presence and absence of sinks and differences in strength, latency, and duration allow a high-performance response profile-based discrimination for most of the sounds as shown using multi-dimensional scaling . The delMeriones unguiculatus) of both sexes . Animals were taken from the breeding colony of the Institute for Cell Biology and Neuroscience, Goethe University, Frankfurt am Main, Germany. Animals were kept in polycarbonate cages (separated sexes) that were filled with nesting material (thickness: 3 to 6 cm). The breeding chamber was constantly kept at 21–22°C and under a 12:12 hours day-night-rhythm. Dry food and water was provided ad libitum. The study was in accordance with international guidelines for animal experimentation  and with ethical standards for the care and use of animals in research defined by German Law for the protection of experimental animals . Ethics and animal experiments with ketamine/xylazine anesthesia was approved by the Regierungspräsidium Gießen (Approve number: #F104/60).Experiments were conducted in 11 adult  Mongolian gerbils . The frequency of pure tones  was set at the neuron's characteristic frequency (CF) obtained in the thalamic input layers V/VI. The CF . Communication sounds were attenuated by using the calibration curve and by taking into account the frequency with the highest power in the spectrum of each of the sounds . The root mean square (RMS) values of the entire sound waveforms and the peak-to-peak (P2P) values are displayed in Initial anesthesia was given via intraperitoneal injection of a mixture of ketamine , xylazine  and isotonic sodium chloride solution . Continuous anesthesia was maintained with the same mixture with the aid of an injection pump . The hind limb withdrawal reflex and whisker activity was monitored throughout the experiment. Body temperature was kept constant at 37°C using a thermostatic heating blanket.dura mater was carefully removed with an injection needle. At the end of experiments, animals were euthanized with an overdose of pentobarbital .For gaining access to the brain, the skin and muscle tissue of the upper and temporal part of the head was removed and the skull was cleaned. A custom-made metal rod  was glued onto the skull using dental cement . A square-shaped hole of ~3x3 mm was drilled into the temporal bone to expose the left auditory cortex (AC). After cleaning the brain surface, the Experiments were conducted in a custom-built soundproofed and electrically-shielded chamber. Recordings were made with commercially available linear probes  with 16 contacts (spacing: 100 μm) spanning 1500 μm. Due to the low impedance of the probes [0.5 to 3MΩ (as provided by the manufacturer)] the studied neuronal activity likely represents the summed activity of multiple nearby neurons, and it is therefore termed “multi-unit activity”. In the present manuscript, the term “neuron” is used to refer to multi-unit activity.The orthogonality between the electrode and the brain surface was achieved by adjusting the animal's head several times until the altitude differences of the electrode tip at the four corners of the recording area was less than 60 μm . This teThe cortical location of all 61 recording sites  studied were obtained by using the stereotaxic coordinates of penetration points and the suture intersection of parietal, sphenoidal and temporal bone as reference. Using these coordinates, the sites were plotted in the schematically parcellated AC –52 displnd order) between 300 and 4500 Hz to obtain the MUAs  and recorded using a multichannel recording system . The raw signal, which was down-sampled from 50 to 20 kHz, was digitally bandpass-filtered offline  and below (z0-nΔz) the field potentials at depth z0 (interchannel distance Δz = 100 μm) and divided by the differentiation grid . A modified version of the iCSDplotter toolbox was used for the CSD calculation  is marked at the same spot as in (TIF)Click here for additional data file.S2 FigThe different neuronal-specific receptive fields across the mapped cortical region Click here for additional data file.S3 FigThe figure shows the correlation coefficients (zero time lag) of single MUA, LFP, and CSD waveforms  between low frequency (n = 32) and high frequency tuned neurons (n = 29) of layer IV. Each waveform of one of the two frequency groups was correlated with all the waveforms of the opposite frequency group. Boxplot data is composed of the highest correlation coefficients between the three channels spanning layer IV of each measured stimuli, respectively. The edge of box represents the second and fourth quartiles and the midline represents the median of data. Lowest correlation coefficients (> 0.27) are found in MUA waveforms evoked by stimuli exclusively consisting of high frequencies . These frequency-dependent dissimilarities are mainly related to the spiking activity strength. The overall high correlation coefficients indicate that different receptive fields do not alter the stimulus-specific processing patterns of waveforms in a fundamental way.(EPS)Click here for additional data file.S4 FigA parametric repeated measures ANOVA in combination with a false discovery rate post-hoc test was applied for 61 measurement points at each of the 600 time points and all 16 channels of the MUAs (A); the LFPs (B), and the CSDs (C). Each stimulus-evoked profile was tested against each other with the exception of the profile evoked by stimulus #2 CF125 which is already displayed in (EPS)Click here for additional data file.


System toxicology aims at understanding the mechanisms used by biological systems to respond to toxicants. Such understanding can be leveraged to assess the risk of chemicals, drugs, and consumer products in living organisms. In system toxicology, machine learning techniques and methodologies are applied to develop prediction models for classification of toxicant exposure of biological systems. Gene expression data (RNA/DNA microarray) are often used to develop such prediction models.The outcome of the present work is an experimental methodology to develop prediction models, based on robust gene signatures, for the classification of cigarette smoke exposure and cessation in humans. It is a result of the participation in the recent sbv IMPROVER SysTox Computational Challenge. By merging different gene selection techniques, we obtain robust gene signatures and we investigate prediction capabilities of different off-the-shelf machine learning techniques, such as artificial neural networks, linear models and support vector machines. We also predict six novel genes in our signature, and firmly believe these genes have to be further investigated as biomarkers for tobacco smoking exposure.The proposed methodology provides gene signatures with top-ranked performances in the prediction of the investigated classification methods, as well as new discoveries in genetic signatures for bio-markers of the smoke exposure of humans.The online version of this article (10.1186/s12859-018-2035-3) contains supplementary material, which is available to authorized users. System toxicology aims at understanding mechanisms, both at functional and genetic structural level, by which biological systems respond to toxicants. Such understanding can be leveraged to assess the risk of chemicals, drugs, and consumer products on living organisms. In particular, the identification of effective genomic biomarkers to aid prediction of toxicant/drug exposure levels in biological systems is an emerging research topic in system toxicology.The increasing interest in this field is motivated by the wide applicability of genomic biomarkers for both finding evidence of toxicity in drug therapies and monitoring therapeutic outcomes. Furthermore, in case of acute poisoning, it can be used to detect exposure degree to toxicants/drugs. Indeed, the exposure level evaluation by safety biomarkers may lead to the development of more efficient diagnostic tools for toxicodynamic monitoring like in case of patients receiving immunosuppressive therapy . This reIn the present work we propose a methodology, as well as an experimental pipeline, for finding gene signatures for tobacco smoke exposure characterization and prediction. Our approach integrates different gene selection mechanisms, whose results are studied and compared to extract gene signatures more robust than those produced by a single methodology. In particular, the considered gene selection methods are based on a regression method (LASSO-LARS), a recursive elimination by support vector machines (RFE-SVM), and a feature selection by an ensemble of decision trees (Extra-Trees). While recent works start employing machine learning techniques for gene selection –13, the The sbv IMPROVER project  is a colIn the SysTox Computation Challenge , 16 partgene selection from whole blood gene expression data contained in the training dataset. The result of this task is a robust gene signature to be