FWE(cluster) = 0.015 for StopSuccess > Go trials and pFWE(cluster) < 0.001 for StopSuccess > StopFail trials). These results provide evidence for a neurocognitive mechanism underlying the efficacy of a few-foods diet in children with ADHD.Research into the effect of nutrition on attention-deficit hyperactivity disorder (ADHD) in children has shown that the few-foods diet (FFD) substantially decreases ADHD symptoms in 60% of children. However, the underlying mechanism is unknown. In this open-label nutritional intervention study we investigated whether behavioural changes after following an FFD are associated with changes in brain function during inhibitory control in 79 boys with ADHD, aged 8–10 years. Parents completed the ADHD Rating Scale before (t1) and after the FFD (t2). Functional magnetic resonance imaging (fMRI) scans were acquired during a stop-signal task at t1 and t2, and initial subject-level analyses were done blinded for ARS scores. Fifty (63%) participants were diet responders, showing a decrease of ADHD symptoms of at least 40%. Fifty-three children had fMRI scans of sufficient quality for further analysis. Region-of-interest analyses demonstrated that brain activation in regions implicated in the stop-signal task was not associated with ADHD symptom change. However, whole-brain analyses revealed a correlation between ADHD symptom decrease and increased precuneus activation (p Generally prescribed drugs for ADHD are not effective 24 h per day and can cause sleeping problems, decreased appetite, headache and stomach-ache2, frequently resulting in discontinuation of medication3. Therefore, novel treatments, preferably aiming at the underlying triggers or causes of ADHD, are needed.Attention-deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by inattentiveness, hyperactivity and impulsiveness, and often co-occurs with other psychiatric disorders such as oppositional defiant disorder (ODD)4. Supplementation or restriction of some nutrients or foods, have generally shown small effects in relieving ADHD symptoms6. However, meta-analyses of double-blind placebo-controlled (DBPC) diet restriction studies have reported moderate to large effect sizes 8. A systematic review of these meta-analyses showed that inclusion of researcher’s observation ratings rather than teacher ratings in the meta-analysis of Sonuga-Barke et al. (2013)8—according to Sonuga-Barke et al. (2013)8 observer ratings would be ‘better probably blinded’ than teacher ratings—resulted in an overall SMD of 0.57; p = 0.0246. Based on the evidence provided by the DBPC studies, the FFD was included in an ADHD treatment protocol9.Dietary interventions can modulate behaviour and mental health6. Subsequent open or single-blinded randomized controlled trials did apply an optimal FFD showing large effects (ES = 1.26–2.35)12, while a recent uncontrolled FFD study in children with ADHD showed significant behavioural improvements according to blinded video ratings (ES = 1.42) as well as to non-blinded assessments (ES = 1.54)13. Yorgidis et al.14 stated that research has shown that ‘nutrition is a strong mediator and/or moderator of ADHD symptoms’. However, the impact of the FFD on ADHD is still disputed.To assure the blinded design, the DBPC studies could not apply the most stringent FFD, in which only a few foods are allowed15, we aimed to investigate whether behavioural changes after following an FFD are associated with changes in brain activation during inhibition tasks in children with ADHD.Knowledge about the mechanism underlying the effect of an FFD on ADHD may provide a better understanding of the association between diet and ADHD. Since ADHD has been associated with impaired cognitive control processes in the brain, such as response inhibition and interference inhibition15. Psychostimulants have been shown to increase task-related activation in various brain regions in children with ADHD16. The impact of the FFD on brain activation in children with ADHD has never been investigated, however, some nutritional studies have been performed20, one of which first used an FFD approach to define which foods the participating children with ADHD were susceptible to, and subsequently showed an increased activity in frontotemporal areas after the children consumed ADHD provoking foods18.Brain activation in response inhibition  and interference inhibition (Stroop and Flanker tasks) can be assessed by blood oxygen level dependent (BOLD) changes in functional magnetic resonance imaging (fMRI). Children with ADHD have decreased activation in various brain regions during inhibition, including the prefrontal cortex, putamen, thalamus, precuneus and parietal and temporal lobes21. We hypothesized that the observed changes in ADHD symptoms after the intervention are associated with changes in activation of brain regions that are involved in task execution, using task-related regions-of-interest (ROIs). For our secondary objective we employed a whole-brain approach, investigating the changes in activation for other regions that were not expected a priori.Our primary objective was to investigate whether ADHD symptom changes following an FFD are reflected in neural activation changes in the brain. To assess brain activation, fMRI measurements of BOLD responses during response inhibition  and interference inhibition (Flanker task) were compared before (t1) and after (t2) a 5-week FFD interventionBetween February 19, 2018, and April 30, 2019, 138 children were screened (t0) for participation in the BRAIN study. Of the 100 children that started with the FFD, 12 stopped the diet prematurely and nine were excluded from analysis. Of the remaining 79 children, 53 were included for the stop-signal task and 32 for the Flanker task analyses . Fifty (63%) of 79 participants showed an ARS score decrease of at least 40% and were categorized as responders, with an average decrease of 73.4% (SD = 16.1%), while non-responders showed an average decrease of 10.8% (SD = 17.7%). There were no differences in ARS scores distribution between the whole group of children (n = 79) and the two subgroups included in the fMRI tasks analyses . ARS scores at the screening (t0) and after the baseline period  were comparable , while the Incongruent-Congruent ERR was not different between t2 and t1  for the frequent Go trials (GoRT) and the average time it took to inhibit a response for the infrequent stop-signal trials  across participants did not differ between t1 and t2 . Forty (69%) of 57 participants showed a decrease in ODD symptoms of more than 40%, with an average symptom decrease of 75.4% (SD = 17.4%). The other 17 (31%) participants showed an average symptom decrease of 9.9% (SD = 19.6%). Changes in ODD score and ARS score are strongly correlated , that might have affected the ODD results at the end of the FFD. For these 57 children, the mean ODD score at t1 was 17.8. At t2, the mean ODD score was reduced to 7.6 , however, in the StopSuccess > StopFail contrast, the precuneus finding no longer survived whole-brain correction for multiple comparisons .Thirty-nine (73.5%) of the 53 children that were included in the stop-signal task met the ODD criteria. In order to investigate whether the presence and the severity of ODD at t1 contributed to the correlation between precuneus activation and change in ARS score, we added the t1 ODD score as covariate of non-interest. The precuneus finding in the StopSuccess > Go contrast remained significant  of 79 participants were responders, with an average ARS score decrease of 73%. This response percentage is commensurate with the response observed in previous studies applying an FFD, assessed by different research groups25. Moreover, the present results are also in agreement with other fMRI studies in ADHD that reported reduced activation of the precuneus in children with ADHD versus matched controls during attention and stop-signal tasks15. Although the precuneus is not a typical region involved in response inhibition, it is a region involved in visuospatial processes26 that contribute more generally to performing a visual task such as our stop-signal task. However, an increased activation of the precuneus in ADHD compared to matched controls during a vigilance task has also been reported27, suggesting that