Large Sample Results Research Articles

Neural Networks for Geospatial Data

Analysis of geospatial data has traditionally been model-based, with a mean model, customarily specified as a linear regression on the covariates, and a Gaussian process covariance model, encoding the spatial dependence. While nonlinear machine learning algorithms like neural networks are increasingly being used for spatial analysis, current approaches depart from the model-based setup and cannot explicitly incorporate spatial covariance. We propose NN-GLS, embedding neural networks directly within the traditional Gaussian process (GP) geostatistical model to accommodate nonlinear mean functions while retaining all other advantages of GP, like explicit modeling of the spatial covariance and predicting at new locations via kriging. In NN-GLS, estimation of the neural network parameters for the nonlinear mean of the Gaussian Process explicitly accounts for the spatial covariance through use of the generalized least squares (GLS) loss, thus, extending the linear case. We show that NN-GLS admits a representation as a special type of graph neural network (GNN). This connection facilitates the use of standard neural network computational techniques for irregular geospatial data, enabling novel and scalable mini-batching, backpropagation, and kriging schemes. We provide methodology to obtain uncertainty bounds for estimation and predictions from NN-GLS. Theoretically, we show that NN-GLS will be consistent for irregularly observed spatially correlated data processes. We also provide a finite sample concentration rate, which quantifies the need to accurately model the spatial covariance in neural networks for dependent data. To our knowledge, these are the first large-sample results for any neural network algorithm for irregular spatial data. We demonstrate the methodology through numerous simulations and an application to air pollution modeling. We develop a software implementation of NN-GLS in the Python package geospaNN. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

Weight Inclusive Thinking for Fitness Spaces (WIT FITS): A Three-Month Follow-Up of a Weight Stigma Intervention for Exercise Professionals

Among the few interventions that exist to decrease weight stigma in exercise settings, none include a long-term follow-up to assess if changes are sustained. The aim of this study was to assess if a weight stigma intervention that showed success in improving fat attitudes among university recreation center professionals had sustained effects after three months, compared to a control group. Participants were 46 university recreation center professionals who took the Fat Attitudes Assessment Toolkit before completing a two-hour self-paced online intervention or control course, after course completion, and at a three-month follow-up. At follow-up, participants in the intervention group showed significant improvement in total fat attitudes, F(2, 88) = 7.94, p < .001, η2p = 0.15 and in fat acceptance, F(2, 88) = 4.25, p = .017, η2p = 0.09. Future studies should further develop interventions and assess results in larger samples and after longer periods of time.

The Value of Muscle Thickness of Lower Limbs in Detection of Diabetic Peripheral Neuropathy


 Background: Neuropathy stands out as the highest-prevalence diabetes-related complication, impacting no less than 50% of individuals with diabetes throughout their lifespan. As The most common reason for disability due to walking difficulties, foot ulcerations, and limb loss, DPN is worthy of study, and early diagnosis of DPN signs is required.
 Objectives: This study aims to aid in the identification of diabetic peripheral neuropathy (DPN) by determining the muscle thickness of the lower extremities in patients with DPN.
 Patients and Methods: The study included 24 subjects with diabetic peripheral neuropathy (DPN) and 25 individuals as a control group, subdivided into 8 diabetic patients without DPN and 17 healthy individuals. Both control and DPN subjects underwent bilateral peroneal and tibial motor nerve conduction studies (NCS) and high-resolution muscle ultrasounds to measure muscle thickness. Abductor hallucis muscle (AH), extensor digitorum brevis muscle (EDB), extensor hallucis longus muscle (EHL), tibialis anterior muscle (TA), and rectus femoris muscle (RF) were evaluated on both sides using ultrasound.
 Results: Comparing the tested muscles to the control group, the study revealed a statistically significant decrease in the thickness of many of the muscles being tested when compared to the control group.
 Conclusions: These findings point to the possibility that quantitative muscle ultrasound could be beneficial in identifying muscle changes associated with DPN. It is necessary to conduct additional studies to verify the results in larger samples of diabetic patients.
 Keywords: Neuromuscular Ultrasound, Muscle Thickness, Diabetic Peripheral Neuropathy

