Several models have tentatively associated improving attention-deficit/hyperactivity disorder (ADHD) symptoms with arousal and external environmental stimulation. In order to further clarify the relationships between ADHD symptoms, arousal, and external stimulation, this study focused on exploring the “simultaneous” effects of white noise on intrinsic attentional performance and extrinsic on-task behaviors in preschoolers with and without ADHD. By using the computerized task (K-CPT 2), 104 preschoolers, including 52 ADHD children and 52 typically developing (TD) children, were tested and analyzed for their intrinsic attention . Simultaneously, these preschoolers’ external on-task behaviors were recorded for analysis through systematic observation. This study showed that white noise could effectively improve attention performance, including enhancing the ability to differentiate non-targets from targets and decreasing omission errors. It could also reduce the extrinsic hyperactive behaviors of preschoolers with ADHD. The findings of this study highlighted that white noise stimulation is a beneficial non-pharmacological treatment for preschoolers with ADHD. In contrast, for TD preschoolers, the results of this study showed that the external white noise stimuli were not only unhelpful but also a burden. Attention-deficit/hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders, with a global prevalence rate of 5% in children and adolescents . Of noteEmpirical research has investigated the arousal states of ADHD individuals over the last two decades and suggested that ADHD is related to cortical hypoarousal of the brain, which may impact cognitive performance . For insThe problem of hyperactive behaviors (such as fidgeting and restlessness) is also one of the core diagnostic features of children with ADHD , which cStochastic resonance (SR), a phenomenon wherein the response of a nonlinear system to a weak periodic input signal is optimized by the addition of a particular level of noise, has been widely demonstrated across various modalities . The MBAPrevious studies examined the effects of white noise on cognitive performance and hyperactive problems separately; these results are difficult to compare and cannot simultaneously examine the impact of white noise on the overall intrinsic and extrinsic problems in children with ADHD, especially when past studies have shown inconsistent results. Furthermore, most white noise studies recruited school-aged children or older than this age group as research subjects and rarely researched young children. To the best of my knowledge, the present study is the first to simultaneously explore the effects of white noise on intrinsic attentional performance and extrinsic on-task behaviors in preschoolers with and without ADHD. Three hypotheses were explored in this study. First, based on various symptoms exhibited by individuals with ADHD, this study hypothesized that preschoolers with ADHD would show significant differences from their TD peers in most intrinsic attentional and extrinsic on-task behavioral indicators in the condition of no background sound. Second, based on the fact that several models have tentatively associated improving ADHD symptoms with arousal and external environmental stimulation, the author hypothesized that the external sound stimulation of white noise could effectively improve the intrinsic attentional performance and extrinsic on-task behaviors of preschoolers with ADHD. Third, although the MBA model emphasizes that external sound stimulation can effectively increase functional performance for children with ADHD by improving their hypoarousal of the dopamine system, this effect may not show in TD children who already have an intact dopamine system. Therefore, the current study hypothesized that most indicators of the intrinsic attentional and extrinsic behavioral performance of TD preschoolers would not show significant differences between the conditions with and without background white noise.A two-stage randomized crossover design was applied in the present study for both children groups to balance the order of the two experimental conditions  . The crot-test by , version 3.1.9 [Two groups of preschoolers (children with and without ADHD) were invited to participate in this study. The group of preschoolers with ADHD was recruited first; these participants were drawn from local ADHD clinics, non-profit associations, and special education preschools. In order to form an age- and gender-matched control group, the group of preschoolers without ADHD was recruited after determining the number of eligible participants for preschoolers with ADHD; these healthy development young children were drawn from local preschools. For confirming the ability to detect differences within and between groups in this study, a minimum sample size of 84  was calculated using the on 3.1.9  with an Clinical diagnoses for the group of preschoolers with ADHD were made by a licensed psychologist and two senior pediatric neurologists with more than ten years of experience in ADHD. The diagnosis procedure was conducted through a five-part process, including parent interviews, educational and medical records review, formal IQ testing  , a diagnThe experimental setting for performing computerized attentional tests was conducted in a clinic soundproofing therapy room, which contained a testing table, several chairs, computerized testing equipment, some small toys (two teddy bears and three toy cars), and a one-way mirror for test session behavioral observation. All participants had to attend the testing meeting twice in this therapy room at a one-week interval. In each appointment, every child was administered the computerized attentional test (K-CPT 2) once in this quiet room.Previous studies tested several noise levels in school-age children ,36,37; t(1)Detectability: An indicator of inattention represents the ability to differentiate non-targets from targets;(2)Omissions: An indicator of inattention represents the results of the failure to respond to targets;(3)Commissions: An indicator of impulsivity represents the degree of response to non-targets;(4)Perseverations: An indicator of impulsivity represents those that are made in less than 100 milliseconds following the presentation of a stimulus;(5)Hit Reaction Time (HRT): An indicator of inattention or impulsivity represents the response speed of correct responses for the whole administration. An atypically slow HRT (higher T-scores) may indicate inattentiveness; alternatively, a speedy HRT (lower T-scores) may indicate impulsivity;(6)HRT Standard Deviation (HRT SD): An indicator of inattention represents the measure of response speed consistency during the entire administration. A high HRT SD indicates a more inconsistent response speed.The Conners’ Continuous Performance Test (CPT)  and its Systematic direct observations of children’s behavior are essential to clinical evaluation. The Restricted Academic Situation Scale (RASS) , which iBoth participating groups (children with and without ADHD) were randomly assigned to two subgroups for different procedures of taking the K-CPT 2 see . ParticiTwo trained senior therapists were blinded to research purposes, participants’ diagnoses, and group assignments and conducted behavioral coding. The two coders received detailed definitions of behavior types and were trained on practice videos. In addition, to create a blind context when coding the on-task behaviors, all subjects had to wear headphones to perform the computerized attention test regardless of the test situation (no sound stimuli were delivered through headphones under the condition of “no background sound”). Only the author, who set the K-CPT 2 and white noise settings, knew whether the white noise was delivered. This procedure created the blind condition to reduce bias on the part of the coders. The interobserver agreement (IOA), which was collected from K-CPT 2 task practice sessions in the present study, was calculated by dividing the number of intervals in agreement by the sum of agreements and disagreements and then multiplying by 100 . Accordip < 0.05. An independent sample t-test was used to compare the data between the two children groups, and a paired sample t-test was adopted to compare the data between the two background sound conditions for each children group. Cohen’s d was adopted to calculate the effect size, a standardized quantitative index representing the magnitude of change that one variable produces in another variable as reflected in the difference between two means independent of sample size [d is based on the convention proposed by Cohen, such as 0.20 being the small effect size, 0.50 being the medium, and 0.80 or more being the large effect size. The statistical significance in this study was sufficient to explain most cases only if the effect size d was considered a large magnitude.The data of intrinsic attentional performance (K-CPT 2 testing scores) and extrinsic on-task behaviors (the data collected from test session observations) were analyzed with the statistical software package SPSS Version 21 . All data analyses were two-tailed, and the significance was set at ple size . The intt-tests showed that there was no significant difference between groups in terms of age  and IQ . In addition, the typically developing children (n = 52) were selected to match the children with ADHD (n = 52) for age and gender. Both preschooler groups in the present study were well-matched concerning age, IQ, gender, and grade of schooling in preschool.The main demographic information for the sample is reported in t = 2.89, p < 0.05), omissions , commissions , perseverations , and HRT SD , showed a significant difference between groups. Similarly, most extrinsic behavioral indicators of ADHD participants, including vocalizations , off task , out of seat , and fidgets , also showed significantly worse performance than their TD peers.As illustrated in t = 3.24, p < 0.05) showed a significant difference between groups; however, this difference was not sufficient to explain most preschoolers in this study (d < 0.8).In the white noise condition see , most int = 3.18, p < 0.05) and fidgets , showed significant differences between groups.As illustrated in t-testing revealed that half indicators, including detectability , omissions , and HRT SD , of intrinsic attentional performance in ADHD preschoolers between two auditory conditions were statistically significant , off task , out of seat , and fidgets  of these ADHD preschoolers, showed significant differences between two background sound conditions.The results from the paired cant see . Additiot = 1.26, p > 0.05), omissions , commissions , perseverations , HRT , and HRT SD  showed no significant difference between two sound conditions. Similar results were also presented on extrinsic on-task behavior; all five extrinsic behavioral indicators, including vocalizations , off task , out of seat , fidgets , and play with objects , showed no significant difference between two background sound conditions.For typically developing preschoolers, white noise presented during taking computerized attentional tasks had no effect on intrinsic attentional performance see . All sixSeveral methodological advantages over previous white noise research were adopted in the present study. First, a two-period crossover design was used in the present study for two groups of children to balance the order of testing under two sound conditions; this methodologic design decreased the impact of fatigue effects in performing two sufficiently long laboratory tests, especially for the little kids. Second, because age and gender truly affect attentional performance ,49, the In the present study, we considered the attentional function and on-task behaviors of TD preschoolers in the no background sound condition as a benchmark for appropriate performance in this age group. Based on comparisons with TD preschoolers, the research results see  support d = 0.91), off task (d = 0.93), and fidgets (d = 1.69). However, only the index of HRT SD (d = 0.90) obtained large effect sizes among attentional indexes. Furthermore, although most attention indexes showed significance between the two groups in the condition of no background sound, five of six attentional indexes showed that the ADHD sample was not 1 SD above the mean. These results were mainly influenced by the presentations of participants with ADHD in this study. Most of the preschoolers with ADHD recruited in this study were categorized into the hyperactive–impulsive presentation (51.9%) based on DSM-5 , a phenomenon in which a signal that is usually too weak to be detected by a sensor can be boosted by adding white noise to the signal, to explain how white noise improves the performance of children with ADHD ,12,21. TRegarding TD preschoolers see , althougThe main strength of this study lies in simultaneously exploring the effects of white noise on intrinsic attentional performance and extrinsic hyperactive behaviors for preschoolers with ADHD, which has not been examined in past research. However, there are also several limitations. First, the author only recruited ADHD preschoolers from clinical settings, and this clinic sampling limits the generalizability of this study’s results. Thus, future research is needed to collect more representative samples by recruiting community participants. Second, this study sample only included preschool children (4–6 years old), so results should not be extrapolated to children outside this age group. Third, only one type of external sound stimulation was used in the present study. Certain types of music may also positively affect ADHD patients. For example, recent studies ,60 confiIn conclusion, the findings of this study highlight that white noise stimulation is a beneficial non-pharmacological treatment for preschoolers with ADHD. Furthermore, this study’s results confirmed that white noise improves not only intrinsic attentional performance (such as decreasing omission errors and reaction time variability) but also decreases extrinsic hyperactive behaviors (such as severe fidgeting). However, these significant benefits of white noise in preschoolers with ADHD were not present in their TD peers. For TD preschoolers, although the results of this study showed no significant difference between the two background sound conditions, the stimulation of white noise presented a trend toward worsening attentional performance and on-task behaviors in these TD preschoolers.