used to reduce training and testing data dimensions. Participants had also to develop inductive prediction models based on training data limited to the gene signatures they had previously identified. Inductive models are developed based only on training data. Classification on each test sample could be carried out only with the previously developed model, without retraining. Inductive models are different from transductive models in which training and testing datasets are processed together and used to retrain models prior to classification prediction. After all participants had submitted their results, in terms of both gene signatures and prediction models, the second stage of the challenge started: testing dataset of gene expression data from human (or human/rodent) blood samples were made available to participants. By using their proposed signatures and predictors, participants had to produce predictions (in terms of probabilities) on testing (unlabeled) samples.Figure gold labels. Prediction models scores and rankings are reported on the sbv IMPROVER SysTox Challenge website.After the competition closing, challenge organizers evaluated results submitted by participants only on a subset of testing samples which had been provided during the competition, the so called H1 training dataset: a clinical case-control study conducted at the Queen Ann Street Medical Center (QASMC), London, UK and registered at ClinicalTrials.gov with the identifier NCT01780298 [01780298 , 17. TheH2 testing dataset: a transcriptomics dataset (BLD-SMK-01) produced from PAXgene TM blood samples obtained from a biobank repository  [MD, USA) . At the Human blood sample data are organized in two datasets: Sample data of H1 and H2 consist of DNA microarray experiments obtained with GeneChip Human Genome U133 Plus 2.0 Array and GeneChip Mouse Genome 430 2.0 Array (Affymetrix), on blood samples. Microarray data of both H1 and H2 are available in the ArrayExpress database , respectFeature selection by importances in forests of trees (Extra-Trees) Cross-validated Lasso, using the LARS algorithm Recursive Feature Elimination with SVM estimator The basic idea of our gene signature extraction approach is to identify an overlapping among the most discriminant genes we found out by applying three different feature selection techniques: Extra-Trees belong to the class of ensemble learning methods. They are based on bagging several instances of a black-box estimator (e.g. a decision tree) on random subsets of the original training set and then combining their individual predictions to form a final prediction. Bagging estimators is a very simple way to improve with respect to a single model without making it necessary to adapt the underlying base algorithm. In many cases, bagging methods reduce overfitting as well as the variance of a base estimator. In this work we use the feature selection facility of the Extra-Trees implementation available in the Python Scikit-learn .LASSO (Least Absolute Shrinkage and Selection Operator) is a regression method performing feature selection by regularization of regression parameters . The computation of the LASSO solutions is a quadratic programming problem, and can be tackled by standard numerical analysis algorithms that estimate sparse coefficients. It is widely recognized that the Least Angle Regression procedure (LARS) is the better approach since it exploits the special structure of the LASSO problem, and it provides an efficient way to compute the solutions simultaneously for all values of the regularization parameter. In this work we use the LASSO method with LARS algorithm for feature selection. In the remaining of the paper we will refer to this feature selection method as LASSO-LARS. In particular we use its implementation available in the Python Scikit-learn library.Recursive Feature Elimination with SVM (RFE-SVM) By starting with the complete set of features, RFE-SVM repeats the following three steps until no more features are left: 1) train a SVM model; 2) compute a ranking of features as the squares of the hyperplane coefficients of the SVM model; and 3) remove the features with the worst ranks. In this work we use the RFE-SVM implementation available in Weka Data Mining Software .The three methods produce as outputs three lists of ranked genes in reversal order. Regardless of the ranking criteria  the three lists of genes are cut-off to the first hundred of genes with higher ranks.The focus of this work is on the data processing methodology to get a robust gene signature. The idea is that if the gene signature is biologically relevant, then classifiers will provide statistically significant results. Therefore, in order to assess the quality and robustness of our gene selection method, on the basis of signatures produced by it, we built a large set of prediction models exploiting well-known supervised learning techniques. We considered a set of nine classifiers, ranging from decision trees to support vector machines, from artificial neural networks to clustering and statistic methods. For the purpose, we used implementations of machine learning techniques available in the opensource Python Scikit-learn library . The lisTo understand the importance of gene signatures with respect to biological function and toxicological effects, we used Comparative Toxicogenomics Database (CTD)  and TranEach feature selection technique has been applied to the datasets, in both SvsNCS and FSvsNS classification problems, by setting a limit to the maximum number of selected genes (one hundred). For each problem the three sets found have been intersected to find a robust gene signature.In the case of SvsNCS problem the results of the first hundred top-ranked genes by applying the three selection criteria are presented in Tables In the case of FSvsNS problem, the results of the first hundred top-ranked genes by applying the three selection criteria are presented in Tables With respects to the SvsNCS problem, the lists of the first hundred of top-ranked genes are reported in Tables To analyze these signatures, we obtained the gene-chemical association results from CTD database and we selected genes which interact with tobacco smoke pollution with higher inference score. Later, we carried out inferred gene-disease association, pathways and gene ontology enrichments analysis. The results are provided in the supplementary tables reported, in the ‘Additional files’ section, from ‘Additional files We also observed that the diseases associated to these genes are respiratory tract, pregnancy complications, cardio-vascular, neoplasm, fetal disorder, congenital abnormalities, endocrine system diseases. Similarly, these genes share 74 common pathways, and some of these pathways  are known to be involved in cancer progression.The gene ontology enrichment and comparative analysis also suggest that most of these genes are involved in protein binding, membrane, localization, ion binding, regulation of biological process and signal transduction. In the light of these results, we deduce that the three gene signatures produced by our selection criteria, with respect to the smokers versus non-current smokers case study, although different still share the same biological and toxicological characteristics. The overlap analysis among the three methods reported more stronger gene signature. We selected the genes common to all three methods and carried out the enrichment analysis.The enrichment analysis of the gene signature we identified for the SvsNCS problem shows that all 14 genes are enriched , are known to contain SNPs or somatic mutations and differential expressed in lung/bladder cancers. The toxicogenomics gene-chemical-disease association study and the resulting biological and toxicogenomics data are provided in the supplementary tables reported, in the ‘Additional file’ section, from ‘Additional files common gene signature, while the set of 6 genes proposed only by us will be referred as specific gene signature. The completed set of 14 genes resulting from our method is referred as total gene signature.Table We focused on these genes and carried out gene-chemical-pathways association studies using CTD database. The results are showed in Figs. We also determined the disease association for these 14 genes with inference score greater than threshold (≥ 50) with respect to respiratory tract disease and respiratory insufficiency. Both these diseases of respiratory tract are well characterized in literature as a negative result of tobacco smoking.We also carried out the pathways enrichment analysis for both the common gene signature and the specific gene signature in the case study of smokers versus non-current smokers. Biological and toxicogenomics analysis suggest that these 6 genes specific to our analysis are also very interesting with respect to smoking and could be further investigated as potential biomarkers for tobacco smoking exposure.On comparing the