the precuneus may be under- or over-activated in ADHD depending on the type of task that is used. The precuneus is involved in visuospatial attention, but is also part of the brain’s default mode network; a network of brain areas that is active when an individual is awake and alert but not actively engaged in a task26. Functional MRI resting state studies investigating the default mode network showed an association between decreased functional connectivity and ADHD28. However, our study did not investigate the activation of the precuneus under resting state conditions, which should be addressed in future research.The fMRI results of the current study, i.e., an increase in precuneus activation in FFD responders, are in line with results from medication studies that demonstrated that methylphenidate increased precuneus activity in boys with ADHD during the stop-signal task or a sustained attention task29. Indeed, changes in brain activation have been considered to be better predictors for ADHD treatment response than differences in task performance31.Contrary to the observation that brain function was correlated with behavioural improvement following an FFD, no relationships were detected between task performance and changes in ARS score. This result is in agreement with studies investigating the effect of medication on ADHD, often detecting no differences in task performance between the medication and placebo group10. Exploratory post-hoc fMRI analyses did not provide indications that the increase of precuneus activation in children showing a decrease of ADHD symptoms was driven by co-morbid ODD. Indeed, post-hoc analyses showed that we could consistently reproduce our ARS precuneus finding independently of comorbid ODD. This is an important observation, showing that our results are uniformly applicable to children with ADHD, whether or not suffering from comorbid ODD.Forty of 57 children included for ODD analysis were ODD-responders, 32 of these responders were also ADHD-responder, i.e., showing ADHD and ODD symptom decreases of at least 40%. These results indicate that nutritional intervention may not only have beneficial effects on ADHD, but also on ODD, which is in line with previous research results25. We cannot explain well why task-related regions did not show effects in our study. However, an ROI analysis does limit the outcomes to specific brain regions, while the observed effects can also be found in regions that are not activated by the task across subjects and time points. For instance, a dietary intervention could affect brain function by compensatory changes in brain regions that are not generally involved in a task. Moreover, group task effects—that our ROIs were based upon—can miss out on important inter-individual variability33. Our whole-brain analysis, on a cluster-level corrected threshold, revealed that the decrease of ADHD symptoms was significantly correlated with increased precuneus activation in two different stop-signal contrasts.In the ROIs that were based on the main task effects across subjects and time points, no significant associations of brain function and behavioural changes were found, even though previous medication studies in ADHD have found effects in fronto-parietal regions more typically involved in executive control34. Therefore, we were able to apply the most restricted diet, i.e., the FFD, in order to achieve the best possible behavioural response6. Another limitation of the present study was that the study population was confined to boys of a narrow age range, which hampers generalization of the results to girls and other age groups. Furthermore, 26 (33%) of the participants could not be included in the stop-signal task analysis, due to movement in the scanner or technical issues. However, both the ARS scores and the baseline characteristics were not different between the complete study population (n = 79) and the children included in the stop-signal task analysis (n = 53). Finally, although our study clearly identified activation of the precuneus to be related to ADHD symptom improvement after following an FFD, the underlying molecular mechanism that explains this activation remains to be deciphered.A limitation of the current study was the lack of a control group and blinded design. An expectancy or a disappointment effect, both on parental ratings and the child’s behaviour, may have affected the behavioural outcomes. However, our purpose was not to evaluate whether or not the FFD affects the behaviour of a child with ADHD, but to investigate whether the observed behavioural improvements after following an FFD are correlated with changes in brain activation, comparably to the study goal of Stevens et al. (2015)4. Our study results are clinically relevant for children with ADHD and for the scientific community, providing new leads towards improved understanding of the impact of nutrition on ADHD.This study showed that the parent-reported behavioural improvements in children with ADHD following an FFD, were reflected in the brain activity of the children. The correlation between the increase in inhibition-related activation of the precuneus and the clinical symptom decrease at the end of the FFD, objectively points to an underlying neurocognitive mechanism. Future research should not only focus on further investigating the role of the precuneus in food-induced ADHD, but also on the underlying biological mechanism and the impact of the gut-brain axis, which is highly modifiable by diet21, 100 children were included. The required samples size for the main hypothesis of this manuscript was estimated at 4621.Participants in the Biomarker Research in ADHD: the Impact of Nutrition (BRAIN) study were recruited via the media and healthcare institutions. Eligibility for participation in the study was assessed during a personal intake interview with the parents. Inclusion criteria were right-handed boys, aged ≥ 8 and ≤ 10 years, and meeting the Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM-IV) criteria of ADHD. Exclusion criteria were (1) diagnosis of autism spectrum disorder, developmental coordination disorder, chronic gastrointestinal disorder, autoimmune disorder, dyslexia or dyscalculia, (2) premature birth (< 36 weeks) and/or known oxygen deprivation during birth, (3) vegetarian/vegan, (4) IQ < 85, (5) use of systemic antibiotics, antifungals, antivirals or antiparasitics in the past 6 months, (6) insufficient command of the Dutch language by either parents or child, (7) family circumstances that may compromise following or completion of the diet, and (8) having a contraindication to MRI scanning. After t1, participants were withdrawn or excluded from the study if the child or family did not comply with the instructions, or if family circumstances interfered with study compliance. Participants and/or the parents of the participants could withdraw from the study at any time. After the procedures had been fully explained, all parents gave written informed consent, and all children gave written assent. Based on the sample size estimates per outcome variable of the BRAIN study , as calculated in the published protocol of this studyChildren meeting the BRAIN study criteria attended a screening session at Wageningen University and Research, The Netherlands, for further assessments (t0). The ADHD diagnosis was established by a trained paediatrician. An abbreviated IQ test based on the Wechsler Intelligence Scale for Children-III was conducted if there were no IQ test results available from the past year, and family circumstances and baseline ADHD symptom scores were assessed. Children were familiarized with the scanning procedure in a mock scanner.21. Then, following a 1-week transition period applying a gradually adapted diet to habituate to a different eating pattern, the children started the FFD, which lasted 32 or 33 days . The most stringent FFD consisted of rice, turkey, vegetables , pears, olive oil, ghee, salt, rice drink with added calcium and water. During the first 2 weeks of the diet, the FFD was extended with some other foods, allowing lamb, butter and small portions of wheat, corn, potatoes, some fruits, and honey24. If no behavioural improvement was reported within 2 weeks, the extended FFD was gradually adapted to