Reweighted and circularised Anderson-Darling tests of goodness-of-fit

This paper takes a look at omnibus tests of goodness of fit in the context of reweighted Anderson-Darling tests and makes threefold contributions. The first contribution is to provide a geometric understanding. It is argued that the test statistic with minimum variance for exchangeable distributional deviations can serve as a good general-purpose test. The second contribution is to propose better omnibus tests, called circularly symmetric tests and obtained by circularising reweighted Anderson-Darling test statistics or, more generally, test statistics based on the observed order statistics. The resulting tests are called circularised tests. A limited but arguably convincing simulation study on finite-sample performance demonstrates that circularised tests have good performance, as they typically outperform their parent methods in the simulation study. The third contribution is to establish new large-sample results.

Plasma levels of neurology-related proteins are associated with cognitive performance in an older population with overweight/obesity and metabolic syndrome

Cognitive impairment is present in a broad spectrum of medical conditions and in aging. Here, we aimed to identify plasma proteins related to cognitive function in a sample of older adults with overweight/obesity and metabolic syndrome. A total of 129 subjects (mean age 64.7 years; 36% females) were grouped according to low (l-GCF, N=65) or high (h-GCF, N=64) global cognitive function and matched according to education, sex, age, and body mass index. Cognitive performance was assessed using neuropsychological tests. Plasma levels of 92 neurology-related proteins were assessed using a proximity extension assay. An elastic net regression analysis was used to identify proteins more associated with cognitive performance. Additionally, the protein expression levels were compared between the two groups by means of a t-test with false discovery rate correction. Pearson correlations were used to assess associations between the protein levels and scores from the neurocognitive tests. Six proteins (alpha-2-MRAP, HAGH, Siglec-9, MDGA1, IL12, and EDA2R) were identified as potential contributors to cognitive performance, remaining significantly increased in l-GCF compared to h-GCF participants after correction for multiple testing. Negative correlations (r= −0.23 to −0.18, i.e., lower protein levels, higher cognitive function) were found between global cognitive function and Siglec-9, NMNAT1, HAGH, LXN, gal-8, alpha-2-MRAP, IL12, PDGF-R-alpha, NAAA, EDA2R, CLEC1B, and LAT. Mini-mental state examination z scores showed the strongest correlations with protein levels, specifically negative correlations with CLEC1b, LXN, LAT, PLXNB3, NMNAT1, gal-8, HAGH, NAAA, CTSS, EZR, KYNU, MANF (r=−0.38 to −0.26) and a positive correlation with ADAM23 (r= 0.26). In summary, we identified several plasma proteins that were significantly associated with cognitive performance in older adults with obesity and metabolic syndrome, although further research is needed to replicate the results in larger samples and to include a predictive perspective.

Transcranial focused ultrasound modulates the emergence of learned helplessness via midline theta modification

BackgroundHelplessness and hopelessness are states closely related to depressive disorders. They ensue following the anticipated absence of valid behavioral options to alleviate an aversive state. One neural structure involved in the detection of such options, is the cingulate cortex (CC), which conveys the evaluation of behavior against the value of its outcome. Accordingly, CC-related EEG measures are thought to correlate to feedback evaluation but also to anxious and conflict-related states, signaling the need for further adaptation to current requirements. Against this background, this study investigated the role of CC functioning in the emergence and prevention of helpless/hopeless states by applying low-intensity transcranial focused ultrasound (litFUS) neuromodulation prior to a learned helplessness task. MethodIn a randomized controlled double blind experimental setup with 55 participants, litFUS was used to inhibit the right lateral prefrontal cortex (lPFC), a region closely connected to CC modulation. Participants were instructed to play 8 games of chess against a computer that was unbeatable to them, while an EEG was recorded. Theta oscillation in midline electrodes, playing performance and self-rate measures on cognitive, affective and physiological parameters were assessed. ResultsThe results show a considerable influence of litFUS neuromodulation of the lPFC on midline theta activity (Fz and Pz electrode position) which in turn correlated to several psychological variables including self-report data on emotion, cognition, and arousal as well as behavioral measures (playing performance). LimitationsDue to the small sample size and sample characteristics, results cannot be generalized to the field of clinical application. A replication of results in larger samples and in context of other sonication parameters is needed to increase the robustness of results and to maximize the efficacy of litFUS application. ConclusionsWe conclude that the development of learned helplessness/hopelessness could be positively influenced in its course by litFUS neuromodulation of the right lPFC. In line with previous results, especially the posterior midline electrode Pz seems to be an interesting target for further research in this field as theta activity at this electrode is correlated to control perception and motivated behavior. To our knowledge, this is the first study to use neuromodulation to monitor and manipulate the development of helplessness in the laboratory.