enriched pathways in both common and specific gene signature with respect to the whole set of pathways associated with tobacco smoking, we determined the significant overlapped pathways for these 14 genes. Some of the main pathways are Class A/1 (rhodopsin-like receptors), GPCR downstream signaling, GPCR ligand binding, signal transduction and signaling by GPCR. The results are shown in Fig. The tabular results of pathways information associated with common and specific gene signature as well as the overlap analysis with tobacco smoking is provided in the ‘Additional file With regards to the former smoker versus never smokers classification problem, we compared the gene signatures from the three selection methods and extracted three overlapping genes: CLUL1, NS3BP and HSD11B1. Biological and toxicological analysis of these three genes  samples from the H2 testing dataset, which of course had been previously reduced to the signature data.With respect to the smokers versus non-current smokers classification problem, the prediction results of the nine selected classifier, in terms of AUPR and MCC scores, are summarized in Table With respect to the former smokers versus never smokers classification problem, the AUPR and MCC scores of the selected classifiers are summarized in Table The focus of this work is our contribution to the crowdsourcing initiative, namely the SysTox Computational Challenge, proposed by sbv IMPROVER project. The challenge initiative aims at identifying by crowdsourcing chemical cigarette smoking exposure biomarkers from human whole blood gene expression data.In this context, this work proposed a methodology, as well as an experimental pipeline, to extract robust gene signatures from whole blood gene expression data. In addition, this work showed how to build predictive models based on robust gene signatures. Our models discriminate smokers from non-current smokers, as well as former smokers from never smokers subjects. In our computational approach we crossed three very different gene selection techniques to obtain robust gene signatures. Later, in order to assess the quality and robustness of the found gene signatures, we build, on the basis of expression data of selected genes of our signatures, nine prediction models implemented with different supervised machine learning techniques.With regards to the SvsNCS classification problem we obtained high scores for the majority of the explored learning techniques, with AUPR and MCC scores comparable to (even better than) those obtained by the SysTox Challenge winners. Surprisingly, for what concerns the FSvsNS classification problem, the prediction models build on the basis of the found signatures performed far better than those proposed by the challenge winners.The results obtained by our computational approach are strengthened by the functional annotation terms enrichment analysis, as well as by the toxicogenomics analysis  for both the SvsNCS and FSvsNS gene signature. In case of SvsNCS, we obtained highly enriched functional terms such as regulation of steroid genesis, orphan nuclear receptors, nerve growth factor, DNA damage, signal transduction, and membrane associated terms. In the present understanding of negative effects of cigarette smoking on humans, the enriched terms and related genes are known to be associated with either cancer progression or nervous system. On the other hand, in case of FSvsNS, the enriched biological terms are generally associated with inflammatory response, extracellular regions, disulfide bonding. As expected, there are not such harmful effects observed in former smoker when compared to never smokers. The interesting observation about this list is that some of these genes such as ADAMS14, SLC38A3, HSD11B1 accommodate structure variation (SNPs) due to tobacco smoking exposure for longer period of time frame.Additional file 1Gene-disease-chemical study of Extra-Trees signature in SvsNCS. Gene-disease-chemical association studies for gene signature predicted by Extra-Trees method for smokers versus non-current smokers case study. (CSV 59 kb)Additional file 2Gene-disease-chemical study of LASSO-LARS signature in SvsNCS. Gene-disease-chemical association studies for gene signature predicted by LASSO-LARS method for smokers versus non-current smokers case study. (CSV 30 kb)Additional file 3Gene-disease-chemical study of RFE-SVM signature in SvsNCS. Gene-disease-chemical association studies for gene signature predicted by RFE-SVM method for smokers versus non-current smokers case study. (CSV 50 kb)Additional file 4Gene-disease-chemical of Extra-Trees signature in FSvsNS. Gene-disease-chemical association studies for gene signature predicted by Extra-Trees method for former smokers versus never smokers case study. (CSV 44 kb)Additional file 5Gene-disease-chemical of LASSO-LARS signature in FSvsNS. Gene-disease-chemical association studies for gene signature predicted by LASSO-LARS method for former smokers versus never smokers case study. (CSV 15 kb)Additional file 6Gene-disease-chemical of RFE-SVM signature in FSvsNS. Gene-disease-chemical association studies for gene signature predicted by RFE-SVM method for former smokers versus never smokers case study. (CSV 32 kb)Additional file 7GO-enrichment of common gene signature. Gene ontology enrichment analysis for 8 genes in common with other participants of the SysTox Computational Challenge. (CSV 11 kb)Additional file 8GO-enrichment of specific gene signature. Gene ontology enrichment analysis for 6 genes not in common with other participants of the SysTox Computational Challenge. (CSV 9 kb)Additional file 9Pathways-enrichment of common gene signature. Pathways enrichment analysis for 8 genes in common with other participants of the SysTox Computational Challenge. (CSV 2 kb)Additional file 10Pathways-enrichment of specific gene signature. Pathways enrichment analysis for 6 genes not in common with other participants of the sbv IMPROVER SysTox Computational Challenge. (CSV 2 kb)Additional file 11Pathways mapping of common versus specific gene signature. Mapping pathways enrichment for both common and specific gene signature with respect to the complete pathways set associated with tobacco smoke pollution. (CSV 93 kb)


Dear Editor,Pestivirus within the family Flaviviridae. Infections of pigs with other pestiviruses occasionally occur and need to be carefully ruled out because having a CSF-positive status has a great impact on a country's ability to engage in international trade of pigs and pig products.One of the most important viral diseases of swine is classical swine fever (CSF), which causes tremendous losses in pig production worldwide and is, therefore, notifiable to the World Organization for Animal Health (OIE). In many countries worldwide, the CSF situation is monitored by PCR-based genome detection and serological screening to demonstrate the absence of the disease. The causative agent of CSF, classical swine fever virus (CSFV), is a single-stranded RNA virus that belongs to the genetically highly variable genus Myoclonia congenita.1 Very recently, it was reported that a newly discovered atypical porcine pestivirus (APPV) is also associated with CT in newborn piglets.4 Consequently, APPV-associated CT must be considered in the clinical differential diagnosis of CSF. Against this background, it is of utmost importance to determine whether the presence of this porcine pestivirus interferes with established procedures for CSF diagnosis. In this study, both the virological and serological differentiation of CSFV and APPV were addressed by applying well-established routine diagnostic methods and newly developed assays.Feto-pathogenicity is a common feature of pestivirus infections that occurs during gestation. The infection of gravid sows with low-virulent CSFV isolates can result in the birth of piglets with neurological disorders. This clinical syndrome is designated congenital tremor (CT) or n=14) obtained from different regions in Germany and Italy that contained ample amounts of APPV RNA (quantitation cycle (Cq) values between 16 and 35) were tested with three established quantitative real-time PCRs with reverse transcription (qRT-PCRs) that are routinely performed at the EU and OIE Reference Laboratory for CSF. These tests comprised one pestivirus-specific qRT-PCR5 and two CSFV-specific qRT-PCRs.7 No false-positive results were obtained with these qRT-PCRs (rns of the German APPV sequence S5/9 (GenBank NC_030653) was expressed in Leishmania tarentolae using the LEXSYcon2 Expression Kit . The Erns antigen was cloned into the pLEXSY-hyg2 plasmid in frame with the N-terminal LmSAP signal peptide for secretory expression and the C-terminal 6x His Tag for antigen purification. To confirm that the novel ELISA was specific for APPV, multiple samples of the reactive sera were analyzed via immunoblot using the purified Erns protein. One specific protein band was detected in all tested sera