Emotional Dysregulations (ED) represent a major health risk present in about 5% of children and are associated with diverse forms of childhood psychiatric disorders and symptoms such as Attention-Deficit/Hyperactivity Disorder (ADHD) . Early CWithin the published studies, children over 10 years old prevail, which makes it difficult to assess the oral health only on the basis of permanent teeth. Studies regarding younger children, i.e., those under 8 years of age with primary dentition, lack definitiveness. This is most probably associated with the point of age at which the diagnosis is made, i.e., between the ages of 6 and 8.Literature regarding children affected by ADHD suggests that caries prevalence in increased in this group ,4,5,6. BAuthors of these and others studies underline that caries development is influenced by a significantly more frequent intake of cariogenic carbohydrates, irregularity in eating patterns as well as less systematic and shorter brushing ,12,14. IIncreased plaque accumulation and gingival inflammation may result from the high-carbohydrate diet that is preferred by such patients. There is a tendency to eat more frequently and snack between meals in children with ADHD. If there are no limits placed by the caretakers and if they do not control the process, the increase in the number of meals may exert a huge influence on the progress of caries in deciduous and permanent dentition. However, there are conflicting views in the literature—on the one hand, some researchers confirm that irregular brushing influences high caries prevalence ; on the other, other researchers do not observe such correlation . Bretz eA higher number of parent–child conflicts has been reported in families with a child affected by ADHD, which may also influence oral health behaviors. Quite significant reduction in child functioning in comparison to a healthy child, requires the parents to support the child in a variety of everyday chores, including oral hygiene maintenance and keeping the dietary regime. It should be an alarming sign for those responsible for planning oral health and should motivate them to keep track of dental appointments in such children. According to questionnaire-based studies, the numbers of appointments differ between healthy children and ADHD-affected children ,12. The ADHD may constitute a risk factor for higher caries incidence; however, the role of medications used in such children should not be overlooked . The infAdditionally, anthropometric studies indicated that children with ADHD may represent a population at risk of overweight ,20,21. TTo help accelerate progress related to the extension of the diagnostic framework for ADHD patients, there is a need to monitor children at risk of oral diseases. From the patient’s side, the action of arresting dental plaque deposit may start from regular tooth brushing and mouth rinsing . PreventProfessional dental diagnosis desires an accurate severity evaluation. The following methods to detect carious lesions are considered as macroscopic visual examination, bitewing X-ray pictures, tooth separation by orthodontic rubber wedges and non-invasive transillumination of dental tissues. Clinical and/or digital diagnosis methods differ by repeatability and sensitivity; therefore, the combination of two procedures has to be used at the same time. In general, carious lesion activity is the next step, and its assessment will select a proper management strategy such as non-operative care (NOC) or tooth-preserving operative care (TPOC). Only in low caries risk will a dental professional decide on lesion monitoring at recall appointments. In moderate/high caries risks, lesion arresting by remineralization is needed. Remineralization efficacy is possible only when acid and sucrose (refined carbohydrate) intake limitations, oral hygiene  and frequency of dental visit improvements occur. The most common dental treatment planning and recommendations are shown in Clinical data of ADHD trials suggest that effective prevention may depend on level of caries risk, patient’s cooperation and therapeutic management;Early strategies for individual patient cases and choices of remineralization products are needed to defend the oral health of children with ADHD against dental caries;All abovementioned preventive directions may be applied, and longer follow-up periods are suggested to evaluate the clinical approach in oral health among ADHD patients.The outcomes of the present survey allowed us to draw the following conclusions:


In the wake groups , the time schedule was reversed. Odor memory consolidation was superior in the ADHD sleep group compared to the TDC sleep and the ADHD wake groups. Intensity and familiarity ratings during encoding were substantially higher in ADHD compared to TDC. Sleep-associated odor memory consolidation in ADHD is superior to TDC. Abundant pre-experience due to lower perceptive thresholds is suggested as a possible explanation. Olfaction might serve as a biomarker in ADHD.Prior experience represents a prerequisite for memory consolidation across various memory systems. In the context of olfaction, sleep was found to enhance the consolidation of odors in adults but not in typically developing children (TDC), likely due to differences in pre-experience. Interestingly, unmedicated children with attention deficit hyperactivity disorder (ADHD), a neurodevelopmental condition related to dopamine dysfunction, showed lower perceptive thresholds for odors, potentially allowing for more odor experience compared to TDC. We investigated sleep-associated odor memory consolidation in ADHD. Twenty-eight children with ADHD and thirty age-matched TDC participated in an incidental odor recognition task. For the sleep groups (ADHD:  Sleep fosters the formation of long-term memory by integrating newly encoded information traces into pre-existing memory systems . This isWith a prevalence of 5–7%, attention deficit hyperactivity disorder (ADHD) , charactLikewise, altered olfaction has been found in other dopamine-related neuropsychiatric conditions, such as Parkinson’s disease . ConsequAltered dopamine function is also thought to reduce sleep-related functionality in the prefrontal cortex (PFC) ,34. SlowWhile the initial sensory processing of odors appears to be characterized by lower thresholds for perception ,31,32, tTwenty-eight boys diagnosed with ADHD according to DSM-IV-TR  were incp > 0.1). Consistent with our diagnostic interview, psychopathology by means of ASEBA-scores was highly different between ADHD and TDC. Sample characteristics are given in ADHD patients were randomly assigned to a sleep or wake group with 14 children in each group in same way as it was done before in case of TDC . There wWritten informed consent was obtained from all children and their caretakers and all children were reimbursed with a voucher for their participation. The study was approved by the ethics committee of the Medical Faculty of the University of Kiel and followed the Declaration of Helsinki. ADHD patients were recruited via the clinic and the outpatient unit of the Department of Child and Adolescent Psychiatry and Psychotherapy of the Center for Integrative Psychiatry in Kiel, Germany, or by newspaper advertisements through which typically developing children (TDC) were recruited as well. For the sleep groups, encoding of odors took place at 8 p.m., followed by a night of sleep, and recognition was tested during retrieval after a 12-h retention interval at 8 a.m. In the same way for the wake groups, odors were encoded at 8 a.m. and retested at 8 p.m. Wake group participants were told not to sleep during the 12-h interval. Participants were asked to refrain from eating or drinking flavored drinks at least 30 min prior to each testing session. Twenty odorants of the University of Pennsylvania Smell Identification Test   were uset-tests for dependent or independent samples. Effect sizes are presented as eta squared (η2) and Cohen´s d. To analyze whether odor perception could be used to discriminate between ADHD and TDC, we used binary logistic regression. The accuracy, sensitivity, and specificity of the classification were computed using the caret toolbox in R. Sample size calculation by G*Power,  . Mean differences between sleep and wake groups both for ADHD and TDC did not reach significance (p > 0.15). In order to exclude any influence of subjective ratings on memory performance, intensity, or familiarity ratings at encoding, which were largely different among children with and without ADHD, these variables were introduced as covariates in the ANOVA. ANCOVA of odor recognition likewise revealed no main effects of GROUP  = 0.2, p = 0.618 η2 = 0.005) or CONDITION  = 0, p = 0.870 η2 = 0.001) but again a highly significant ADHD X CONDITION interaction  = 15.2, p < 0.001 η2 = 0.240). Ratings for mood, tiredness, and arousal are given in Ratings of mood, tiredness, and arousal served as a control for possible circadian effects. Repeated measure ANOVAS with the within factor SESSION encoding vs. retrieval) and the between factors ADHD (adhd vs. tdc) and CONDITION (sleep vs. wake) revealed the main effects of ADHD for each mood, tiredness, and arousal , the main results of superior sleep-associated memory consolidation of odors in ADHD remained unchanged. Additionally, serving as a control for circadian factors, subjective ratings of mood, tiredness, and arousal, which were quite different in ADHD and TDC but not different among the sleep or wake groups, had no influence whatsoever on odor memory consolidation. Hence, we can exclude that differences in memory consolidation can be ascribed to circadian factors.We previously found less effective declarative memory consolidation linked to less functional slow oscillations ,21,35. AThe majority of studies covering neuropsychological functions in ADHD report impairments or deficits, mainly in the context of executive functions, e.g., inhibition, vigilance, working memory, planning as well as in set shifting, selective attention, reaction time fluency, and decision making ,33,34. MIn contrast, improved olfaction in ADHD might become clinically significant in the context of diagnostics. Despite extensive research in the past decades, ADHD, based on the observation of behavioral symptoms, remains a major challenge, even for experienced clinicians . For exaThis study is the first to demonstrate superior sleep-associated odor memory in children with ADHD in contrast to TDC. Of course, the small sample size calls for larger confirmatory studies after our proof of principle. In this regard, one may assume our study as being not more than a pilot study. On the other hand, rigorous interviewing with strict inclusion criteria in children with ADHD according to research criteria and with no comorbidities except ODD and the exclusion of any disorder in the TDC group guaranteed homogeneous groups, reducing the probability of type 1 errors. A further strength is the use of a reliable, standardized test kit . Due to In conclusion, sleep-associated odor memory consolidation is superior in children with ADHD compared to TDC. As indicated by higher familiarity ratings, more abundant pre-experience because of lower olfactory thresholds is suggested as a possible explanation. We suggest that attenuated dopaminergic inhibition in the olfactory bulbs ,31,32 al