Use of explicit priming to phenotype absolute pitch ability

Musicians with absolute pitch (AP) can name the pitch of a musical note in isolation. Expression of this unusual ability is thought to be influenced by heritability, early music training and current practice. However, our understanding of factors shaping its expression is hampered by testing and scoring methods that treat AP as dichotomous. These fail to capture the observed variability in pitch-naming accuracy among reported AP possessors. The aim of this study was to trial a novel explicit priming paradigm to explore phenotypic variability of AP. Thirty-five musically experienced individuals (Mage = 29 years, range 18–68; 14 males) with varying AP ability completed a standard AP task and the explicit priming AP task. Results showed: 1) phenotypic variability of AP ability, including high-accuracy AP, heterogeneous intermediate performers, and chance-level performers; 2) intermediate performance profiles that were either reliant on or independent of relative pitch strategies, as identified by the priming task; and 3) the emergence of a bimodal distribution of AP performance when adopting scoring criteria that assign credit to semitone errors. These findings show the importance of methods in studying behavioural traits, and are a key step towards identifying AP phenotypes. Replication of our results in larger samples will further establish the usefulness of this priming paradigm in AP research.

Speech recognition can help evaluate shared decision making and predict medication adherence in primary care setting.

ObjectiveAsthma is a common chronic illness affecting 19 million US adults. Inhaled corticosteroids are a safe and effective treatment for asthma, yet, medication adherence among patients remains poor. Shared decision-making, a patient activation strategy, can improve patient adherence to inhaled corticosteroids. This study aimed to explore whether audio-recorded patient-primary care provider encounters can be used to: 1. Evaluate the level of patient-perceived shared decision-making during the encounter, and 2. Predict levels of patient’s inhaled corticosteroid adherence.Materials and methodsShared decision-making and inhaled corticosteroid adherence were assessed using the SDM Questionnaire-9 and the Medication Adherence Report Scale for Asthma (MARS-A). Speech-to-text algorithms were used to automatically transcribe 80 audio-recorded encounters between primary care providers and asthmatic patients. Machine learning algorithms (Naive Bayes, Support Vector Machines, Decision Tree) were applied to achieve the study’s predictive goals.ResultsThe accuracy of automated speech-to-text transcription was relatively high (ROUGE F-score = .9). Machine learning algorithms achieved good predictive performance for shared decision-making (the highest F-score = .88 for the Naive Bayes) and inhaled corticosteroid adherence (the highest F-score = .87 for the Support Vector Machines).DiscussionThis was the first study that trained machine learning algorithms on a dataset of audio-recorded patient-primary care provider encounters to successfully evaluate the quality of SDM and predict patient inhaled corticosteroid adherence.ConclusionMachine learning approaches can help primary care providers identify patients at risk for poor medication adherence and evaluate the quality of care by measuring levels of shared decision-making. Further work should explore the replicability of our results in larger samples and additional health domains.