and by the His-tag specific antibody (data not shown). Pig sera from Germany and Italy (n=189), that is, countries that are free of CSF and are not applying vaccination, were used to determine the APPV Ab statuses and the reactivities in two ELISAs targeting the E2 glycoprotein of CSFV  and one pestivirus-specific ELISA targeting NS3 . The APPV ELISA demonstrated an intermediate  to high  reactivity in 62% (118/189) of the investigated sera, which indicated high sero-prevalences in both countries. Independently of the APPV Ab status, all the tested sera were Ab-negative in the pestivirus ELISA and in one of the CSFV-specific ELISAs (IDEXX). Three doubtful and one false positive results in the other CSFV-specific ELISA (IDvet) were not correlated with the APPV Ab status. Accordingly, there is no evidence for serological cross-reactivity of the APPV Ab-positive samples with the ELISAs that are routinely applied for the detection of pestivirus- or CSFV-specific antibodies.APPV genome-positive samples  in a 24-well dish . The success of virus isolation was monitored via the APPV-specific SYBR Green based qRT-PCR of the cellular RNA as previously described.3 One of the fourteen inoculated wells was found to contain significant amounts of APPV genome  after the third passage, and this amount increased after repeated passages , which provided strong evidence for APPV replication. Immunostaining with a field serum that was found to be reactive against the recombinantly expressed Erns antigen confirmed the presence of APPV-infected cells .Attempts to isolate APPV from the cerebrospinal fluid of a CT-affected piglet were successful when using a porcine kidney cell line, as recently described.ed cells . Staininlication , althougCSFV-induced CT cannot be clinically distinguished from APPV-associated neurological disorders in newborn piglets. Nevertheless, the obtained data provide no evidence that the presence of APPV genomes or APPV-specific Abs interfere with the diagnostic tests that are routinely applied in the diagnosis of CSF. In conclusion, it seems unlikely that the newly discovered porcine pestivirus APPV will negatively influence the established CSF diagnosis and surveillance programs that are performed in many countries worldwide.


In an era of a rapidly aging population who requires home care services, clients must possess or develop therapeutic self-care ability in order to manage their health conditions safely in their homes. Therapeutic self-care is the ability to take medications as prescribed and to recognize and manage symptoms that may be experienced, such as pain. The purpose of this research study was to investigate whether therapeutic self-care ability explained variation in the frequency and types of adverse events experienced by home care clients.A retrospective cohort design was used, utilizing secondary databases available for Ontario home care clients from the years 2010 to 2012. The data were derived from (1) Health Outcomes for Better Information and Care; (2) Resident Assessment Instrument-Home Care; (3) National Ambulatory Care Reporting System; and (4) Discharge Abstract Database. Descriptive analysis was used to identify the types and prevalence of adverse events experienced by home care clients. Logistic regression analysis was used to examine the association between therapeutic self-care ability and the occurrence of adverse events in home care.The results indicated that low therapeutic self-care ability was associated with an increase in adverse events. In particular, logistic regression results indicated that low therapeutic self-care ability was associated with an increase in clients experiencing: (1) unplanned hospital visits; (2) a decline in activities of daily living; (3) falls; (4) unintended weight loss, and (5) non-compliance with medication.This study advances the understanding about the role of therapeutic self-care ability in supporting the safety of home care clients. High levels of therapeutic self-care ability can be a protective factor against the occurrence of adverse events among home care clients. A clear understanding of the nature of the relationship between therapeutic self-care ability and adverse events helps to pinpoint the areas of home care service delivery required to improve clients’ health and functioning. Such knowledge is vital for informing health care leaders about effective strategies that promote therapeutic self-care, as well as providing evidence for policy formulation in relation to risk mitigation in home care.The online version of this article (doi:10.1186/s12913-017-2103-9) contains supplementary material, which is available to authorized users. In recent years, there has been an increasing amount of health resources devoted to home care settings for the management of disease conditions . Home caHome is an unpredictable site of care because home care providers are usually in the home for very short durations, and therefore home care requires the active involvement of the clients and their families . Client As hospitals are discharging patients sooner and sicker, home care will play an important role in restoring and encouraging self-care in clients with chronic conditions. Self-care skills that are lost or not used as a result of health breakdown must be regained . One of The current literature reveals gaps in care that raise concern about the safety of Canadian home care clients, and highlight the need for increased self-care support in home care. A scoping review of adverse events experienced by home care clients reported the overall rates of 3.5 to 15.1% which include the types of events such as adverse drug events, infections, wounds, and falls . A recenIn an era of a rapidly increasing number of older people who require home care services, clients must possess or develop therapeutic self-care ability in order to manage safety in their homes. Therapeutic self-care is a concept developed by Sidani and Doran  to expanThe purpose of this research study was to examine the relationship between home care clients’ therapeutic self-care scores and adverse events. In particular, the study question was to evaluate the relationship between therapeutic self-care, adverse events, and health system utilization; while controlling for client demographics and clinical characteristics. This question provided insight into whether therapeutic self-care ability predicted variation in the frequency and types of adverse events experienced by the home care clients, such as client falls, emergency room visits and unplanned hospitalizations.Self-care is viewed as encompassing a broad set of practices that individuals perform on their own behalf for the purposes of maintaining quality of life and well-being . TherapeThe World Health Organization (WHO)  defined This study focused on clients and their informal caregivers as the care system in home care, and examined the occurrence of two types of outcomes that were most likely sensitive to therapeutic self-care ability: 1) use of health care resources, including new emergency room visits and unplanned hospital admissions; and (2) adverse events, including client falls; unintended weight loss; new urinary tract infection; activities of daily living (ADL) decline; non-compliance with medications; new pressure ulcer or ulcer deterioration; and new caregiver distress. These adverse events were examined for the following two reasons. First, these adverse events were expected to be sensitive to the clients’ therapeutic self-care ability as identified by Blais et al., Lang et al, and Sears et al. , 21, 22. use of hA retrospective cohort design was used to investigate the prevalence and types of adverse events that were associated with home care clients’ ability in therapeutic self-care in Ontario, Canada. In Ontario, clients generally receive home care services through the Community Care Access Centres (CCACs). The CCACs are single-point entry agencies that determine eligibility for the community and institutional services. They also act as case management organizations contracting with the home care agencies to provide services including professional nursing, therapies and homemaking. The cohort consisted of the populations of home care clients who received publicly funded home care services from the province between the periods of April 1, 2010 to March 31, 2012. The study population consisted of long-stay home care clients who were adults with the age of 18 years or older. Long-stay home care clients were defined as those who were expected to receive home care services for 60 days or longer.The study received ethics approval from the University of Toronto Health Sciences Research Ethics Board. Ethics approval was obtained from the Institute of Clinical and Evaluative Sciences (ICES) where secondary data were derived from the following data sources: 1) HOBIC-HC ; 2) Home Care Reporting System RAI-HC (Resident Assessment Instrument-Home Care); 