JCI, Choi, Kim, and co-authors report on a role of viperin in regulating metabolic alterations in cancer cells. The authors showed a correlation between clinical outcomes and viperin expression levels in multiple cancer tissues and proposed that viperin expression was upregulated in the tumor microenvironment via the JAK/STAT and PI3K/AKT/mTOR/HIF-1α pathways. Functionally, viperin increased lipogenesis and glycolysis in cancer cells by inhibiting fatty acid β-oxidation. Viperin expression also enhanced cancer stem cell properties, ultimately promoting tumor initiation in murine models. This study proposes a protumorigenic role for viperin and identifies HIF-1α as a transcription factor that increases viperin expression under serum starvation and hypoxia.Viperin, an IFN-regulated gene product, is known to inhibit fatty acid β-oxidation in the mitochondria, which enhances glycolysis and lipogenesis during viral infections. Yet, its role in altering the phenotype of cancer cells has not been established. In this issue of the  MYC genes  in order to survive . The TMEPrevious studies have shown that viperin is an IFN-γ–inducible gene product , but theMany feedback systems in biology are negative feedback systems. Negative feedback is essential to limit a signaling response that has been activated but is no longer required . Two cla+ cells compared with CD133– cells, and both CD133 and viperin expression increased when the CD133+ cells were serum starved. Viperin knockdown inhibited single-cell–derived spheroid formation, a functional characteristic commonly used to quantify the number of cells with self-renewal properties. The authors also used a Hoechst dye release assay to quantify the number of cells that pumped out the dye (termed the “side population” [SP]). Depleting viperin caused a decrease in the number of cells in the SP, indicating that viperin expression enhanced drug efflux, a mechanism known to promote chemotherapy resistance . CSCs are undifferentiated tumor cells with self-renewal properties that promote tumor formation and cause resistance to therapy . CSCs hasistance , 22. In sistance .To explore the role of viperin in vivo, mice were inoculated with MKN28 control or viperin-knockdown cells isolated from either the SP or from single-cell–derived spheroids. Mice injected with viperin-knockdown cells developed smaller tumors that had lower levels of CD44 than did those injected with control cells. Importantly, Choi, Kim, and colleagues demonstrated that increased expression of viperin was associated with the presence of CSCs, which promote tumor initiation .Choi, Kim, and co-authors demonstrated that the IFN-regulated protein viperin plays two important roles in cancer progression: (a) regulating metabolic reprogramming by activating glycolysis and lipogenesis and (b) enhancing stem-like properties in CSCs . These oNot all gastric, lung, and breast cancer cell lines analyzed in this work had detectable levels of viperin. Cancer cells such as MKN45, with an undetectable level of viperin protein expression, also had a weaker induction of viperin following IFN-γ stimulation or serum starvation. It would be interesting to determine what controls the basal level of viperin expression in cancer cells with the following questions in mind: (a) Do the basal levels of viperin correlate with the basal levels of HIF-1α expression? (b) Do the relative levels of HIF-1α and viperin induction correlate with one another following serum starvation? Since previous studies have shown that HRE methylation prevents HIF-1α–dependent gene expression , it woulThe expression of viperin was prompted within the TME via oxygen and fatty acid deprivation or exposure to IFN-γ . MultiplMost of the cancer types studied by Choi, Kim, and colleagues showed a correlation between high viperin levels and poor survival rates, except for patients with melanoma, for whom viperin levels were not directly correlated to poor survival rates . In addi


Low birth weight (LBW) is associated with intestinal inflammation and dysbiosis after birth. However, the underlying mechanism remains largely unknown.In the present study, we aimed to investigate the metabolism, therapeutic potential, and mechanisms of action of bile acids (BAs) in LBW-induced intestinal inflammation in a piglet model.The fecal microbiome and BA profile between LBW and normal birth weight (NBW) neonatal piglets were compared. Fecal microbiota transplantation (FMT) was employed to further confirm the linkage between microbial BA metabolism and intestinal inflammation. The therapeutic potential of ursodeoxycholic acid (UDCA), a highly differentially abundant BA between LBW and NBW piglets, in alleviating colonic inflammation was evaluated in both LBW piglets, an LBW-FMT mice model, and a DSS-induced colitis mouse model. The underlying cellular and molecular mechanisms by which UDCA suppresses intestinal inflammation were also investigated in both DSS-treated mice and a macrophage cell line. Microbiomes were analyzed by using 16S ribosomal RNA sequencing. Fecal and intestinal BA profiles were measured by using targeted BA metabolomics. Levels of farnesoid X receptor (FXR) were knocked down in J774A.1 cells with small interfering RNAs.We show a significant difference in both the fecal microbiome and BA profiles between LBW and normal birth weight animals in a piglet model. Transplantation of the microbiota of LBW piglets to antibiotic-treated mice leads to intestinal inflammation. Importantly, oral administration of UDCA, a major BA diminished in the intestinal tract of LBW piglets, markedly alleviates intestinal inflammation in LBW piglets, an LBW-FMT mice model, and a mouse model of colitis by inducing M2 macrophage polarization. Mechanistically, UDCA reduces inflammatory cytokine production by engaging BA receptor FXR while suppressing NF-κB activation in macrophages.These findings establish a causal relationship between LBW-associated intestinal abnormalities and dysbiosis, suggesting that restoring intestinal health and postnatal maldevelopment of LBW infants may be achieved by targeting intestinal microbiota and BA metabolism.Video AbstractThe online version contains supplementary material available at 10.1186/s40168-022-01458-x. Intrauterine growth restriction (IUGR), with approximately 30% prevalence in developing countries and approximately 8% in developed countries, is a significant public health concern worldwide . IUGR im10–1012 per gram of digesta) with high diversity  [Lactobacillus, Erysipelotrichaceae, Lachnospiraceae, Clostridium, and Bacteroides encode the bile salt hydrolase (bsh) gene, while other Lactobacillus, Lachnospiraceae, Ruminococcaceae, Clostridiaceae, Eubacterium, and Peptostreptococcus carries the BA-inducible (bai) operon with the 7α-dehydroxylase activity [The mammalian intestine, especially the large intestine, is colonized by a large number of bacteria (approximately 10species) , 14. Onespecies) , 16. Ceractivity –19. The activity . Conjugaactivity .However, a mechanistic understanding of these microbiome-BA-disease associations is lacking. Thus, the role of BA and its relationship with the intestinal microbiome in enteric diseases need to be elucidated and explored for potential therapeutic options . The intWe hypothesized that the difference in the intestinal microbiota composition between LBW and NBW piglets alters the BA metabolism in the intestinal tract, resulting in heightened intestinal inflammation in LBW piglets. To test this hypothesis, targeted BA metabolomic profiling and microbiome analysis was employed to investigate the difference in the BA profile and microbiota composition in the intestines of LBW and NBW piglets in early life (two days after birth). Furthermore, both a DSS-induced colitis mouse model and a cell culture model were further employed to explore the role of ursodeoxycholic acid (UDCA), a secondary BA that is overly reduced in LBW piglets, in regulating intestinal inflammation and the potential underlying mechanisms involved. These studies will shed light on the dysregulation of the intestinal microbiota and BA metabolism in LBW, which will help elucidate the causes of the developmental defects associated with LBW and provide new approaches to the prevention and treatment of LBW-induced intestinal inflammation and postnatal maldevelopment.A total of 14 litters of Duroc × (Landrace × Large White) piglets were naturally delivered from 14 multiparous sows  after 113–114 days of gestation. Sows were fed a corn-soybean meal-based diet according to the NRC recommendations, with free access to water. The weights of newborn piglets were individually recorded immediately after birth, and one LBW piglet (0.75∼0.95 kg) and one NBW piglet (1.35∼1.55 kg) were obtained from each litter and housed individually in the same house. No cross-fostering was used in this study. None of the sows and piglets were administered antibiotics or any other drugs throughout the experiment. Fresh fecal samples were collected from the terminal rectum by a sterile cotton swab of 14 LBW and 14 NBW piglets on day 2 and then divided into two portions, with one flash-frozen in liquid nitrogen and then stored at −80°C until the microbiome and BA profiling analysis and the other portion used for fecal microbiota transplantation (FMT).Fresh feces from both LBW and NBW piglets were diluted 5-fold individually in sterile PBS containing 15% glycerol (v/v), homogenized, aliquoted, and stored at −80 °C for future FMT to mice. A total of 16, 4-week-old male C57BL/6 SPF mice  were treated with antibiotics   to deplete the intestinal microbiota as described . The antn = 8). Acute experimental colitis was induced with 3% DSS  in drinking water given ad libitum for 7 days. Body weight was measured daily throughout the experiment. Acute colitis was evaluated using a disease activity index (DAI)  group, and a DSS plus UDCA  enema group (50 mg/kg BW) (n = 10 in each group). All piglets were housed in the same environment. No cross-fostering was used in this study. None of the sows and piglets