the most stringent FFD23. At the end of the FFD both fMRI and ADHD symptom score assessments were repeated (t2).Children meeting the criteria for participation started with a 2-week baseline period, during which they stayed on their regular diet, while parents kept a diary meticulously documenting their child’s food, behaviour and activities. The period between t0 and t1 was called the baseline period. At t1, after the baseline period, the first fMRI scan was performed, ADHD symptom scores were assessed again, and parents received an elaborate explanation of the FFD36. This questionnaire was completed by the parents at t0, t1 and t2; if possible, teachers completed the ARS at t1 and t2. Prior to the measurements, parents and teachers were asked to focus on the child’s behaviour during the past week only. The percentage change in ARS score at t2 relative to t1 (100 × [t1 − t2]/t1) was used to assess a child's response to the FFD. Children with an ARS reduction of at least 40% at t2 compared to t1 were designated responders21. ODD, a co-morbid disorder commonly occurring in children with ADHD, was assessed at t1 and t2 by means of a structured psychiatric interview using the eight ODD-symptoms (DSM-IV), applying a four-point scale . ODD criteria were met when at least four out of eight symptoms occurred at least three times a week. Children with an ODD symptom score reduction of at least 40% at t2 compared to t1, were designated ODD responders (22).ADHD symptom scores before and after the FFD were measured using the ARS, consisting of 18 ADHD symptoms and applying a four-point scale www.neurobs.com). An optical button box with four buttons was used. Participants were instructed to only use the left and right button, pressing the left button with the left hand and the right button with the right hand. A screen was used in the scanner to present the tasks to the participants. Before the scans, participants were familiarized with the tasks. On both fMRI-assessment days (t1 and t2), participants were scanned, at the same time of day, on a 3 T-Siemens Magnetom Verio with a 32-channel head coil. Participants underwent: (1) a functional MRI scan during a stop-signal task (duration ± 8 min), (2) an anatomical MRI scan (duration ± 5 min), and (3) a functional MRI scan during a Flanker task (duration ± 5 min). See The fMRI tasks were programmed in Presentation  and ERR of incongruent and congruent trials. The main congruency effect of the Flanker task was assessed with the contrast of incongruent versus congruent trials.The stop-signal task40, as described in more detail in the 41 were collected as potential noise regressors for the first level fMRI analysis (see next section).Anatomical and functional MRI pre-processing was done using fmriprep42, the six rigid-body realignment parameters, the mean signal from the cerebrospinal fluid, and four physiological regressors (CompCor)43. Finally, we added the time courses of the independent noise components of ICA-AROMA as regressors of non-interest, with the restriction that we only included those that could not regress out significant amounts of task-related  variance. All task regressors were convolved with the canonical hemodynamic response function and high pass filtered (128 s). At the first level, two contrasts were specified to assess response inhibition during the stop-signal task: StopSuccess > StopFail and StopSuccess > Go. To assess performance monitoring and error processing, we use the contrast: StopFail > StopSuccess contrast. To generate a motor contrast, we used: Go > StopSuccess.At the first, subject-specific level, we specified a model with regressors of interest (task-related events) and regressors of non-interest (noise regressors). For the stop-signal task, this model included three event regressors of interest per session (t1 and t2), reflecting: 1. Go trials; 2. StopSuccess trials, 3. StopFail trials. Missed Go trials were added as regressor of non-interest. Other noise regressors included the framewise displacementThe Flanker task was analysed in the same way as the stop-signal task, except that different regressors of interest were used: 1. congruent trials; 2. Incongruent trials. Response conflict during the Flanker task was assessed using the contrast: Incongruent > Congruent.For a full description of the first level analysis, see FWE) < 0.05 (cluster level). Regionally averaged beta weights were extracted for both timepoints from all voxels in the identified ROIs using MarsBar44.The primary objective of the BRAIN study was to explore the relation between ARS score changes and BOLD response changes after the FFD in ROIs during task performance. To this end, functional ROIs were used, defined by the main task effect, i.e., across participants and timepoints, independent of the ARS score decrease (%). The functional ROIs were determined using a GLM for each of four stop contrasts (StopSuccess > / < StopFail and StopSuccess > / < Go) and two Flanker contrasts (Congruent > / < Incongruent) across sessions  and across participants. ROIs were defined as the clusters showing a main task effect using a family-wise-error-corrected p-value  as covariate of interest in the voxel-wise t2 − t1 difference. Both positive and negative between-subject correlations were investigated. To explore the effects of ODD symptom severity at t1 on the outcomes of the above mentioned whole-brain analyses, we repeated the analyses with the t1 ODD score per subject as a covariate of non-interest.FWE < 0.05 as threshold for multiple comparisons over the whole brain . All analyses were performed using the SPM software package (SPM12).To correct for the possibility that ADHD symptom severity is related to movement during the scan, potentially causing a noise bias, the average movement per subject  across t1 and t2, was added in all group-level analyses as a covariate of non-interest. The results of these group-level fMRI analyses were assessed at the cluster level, by applying pAll statistical analyses described in this section were carried out using SAS® (version 9.4).t test including only those children meeting the ODD criteria at t1. If the assumption of normality was violated then non-parametric tests were applied . Normality was assessed by the Shapiro–Wilk test.ARS scores at t0 and t1 were compared using a paired t-test. The same test was used to compare ARS scores before (t1) and after (t2) the FFD intervention. The ODD scores at t1 and t2 were compared by a paired t test if the differences (results at t2 minus results at t1) were normally distributed, if not, a Wilcoxon signed rank test was used. The association between task performance results at t2 and the percentage change in ARS score at t2 compared to t1  was assessed using a general linear model (ANCOVA), including the task performance results at t1 as covariate of non-interest. If the model residuals were not normally distributed, a nonparametric Kruskal–Wallis test was applied  or a Spearman rank correlation  was estimated.The task performance results obtained at t2 and t1 were compared with a paired The t2 beta weights extracted from the ROIs in the fMRI analysis were further analysed using a general linear model (ANCOVA) with the change in ARS score  as covariate of interest and including t1 beta weights, age, IQ, and previous use of ADHD medication as covariates of non-interest.21 and pre-registered at Openscience (https://osf.io/6aeq3). The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008.This study is registered as “Biomarker Research in ADHD: the Impact of Nutrition (BRAIN)” at ClinicalTrials.gov, number NCT03440346 (22/02/2018). It was approved by the Medical Research and Ethics Committee of Wageningen University ; the protocol has been publishedIn this open-label intervention study all participants received the FFD, and parents, children and researchers involved in applying the FFD were not blinded. However, during the data quality checks, pre-processing and subject-level fMRI analysis, data were evaluated by researchers blinded to the ADHD symptom scores on both t1 and t2 .The sponsor, Porticus, was not involved in study design and execution, data analysis and its interpretation, writing the manuscript and the submission for publication.Supplementary Information.