Internet-based CBT for adolescents with low self-esteem: a pilot randomized controlled trial

ABSTRACT Low self-esteem is a common problem among adolescents and is related to psychiatric problems such as depression and anxiety. However, effective and available interventions primarily targeting low self-esteem are scarce, in particular for youths. To address this gap, the aim of this pilot study was to evaluate a novel internet-based Cognitive Behavioral Therapy (ICBT) program for low self-esteem in adolescents using a randomized controlled design. Fifty-two participants (15-19 years) were recruited and randomly allocated to seven weeks of therapist-supported ICBT (n=26) or to a waitlist control condition (n=26). The primary outcome was the Rosenberg Self-Esteem Scale (RSES). Secondary outcomes measured domain-specific aspects of self-esteem, self-compassion, quality of life, depression and anxiety. The treatment group showed significantly higher levels of self-rated self-esteem compared to the control group at post-treatment, with a large between-group effect-size (RSES, d = 1.18). Further, the treatment had significant positive impact on secondary measures of self-esteem, self-compassion, quality of life, depression and anxiety. The results of this pilot-RCT suggest that ICBT can be effective for treating low self-esteem in adolescents, decrease depression and anxiety levels, and increasing quality of life. Replication of the results in larger samples is needed.

Treatment for Pediatric Functional Abdominal Pain: An Initial Examination of Reciprocal Associations Between Pain, Functional Impairment, and Parental Distress.

While cross-sectional studies underline that child and parent factors in pediatric chronic pain are reciprocally related, so far, little is known on their prospective relationship, especially in treatment contexts. This study aims to analyze directions of influence between child and parental outcomes using data from an intervention study. The sample covered 109 families with children aged 7-13 years diagnosed with functional abdominal pain (FAP). Child outcomes included pain and impairment, and parental outcomes covered caregiver-specific distress including both parental personal time burden (i.e., less time available for personal needs) and emotional burden due to child's pain (i.e., increased worries). Cross-lagged panel analyses examined the directions of the relations between child and parental outcomes across time (pretreatment T1, post-treatment T2, and 3-month follow-up and 12-month follow-up T3/T4). First, a significant improvement over time in all measures was observed. Cross-lagged effects were found for less parental personal time burden at T2, predicting both less pain (β = -0.254, p = .004) and less impairment (β = -0.150, p = .039) at T3. Higher baseline pain was predictive for higher parental emotional burden after treatment (β = -0.130, p = .049) and, reversely, for less emotional burden at 12-month follow-up (β = 0.261, p = .004). Addressing parental personal time burden in FAP treatment might possibly support the improvement on the child level. Replication of results in larger samples is warranted to gain more insight into the directions of influence and, in that way, to optimize treatment for pediatric FAP.

The partly parametric and partly nonparametric additive risk model

Aalen’s linear hazard rate regression model is a useful and increasingly popular alternative to Cox’ multiplicative hazard rate model. It postulates that an individual has hazard rate function h(s)=z_1alpha _1(s)+cdots +z_ralpha _r(s) in terms of his covariate values z_1,ldots ,z_r. These are typically levels of various hazard factors, and may also be time-dependent. The hazard factor functions alpha _j(s) are the parameters of the model and are estimated from data. This is traditionally accomplished in a fully nonparametric way. This paper develops methodology for estimating the hazard factor functions when some of them are modelled parametrically while the others are left unspecified. Large-sample results are reached inside this partly parametric, partly nonparametric framework, which also enables us to assess the goodness of fit of the model’s parametric components. In addition, these results are used to pinpoint how much precision is gained, using the parametric-nonparametric model, over the standard nonparametric method. A real-data application is included, along with a brief simulation study.

Categorical differentiation of the unipolar and bipolar disorders

There has been a longstanding debate as to whether the bipolar disorders differ categorically or dimensionally, with some dimensional or spectrum models including unipolar depressive disorders within a bipolar spectrum model. We analysed manic/hypomanic symptom data in samples of clinically diagnosed bipolar I, bipolar II and unipolar patients, employing latent class analyses to determine if separate classes could be identified. Mixture analyses were also undertaken to determine if a unimodal, bimodal or a trimodal pattern was present. For both a refined 15-item set and an extended 30-item set of manic/hypomanic symptoms, our latent class analyses favoured three-class solutions, while mixture analyses identified trimodal distributions of scores. Findings argue for a categorical distinction between unipolar and bipolar disorders, as well as between bipolar I and bipolar II disorders. Future research should aim to consolidate these results in larger samples, particularly given that the size of the unipolar group in this study was a salient limitation.