were administered antibiotics or any other drugs throughout the experiment. On day 8, six piglets were randomly selected from each group and sacrificed to collect the colonic tissue after weighing. Tissue samples were stored at −80°C until further analysis.A total of 10 multiparous sows  were fed a corn-soybean meal-based diet to meet the NRC recommendations, with free access to the water throughout the entire experiment. Ten litters of Duroc × (Landrace × Large White) piglets were spontaneously delivered from sows after 113∼114 days of gestation. The weight of the newborn piglet was recorded immediately after parturition, and 2 LBW piglets (0.75∼0.95 kg) were obtained from each litter and randomly allocated into either the control group with oral administration of 1 mL saline or the UDCA group with administration of 1 mL UDCA (50 mg/kg BW) daily (n = 8 in each group). On day 8, mice were sacrificed for sample collection and further analysis as mentioned above.Macrophage depletion was performed by intraperitoneal injection of 0.2 mL clodronate liposomes  into mice 2 days before 3% DSS treatment, and also on day 1 and day 4 during DSS treatment as described . Animalsn = 8 in each group). As for UDCA treatment, mice were orally administered 0.2 mL UDCA solution (50 mg/kg BW) every day. Macrophage depletion was performed 2 days before UDCA treatment, and on day 1 and day 4 during UDCA treatment. On day 8 of UDCA treatment, all the mice were sacrificed to collect the colonic tissue after weighing. Tissue samples were stored at −80°C until further analysis.A total of 40, 4-week-old healthy male C57BL/6 SPF mice were selected. After 1 week adaptation period, 2 weeks of antibiotics intestinal microbiota were depleted, and 4 weeks of FMT of NBW or LBW fecal microbiota. Relevant experiment methods refer to the above description. Mice were divided into five groups, including an NBW-FMT group, an LBW-FMT group, an LBW-FMT + UDCA group, an LBW-FMT plus macrophage depletion group, and an LBW-FMT plus UDCA plus macrophage depletion group  overnight and then treated with 1 μg/mL LPS and 1 mM UDCA individually or in combination for another 24 h. The cells were harvested for RNA and protein extraction and subsequent RT-qPCR, RNA sequencing, Western blot, and flow cytometry analysis.Murine J774A.1 macrophages were cultured in DMEM  supplemented with 10% fetal bovine serum  and 1% penicillin/streptomycin  at 37°C with 5% COIL-1β and protein expressions of p65, p-p65, and FXR.Macrophages were transfected with double-stranded, FXR-specific small interfering RNA (forward 5′-CCA AGA ACG CCG UGU ACA ATT-3′ and reverse 5′-UUG UAC ACG GCG UUC UUGGTT-3′) or scrambled siRNA in 6-well plates using Lipofectamine® 3000 Reagent , followed by 1 mM UDCA treatment for 24 h. IL-1β concentrations in the supernatants were measured by ELISA. The cell samples were collected for gene expression of BAs in the feces and colonic digesta were profiled as previously described . Brieflyhttps://cloud.majorbio.com/). Raw sequence reads were deposited into the NCBI Sequence Read Archive (SRA) database under accession number PRJNA646844 for the feces of piglets and PRJNA714049 for the colonic digesta of mice after FMT.Total microbial genomic DNA in the feces and colonic digesta was extracted using QIAamp Fast DNA Stool Mini Kit . The V3–V4 region of the 16S rRNA gene was amplified with the universal primers 341F (5′-ACTCCTACGGGAGGCAGCAG-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′). Raw sequences were analyzed using QIIME 2 (version 2020.2) . InitialColonic tissue samples were fixed in 4% paraformaldehyde for 24 h, followed by dehydration, embedding in paraffin, sectioning, and staining with hematoxylin and eosin or periodic acid-Schiff (PAS). Subsequent microscopic evaluations were made blindly by an experienced pathologist. The extent of tissue damage was scored as previously described . The deg6 cells per sample were collected after experimental treatment. Cell suspensions were then passed through a 70-μm cell strainer and collected for subsequent flow cytometry analysis. Lamina propria mononuclear cells (LPMCs) or J774A.1 cells was stained with the following antibodies: APC anti-mouse/human CD11b (Biolegend/M1/70), PE anti-mouse CD11c (Biolegend/N418), PE anti-mouse F4/80 (Biolegend/BM8), and APC anti-mouse CD206 (MMR) (Biolegend/C068C2). CD11b+CD11c+ cells were considered M1 macrophages, while F4/80+CD206+ cells were treated as M2 macrophages [Flow cytometry was conducted as previously reported . Brieflyrophages .Total RNA was extracted from colonic tissues or J774A.1 cells using TRIzol , and 1 μg RNA was reverse-transcribed using PrimeScript® RT Reagent Kit with cDNA Eraser . RT-qPCR was performed with gene-specific primers . Based on GO annotation [For RNA sequencing, J774A.1 cells in 6-well plates were treated with UDCA at 1 mM with or without LPS (1 μg/mL) for 24 h. Total RNA was isolated using TRIzol, and RNA quality was assessed using a 2100 Expert Bioanalyzer  and sent for commercial library preparation and sequencing by Majorbio Biotech  on Illumina HiSeq 2000. Short sequence reads were analyzed on the Majorbio I-Sanger Cloud Platform , respectively, according to the manufacturer’s instructions.Frozen mouse colonic tissues and J774A.1 cell pellets were ground using a mortar and pestle under liquid nitrogen and then lysed in RIPA buffer  containing a cocktail of protease inhibitors . After centrifugation at 13,000×g for 10 min at 4°C, the protein concentration in the supernatant fluid was determined using a BCA Protein Assay Kit . For Western blot, an equal amount of protein (30 μg) was run in SDS-PAGE together with pre-stained protein markers, transferred to polyvinylidene difluoride (PVDF) membranes , and blocked in 5% fat-free milk at room temperature in TTBS  for 1 h. Membranes were incubated with primary antibodies, FXR , p65 , p-p65 , p38 , p-p38 , ERK1/2 , p-ERK1/2 , JNK1/2 , and p-JNK1/2  at 4 °C overnight with gentle rocking. All primary antibodies were purchased from Cell Signaling Technology . After three washes in TTBS, the membranes were incubated with horseradish peroxidase-conjugated secondary antibodies  at 1:5000 dilution for 1 h at room temperature. The signal was developed using Supersignal West Dura Extended Duration Substrate . Chemiluminescence was detected and quantified using Alpha Imager 2200  and normalized to the β-actin expression level in each sample.P < 0.05. Orthogonal partial least squares-discriminant analysis (OPLS-DA) was performed to discriminate the intestinal BA profiles between treatment groups. Scatter plots were drawn using GraphPad Prism 6.0.All statistical significance was assessed using one-way analysis of variance (ANOVA), followed by post hoc Tukey’s test for pairwise comparisons . All results were expressed as means ± SEM. Differences were considered statistically significant if 2Y value of 0.511 and an R2Y value of 0.819   showed an increased level of NBW than LBW piglets  throughout the entire experiment  from day 24 after FMT  and downregulation of mucin-2 (MUC2) and BA receptor (FXR) in the colon (P < 0.05)   , weighted unifrac distance  and unweighted unifrac distance  were observed between NBW and LBW piglets    , relative to NBW-FMT mice  Fig. D and G. ets Fig. E and H, 05) Fig. F and 4I.P < 0.05)  gene was significantly decreased, while BA 7-dehydroxylation (baiJ)-active bacteria were significantly increased in LBW piglets compared with NBW piglets (P < 0.05)   . Perhaps it is unsurprising that the gene copy numbers of 7α-HSDH and 7β-HSDH showed a significant positive correlation with fecal levels of UDCA (P < 0.05), while baiJ was negatively correlated with UDCA (P < 0.05)  Fig. A, the re05) Fig. B. The re05) Fig. C. Using 05) Fig. D, E. It 05) Fig. F were si05) Fig. G. These 2Y and R2Y values, was suggested to be effective   showing a significant reduction in LBW-FMT mice and that such a reduction is associated with heightened colonic inflammation in both LBW piglets and LBW-FMT mice, we attempted to explore whether UDCA could directly prevent intestinal inflammation and barrier dysfunction. A mouse model of DSS-induced colitis was first employed with or without oral administration of UDCA at the beginning of a 7-day DSS treatment  in the colon (P < 0.05)  , as well as FXR, were also significantly increased in the colon of LBW piglets in response to UDCA treatment  was orally administered to newborn LBW piglets daily for 7 days Fig. A. Body w97) Fig. B. UDCA a97) Fig. C. Moreov97) Fig. D. The exent Fig. D. These Macrophages are known to play a critical role in the progression of colitis . To evalTo further confirm the role of the macrophage in UDCA-mediated alleviation of LBW-induced intestinal inflammation, an LBW-FMT mouse model was used and macrophages were depleted in mice through intraperitoneal injection of clodronate-containing liposomes during UDCA intervention Fig. A. Macrop+CD11c+) was increased and M2 macrophages (F4/80+CD206+) decreased in response to DSS treatment, while UDCA administration reversed the trend   after 24-h treatment (data not shown). While it appeared to amplify LPS-induced inflammatory response at 6 and 12 h, UDCA markedly suppressed inflammatory cytokine expressions at 24 h , and a modest induction occurred at 6 and 12 h, but not 24 h, in the presence of LPS ), different sampling time points (day 3 after birth vs day 2 after birth), and different feces sampling methods  in the present study. FMT can provide new intuitive evidence for the mechanisms of gut microbiota-related diseases [Early colonization of microbes in the neonatal gut plays an important role in regulating intestinal health. Compared with NBW piglets, the microbial α-diversity was higher in LBW piglets, which is inconsistent with the results of our previous study that the LBW piglets do not affect fecal microbial diversity at any age . These ddiseases . After Fdiseases –52. It mClostridium, Eubacterium, Bacteroides, E. coli, and Eggerthella lenta [P-value; (2) UDCA was significantly decreased in the colonic digesta of mice following FMT of LBW piglets; (3) Both 7α-HSDH and 7β-HSDH gene copy numbers were significantly reduced in the intestinal tract of both LBW piglets and LBW-FMT mice; (4) Relative abundances of 7α-HSDH- and 7β-HSDH-producing bacteria such as Escherichia-Shigella and Clostridium sensu stricto 2 in the feces of LBW piglets as well as Bacteroides and Eggerthella in the colon of LBW-FMT mice were significantly decreased; (5) A strong negative correlation was observed between the concentration of UDCA and the 7α-HSDH/7β-HSDH gene copy number in both LBW piglets and LBW-FMT mice; (6) UDCA is the only BA have been used therapeutically for the treatment