The overall risk of ADHD in the FS+/preterm+ group was higher than that in the FS+/preterm− group . In addition, children with FS+/preterm+ had 16.9 times  higher odds of having ASD compared with those with FS+/preterm−. Preterm birth may be a risk factor for subsequent epilepsy, ASD and ADHD in children with FS.Febrile seizure (FS) is the most prevalent childhood seizure; it is significantly related to subsequent epilepsy and has possible links to childhood neurodevelopmental disorders. Separately, premature births are believed to increase the risk of attention deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD). Therefore, this study investigated whether preterm birth is a risk factor for subsequent epilepsy, ASD, and ADHD in children with FS. We retrospectively collected data for children aged < 5 years with FS from 1 January 2005, to 31 December 2013. We divided these children into two groups—the premature birth group and the full-term group—and compared their incidence rates of epilepsy, ASD and ADHD. The data of 426 patients with history of febrile convulsion were retrospectively collected. The premature birth group (FS+/preterm+) had 108 patients and the full-term group (FS+/preterm−) had 318 patients. The overall epilepsy risk in the FS+/preterm+ group was higher than in the FS+/preterm− group (odds ratio [OR], 2.52; 95% confidence interval [CI], 1.14–5.58;  Autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) are two common neurodevelopmental disorders (NDDs) in children. ASD, which commonly manifests in the first years of life, is a disease with multifactorial etiology and typically manifests as social and communication impairments as well as stereotyped behaviors, whereas ADHD is characterized by inattention, hyperactivity and impulsivity. These disorders are thought to have a strong heritable component and identified risk factors include extremes of maternal or parental age, mothers with chemical intolerances, low socioeconomic status, prenatal exposure to smoking and alcohol and epilepsy ,4,5,6,7.Febrile seizure (FS), which has an onset age mostly between 14 and 18 months, is the most common childhood seizure, affecting 2–5% of children aged < 5 years . CertainIn this small, but clinically significant study, based on the abovementioned considerations, we sought to examine whether preterm birth acts as a prognostic factor for subsequent neurodevelopmental sequelae in children with FS. Our hypothesis was that preterm birth is a risk factor for not only epilepsy but also subsequent ASD and ADHD in children with FS.The study protocol was approved by the Ethics Review Board of the China Medical University Ethics Committee (CMUH108-REC1-023). In this retrospective study, we collected data from children aged 0–5 years who were born preterm and full-term and had been diagnosed with FS between 1 January 2005 and 31 December 2013. All subjects in the study (FS+/preterm+ and FS+/preterm−), were born in the China Medical University Children’s Hospital and its three branches and their birth medical records were complete and detailed. Preterm birth in the present study was defined as any birth before 37 completed weeks of gestation. The diagnosis of FS was made by child neurologists in accordance with the following generally accepted criteria: (1) convulsions associated with an elevated temperature greater than 38 °C; (2) child aged older than 6 months and younger than 5 years; (3) absence of central nervous system infection or inflammation; 4) absence of acute systemic metabolic abnormality that may produce convulsions; and (5) no history of afebrile seizures [ absence All patients included in the study were followed up until the end of follow-up (31 December 2017). We followed up patients by reviewing their medical records and contacting their families via telephone or e-mail quarterly since the beginning of 2014, to investigate whether a first diagnosis of epilepsy, ASD, or ADHD after FS was confirmed. Since we included patients who were sure to be contacted, there were three lost cases in the follow-up .Subsequently, we comprehensively reviewed the respective medical records toward the end of 2017. The exclusion criteria for study participants were as followings: those individuals who had died during follow-up, those that had been diagnosed with neoplasms, had undergone organ transplantation, or had any serious disease affecting the immune system  before or after FS. The children enrolled in the study were relatively healthy; that is, they had no epilepsy or neurological, metabolic, autoimmune, or any known congenital disorders before the onset of FS. The final study population comprised 108 patients (FS+/preterm+).In addition, each patient in the FS+/preterm+ cohort was 1:3 propensity matched by age, sex, index year, urbanization to form a control group of 318 full-term children with FS (FS+/preterm−). Epilepsy was defined as two unprovoked seizures more than 24 h apart as diagnosed by a pediatric neurologist. Patients who met the relevant diagnostic criteria in the Fourth and Fifth Editions of the Diagnostic and Statistical Manual of Mental Disorders  were diagnosed with ASD and ADHD and their diagnoses were made by a pediatric psychiatrist or pediatric neurologist in an inpatient or outpatient setting in the China Medical University Children’s Hospital between 1 January 2005 and December 31, 2017. Confounding factors were sex, gestational age, birth body weight, first FS duration and onset age, brain condition within 6 months of age and number of FSs .p < 0.05 to be statistically significant.Categorical variables between groups were analyzed using χ2 tests. Furthermore, we calculated the incidence density rates of epilepsy, ADHD and ASD in both groups . In addition to term and preterm, other confounders, such as sex, gestational age, birth body weight, first FS duration and onset age, brain condition within 6 months of age and number of FSs were analyzed and controlled by propensity matching and stratification in the process of logistic regression model . Howevern = 45, 41.6%), intraventricular hemorrhage , hydrocephalus or ventriculomegaly , white matter damage or periventricular leukomalacia  and others . In total, 426 children who had been diagnosed with FS between 1 January 2005 and 31 December 2013 were enrolled into this study. p = 0.02). The overall risk of ADHD in the FS+/preterm+ group was higher than that in the FS+/preterm− group . Furthermore, the risk of ADHD among patients in the FS+/preterm+ group who experienced FS without recurrence  and whose age of FS onset ranged between 1 and 5 years were even higher than those in the FS+/preterm− group .p = 0.0001) higher odds of having ASD compared with those in the FS+/preterm− group, which was the same in each stratification subgroup. Among other things, patients in the FS+/preterm+ group who had experienced one episode of FS and whose age at FS onset was greater than 1 year had an even higher risk of ASD than those in the FS+/preterm− group In addition, children in the FS+/preterm+ group had 16.9 times  and multiple FS recurrences. Multiple FSs increased the risk of epilepsy . NeuroloAlthough not the main purpose of this study, our results revealed that 11.10% of preterm patients with FS developed epilepsy, whereas 4.7% of full-term patients with FS developed epilepsy, both of which were higher than figures previously reported in the literature. Moreover, we found that preterm birth might be a risk factor in combination with FS for the development of childhood epilepsy. However, there was no statistical significance in which we stratified according to the onset age and recurrence of FS. We believe that the formation of epilepsy is much more complicated and so individual risk factors selected in the present study cannot exist independently, that is, there is an interaction between various potential risk factors ,19,20. AStudies have noted the relationship between FSs and ADHD, with some reporting FS to be a risk factor for subsequent ADHD ,12,13. KOur results, on the contrary, revealed some differences than before. After taking gestational age into account, we found that FS onset above 1 year of age might significantly increase the risk of ASD and ADHD, but the puzzling thing is that children without recurrence of FS had an even higher risk of developing ASD and ADHD. The above phenomenon did not appear when considering epilepsy. Although we have obtained new findings from our observational data, there is no reasonable explanations for them yet. Therefore, further studies to determine the role of prematurity in the development of ADHD and ASD across the different stages of the developing brain will be essential to elucidate our findings ,31. Preterm birth is the leading cause of neurobehavioral impairment and disability, causing a large economic burden ,33. StudPathophysiologically speaking, myelination processes and brain development are interrupted in prematurely born children and the connectivity between brain regions could be disrupted as well; this may explain the occurrence of NDDs and the emotional/behavioral problems in this population . DespiteThis study had some limitations. First, some confounding factors may have affected the results, such as the mother’s medical condition during pregnancy, socioeconomic status, pharmacological treatment of children with preterm birth, malnutrition and environmental factors. Second, our sample size was relatively small and thus a population-based cohort study should be designed to prove our findings. Third, we did not perform an analysis of seizure semiology because of restrictions on the study design. Fourth, preterm children are monitored much more closely for developmental delay and therefore are more likely to be diagnosed having ASD or ADHD.A large prospective cohort longitudinal study that investigates whether the incidence rates of ASD and ADHD are significantly different between preterm and control groups may help to confirm our findings. Furthermore, additional research that focuses on neuroimaging, genetic susceptibility, iron homeostasis, or proinflammatory cytokines may identify potential mechanisms.This study investigated whether preterm birth is a risk factor for subsequent epilepsy, ASD and ADHD in children with FS. It is crucial for pediatricians to identify the early symptoms of ADHD or ASD in children with a history of FS—especially those born prematurely—to initiate proper assessment and treatment. Additional patient data and more in-depth studies are warranted to elicit potential cofounders to clarify the influence of preterm labor on FS and its association with ADHD and ASD.