Adolescent girls’ physiological reactivity to real-world peer feedback: A pilot study to validate a Peer Expressed Emotion task

Peer feedback becomes highly salient during adolescence, especially for girls. The way in which adolescents react to social feedback is associated with psychosocial adjustment and mental health. Consequently, researchers are increasingly interested in understanding the physiological and neural underpinnings of adolescent response to feedback by simulating the experience of rejection and acceptance using computer-based paradigms. However, these paradigms typically use nonfamiliar peers and the facade of internet chatrooms or games to present artificial peer feedback. The current study piloted the use of a novel and potentially more ecologically valid peer expressed emotion paradigm in which participants listen to prerecorded clips of ostensible personalized feedback made by their close friend. Physiological data measuring autonomic nervous system response were collected as an index of emotional reactivity/arousal and cognitive-affective processing. Results show promising preliminary evidence validating the task for future use. Participants (N = 18 girls, aged 11–17 years) reported feeling more positive following praise, relative to critical and neutral feedback, and reported feeling more upset following criticism, relative to praise and neutral feedback. Girls exhibited greater pupillary dilation, skin conductance levels (N = 17), and/or heart rate (N = 17) while listening to affectively charged, peer feedback compared with neutral yet personally relevant statements. Girls also exhibited variable physiological response when listening to praising versus critical feedback. Findings from this pilot study validate the use of this novel Peer Expressed Emotion task for the investigation of adolescents’ emotional and physiological reactivity in response to real-world peer evaluation. However, it is important to recognize that this study provides only preliminary findings and that future research is needed to replicate the results in larger samples.

How am I doing compared to different standards? Comparative thinking and well-being following exposure to a vehicle-ramming attack

ABSTRACT Background: Exposure to potentially adverse events might intensify thinking about different comparison standards in relation to one’s own well-being. Objective: To examine how frequently survivors of a recent potentially traumatic event use different comparison standards to evaluate their current well-being. Method: A survey with 223 participants directly or indirectly exposed to a vehicle-ramming attack was conducted. Symptoms of post-traumatic stress disorder (PTSD) and depression, quality of life, and the sum score of the frequency of different types of comparison standards were assessed. The latter consisted of temporal, counterfactual, social, dimensional, and criteria-based comparisons. Results: In total, 98% of participants reported some form of comparative thinking during the last two weeks. The most frequent comparison types were temporal and dimensional comparisons, with 94 and 87% of participants reporting them, respectively. Notably, comparative thinking predicted unique variance in PTSD symptoms, over and above depressive symptoms. Conclusion: The results suggest that comparative thinking may be a significant factor in understanding psychological distress following exposure to aversive events. Replication of the results in larger samples and using longitudinal and experimental designs is clearly necessary.

Prediction of depression symptoms in individual subjects with face and eye movement tracking.

Depression is a challenge to diagnose reliably and the current gold standard for trials of DSM-5 has been in agreement between two or more medical specialists. Research studies aiming to objectively predict depression have typically used brain scanning. Less expensive methods from cognitive neuroscience may allow quicker and more reliable diagnoses, and contribute to reducing the costs of managing the condition. In the current study we aimed to develop a novel inexpensive system for detecting elevated symptoms of depression based on tracking face and eye movements during the performance of cognitive tasks. In total, 75 participants performed two novel cognitive tasks with verbal affective distraction elements while their face and eye movements were recorded using inexpensive cameras. Data from 48 participants (mean age 25.5 years, standard deviation of 6.1 years, 25 with elevated symptoms of depression) passed quality control and were included in a case-control classification analysis with machine learning. Classification accuracy using cross-validation (within-study replication) reached 79% (sensitivity 76%, specificity 82%), when face and eye movement measures were combined. Symptomatic participants were characterised by less intense mouth and eyelid movements during different stages of the two tasks, and by differences in frequencies and durations of fixations on affectively salient distraction words. Elevated symptoms of depression can be detected with face and eye movement tracking during the cognitive performance, with a close to clinically-relevant accuracy (~80%). Future studies should validate these results in larger samples and in clinical populations.