3) NACRS  for emergency admissions; 4) DAD (Discharge Abstract Database) for hospital admissions. ICES staff ensured all data were made anonymous by removing personal identifiers prior to sharing the data with the researchers.This study involved using HOBIC-HC assessments to investigate the independent effect of Therapeutic Self-Care (TSC) on the occurrence of adverse events. HOBIC assessments are collected to capture standardized client outcomes data related to nursing care in four sectors: acute care, long-term care, complex continuing care and home care . The focThe Therapeutic Self-Care scale is a 12-item instrument that measures the domains of self-care: taking prescribed medications; recognizing and managing symptoms; performing and adjusting regular activities; and managing changes in condition . The iteRAI-HC assessments were used to provide information on client demographics and clinical characteristics, including age, sex, education; client living situation; comorbidities, the Cognitive Performance Scale; the Activities of Daily Living (ADL) Hierarchy score; the Depression rating score; pain scale and the Changes in Health, End-Stage Disease, and Symptoms and Signs Scale (CHESS). The assessments were also used to provide information on other adverse events, including (1) client falls; (2) unintended weight loss; (3) new urinary tract infection; (4) ADL decline; (5) new pressure ulcer or ulcer deterioration; (6) compliance/adherence to medications; and (7) new caregiver distress. The RAI-HC assessment provides a comprehensive profile of Canadian home care clients, their environment, services and outcomes . The assThe therapeutic self-care scores from HOBIC assessments were examined in association with the National Ambulatory Care Reporting System (NACRS) to investigate the relationship between therapeutic self-care and emergency room visits (ER visits) during the follow-up period. NARCS is a data system designed to capture information on client visits to facility and community-based ambulatory care in which data about visits are collected at the time of service in participating facilities . This syThe study cohort was constructed by linking HOBIC-HC assessments and baseline RAI –HC assessments using a common variable called Identification Key Number (IKN). The accrual of study cohort started from April 1, 2010 and ended on September 30, 2011, which resulted in a final sample of 1470 linked HOBIC-HC and baseline RAI-HC assessments. After the creation of study cohort, the 1470 linked assessments were followed-up for a 12-month period to investigate the occurrence of adverse events. The 1,470 HOBIC-HC assessments on therapeutic self-care measures were linked with the data from: (1) DAD; (2) NACRS; (3) subsequent RAI-HC assessments to determine adverse events. The baseline RAI-HC assessment was used to measure the client clinical characteristics and to look back in time for prior health system utilization. Subsequent RAI-HC assessments were used in the follow-up period, and were linked with the therapeutic self-care score to examine the occurrence of adverse events and their relationship with therapeutic self-care.Therapeutic self-care scores were the independent variables that were posited to influence adverse events in home care. The reliability of the multi-item Therapeutic Self-Care Scale was assessed using Cronbach’s alpha. A Cronbach’s alpha of 0.88 was found in an earlier study by Doran et al. in which the Therapeutic Self-Care Scale was correlated with ability to resume activities of daily living and role activities at hospital discharge, supporting its construct validity . In thisThe frequency distribution of the 12-item therapeutic self-care scores indicated that the distribution of scores was skewed with high numbers of scores 5 (45%) for our study cohort. A decision was made to dichotomize this continuous variable into a binary variable by creating two groups for comparison in order to address the skewed distribution of therapeutic self-care scores. A final decision was made to use the cut-off value of score 5 by dichotomizing the therapeutic self-care score into a low self-care group (score 0 to 4) and a high self-care group (score 5). The cut-off value of score 5 is most appropriate to differentiate between high and low therapeutic self-care ability by creating a balance between the low self-care group  and high self-care group . Sensitivity analyses were performed with different cut-off values including the scores 5, 4 and 3. Results of sensitivity analyses are presented in Table p > 0.1) were dropped from the model, age and gender were the two variables that were always retained in each model. The reason for keeping age and gender in the model was that past research has found these two variables were considered important when adjusting for biological differences among individuals [The independent variables that were entered included the risk adjustment variables followed by the binary variables of low and high therapeutic self-care, as well as interaction terms. Risk adjustment was undertaken to adjust for different populations of clients who may be at greater risk of experiencing a poor outcome as a function of their demographic characteristics and clinical status . A complividuals , 34, 35.The results that describe the characteristics of study cohort can be found in Table The study cohort consisted of 820 individuals who were in the low self-care group and 650 individuals who were in the high self-care group. The majority were female who were over the age of 65. Seventy-seven percentage of the study cohort were living with their informal caregivers at home. The low self-care group was found to be more functionally dependent in nearly all the measures. For example, the low self-care individuals were characterized as having more complexity in clinical status such as having recent hospitalizations; multiple chronic diseases, polypharmacy; physical symptoms such as edema; and higher CHESS  scores. The CHESS score is a composite measure of change in health status, end-stage disease and symptoms and signs , and has been shown to be a strong predictor of adverse events . FurtherNew ER visits, new ADL decline, new hospital visits , new client falls, and new caregiver distress were ranked among the top most frequently occurring adverse events. Unintended weight loss, non-compliance or adherence with medications, newly detected urinary tract infections, and new pressure ulcer/ulcer deterioration were less frequently identified events. The prevalence rates of adverse events identified in RAI-HC, DAD and NACRS for the home care clients from the year of 2011 to 2012 are presented in Table Each logistic regression model was built using a backward stepwise selection process for the nine adverse events: (1) use of health care resources, including new emergency room visits and new hospital visits; (2) adverse events, including ADL decline; new falls; unintended weight loss; non-compliance/adherence with medication; new urinary tract infections; new pressure ulcers or ulcer deterioration; and new caregiver distress. The unadjusted and adjusted odds ratio estimates for therapeutic self-care in relation to adverse events are presented in Table The results of the logistic regression models revealed that home care clients’ use of health care resources was found to have association with therapeutic self-care scores. More specifically, the high self-care group was found to have a lower likelihood of new hospital visits than the low self-care group. The findings revealed that 47.4% of home care clients with high therapeutic self-care ability had unplanned hospital visits in comparison to 53.2% of individuals in the low self-care group. The result of the analyses indicated that the odds of having hospital visits for the high self-care group were 26% lower than the low self-care group. The results also indicated that the client factors that increased the odds of hospital visits were: being male, having an increase in heath instability as indicated by high CHESS scores, as well as having the diagnoses of Congestive Heart Failure or Alzheimer’s disease. In particular, polypharmacy (taking nine or more medications) and therapeutic self-care was found to be significant, which indicated that there was interaction between polypharmacy and therapeutic self-care ability in the occurrence of new hospital visits. High polypharmacy was associated with the increased odds of experiencing new hospital visits among the individuals with low therapeutic self-care ability. Sixty-one percent of home care clients with polypharmacy were found to have unplanned hospitalizations in this study cohort. The interaction model with adjusted odds ratio estimates for therapeutic self-care in relation to hospital visit is presented in Table New ADL decline, new client falls, unintended weight loss, and non-compliance with medication were the four adverse events that