K-edge X-ray absorption spectra of UO2(S2PR2) (R = Ph and o-CF3C6H4) is entirely attributed to the transitions from S 1s orbitals to the unoccupied molecular orbitals due to the mixing between U 5f and S 3p orbitals.The first pre-edge feature with an intensity of 0.16 in the S  2PR2) have been used for the selective separation of trivalent actinides (AnIII) from lanthanides (LnIII) over the past decades. The substituents on the dithiophosphinic acids dramatically impact the separation performance, but the mechanism is still open for debate. In this work, two dithiophosphinic acids with significantly different AnIII/LnIII separation performance, i.e. diphenyl dithiophosphinic acid (HS2PPh2) and bis(ortho-trifluoromethylphenyl) dithiophosphinic acid [HS2P(o-CF3C6H4)2], are employed to understand the substituent effect on the bonding covalency between the S2PR2− anions (R = Ph and o-CF3C6H4) and the uranyl ion by sulfur K-edge X-ray absorption spectroscopy (XAS) in combination with density functional theory calculations. The two UO2(S2PR2)(EtOH) complexes display similar XAS spectra, in which the first pre-edge feature with an intensity of 0.16 is entirely attributed to the transitions from S 1s orbitals to the unoccupied molecular orbitals due to the mixing between U 5f and S 3p orbitals. The Mulliken population analysis indicates that the amount of p character in these orbitals is essentially identical for the UO2(S2PPh2)2(EtOH) and UO2[S2P(o-CF3C6H4)2]2(EtOH) complexes, which is lower than that in the U 6d-based orbitals. The essentially identical covalency in U—S bonds for the two UO2(S2PR2)2(EtOH) complexes are contradictory to the significantly different AnIII/LnIII separation performance of the two dithiophosphinic acids, thus the covalency seems to be unable to account for substituent effects in the AnIII/LnIII separation by the dithiophosphinic acids. The results in this work provide valuable insight into the understanding of the mechanism in the AnIII/LnIII separation by the dithiophosphinic acids.The dithiophosphinic acids (HS Soft-donor ligands have demonstrated good performance in AnIII/LnIII separation, in which the dithiophosphinic acids (HS2PR2) show great potential in the AnIII/LnIII separation process  dithiophosphinic acid [HS2P(o-CF3C6H4)2] can afford an AmIII/EuIII separation factor higher than 104, which is three orders of magnitude higher than that for diphenyl dithiophosphinic acid (HS2PPh2)  the deprotonation properties of the dithiophosphinic acids 2 may destabilize the anion to a greater extent and increase selectivity towards actinides 2− formed up to 3:1 complexes 2− anion was a ‘softer’ extractant as compared with the S2PPh2− anion, which promoted the selectivity towards actinides  chemical bonding in the dithiophosphinate complexes, to the best of our knowledge, has not been reported up to now.Several factors emerged in the previous studies that are responsible for the substituent effect in the AnM—S bonds would account for the separation performance. The technique of ligand K-edge XAS in combination with density functional theory (DFT) calculations is employed. The intensity of the pre-edge features in XAS directly reflects the amount of ligand p character in metal-derived molecular orbitals (MOs) and thus the covalency in metal–ligand bonds 2(EtOH)  complexes by sulfur K-edge XAS to detect the substituent effect on the bonding covalency between the dithiophosphinate anions and the uranyl ion. Results in this work will be informative in terms of the substituent effect on the bonding covalency between the dithiophosphinate anions and trivalent actinides, and thus helpful for the understanding of the mechanism in the AnIII/LnIII separation by the dithiophosphinic acids.In this work, we are motivated to address whether the extent of covalency in the 2.2.1.22+ with S2PR2−  ligands were prepared and isolated as highly pure crystalline solids before the XAS experiments. Note that the complexes of UO22+ with the S2PPh2− ligand have been crystalized previously 2− ligand have not been reported. In this work, we successfully synthesized single crystals of UO22+ with the two S2PR2− ligands. The crystal structures of the UO2(S2PR2)2(EtOH) complexes are shown in Fig. 1Table S1 of the supporting information. Both the two S2PR2− ligands form up to 2:1 complexes with UO22+, similar to previous reports 2(EtOH) complexes contain four sulfur atoms from the two bidentate dithiophosphinate ligands and one oxygen atom from the coordinated ethanol in the first coordinated sphere of the equatorial plane of UO22+. The crystals of the two ligands S2PR2− were also obtained by employing tetraphenylphosphonium (Ph4P+) as the cation according to the procedure reported in the literature 2(EtOH) complexes are provided in Table 12PPh2− and S2P(o-CF3C6H4)2−, respectively, and these values prolong to 2.010 Å in the two UO2(S2PR2)2(EtOH) complexes, suggesting comparable bonding interactions of the two S2PR2− ligands with the uranyl ion. The S—U—S bond angles are 70.74° and 70.23° for UO2(S2Ph2)2(EtOH) and UO2[S2P(o-CF3C6H4)2]2(EtOH), respectively, and the S—P—S bond angles are 111.09° and 109.78° for UO2(S2PPh2)2(EtOH) and UO2[S2P(o-CF3C6H4)2]2(EtOH), respectively. The comparable S—U—S and S—P—S bond angles suggest comparable bonding interactions of S2PPh2− and S2P(o-CF3C6H4)2− with the uranyl ion.The selected bond lengths and bond angles for the crystal structures of the [S2PR2] ligands and the UO2(S2PR2)2(EtOH) complexes. The background-subtracted and normalized P K-edge XAS spectra are shown in Fig. 2Fig. S1 of the supporting information. The spectrum of PPh4Cl was also collected to compare with the spectra of the [PPh4][S2PR2] ligands. According to the second derivatives , the spectrum of PPh4Cl contains three pre-edge features at 2147.6, 2149.4 and 2150.7 eV. The spectra of the [PPh4][S2PR2] ligands both contain four pre-edge features at about 2147.6, 2149.2 and 2149.8, 2150.5 eV. The spectra of the UO2(S2PR2)2(EtOH) complexes both contain three pre-edge features at about 2147.6, 2148.9 and 2150 eV. The P K-edge XAS spectra are not very informative for the bonding interactions between the uranyl ion and the dithiophosphinate, thus collections of the S K-edge XAS proceeded for both the [PPh4][S2PR2] ligands and the UO2(S2PR2)2(EtOH) complexes.Before the collection of S 2.3.K-edge XAS spectra of the [PPh4][S2PR2]  ligands and the UO2(S2PR2)2(EtOH) complexes are shown in Fig. 3Fig. S4), the spectrum of [PPh4][S2PPh2] contains three pre-edge features at 2471.3, 2472.4 and 2473.6 eV, and the spectrum of [PPh4][(S2P(o-CF3C6H4)2] contains four pre-edge features at 2471.3, 2472.4, 2473.4 and 2474.3 eV. This result is in agreement with the previous observations by Daly and co-workers, except that there