Associations of maternal early-pregnancy blood glucose and insulin concentrations with DNA methylation in newborns

BackgroundIntrauterine exposure to a disturbed maternal glucose metabolism is associated with adverse offspring outcomes. DNA methylation is a potential mechanism underlying these associations. We examined whether maternal early-pregnancy glucose and insulin concentrations are associated with newborn DNA methylation. In a population-based prospective cohort study among 935 pregnant women, maternal plasma concentrations of non-fasting glucose and insulin were measured at a median of 13.1 weeks of gestation (95% range 9.4–17.4). DNA methylation was measured using the Infinium HumanMethylation450 BeadChip (Ilumina). We analyzed associations of maternal early-pregnancy glucose and insulin concentrations with single-CpG DNA methylation using robust linear regression models. Differentially methylated regions were analyzed using the dmrff package in R. We stratified the analyses on normal weight versus overweight or obese women. We also performed a look-up of CpGs and differently methylated regions from previous studies to be associated with maternal gestational diabetes, hyperglycemia or hyperinsulinemia, or with type 2 diabetes in adults.ResultsMaternal early-pregnancy glucose and insulin concentrations were not associated with DNA methylation at single CpGs nor with differentially methylated regions in the total group. In analyses stratified on maternal BMI, maternal early-pregnancy glucose concentrations were associated with DNA methylation at one CpG (cg03617420, XKR6) among normal weight women and at another (cg12081946, IL17D) among overweight or obese women. No stratum-specific associations were found for maternal early-pregnancy insulin concentrations. The two CpGs were not associated with birth weight or childhood glycemic measures (p values > 0.1). Maternal early-pregnancy insulin concentrations were associated with one CpG known to be related to adult type 2 diabetes. Enrichment among nominally significant findings in our maternal early-pregnancy glucose concentrations was found for CpGs identified in a previous study on adult type 2 diabetes.ConclusionsMaternal early-pregnancy glucose concentrations, but not insulin concentrations, were associated with DNA methylation at one CpG each in the subgroups of normal weight and of overweight or obese women. No associations were present in the full group. The role of these CpGs in mechanisms underlying offspring health outcomes needs further study. Future studies should replicate our results in larger samples with early-pregnancy information on maternal fasting glucose metabolism.

Taxonomic and Functional Differences in Cervical Microbiome Associated with Cervical Cancer Development

The cervical microbiome is associated with cervical cancer risk, but how microbial diversity and functional profiles change in cervical cancer remains unclear. Herein, we investigated microbial-compositional and functional differences between a control group and a high-grade cervical intraepithelial neoplasia and cervical cancer (CIN2/3-CC) group. After retrospective collection of 92 cervical swab samples, we carried out 16S rRNA amplicon sequencing on 50 and 42 samples from the control and CIN2/3-CC groups, respectively. The EzBioCloud pipeline was applied to identify the genomic features associated with the groups using 16S rRNA data. A linear discriminant analysis effect size (LEfSe) was performed to assess the enrichment in the assigned taxonomic and functional profiles. We found a lower richness in the control group relative to the CIN2/3-CC group; however, the β-diversity tended to be similar between the groups. The LEfSe analysis showed that a phylum Sacchaaribacteria_TM7, 11 genera, and 21 species were more abundant in the CIN2/3-CC group and that one uncharacterized Gardnerella species was more abundant only in the control group. Further characterization of the functional pathways using EzBioCloud showed that the 4 KEGG orthologs (Phosphotransferase system [PTS] sucrose-specific IIA, IIB, IIC components and PTS cellubiose-specific IIC component) were involved in the KEGG pathway of starch and sucrose metabolism. The two pathways of folate biosynthesis and oxidative phosphorylation were more abundant in the CIN2/3-CC group. Further confirmation of these results in larger samples can help to elucidate the potential association between the cervical microbiome and cervical cancer.