were found to have associations with therapeutic self-care scores. In particular, the low therapeutic self-care group was associated with an increased odds of ADL decline, new falls, unintended weight loss and non-compliance/adherence with medication. In summary, there were five adverse events that were found to have associations with therapeutic self-care scores: (1) new hospital visits; (2) new ADL decline; 3) new falls; 4) unintended weight loss; and 5) non-compliance with medication. In particular, the high therapeutic self-care group was associated with the decreased odds of adverse events, whereas the low therapeutic self-care group was associated with the increased occurrence of a hospital visit, ADL decline, falls, unintended weight loss and non-compliance with medication. The Tables  new fall unintend non-compThe prevalence rates of adverse events for home care clients were identified during the study period from 2011 to 2012. This research found the five most prevalent adverse events were: ADL decline (51.7%); client falls (35%); caregiver distress (27%); unintended weight loss 13.5%); and medication non-compliance (11%). Previous research found similar findings in which client falls, medication-related events and caregiver distress were the most frequent types of adverse events in home care observed through chart review and secondary data analysis during the year of 2008 and 2009 [.5%; and The study findings suggest that the level of home care clients’ engagement in therapeutic self-care may be explained by the theory of patient activation. Patient Activation is the process that clients go through in becoming fully competent self-managers of their own health . This prThese findings are consistent with Sidani and Doran’s definition of therapeutic self-care as the clients’ knowledge and ability to manage their health condition, manage symptoms, and follow the prescribed treatments . The domSimilarly, individuals with low therapeutic self-care ability may lack the ability or knowledge to follow through on recommendations and comply with medication regimens. Therefore, the lack of self-sufficiency in therapeutic self-care may lead to the increased risk for adverse events among home care clients. This study’s conceptualization of therapeutic self-care recognizes that the notion of self-care is not only dependent upon the “self”, but it is also dependent on the support of other people . For insWith regards to the use of health care resources, our study findings revealed that the prevalence rate for ER visits was 56.9% and the prevalence rate for hospital visits was 48.8%. These rates are consistent with the findings of recent home care studies in which researchers determined that unplanned visits to ER (60.5%) and unplanned admissions to hospital (38.3%) were among the most frequent adverse events for home care clients , 14. OurA possible explanation for these findings is that home care clients with a higher level of therapeutic self-care ability were more likely to become activated to engage in self-care practices. With a higher level of therapeutic self-care ability, clients may have the knowledge and skills needed to comply with medication regimens, and therefore may be more able to manage their polypharmacy. On the other hand, clients with a low therapeutic self-care level may lack the ability or knowledge to manage multiple medications, or access appropriate care to prevent health declines. Our findings are consistent with the polypharmacy literature that indicates the burden of taking multiple medications is associated with greater costs in health care utilization due to drug interactions, medication non-adherence and reduced functional capacity . In the Understanding home care clients’ risk profile is foundational to effective patient care management . This stThis study points out that the adverse events associated with therapeutic self-care ability could be potentially preventable, such as the occurrence of further ADL decline, client falls, unintended weight loss, and medication non-compliance. An understanding of the adverse events associated with therapeutic self-care ability should enable health leaders to make informed decisions about service priorities in home care settings. For example, the occurrence of unintended weight loss and medication non-compliance points to the need for service priorities to support clients’ IADL ability, including nutritional and medication management. Health care leaders need to allocate appropriate resources to support clients’ development of therapeutic self-care ability, as well as implementing appropriate measures in place that monitor clients’ risk for adverse events at home . For exaThis is the first study that investigated the relationship between therapeutic self-care and adverse events in home care, and thus it makes an important contribution to the field. This research advances the understanding of the relationship between therapeutic self-care ability and the types and frequency of adverse events experienced by home care clients. High therapeutic self-care ability was found to be a protective factor against safety problems, while low self-care ability could be a risk factor in the occurrence of adverse events. This study highlights the importance of therapeutic self-care ability in influencing the occurrence of safety outcomes in the management of disease conditions at home. It also underscores the need for policy formulation that supports the development of therapeutic self-care ability in the prevention of adverse events for home care clients . Our conThis research study has a number of limitations. First, the study cohort of 1470 individuals is considered a small sample in secondary data research. The sample was small because there were a limited number of home care organizations submitting HOBIC-HC data to ICES at the time of the research. The sample size was further limited by including only long-stay clients who qualified for a RAI-HC assessment. Therefore, the study findings are only generalizable to the long-stay clients. Future research, involving more home care agencies across different geographical areas could further validate the evidence generated from the present study .Another limitation of this research study is the under-reporting or over-reporting of the actual experience. For example, the frequency distribution of the HOBIC-HC therapeutic self-care scores was skewed with a high number of scores 5 for our study cohort. This situation may be due to the possibility that home care clients, informal or formal caregivers have over-reported or over-estimated the self-care ability of the individuals. Clients’ perceived ability to perform self-care behaviors is a subjective phenomenon . TherefoIn summary, the findings revealed that a low level of therapeutic self-care ability could be a risk factor associated with the occurrence of adverse events in home care. The reasons why clients with high levels of therapeutic self-care were less likely to experience adverse events could be related to their active engagement in disease management. Therapeutic self-care ability is the level of knowledge and skill that enables clients to make informed choices regarding the management of their disease conditions. As a result, high therapeutic self-care ability is viewed as an enabling factor that could protect the clients against safety problems in the home care settings. It was important to study the enablement perspective of therapeutic self-care because of its potential role in risk mitigation, as well as its role in reducing the care burden of informal caregivers. The findings underscore the importance of assessing clients’ readiness for therapeutic self-care, and supporting their level of self-sufficiency in caring for themselves and managing their self-care needs at home. A clear understanding of the nature of relationship between therapeutic self-care ability and adverse events helps to pinpoint the areas of health care service delivery required to improve clients’ health and functioning . Such kn