is no small contribution to the spectra (>2480 eV) from sulfate contaminant   (EtOH) complexes display pre-edge features around 2472.0, 2473.5 and 2474.8 eV and also contain a shoulder feature at about 2470.5 eV . This shoulder feature in the S K-edge XAS of the UO2(S2PR2)2(EtOH) complexes is ascribed to the covalent interaction between the uranyl ion and the S2PR2− ligands.The normalized and background-subtracted S K-edge XAS spectra of the UO2(S2PR2)2(EtOH) complexes were modeled using pseudo-Voigt functions with a 1:1 ratio of Lorentzian and Gaussian function contributions and a step function with a 1:1 ratio of arctangent and error function contributions. The energy positions of the features determined by the second derivatives are fixed during the curve-fits . The curve-fits parameters are summarized in Table S2.To quantify the intensities of the pre-edge features, the S 2(S2PPh2)2(EtOH) is best modeled by four pseudo-Voigt functions at 2470.5, 2472.0, 2473.5 and 2474.8 eV, and that of UO2[S2P(o-CF3C6H4)2]2(EtOH) by five pseudo-Voigt functions at 2470.6, 2471.9, 2473.1, 2474.0 and 2474.7 eV, as shown in Fig. 42(S2Ph2)2(EtOH) and UO2[S2P(o-CF3C6H4)2)]2(EtOH). Resembling the spectra of [PPh4][S2PR2], the fourth feature observed in the spectrum of UO2(S2PPh2)2(EtOH) at 2474.8 eV splits into two features at 2474.0 and 2474.7 eV in the spectrum of UO2[S2P(o-CF3C6H4)2]2(EtOH), which may be attributed to the orbital splitting resulting from symmetry change and nonuniform C—P—S angles, according to the results reported by Daly and co-workers 2(EtOH) complexes.The electronic structure of the Sf and 6d orbitals in the covalent bonds between uranium and axial oxygen (Oyl) atoms induces a geometrically linear and redox-stable uranyl ion 2(EtOH) complexes in this work. The energy level diagram of the truncated unoccupied MOs for the two UO2(S2PR2)2(EtOH) complexes is presented in Fig. 5s orbitals are equivalent to each other, in order to directly compare with the S K-edge XAS. The U 5f- and 6d-dominant MOs are shown by red and blue lines, respectively. There are four orbitals (1–4a) belonging to the σ- and π-type mixing of the S2P orbitals with U 5f orbitals near −3 eV, and the contours of these four orbitals are illustrated in Fig. 6f-dominant MOs locate around −1.25 eV showing the orbital mixing between U 5f and S 3p orbitals, blending in with some orbitals  that contain significant phenyl character  are distributed ranging from −0.5 eV to 2.0 eV, interspersing with some σ* S2P orbitals (gray lines) containing little S 3p character. The contours of the U 6d-dominant MOs (5–9a) for UO2(S2PR2)2(EtOH) are illustrated in Fig. 72(S2PR2)2(EtOH) are illustrated in Fig. S7.The involvement of both U 5K-edge XAS of the UO2(S2PR2)2(EtOH) complexes in Fig. 4s orbitals to the U 5f-dominant MOs (1–4a). The second pre-edge features around 2472.0 eV are dominated by the transitions associated with primarily phenyl character 2(EtOH) complexes were simulated by TDDFT calculations to directly compare with the experiment XAS 2(EtOH) complexes have been shifted by +49.7 eV to account for the omission of the atomic and extra-atomic relaxation associated with the core excitation, relativistic stabilization, and errors associated with the functional 2(EtOH) complexes, which is entirely attributed to the transitions from S 1s orbitals to the U 5f-dominant unoccupied MOs (1–4a), indicating the covalent mixing between S 3p orbitals and U 5f orbitals. The transitions from S 1s orbitals to the primarily phenyl character     for each S-ligand  to % S 3p character. Therefore, we herein use the data from Mulliken population analysis that are associated with the experimental XAS data to evaluate the bonding covalency between the uranyl ion and the S2PR2− ligands 2(EtOH) is 2.68%, 3.75%, 1.95% and 3.04%, respectively. For UO2{[S2P(o-CF3C6H4)]2}2(EtOH), these values are 1.95%, 4.09%, 2.48% and 2.73%, respectively. The total amount of S 3p character of the orbitals of 1a, 2a, 3a and 4a are 11.42% and 11.25%, corresponding to 2.86% and 2.81% per U—S bond in UO2(S2PPh)2(EtOH) and UO2{[S2P(o-CF3C6H4)]2}2(EtOH), respectively. The DFT calculations also show that the amount of S 3p character in the U 6d-dominant unoccupied MOs of 5a, 6a, 7a, 8a and 9a for UO2(S2PPh)2(EtOH) is 12.11%, 9.0%, 13.43%, 14.43% and 16.78%, respectively. For UO2[(S2P(o-CF3C6H4))2]2(EtOH), these values are 13.00%, 21.93%, 8.94%, 6.29% and 13.39%, respectively. The average amount of S 3p character in U 6d-based orbitals (5–9a) obtained from the DFT calculations are 13.15% and 12.71% for UO2(S2PPh)2(EtOH) and UO2{[S2P(o-CF3C6H4)]2}2(EtOH), respectively, thus the S 3p orbitals are engaged more in the U 6d orbitals than that in the U 5f orbitals, consistent with the previous reports that the 6d orbitals play a significant role in actinide bonding relative to the 5f orbitals 2(EtOH) and UO2{[S2P(o-CF3C6H4)]2}2(EtOH), respectively, indicating an important contribution to the pre-edge features. Although only the first pre-edge feature around 2470.5 eV in each spectrum is exclusively attributed to the transitions from S 1s orbitals to the U 5f-dominant unoccupied MOs (1–4a), the XAS data and DFT calculations both suggest that the mixing of U 5f and 6d orbitals with S 3p orbitals are similar in the two UO2(S2PR2)2(EtOH) complexes, indicating that introduction of o-CF3 into phenyl has little effect on the covalent bonding between S2PR2− and UO22+.The DFT calculations show that the amount of S 33.K-edge XAS technique and DFT calculations has been conducted on UO2(S2PR2)2(EtOH) (R = Ph and o-CF3C6H4) complexes to obtain direct insight into the contributions of U 6d and especially 5f orbitals to the covalency in the U—S bonds, in order to illuminate the role of the bonding covalency in the AnIII/LnIII separation by the dithiophosphinic acids. The two UO2(S2PR2)2(EtOH) complexes display similar pre-edge features in the S K-edge XAS, the first of which is entirely attributed to the transitions from S 1s orbitals to the U 5f orbitals mixing with the S 3p orbitals. Curve-fitting analysis indicates identical intensities of 0.16 for the first pre-edge feature of the two UO2(S2PR2)2(EtOH) complexes. Consistently, the amounts of S 3p character per U—S bond for UO2(S2Ph2)2(EtOH) and UO2[S2P(o-CF3C6H4)2]2(EtOH) by Mulliken population analysis are essentially identical to each other. In addition, the DFT calculations show that the amounts of S 3p character in U 6d-based orbitals are also nearly equivalent for the two UO2(S2PR2)2(EtOH) complexes. The XAS data and DFT calculations demonstrate essentially identical bonding covalency in the two UO2(S2PR2)2(EtOH) complexes, indicating that the introduction of o-CF3 into phenyl has little effect on the covalent bonding between the S2PR2− ligands