of a range of cholestatic disorders such as biliary atresia [E. coli infection in sepsis and colitis in neonatal mouse models [BAs are increasingly appreciated to be critically involved in the regulation of inflammation through interacting with both microbiota and host receptors . UDCA isla lenta , 55. Amo atresia ; and (7) atresia –59. We h atresia –59. In f atresia , 61. In e models .Mechanistically, we have demonstrated for the first time that, at the cellular level, UDCA amelioration of intestinal inflammation is macrophage-dependent and achieved through preferential polarization of macrophages from the proinflammatory M1 phenotype toward the anti-inflammatory M2 phenotype , in agreTNF and IL-1β loci [A growing body of evidence suggests that FXR is critical for mucosal homeostasis and generally suppressed during chronic intestinal inflammation . BA-depe-1β loci , 68. At -1β loci –71. Conv-1β loci . Taken tIt is worth noting that, in addition to FXR, BAs are ligands to several other nuclear receptors such as pregnane X receptor (PXR), glucocorticoid receptor (GR), and vitamin D receptor (VDR) as well as a cell surface receptor (TGR5) . Some ofAlthough UDCA was chosen in this study and subsequently confirmed to alleviate intestinal inflammation upon oral administration into LBW piglets, other differentially abundant BAs are likely to play a role as well. It will be important to examine the efficacy of other BAs or a combination of BAs in mitigating LBW-associated metabolic and immunological abnormalities. Secondly, because 7α-HSDH and 7β-HSDH, two major enzymes involved in the biosynthesis of UDCA, are significantly reduced in LBW piglets, it will be interesting to investigate the potential of transferring 7α-HSDH- and 7β-HSDH-positive bacteria for the treatment of postnatal maldevelopment of LBW infants. Thirdly, because we have shown the intestinal microbiota is responsible for much of the intestinal health issues associated with LBW piglets, it is possible to achieve a positive outcome by considering the FMT of normal individuals to LBW infants. Fourthly, it does not fully explain the mechanism of UDCA improving LBW-induced intestinal inflammation through macrophage polarization using an in vitro macrophage cell model. Further macrophage adoptive transfer in vivo is still needed.In summary, we have revealed for the first time that LBW piglets produce a significantly lower amount of UDCA and harbor less 7α-HSDH- and 7β-HSDH-positive bacteria than their normal counterparts. Oral supplementation of UDCA significantly improves the intestinal health of LBW piglets. We have further demonstrated that UDCA alleviates intestinal inflammation by regulating macrophage polarization, engaging FXR, and suppressing NF-kB activation. This study provides a direct link between LBW-associated intestinal abnormalities and altered microbiota-mediated BA metabolism. Taken together, we have not only revealed the mechanisms of LBW-associated intestinal disorder, but also proposed several independent, but related, intervention strategies for the treatment of LBW-associated postnatal abnormalities.Additional file 1: Supplemental Table S1. Scoring standards for the disease activity index (DAI)1,2. Supplemental Table S2. Parameters and criteria of histological damage evaluation1,2. Supplemental Table S3. Primer pairs used in this experiment1. Supplemental Figure S1. Fecal bile acids (BAs) profiles of low birth weight (LBW) and normal birth weight (NBW) piglets 2 days after birth (n = 10). Supplemental Figure S2. Verification of depletion of the intestinal microbiota in mice (n = 8). Supplemental Figure S3. Microbial structure of NBW and LBW donor piglets and NBW-FMT and LBW-FMT recipient mice. Supplemental Figure S4. Microbial composition of NBW and LBW donor piglets and NBW-FMT and LBW-FMT recipient mice. Supplemental Figure S5. Colonic BA profiles of mice transplanted with the feces of LBW or NBW piglets (n = 8). Supplemental Figure S6. Effect of UDCA on inflammatory cytokines gene expression and macrophage polarization in J774A.1 cell.


Adults with attention-deficit/hyperactivity disorder (ADHD) report increased spontaneous mind wandering (MW) compared to control adults. Since MW is associated with ADHD severity and functional impairment, elucidating the brain mechanisms underlying MW may inform new interventions targeting MW and point to neural markers to monitor their efficacy. Population-based electroencephalographic (EEG) studies suggest that weaker event-related decreases in occipital alpha power characterise periods of MW, but no study has examined event-related brain oscillations during MW in individuals with ADHD. Using an experience-sampling method, we compared adults with ADHD (N = 23) and controls (N = 25) on event-related EEG measures of power modulations and phase consistency during two tasks with high and low demands on working memory and sustained attention, and during periods of MW and task focus. Compared to controls, individuals with ADHD showed weaker alpha power decreases during high working memory demands and across sustained attention demands, weaker theta power increases and phase consistency across working memory demands and during low sustained attention demands, and weaker beta power decreases during low working memory demands. These EEG patterns suggest broadly deficient attentional and motor response processes in ADHD. During MW episodes, adults with ADHD showed weaker alpha power decreases in the sustained attention task and lower theta phase consistency in the working memory task compared to controls. These findings suggest that atypical EEG patterns thought to reflect reduced inhibition of task-irrelevant processes and inconsistent stimulus processing underlie increased MW in adults with ADHD and may be useful for future real-time monitoring of treatment effects. ADHD afFunctional magnetic resonance imaging (fMRI) studies have identified the default mode network (DMN) as a neural correlate of MW . HoweverRecent reviews have found that weaker event-related alpha decreases during attentional and working memory tasks are reliably observed in individuals with ADHD relative to controls . These fWe have recently found that event-related power modulations distinguishing adults with ADHD from controls  were associated with self-reported spontaneous MW . These fTo further elucidate the neural mechanisms of MW, we carried out a new in-depth analysis of the data presented in our previous publication , using e2EEG analyses/data screening’ section) from our initial sample of 27 adults with ADHD and 29 controls. Recruitment centres for adults with ADHD were the South London and Maudsley NHS Trust, the Barnet, Enfield and Haringey Mental Health Trust adult ADHD clinics, online platforms, UKAAN (the UK Adult ADHD Network) and mental health professionals. Adults without ADHD , and no prior diagnosis for mental health conditions were recruited via online recruitment platforms. Exclusion criteria for both groups included a current or past diagnosis of major physical illness , severe mental health difficulties , current or past substance abuse , or an IQ below 80.The sample consisted of 23 participants with ADHD and 25 controls with good quality EEG data  during clinical assessments ADHD . Twelve 2.1The testing session for all participants lasted approximately 3–4 h and included a clinical interview for ADHD (DIVA 2.0), IQ testing and self-report measures, and two computerised tasks with simultaneous EEG recordings preceded by a practice session for each task . Partici33.1This task consists of a 0-back and 1-back condition. The 0-back is a choice reaction condition designed to capture alertness levels and motor activity. By contrast, the 1-back is a working memory condition, which aims to assess visual working memory. In the 0-back condition, a sequence of black shapes (separated by a blue line) is presented to the participant in the middle of the computer screen. The participant is instructed to observe these shapes before a blue target appears . Once the target is presented, the participant had to use the left or the right arrow to select the location of the bigger shape matching the location of the middle target shape. In the 1-back condition, the same sequence of black shapes (separated by a red line) was occasionally paired with two red question marks (‘?’) with a small red shape (target) between the question marks. Upon presentation of the question marks, a manual response is required to indicate the location (left or right) of the target shape in the previous trial. The colored question marks appeared randomly, which required encoding and retaining in memory the location of each black shape in the previous trial .These two task conditions occurred in a counterbalanced fashion. During each trial, 2 to 6 non-targets appeared before the target. The duration of each non-target was 1 to 3 s with increments of 0.1 s in each trial (the maximum duration was 3 s). The task had a total of 128 targets (64 in each condition) and 580 non-targets (290 in each condition). The duration of each target was 4 s, which allowed a 4 s time window for a response before the trial ended. A fixation cross was included before and after all task stimuli, which lasted 2 to 4 s with increments of 0.1 s.The total number of trials in each block was 8 for each condition. The total number of blocks was also 8, and the duration of each block varied from 40 s to 120 s. The end of each block included two on-screen messages “STAY” or “SWITCH”, indicating to the participants that they were about to either remain the same condition or enter the other condition. The duration of both messages “SWITCH” and “STAY” was 5 s. The task lasted approximately 30 min split into two 15-min sessions.3.2For this study we used a modified version of the SAT . The ori3.3Where was your attention just before this probe?” with two response options “On task” and “Off task”. If the participants responded, “Off task”, an additional question appeared “Were you aware of your attention drifting away from the task?” with two response options “Aware” and “Unaware”. To capture episodes of MW and task focus, we used the 15 s-time window prior to each probe, consistent with our previous ERP study  at approximately 1-minute intervals. The targets in the MWT and the stimulus following the unpredictable delays in the SAT were occasionally substituted by the MW probes. Our version of the SAT included 26 delays per session  in contrast to 20 delays  in the previous version of the task . The incRP study  and prevRP study .3.4As described in our previous publication , we reco3.5For Analysis 1, we carried out separate EEG analyses on the working memory (1-back) and choice reaction (0-back) conditions of the MWT and on the delays  of the SAT. This analysis did not include the trials preceding or containing MW probes to ensure consistency with previous studies using these tasks without thought probes. Instead, Analysis 2 specifically focused on the trials in the 15 s period preceding probes.Time-frequency analyses were adopted to investigate changes in power and phase consistency related to task conditions  and periods of MW and task focus . Power changes were quantified as an event-related spectral perturbation (ERSP) index , using MIn the MTW, time–frequency analyses were applied for working memory/choice-reaction (1-back/0-back) conditions and periods of MW and task focus between −1500 to 1500 ms, normalized with respect to the mean log-power spectrum from a pre-stimulus period (baseline) between −500 to 0 ms. During the SAT, time–frequency analyses were applied between −1500 and 1500 ms for the 1 s condition, using a pre-stimulus baseline period between −500 to 0 ms; between −2500 and 2500 ms for the 2 s condition, using a baseline period between −1000 and 0 ms; between −5500 and 2500 ms for the 5 s condition, with a baseline period between −4000 and −3000 ms; and between 8500 ms and 2500 ms for the 8 s condition, with a baseline period between −7000 and −6000 ms. This different segmentation allowed us to ensure comparability of the baseline period across 2 s, 5 s, 8 s delays conditions in within-group comparisons, as the same 1000 ms period after presentation of the previous stimulus was used as a baseline for the ERSP/ITC indices across delays . A shorter baseline (-500–0 ms) was used for the 1 s condition because a longer baseline (-1000–0 ms) would have captured the time of the response . Within-group comparisons only focused on the longer delays , which place varying demands on sustained attention, and not on the 1 s delay condition, which instead place high demand on sensorimotor function . As suchThe choice of time windows and scalp locations was based on the location and timing of maximal power changes in the relevant frequencies in previous time–frequency studies during similar tasks. These parameters were then confirmed based on maximal changes in the same frequencies in our data , Fig. 4., we further removed 4 additional controls and 2 individuals with ADHD due to the absence of MW and task focus episodes in the MWT, leaving data from 21 individuals with ADHD and 21 controls for this analysis. Seven controls did not have sufficient MW episodes (>3 episodes) in the SAT, leaving us with 23 individuals with ADHD and 18 controls for analysis. For more details on the average number of trials included in each ESRP/ITC measure, please refer to Supplementary Table 3.Participants were included in the ERSP/ITC analyses if they had at least 20 artefact-free EEG segments in each condition or probe. This approach is in line with previous requirements of at least 20 artefact-free EEG segments to observe reliable neural effects . For Ana4In Analysis 1, we studied the effects of condition , group (ADHD/control) and condition-by-group interactions on each ESRP/ITC in repeated measures general linear models. For alpha and beta, different time windows were tested in a separate repeated measures general linear model. To understand the effect of MW frequency on the EEG variables, we then repeated the same analyses adding MW frequency as a covariate. In Analysis 2, we measured the effects of probe (MW/task focus), group (ADHD/control) and probe-by-group interactions on each EEG measure in repeated measures general linear models. In both analyses, for measures showing significant main effects of group and/or condition/probe, we carried out additional post-hoc analyses comparing groups in each condition/probe separately and comparing conditions in each group based on the following a-priori predictions :Prediction 1 : Individuals with ADHD would show weaker event-related alpha, beta and theta power modulations and lower theta phase consistency than controls across task conditions.Prediction 2 : Only the ADHD group would show a within-group reduction in EEG power modulations and phase consistency from the 0-back condition to the 1-back condition of the MWT.Prediction 3 : In both tasks, the ADHD group would display atypical EEG patterns compared to controls during periods of MW, but not during task focus.Prediction 4 : Controls, but not adults with ADHD, would show a within-group enhancement in EEG power modulations and phase consistency from MW to task focus.All ERSP/ITC measures were normally distributed. A false discovery rate (FDR) threshold for between- and within-group effects separately in Analysis 1 and 2 was used to address multiple testing . FDR sigWe have also conducted sensitivity analyses to ensure group differences were not driven by participants with ADHD taking stimulant and non-stimulant medication. We compared controls and individuals with ADHD, who have not been treated with stimulants and/or non-stimulants, on ERSP and ITC measures. The sensitivity analyses provided similar results  to Analysis 1 and 2 . Adults with ADHD compared to controls showed weaker alpha power decreases during high demand on working memory and during both high and low demand on sustained attention, reflecting reduced inhibition of task-irrelevant information. These effects in alpha modulations in both tasks extend replicated oscillatory findings in ADHD samples  and suppIn analyses examining within-group adjustments in brain oscillations with changing cognitive demands, we found a general pattern of improvements in oscillatory activity from low to high demand on working memory in both groups, evidence by stronger beta decrease, theta increase and theta phase consistency. While controls showed significant improvement in alpha activity from low to high working memory demands, this difference was not significant in the ADHD group, potentially suggesting lower ability to suppress task-irrelevant information in response to increasing cognitive demands. In the SAT, controls maintained consistent inhibition of task-irrelevant information across cognitive demands, as reflected by comparable alpha decreases across delays, the ADHD group showed weaker alpha decreases during low compared to high sustained attention demands. Together, these results extend our previous findings pointing to context regulation of MW frequency and associated ERPs across groups in the MWT, and in controls – but not adults with ADHD – in the SAT .MW frequency statistically explained most of the group differences alpha power decreases during varying demands on working memory and sustained attention. Nevertheless, as the group difference on alpha decrease during low and high sustained attentions demand did not reach statistical significance, further research in larger samples is needed to confirm these findings. Group differences in response execution (beta decrease), attention allocation (theta increase) and variability of stimulus processing (theta phase consistency) were only partly explained by MW frequency, suggesting that processes independent of MW and related to task demands may also play a role in group differences in these EEG patterns. Conversely, MW frequency did not statistically explain any of the within-group effects, suggesting a more limited effect of MW on adaptations to changing cognitive demands.Our second aim was to assess the relationship between MW and oscillatory activity more directly by contrasting periods of MW and task focus in individuals with and without ADHD . Adults with ADHD showed lower inhibition of task-irrelevant information  compared to controls during task focus in the MWT and during MW in the SAT, with similar patterns not reaching statistical significance during MW in the MWT and during task focus in the SAT. These findings suggest that atypical EEG patterns reflecting difficulties inhibiting task-irrelevant information in ADHD may be particularly pronounced during periods of task focus in cognitively challenging tasks like the MWT, as well as during MW in less challenging tasks like the SAT. By contrast, the lack of group differences in alpha power during MW in the MWT suggest that individuals with and without ADHD have comparable suppression of task-irrelevant information during MW in this challenging task, consistent with well-established models of MW . Adults 7d < 0.50) and likely resulted in non-significant effects, such as non-significant interactions. Second, in the analyses comparing periods of MW and task focus, some participants had an insufficient number of trials and had to be excluded. This might explain why we did not find the hypothesised within-group differences in EEG activity between MW and task focus. Future research should replicate these findings in larger samples, perhaps using paradigms that induce a greater proportion of MW episodes, for example by manipulating the task difficulty. Future studies could also consider using other methods for detecting the onset of MW and its fluctuations during the task, such as pupil diameter (First, the small sample size did not allow for an exploration of more subtle effects (diameter  to prese8To the best of our knowledge, this is the first study to identify event-related brain oscillatory patterns associated with MW and task focus in individuals with ADHD. Alpha decrease and theta phase consistency distinguished between ADHD and control groups in task conditions eliciting high cognitive demands and associated with MW, as well as during MW episodes identified through a rigorous experience-sampling approach. These EEG patterns may thus reflect key neural mechanisms of increased MW frequency in ADHD. Since MW shows strong associations with ADHD symptoms and daily life difficulties, future studies should test whether these neural markers can be used to monitor the effects of treatments aiming to regulate MW in adults with ADHD, such as meditation techniques .Professor Jonna Kuntsi has given talks at educational events sponsored by Medice: all funds are received by King’s College London and used for studies of ADHD. Professor Philip Asherson has received honoraria for consultancy to Shire/Takeda, Flynn-Pharma, Eli-Lilly, Janssen, Novartis, Lundbeck and Medice; educational/research awards from Janssen, Shire, Lilly, Novartis, Flynn Pharma, Vifor Pharma, GW Pharma and QbTech; speaker at sponsored events for Shire/Takeda, Lilly, Novartis, Medice, Janssen-Cilag and Flynn Pharma. Professor Katya Rubia has received a grant from Shire/Takeda for another project and consultancy fees from Lundbeck and Supernus pharmaceuticals which were received by King’s College London and used for studies of ADHD.