South Africa has a large domestically funded HIV programme with highly saturated coverage levels for most prevention and treatment interventions. To further optimise its allocative efficiency, we designed a novel optimisation method and examined whether the optimal package of interventions changes when interaction and non-linear scale-up effects are incorporated into cost-effectiveness analysis.The conventional league table method in cost-effectiveness analysis relies on the assumption of independence between interventions. We added methodology that allowed the simultaneous consideration of a large number of HIV interventions and their potentially diminishing marginal returns to scale. We analysed the incremental cost effectiveness ratio (ICER) of 16 HIV interventions based on a well-calibrated epidemiological model that accounted for interaction and non-linear scale-up effects, a custom cost model, and an optimisation routine that iteratively added the most cost-effective intervention onto a rolling baseline before evaluating all remaining options. We compared our results with those based on a league table.The rank order of interventions did not differ substantially between the two methods- in each, increasing condom availability and male medical circumcision were found to be most cost-effective, followed by anti-retroviral therapy at current guidelines. However, interventions were less cost-effective throughout when evaluated under the optimisation method, indicating substantial diminishing marginal returns, with ICERs being on average 437% higher under our optimisation routine.Conventional league tables may exaggerate the cost-effectiveness of interventions when programmes are implemented at scale. Accounting for interaction and non-linear scale-up effects provides more realistic estimates in highly saturated real-world settings.The online version of this article (doi:10.1186/s12889-017-4023-3) contains supplementary material, which is available to authorized users. Currently, over 35 million people are living with HIV globally. In 2013, there were 2.1 million new infections and 1.5 million AIDS-related deaths worldwide .Some analysts have risen to the challenge of informing policy priorities by comparing the cost-effectiveness of single interventions, often using mathematical models. A recent combined analysis of 12 mathematical models examined the cost and cost-effectiveness of expanded treatment coverage and/or eligibility criteria for antiretroviral therapy (ART) . The BärGoing one step further, several models moved beyond comparing individual interventions and comprehensively assessed the cost and impact of entire national HIV programmes. Three models in particular—the AIDS Epidemic Model , GOALS , and OptIn 2014, we were tasked by the South African government with providing the analytical framework for the country’s HIV Investment Case, which aimed to calculate the most cost-effective mix of interventions against HIV and inform relevant domestic and donor budgets. During that process, which has been described in detail elsewhere , we encoSouth Africa is in the unique position of having a pre-existing, well-calibrated HIV transmission model that already takes into account those non-linear effects as well as interaction effects between interventions, the Thembisa model , which wThis paper describes the optimisation routine we developed for the South African HIV Investment Case and compares its results with those generated using conventional cost-effectiveness analysis methods to examine the incremental benefit of accounting for interaction effects between interventions and non-linear effects across scale up.Selection of intervention and coverage optionsAfter aprocess of evidence review and intervention selection which has been described elsewhere , we inclWe projected the epidemiological and cost impact of each intervention-coverage option over 20 years. We estimated the epidemiological impact in terms of life years saved relative to the West level 26 life table without applying any discounting . Based oThese results were then used as inputs for a cost-effectiveness analysis using two different methods: firstly, the conventional league table method, and secondly using our optimisation routine that iteratively added the most cost-effective intervention onto a rolling baseline before evaluating all remaining options. We compared the results generated between the two methods to examine the incremental impact of our optimisation routine.We first analysed the cost-effectiveness of the interventions included in the Investment Case using the conventional league table method that is well established in existing literature –22. AssuFirst, we grouped different coverage levels of each intervention into separate categories. Second, we sorted the intervention-coverage level option by incremental cost in ascending order within each category. Third, we excluded options that were strongly dominated by another option in the same category—an option was strongly dominated if it was both less effective and more costly than an available alternative. Fourth, we calculated the incremental cost-effectiveness-ratio (ICER) of each successive option using the immediate intervention above it in the list as the baseline, and removed interventions that were weakly dominated.The fact that these CERs are calculated by comparing the impact of the intervention on baseline implicitly assumes that an intervention’s effectiveness is independent of what other interventions have already been implemented.Given that the South African HIV programme already consists of a wide range of interventions, we hypothesised that interaction effects between interventions were important and would challenge the assumption of independence between interventions that underlines the conventional league table method. Given that many interventions are also already implemented at high levels of coverage , we hypoWe therefore added a custom costing and optimisation component to Thembisa in order to preserve the non-linear scale-up effects inherent in the transmission model and produce more realistic cost-effectiveness results. First, we projected the cost and effect of the HIV programme at baseline. We then calculated the incremental cost and effect (in terms of life years saved) over baseline of each intervention-coverage level option. Next, we conducted a pairwise comparison between all interventions and filtered out all strongly dominated interventions.We then ranked the remaining options by their ICER over baseline. The option with the lowest ICER was the most cost-effective. However, we also ranked between competing cost-saving options, which conventional cost-effectiveness analysis does not allow for since cost-saving options have negative ICERs that are not interpretable. In order to rank these cost-saving interventions, we ranked them both by life years saved (in descending order), and incremental cost (in ascending order). We then summed these two ranks and chose the intervention that had the lowest combined rank, representing the most effective and cost-saving intervention in aggregate. After choosing the most cost-effective intervention, we removed all lower coverage level options of the chosen intervention from the remaining pool.In order to take into account the possibility of diminishing marginal returns as a result of an increasingly saturated programme, we then modified the baseline to include the intervention-coverage level option that was found to be most cost-effective and re-evaluated the cost-effectiveness of the remaining options using the same algorithm relative to this new baseline. We repeated this process iteratively until all intervention-coverage level options were exhausted . The order in which intervention-coverage level options were added to the baseline represents the relative cost-effectiveness of each intervention, and allows us to construct a ranking comparable with those from conventional league table analyses.Figure Figure Using the conventional league table method, scaling up condom availability and medical male circumcision were the most cost-effective options. The model in fact suggests that they were cost-saving overall, since both interventions prevent significant numbers of new infections which translate into savings in treatment costs and net overall savings. These were followed by social and behaviour change campaign (SBCC) 1 (a mass media campaign with a message of encouraging testing and discouraging multiple partners) and scaling up ART under current guidelines and prevention of mother to child transmission (PMTCT). Towards the bottom of the list, interventions with relatively poor cost-effectiveness over 20 years included HIV testing for adolescents, PrEP for young women, and early infant male circumcision.The order of interventions in the league table using our optimisation routine was similar to the conventional league table method Fig. , with soFocusing on the difference in magnitudes of the ICERs of each intervention between the two methods, we found that all interventions were less cost-effective (i.e. more costly and/or less effective) when evaluated against a stacked baseline of interventions with high coverage levels. Restricting our analysis to interventions with positive ICERs, the ICERs calculated using the optimisation routine were on average 437% higher than their counterparts under the conventional league table method. This shows substantial diminishing marginal returns to investment since interaction effects between interventions become more significant as the HIV response becomes increasingly saturated.Table While the model selected the maximum coverage level ahead of all other coverage levels in the cases of condom availability and medical male circumcision, the model recommended scaling up