and UO22+. The essentially identical covalency in the U—S bonds for the two UO2(S2PR2)2(EtOH) complexes are contradictory to the significantly different AnIII/LnIII separation performance of the two dithiophosphinic acids. The M—S bonding covalency seems to be unable to account for the substituent effect in the AnIII/LnIII separation by the dithiophosphinic acids. According to the results in the previous work as mentioned in the Introduction2PPh2 and HS2P(o-CF3C6H4)2 in the AnIII/LnIII separation. Nevertheless, it is worthwhile conducting an experimental investigation on the bonding covalency between the trivalent actinides and dithiophosphinate ligands with different substituents in the future.A combination of the S 4.4.1.4][S2PR2] suitable for X-ray diffraction characterization were obtained by recrystallization from a 1:1 acetonitrile/toluene solution under air and at ambient conditions, according to the procedure reported in the literature 2(EtOH) complexes, two dithiophosphinate ligands in the ammonium form [NH4][S2PR2] were used according to the previous procedures 2(EtOH)UO. Single crystals of the UO2(S2PR2)2(EtOH) complexes were prepared using the reported procedures with slight modifications  in ethanol (2 ml) was mixed with a solution of UO2Cl2 (35.8 mg) in ethanol (2 ml). The mixture was stirred for 20 min at 70°C to give an orange solution. A slight white precipitate was generated over the course of the reaction. The orange solution was taken to dryness and the resulting complexes were extracted from the white–orange solid residue with dichloromethane (10 ml). The products were recrystallized after solvent removal from 1.5 ml ethanol. After one week, single crystals suitable for X-ray diffraction were obtained at room temperature. IR : 923 (asymmetric O=U=O stretching), 559 (symmetric PS2 stretching), 631 (asymmetric PS2 stretching). Raman (cm−1): 840 (symmetric O=U=O stretching). Anal. Calcd for C26H26O3P2S4U: C, 38.33; H, 3.22. Found: C, 38.24; H, 3.27.2[S2P(o-CF3C6H4)2]2(EtOH)]·EtOH[UO. Single crystals of UO2[S2P(o-CF3C6H4)2]2(EtOH) were prepared as described above for UO2(S2PPh2)2(EtOH) from [NH4][S2P(o-CF3C6H4)2]  and UO2Cl2 (35.4 mg). IR : 930 (asymmetric O=U=O stretching), 560 (symmetric PS2 stretching), 646 (asymmetric PS2 stretching). Raman (cm−1): 845 (symmetric O=U=O stretching). Anal. Calcd for C32H28F12O4P2S4U: C, 33.93; H, 2.49. Found: C, 33.79; H, 2.66.4.2.4][S2PR2] and UO2(S2PR2)2(EtOH) complexes were collected on a Rigaku Super Nova, Dual, Cu at zero, AtlasS2 diffractometer. The measurements were performed with Cu/Kα (λ = 1.54184 Å) or Mo/Kα (λ = 0.71073 Å) radiation. All crystals were kept at 173 K during data collection. Data collection and reduction were carried out in CrysAlisPro, Version 1.171.39.46 2(EtOH) are available through the Cambridge Crystallographic Data Centre (CCDC 2103608–2103611).The single-crystal X-ray diffraction data for [PPh4.3.K-edge XAS measurements were conducted on beamline 4B7A of Beijing Synchrotron Radiation Facility (BSRF) over an energy range from 1750 eV to 6000 eV. The energy of the electron beam is 2.5 GeV in the storage ring with a maximum beam current of 250 mA. A beam spot tightly focused at a sample is about 1.5 mm × 0.4 mm, and the measured flux is over 3 × 1010 photons s−1 (250 mA)−1  was used as the transition intensity.Background subtraction and normalization of S and P 4.4.Amsterdam Density Functional (ADF 2019) program 2(EtOH) complexes  I, II, III, IV. DOI: 10.1107/S160057752101198X/yw5001Isup2.hklStructure factors: contains datablock(s) I. DOI: 10.1107/S160057752101198X/yw5001IIsup3.hklStructure factors: contains datablock(s) II. DOI: 10.1107/S160057752101198X/yw5001IIIsup4.hklStructure factors: contains datablock(s) III. DOI: 10.1107/S160057752101198X/yw5001IVsup5.hklStructure factors: contains datablock(s) IV. DOI: 10.1107/S160057752101198X/yw5001sup6.pdfSupporting information: Tables S1 and S2; Figures S1 to S7. DOI: 2103608, 2103609, 2103610, 2103611CCDC references:


Attention deficit and hyperactivity disorder (ADHD) is a widespread neurodevelopmental disorder that begins in childhood and has negative consequences throughout adult life. The etiology and pathogenesis of ADHD are still unclear. Tau protein is a soluble microtubule-related protein expressed by neurons and localized in the cytoplasm as well as axons. Tau protein provides stability of microtubule in two ways: phosphorylation and isoforms. The excessive phosphorylation of Tau separates the protein from the microtubule, thus making it unstable. In this study, we aimed to investigate whether there is a relationship between serum Tau protein and phospho Tau This study included 26 male children aged 7–12 years with newly diagnosed ADHD, who had previously not used any medication for ADHD, and 26 male healthy children. Serum Tau and p-Tau181 concentrations were performed by enzyme-linked immunosorbent assay (ELISA).  In patients, the Tau levels were not significantly different from those of the controls; the p-Tau181 levels were significantly higher than those of the controls.  We concluded that high p-Tau181 might be associated with the progression of ADHD and cognitive changes in ADHD.  Attention deficit and hyperactivity disorder (ADHD) is one of the common neuropsychiatric disorders of childhood. It is a lifelong disorder that causes significant loss of functionality in many areas. It negatively affects the quality of life of the patient and family, and therefore the society . ADHD is defined as the inability to sustain attention and/or symptoms of hyperactivity and impulsivity that are more severe, continuous, or frequent compared to individuals of similar age and developmental levels [3].Neuropsychiatric disorders, such as ADHD, are quite common in the population. Although they may stand to express modifications in brain duty, they are not defined by clear neuropathology and the underlying biochemical conditions are mainly unrecognized. In other words, the etiology and pathogenesis of ADHD are still unclear [4]. Therefore, new research to explore ADHD is presently ongoing. Tau is a soluble microtubule-associated protein required for polymerization and stabilization of the microtubular cytoskeleton [5]. It is placed in the brain and spinal cord, primarily in axons. Tau protein undergoes many posttranslational modifications, and the most important is phosphorylation, which regulates both normal and pathological functions of Tau. Over phosphorylation reduces Tau (p-Tau) affinity for microtubules, damaging normal functions and decreasing biological activity [6]. p-Tau separates from the microtubule and thus destabilizes it [7]. The neuron loses viability as a result of the cytoskeleton and cellular transport disruption. Tau is released into the