Obstructive sleep apnea syndrome (OSAS) severity, obesity, sex difference, and attention-deficit/hyperactivity disorder (ADHD) had a complex impact on health-related quality of life (HRQoL). However, the interactive effects among these features on HRQoL remained to be clarified. This study aimed to investigate the individual and interactive associations between the four characteristics of interest and HRQoL as determined by 36-Item Short Form Health Survey, Pittsburgh Sleep Quality Index (PSQI), and Epworth Sleepiness Scale (ESS). This non-interventional, prospective, observational study enrolled a total of 132 patients with suspected OSAS for analysis. While OSAS severity and ADHD detected by adult ADHD Self-Report Scale, termed as screened ADHD, interact with each other, all the four studied features were individually associated with HRQoL. After adjusting for potential physiological and polysomnographic confounders, screened ADHD was independently correlated with PSQI > 5 , mental component score < 50  and ESS > 10 . Our results show that ADHD detection is necessary and should be incorporated into clinical practice for OSAS management. Quality of life (QoL) is a complex, multifaceted concept encompassing the physical, psychological, and social domains that broadly indicate an individual’s well-being . For whaObstructive sleep apnea syndrome (OSAS) is characterized by recurrent partial or complete collapse of the upper airway, resulting in hypopnea or apnea ,6. It maObesity and sex difference, which were risk factors for OSAS ,11,12,13Attention deficit hyperactivity disorder (ADHD) is a mental health disorder, common in childhood and often persisting into adulthood . It is cWe hypothesized that the four features, including OSAS severity, obesity, sex difference, and ADHD, may have interactive effects on HRQoL as determined by SF-36, PSQI, and ESS. Therefore, this study aimed to explore the individual association between the four characteristics of interest and HRQoL, and to investigate whether the association was affected by other features.This non-interventional, prospective, observational study was conducted at the sleep clinic of Taichung Veterans General Hospital (TCVGH), a medical center located at central Taiwan, between September 2015 and August 2017. The Institutional Review Board and Ethics Committee of TCVGH approved this study , and informed consent was obtained from all participants. At the sleep clinic, subjects were enrolled if they were aged ≥20 years and scheduled to undergo overnight polysomnography (PSG) for the suspicion of OSAS diagnosis as per sleep specialist’s comprehensive assessment of presenting symptoms and signs, medical history, and physical examination in a continuous, unbiased sampling manner. Those with a history of alcoholism or mental diseases other than ADHD or unable to answer the studied questionnaires were excluded from the study. 2 [At enrollment, physiological characteristics, including age, gender, body mass index (BMI), neck circumference, and waist circumference (WC) were recorded for each participant. Obesity was defined as a BMI ≥ 27 kg/m2 . MeanwhiAs detailed elsewhere , OSAS waThe adult ASRS consists of 18 criteria outlined in the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, and it was used to screen and diagnose ADHD, termed as “screened ADHD” in the present study. The ASRS consists of two subscales, inattention and hyperactivity, and each of which contains nine items. Each item is scored on a 5-point scale ranging from 0 (never) to 4 (very often). Participants with a sum score on either subscale of ≥17 were considered to have screened ADHD. Otherwise, non-screened ADHD was the case ,29. Self-reported HRQoL was assessed using the SF-36, Taiwan version , PSQI, and ESS. The SF-36 consists of eight sections: vitality, physical functioning, bodily pain, general health perception, physical role function, emotional role function, social role function, and mental health. The scales are scored using a Likert method of summarized ratings, and the scores are weighted sums of the questions in each section. Each section is transformed into a 0–100 score. The SF-36 can also be divided into two components, the Physical Component Score (PCS) and the Mental Component Score (MCS). A lower score indicates more disable. In this study, a PCS or MCS score <50 was defined as worse HRQoL ,31.The PSQI is a standardized self-report questionnaire used to assess subjective sleep quality over the past month . It consThe ESS is used to measure daytime sleepiness and ask the subject to rate his or her probability of falling asleep on a scale of increasing probability from 0 to 3 for eight different situations . The scoAs above-mentioned, the patients with worse HRQoL based on SF-36, PSQI, and ESS were defined as those with a PCS or MCS score <50, a score >5, and a score >10, respectively ,31,33,34p < 0.05.In this study, the enrolled participants were categorized into four binary groups: normal to moderate OSAS versus severe OSAS, female versus male, non-obese versus obese, and non-screened ADHD versus screened ADHD. A crossing matrix of three-way ANOVA was used to determine the individual and interactive effects among OSAS severity, obesity, sex difference, and screened ADHD on SF-36, PSQI, and ESS, respectively. A post hoc test was performed once the significant interaction or the main effect was met. Moreover, the univariate analysis was used to identify potential sleep and physiological parameters associated with HRQoL. Finally, combined the significant factors found in the three-way ANOVA analysis and those found in the univariate analysis, the independent variables associated with HRQoL were explored using the multiple logistic regression model. All data were expressed as mean and standard deviation (SD) for continuous variables or number (percentage) for categorical variables. The statistical analyses were performed using Predictive Analysis Software (PASW) version 18.0 . Significance was set at 2, and 39.5 ± 28.5, respectively  and PCS (p = 0.034), respectively. Moreover, the average oxygen saturation during sleep correlated to ESS (p = 0.047) while sleep stages had a relationship with PSQI , PCS , and MCS   . Togethe= 0.002) .We found that, although the four studied features, including OSAS severity, obesity, sex difference and screened ADHD, were associated with HRQoL, as determined by SF-36, PSQI and ESS in the three-way ANOVA analysis, particularly an interactive effect between OSAS severity and screened ADHD on ESS score, only patients with screened ADHD were more likely to have worse HRQoL, comparing to those with non-screened ADHD after adjusting for potential physiological and sleep variables in the multiple logistic regression analysis.Similar to our findings that screened ADHD was independently associated with ESS score > 10 in patients with suspected OSAS, Oğuztürk Ö et al. have shown that, when compared to OSAS patients without ADHD, those with ADHD had a higher anxiety score, a poorer PCS of SF-36, and a higher ESS score . This coIn this study, for patients with suspected OSAS, the relationship between OSAS severity and ESS score was affected by screened ADHD in addition to the correlations between the studied features and the tested questionnaires for HRQoL in the three-way ANOVA analysis. We also have reported that OSAS severity and sex difference had an interactive effect on serum interleukin-6 level, cardiovascular biomarker, in patients suspected to have a diagnosis of OSAS . MoreoveTogether with our findings that OSAS severity was associated with a worse HRQoL as determined by PCS and is interactive with screened ADHD on ESS score, evidence has shown that OSAS decreased HRQoL in different dimensions ,8,9. ThiThe strengths of this study include that a three-way ANOVA analysis was used to examine the association between the four features of interest and the three studied questionnaires related to HRQoL. This made it easier to clarify the complex relationship between these features and HRQoL. Furthermore, we used multiple logistic regression model to incorporate other potential confounders into our analysis, making our results solider for interpretation. This compensates several limitations of our study, including an inherent limitation of studies on OSAS that our results are subject to selection bias toward middle-aged male adults, which may make the results not generalizable to other populations. Moreover, the diagnosis of ADHD was made by adult ASRS, thus termed as screened ADHD, making it less accurate in the detection of this mental health disorder. Meanwhile, the sample size had not been calculated, making the power of the study may be sub-optimal.ADHD is a common mental disorder with a prevalence of 3–16% in children, enduring through adulthood in about 60%–80% of cases ,35. MoreWe found an independent correlation between screened ADHD and ESS score > 10 in patients with suspected OSAS. Meanwhile, OSAS severity, obesity, and sex difference also had individual associations with HRQoL in such population. This information made the detection of ADHD necessary in the management of OSAS.