SBCC campaign 1 more incrementally. Scaling up SBCC campaign 1 to Baseline +1 was the most cost-effective (non-cost-saving) intervention, but the model only suggested further scale-up to maximum coverage once PMTCT, ART at current guidelines, and infant testing at 6 weeks had already been scaled up. A similar phenomenon was observed in the case of general population HCT, whose scaled-up coverage levels were interspersed between different coverage levels of SBCC campaigns 1 and 2. This suggests that there are non-linearities in the incremental effectiveness as interventions are scaled up, which implies the existence of an optimum coverage level in terms of cost-effectiveness, after which further scale-up would lead to diminishing marginal returns.To illustrate this further, Fig. Comparing our novel optimisation routine against conventional cost-effectiveness analysis methods using interventions included in the South African Investment Case as a case study, we found substantial gains in analytical precision from our methodology.Firstly, our custom optimisation routine provided more realistic ICERs that accounted for interaction effects, resulting in diminishing marginal returns to scale. The magnitude of the differences in the ICERs between the two methods was non-trivial (437%), with the optimisation method especially penalising prevention interventions once other highly effective and cost-effective prevention interventions had already been taken to scale . This is particularly important in a relatively saturated environment like South Africa, especially since funding is likely to be limited in the future. Our consideration of interaction effects extends the broader literature on generalised cost-effectiveness analysis , 24, whiSecondly, our approach of adding a custom optimisation routine onto a pre-existing, country-specific epidemiological model contrasted with the established approach of using integrated HIV epidemiological and costing models. We approached the same task of pursuing allocative efficiency by starting from a well-calibrated, context-specific epidemiological model and adding costing and cost-effectiveness evaluation to it subsequently. Our approach illustrated the benefit of drawing on the available detail of a well-parameterised epidemiological model, instead of a cost-effectiveness model designed to simplify the epidemiology for costing purposes. This meant we were able to avoid common practices such as linearising non-linear mechanisms.Our optimisation routine is subject to several key limitations. Firstly, there is limited scope for uncertainty analysis in our optimisation model. The rank order of interventions could vary significantly as uncertainty bounds around the ICERs are likely to overlap. While adopting methods such as stochastic league tables , 28 may Secondly, our optimisation routine is limited to using cost per life years saved as the optimand . This does not allow for optimisation based on specific epidemiological targets such as minimising incidence or mortality. Furthermore, our routine does not allow for optimisation based on multiple criteria, such as efficiency and equity . FurtherFourthly, our algorithm cannot be considered as “optimisation” in the strictest sense of the word. Although we believe it to be a reasonable approximation, iteratively adding the most cost-effective option onto the baseline does not necessarily guarantee the globally optimal bundle of interventions, since it could theoretically be beneficial to remove certain options later on in the routine in favour of others. This is a classic case of the knapsack problem  which caLastly, our optimisation routine’s ability to generate more realistic ICER comes at a cost of the external validity of our results, since the cost-effectiveness estimates of each intervention in the league table is conditional on having implemented all of the interventions above. Since policy makers often pick and choose interventions for reasons other than maximising cost-effectiveness, any deviations from the rolling baseline will render the cost-effectiveness estimates inaccurate and might provide a false sense of precision.Conventional league table methods for cost-effectiveness analyses may exaggerate the cost-effectiveness of interventions when programmes are implemented at scale, such as in the context of the South African HIV programme. This is because the assumption of independence between interventions is challenged, and intervention scale-up often leads to diminishing marginal returns to an investment. We developed an optimisation routine that iteratively added the most-cost-effective intervention onto a rolling baseline to obtain more realistic ICERs and rank orders for interventions in highly saturated real-world settings. The results of our optimisation routine provided decision makers within the South African Department of Health and Treasury with reliable data on the relative cost effectiveness of a vast range of HIV interventions in such a setting, guiding decisions on targets and budgets for the next years and helping to increase the allocative efficiency of the country’s HIV expenditure.


Cerebrovascular dysfunction plays a critical role in the pathogenesis of Alzheimer’s disease (AD): the most common cause of dementia in the elderly. The involvement of neurovasculature disorders in the progression of AD is now increasingly appreciated, but whether they represent initial factors or late-stage pathological changes during the disease is unclear. Using senescence-accelerated OXYS rats, which simulate key characteristics of sporadic AD, we evaluated contributions of cerebrovascular alterations to the disease development. At preclinical, early, and advanced stages of AD-like pathology, in the hippocampus of OXYS and Wistar (control) rats, we evaluated (i) the blood vessel state by histological and electron-microscopic analyses; (ii) differences in gene expression according to RNA sequencing (RNA-Seq) to identify the metabolic processes and pathways associated with blood vessel function; (iii) the amount of vascular endothelial growth factor (VEGF) by western blot and immunohistochemical analysis.1–42 there at the advanced stage of the disease. According to RNA-Seq data analysis, major alterations in cerebrovascular processes of OXYS rats were associated with blood vessel development, circulatory system processes, the VEGF signaling pathway, and vascular smooth muscle contraction. At preclinical and early stages of the AD-like pathology, these processes were upregulated and then downregulated with age. At the advanced stage in OXYS rats, differentially expressed genes (DEGs) were associated with downregulation of cerebrovascular function as compared to Wistar rats. Among the 46 DEGs at the preclinical stage of the disease, 28 DEGs at the early stage, and among 85 DEGs at the advanced stage, using functional analysis and gene network construction, we identified genes  probably playing a significant role in the development of cerebrovascular dysfunction in OXYS rats.We observed a loss of hippocampal blood vessel density and ultrastructural changes of those blood vessels in OXYS rats at the early stage of AD-like pathology. There were significant alterations in the vessels and downregulation of VEGF with an increased amount of amyloid βChanges in expression of the genes functionally associated with cerebrovascular processes already in the early period of life may contribute to the development of AD-like pathology in OXYS rats.The online version of this article (10.1186/s12864-018-4480-9) contains supplementary material, which is available to authorized users. Vascular dysfunction is a universal feature of aging that contributes to the cognitive decline and neurodegeneration observed in Alzheimer’s disease (AD): the most common cause of dementia in the elderly , 2. AD iMultiple epidemiological studies have shown a remarkable overlap among risk factors for a cerebrovascular disorder and sporadic, late-onset AD , but whe1–42 peptide accumulation and tau hyperphosphorylation in the hippocampus, and impairment of the learning ability and memory ). The Newman–Keuls test was applied to significant main effects and interactions to assess the differences between some sets of means. The data were presented as mean ± SEM. The differences were considered statistically significant at Additional file 1:The lists of significantly enriched GO terms according to DAVID and pathways according to KEGG results, and the list of significantly differentially expressed protein-coding genes associated with cerebrovascular function identified by the RNA-Seq platform. (XLSX 97 kb)Additional file 2:The file contains a Venn diagram for significantly differentially expressed protein-coding genes associated with cerebrovascular function. (XLSX 217 kb)Additional file 3:The file contains significant terms associated with blood vessel processes according to GeneMANIA. (XLSX 47 kb)