Alleviation of ADHD symptoms by non-invasive right prefrontal stimulation is correlated with EEG activity.

Attention deficit hyperactivity disorder (ADHD) is a prevalent disorder with effective pharmacological treatment that benefits most patients. However, about one-third fail to benefit while others search non-pharmacological alternatives, and for those options are scarce. One alternative treatment option is to alter abnormal right prefrontal cortex (rPFC) activity, given that rPFC abnormality has been repeatedly implicated in ADHD neurophathology. Here, we evaluated whether targeting the rPFC with multiple sessions of repetitive transcranial magnetic stimulation (rTMS), which can modulate neuronal excitability, activity, and plasticity in a non-invasive manner, will affect clinical symptoms in adults suffering from ADHD. Concomitantly, we used EEG to characterize electrophysiological alterations induced by treatment and to search for correlation between baseline neuronal activity and clinical response.Forty-three drug free adults with ADHD were randomized to receive either Real, Active Control, or Sham treatment (13 females, age ranging 21-46; n = 15, 14, 14, respectively), and underwent three weeks of daily high-frequency (18 Hz) stimulation sessions. We found that Real treatment was safe and resulted in significant improvement of symptoms (η2p = 0.34; Cohen's d(against Sham) = 0.96; Cohen's d(against AC) = 0.68; p = 0.00085). Furthermore, based on EEG recorded within the first treatment session we established a novel biomarker, composed of the Alpha and Low-gamma power, which highly correlated the magnitude of the clinical outcome (r = 0.92, p = 0.0001).Taken together, the results of this pilot study indicate safety and effectiveness of rTMS directed to the rPFC for treatment of adult ADHD patients. The biomarker is suggested to reflect the responsiveness of the cortex to this rTMS intervention. Following validation of the results in larger samples, this study may represent a step towards a non-pharmacological treatment for adults with ADHD using EEG-based selection of optimal candidates for treatment.

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Hsa-miR-584-5p as a novel candidate biomarker in Turkish men with severe coronary artery disease.

Coronary artery disease (CAD) is still the preliminary cause of mortality and morbidity in the developed world. Identification of novel predictive and therapeutic biomarkers is crucial for accurate diagnosis, prognosis and treatment of the CAD. The aim of this study was to detect novel candidate miRNA biomarker that may be used in the management of CAD. We performed miRNA profiling in whole blood samples of angiographically confirmed Turkish men with CAD and non-CAD controls with insignificant coronary stenosis. Validation of microarray results was performed by qRT-PCR in a larger cohort of 62 samples. We subsequently assessed the diagnostic value of the miRNA and correlations of miRNA with clinical parameters. miRNA-target identification and network analyses were conducted by Ingenuity Pathway Analysis (IPA) software. Hsa-miR-584-5p was one of the top significantly dysregulated miRNA observed in miRNA microarray. Men-specific down-regulation (p = 0.040) of hsa-miR-584-5p was confirmed by qRT-PCR. ROC curve analysis highlighted the potential diagnostic value of hsa-miR-584-5p with a power area under the curve (AUC) of 0.714 and 0.643 in men and in total sample, respectively. The expression levels of hsa-miR-584-5p showed inverse correlation with stenosis and Gensini scores. IPA revealed CDH13 as the only CAD related predicted target for the miRNA with biological evidence of its involvement in CAD. This study suggests that hsa-miR-584-5p, known to be tumor suppressor miRNA, as a candidate biomarker for CAD and highlighted its putative role in the CAD pathogenesis. The validation of results in larger samples incorporating functional studies warrant further research.