We design a diagnostic test to evaluate the effectiveness and accuracy of A non-intrusive Wearable Diagnostic Assessment System versus SNAP-IV for auxiliary diagnosis of children with ADHD.This study included 55 children aged 6–16 years who were clinically diagnosed with ADHD by DSM-5, and 55 healthy children . Each subject completes 10 tasks on the WeDA system (Wearable Diagnostic Assessment System) and Parents of each subject complete the SNAP-IV scale. We will calculate the validity indexes, including sensitivity, specificity, Youden's index, likelihood ratio, and other indexes including predictive value, diagnostic odds ratio, diagnostic accuracy and area under the curve [AUC] to assess the effectiveness of the WeDA system as well as the SNAP-IV.p = 0.068), and both of them have high diagnostic accuracy. In addition, the diagnostic efficacy of the WeDA system was higher than that of SNAP-IV in terms of the Youden index, diagnostic accuracy, likelihood ratio, diagnostic odds ratio and predictive value.The sensitivity (94.55% vs. 76.36%) and the specificity (98.18% vs. 80.36%) of the WeDA system were significantly higher than the SNAP-IV. The AUC of the WeDA system (0.964) was higher than the SNAP-IV (0.907). There is non-statistically significant difference between groups (The advantages of the WeDA system in terms of diagnostic objectivity, scientific design and ease of operation make it a promising system for widespread use in clinical practice. Prior to this study, there were no diagnostic trial studies of wearable devices to auxiliary diagnosis of ADHD.Compared to previous wearable devices systems, the self-developed WeDA system is based on the DSM-5 diagnostic criteria and is scientifically designed with a high degree of conviction.The results of the study showed that the WeDA system had a sensitivity of 94.55%, a specificity of 98.18% and a diagnostic accuracy of 96.36%, much higher than the rating scale SNAP-IV.These results demonstrate that the WeDA system has good auxiliary diagnostic value and has great potential for widespread clinical use.Attention-deficit/hyperactivity disorder (ADHD) is one of the most common neuropsychiatric disorders during childhood and adolescence. The worldwide prevalence of ADHD in children is estimated to be 7.2% . It is cChinese Guidelines for the Prevention and Treatment of Attention Deficit Hyperactivity Disorder edited by Zheng et al. [, ADHD currently lacks etiological or pathological changes of diagnostic significance, and there are few objective signs and laboratory tests to assist in the diagnosis. For a diagnosis of ADHD, patient’s symptoms should meet the DSM-5 /ICD-10  diagnostic criteria. However, during the clinical interview, children with ADHD are often not clear in expressing themselves and are uncooperative, while parents or teachers are often subjective in their descriptions of the child's symptoms. The diagnosis of ADHD is a complex and subjective process. In addition, there are very few experts in China that can diagnose ADHD and the demand for its diagnosis is far from being met [According to the g et al. , ADHD cuThe provision of reliable diagnostic aids for clinicians can greatly improve diagnostic efficiency. Currently, the most commonly used diagnostic aids in clinical practice are rating scales . SeveralNowadays, objective diagnostic ADHD methods based on wearable inertial sensors (e.g. accelerometers and gyroscopes) have been studied , 17. TheAt present, using wearable inertial measurement units, the Institute of Computing Technology, Chinese Academy of Sciences and Beijing Anding Hospital co-developed a new Wearable Diagnostic Assessment System based on the clinical diagnostic criteria of DSM-5 and six classical detection paradigms and four interaction modes. In this study, we aimed to evaluate the accuracy and validity of the WeDA system in the diagnosis of ADHD and compare it with the SNAP-IV scale.This study followed the STARD statement  meeting the DSM-5 diagnostic criteria for attention deficit and hyperactivity disorder; (b) aged 6–16 years and normal corrected visual acuity regardless of gender; (c) Wechsler Intelligence Scale for Children, Fourth Edition (WISC-CR) IQ ≥ 70; (d) being cooperative with wearing and completing games; (e) no previous use of medication for ADHD; (f) informed consent were obtained from patient and guardian. The exclusion criteria were as follows: (a) severe physical illness, closed-angle glaucoma, and previous history of epilepsy; (b) comorbidity with various developmental disorders such as mental retardation and autism spectrum disorder; (c) comorbid severe psychiatric disorders such as schizophrenia and bipolar disorder; (d) other conditions that the investigator considers unsuitable for inclusion in the group. The control group recruited children with normal development and excluded other disorders. In addition, children with symptoms of ADHD scored by the SNAP-IV while did not meet the diagnosis of ADHD under the gold standard were also included in the control group. All children were able to complete all the requirements of the trial. The project was approved by the Ethics Committee of Beijing Anding Hospital (201743FS-2).Finally, 110 subjects were successfully recruited and none of them dropped out. All subjects signed an informed consent form before the trial. All subjects completed the SNAP-IV scale and the WeDA system test, and the data were reliable and valid.The gold standard for the diagnosis of ADHD was based on the the opinion of two senior experts (Qi Yanjie and He Fan). According to the DSM-5 diagnostic criteria, all subject had significant symptoms of ADHD that significantly affecting their learning and life after ruling out other disorders . ReliablThe WeDA system is a wearable device diagnostic system based on the DSM-5 and designed to combine a variety of classical psychological paradigms. The system consists of a computer machine with a large touch screen, a set of 3D printed interactive devices, and six wearable motion sensors Fig. . It inclThe parent version of the SNAP-IV was used to evaluate children with ADHD as it was closer to the clinical diagnosis . The 26 The data collection was conducted in a room which was specially decorated and did not make the children feel uncomfortable. After a brief communication with the parents and children, they were separated by the assistants. Parents were asked to finish the SNAP-IV scale and they should not talk to their children nor guide them during the ten tasks. Then the child finished the test of the WeDA system under the guidance. First, the assistant helped the child to put on the wearable sensors on his hands, legs, waist and head and tuned the machine to ensure stable data collection. Next, the assistant told the child how to complete the game tasks until the child was able to understand and complete all the game tasks correctly. When the child started performing the game tasks, the system would capture the movement data and task results from the six wearable sensors and gathered them into a database. The child would decide the order of the tasks on its own preference.All the statistically analyses were performed by using SPSS 25.0 software . Age and gender difference between the ADHD group and normal controls was tested using t-test and chi-square test, respectively. Calculated indicators for diagnostic validity included sensitivity, specificity, Youden index, diagnostic odds ratio, and likelihood ratio. Performance of diagnostic tests included predictive values as well as diagnostic accuracy, and comparisons of sensitivity specificity between the two groups were analysed using McNemar’s chi-squared test for paired data. In addition, the ROC curves were plotted and Z-test was performed to test whether there was a difference in the area under the curves (AUC), it is generally accepted that accuracy is high when the AUC is 0.7 to 0.9 .p = 0.641) and gender ratio  between these two groups.A total of 110 subjects were included in this study, and 55 of them were in the ADHD group and 55 were in the control group. There were no difference in the age  and specificity  compared to that of SNAP-IV. Furthermore, the WeDA system also had higher a Youden Index, predictive efficacy, and diagnostic accuracy than SNAP-IV (Table As shown in Table p = 0.068).The ROC curves of the WeDA system and SNAP-IV were presented in Fig. The current study found that both the WeDA system and SNAP-IV had good diagnostic value as indicated by the AUC. The sensitivity and specificity of SNAP-IV in this trial were similar to that of previous study . CompareUsing accelerometry as an objective diagnostic indicator for children with ADHD has been studied for a long time with favorable results , 28, 29.However, most of the previous studies have suffered from a number of shortcomings, with some studies identifying ADHD by recording motion data in a “free-living” environment being too poorly interpreted and difficult to convince. On the other hand, studies that record motion data in restricted situations do not have a good design concept and lack a theoretical basis for the task. It is also difficult to convince clinicians of the machine's diagnostic results. The rationale for this study was therefore to explore a more objective and convincing evaluation method to help clinicians make a quick, efficient and accurate diagnosis of ADHD.Just like SNAP-IV interprets and quantifies DSM-IV, The WeDA system is also designed to cover all symptom items in the DSM-5. The ten carefully designed games include comprehensive coverage of the 18 DSM-5 diagnostic criteria of “inattention” and “Hyperactivity and impulsivity”, and are designed to incorporate doctors’ expertise. For example, when designing the grasshopper catching game, Professor Zheng Yi once said, “If a child is able to catch a grasshopper in a meadow, then he/she definitely has no ADHD. The reason for this is that catching a grasshopper requires patient observation, slow approach, holding one's breath and coordination between the hands and eyes, all characteristics that are most lacking in people with ADHD.” This is the source of the design of the grasshopper catching game. Similar ideas are used throughout the design of the game. Secondly, to assess the cognitive state of the child more comprehensively, several classical testing paradigms were used simultaneously in the design of the testing scenarios, including the Trail Making Test (TMT), Go/No-Go Paradigm, Continuous Performance Task (CPT), Stroop Effect, Wisconsin Card Sorting Test (WCST), and Stop Signal Task (SST). As a result, these more clinically relevant designs make the WeDA system more scientific. The results of the tests are more interpretative and more convincing to clinicians. Clinicians are able to use the WeDA system to clearly identify a child’s symptoms and match them to the DSM-5, providing evidence for diagnosis and improving diagnostic efficiency.As an auxiliary diagnostic tool, the WeDA system has been designed to improve on the shortcomings of previous research, making the machine diagnostic system more scientific and more accepted among the clinician community. However, the WeDA system, as a very rudimentary prototype, has a number of shortcomings. First, The WeDA system has full coverage of the DSM-5 and incorporates multiple psychological paradigms, which has led to the design of a large number of game tasks and the possibility of some overlap between game tasks. Second, during the study, we found that subjects who completed part of the game tasks were also able to achieve a high diagnostic accuracy rate. Therefore, future study should focus on making WeDA system more simplified and personalized. Third, the WeDA system performs poorly in the classification of ADHD subtypes. The sensitivity and specificity of the ADHD subtype classification are poor. When it comes to ADHD, no single diagnosis or treatment can be applied to everyone, as each patient will have different symptoms and the corresponding treatment will be different. Hence, more efforts are needed to have a complementary diagnostic technique in order to make an accurate diagnosis for ADHD subtypes.The WeDA system has excellent clinical diagnostic performance. Compared with SNAP-IV, the WeDA system has superior sensitivity, specificity, and accuracy in all aspects. The advantages of the WeDA system in terms of diagnostic objectivity, scientific design and ease of operation make it a promising system for widespread use in clinical practice.