cerebrospinal fluid (CSF) and plasma by disrupting cell integrity. Therefore, CSF and plasma Tau/pTau levels can be used as a marker in the diagnosis of some diseases and in determining the severity of axonal damage [8].There are few studies on psychiatric disorders related to Tau and pTau pathology. In a study, serum total Tau and pTau levels were found to be low in schizophrenia [9]. Studies in rats showed that chronic stress triggers Tau hyperphosphorylation [10]. In another study, autopsy samples were examined and Tau was shown to be higher in suicide cases [11]. Total Tau levels were found to be significantly lower in children with autism spectrum disorder compared to the control [12]. These findings suggest that Tau and pTau may also play a role in the pathogenesis of psychiatric disorders. Information on serum Tau and phospho Tau (p-Tau181) protein levels in ADHD and its role in the pathogenesis of the disorder is very weak. There is a study in the literature on ADHD, and only serum Tau levels were evaluated, not pTau, and they were found to be high in the patient group [13]. Differently, in the present study, we aimed to evaluate both serum total Tau and p-Tau181 levels in ADHD.This study was performed in the Biochemistry and Child and Adolescent Psychiatry departments of Erciyes University Medical Faculty. Twenty-six male children aged 7–12 years, with newly diagnosed ADHD, who had formerly not taken any medication for ADHD were included. They were examined at the Child and Adolescent Psychiatry Department, between August 2017 and July 2018. ADHD was diagnosed by two child and adolescent psychiatrists based on the DSM V-TR diagnostic criteria and the short form of the Conners’ Parent Rating Scale.  Twenty-six healthy male children aged 7–12 years who applied to the healthy children polyclinic in the Department of Pediatrics were selected for the control group. These children were included in the study after confirming that they did not have any acute or chronic diseases  and that they did not have any psychiatric disorders (a detailed psychiatric examination was performed by two child and adolescent psychiatrists). Written consent was obtained from the parents of children to participate in the study. 181 concentrations.Laboratory measurements were performed in the Department of Biochemistry. After an overnight (12 h) fast, blood samples were collected from control and patient groups into tubes without anticoagulants using the venipuncture technique. The samples were enabled to clot for 30 min and then centrifuged at 2000 rpm for 10 min as usual. Hemolyzed and lipemic serums were excluded. The aliquoted serum samples were stored at –80 °C for the measurement of Tau and p-TauSerum Tau protein and p-Tau181 concentrations were measured by enzyme-linked immunosorbent assay (ELISA) kits, Sunred and Elabscience, respectively. These kits were based on biotin double antibody sandwich technology to determine human Tau and phospho Tau levels in serum, plasma, CSF, urine, and other body fluids; measurements were performed as per the manufacturer’s instructions. The absorbance of each well was measured under 450 nm wavelength. According to the standard concentrations and their corresponding absorbances, we determined the Tau protein and phospho Tau concentrations by calculating the standard curve nonlinear regression equation.p-value less than 0.05 was considered statistically significant.Statistical analysis was performed using the TURCOSA  software. The normality of the data was evaluated using the Shapiro–Wilk normality test and Q–Q graphs. Data were expressed as the number for categorical variables and mean ± SD or median (25th–75th percentile) for continuous variables. Age comparison between the two groups was performed using the Independent Samples t-test. In terms of Tau and phospho Tau, comparisons between groups were performed with the Mann–Whitney U test. The relationship between Tau and p-Tau was evaluated with the Spearman correlation analysis. A The ethical and methodological aspects of this investigation were approved by the Institutional Review Board of Erciyes University Medical Faculty. Written informed consent was provided by the participants to join this study. We confirm that all the methods were performed in compliance with the relevant guidelines and regulations (Project No: TTU-2017-7506).p = 0.664). Our study involved 26 male children with ADHD and 26 male controls. The mean age of the controls was 9.08 ± 1.92 and the mean age of the children with ADHD was 8.85 ± 1.89 . p-Tau181 levels in children with ADHD were found to be statistically significantly higher than those in controls (p = 0.046) .Total Tau and p-Tau181 levels (r = –0.435 p = 0.026), while this correlation was not found in the ADHD group (p = 0.584).In the control group, Tau levels were significantly negatively correlated with p-Tau181 levels can be used as a marker in the diagnosis of some diseases and in determining the severity of axonal damage [6–8]. Although plasma/CSF levels have been evaluated in many neurological diseases, there are limited studies in psychiatric disorders. Previous studies have suggested that CSF and plasma Tau/p-TauAs we mentioned above, there is a study on ADHD in the literature. In this study, only serum total Tau levels were evaluated, not pTau, and were found to be high in the patient group. This finding was interpreted as that ADHD may share a common mechanism with other diseases in terms of tau pathology and may indicate a disturbance in microtubule transport in the brain in this disorder [13].In this study, we found no statistically significant difference in Tau levels between the patient and control groups. However, the numerical differences in Tau levels (lower in ADHD) suggest that there may be a relationship between Tau and ADHD by an as yet unknown mechanism. A protein produced in the central nervous system can also be detected in serum, but its concentration is approximately 10-fold lower compared to that in CSF. One explanation for this condition is that it leaks into the circulation due to the protein gradient through the impaired blood-brain barrier (BBB) [14]. The low serum Tau levels in our study may be due to the small amount of penetration of this protein through the BBB into the serum. On the other hand, Tau levels may be positively correlated with the number of intact neurons. Tau concentration may vary depending on the dynamic balance between CSF and serum leakage and clearance. Therefore, less disabled patients with a larger number of intact neurons secrete more Tau than patients with pronounced brain atrophy. The numerical higher Tau levels in the control group may be due to this reason. To validate these numerical results, these preliminary findings should be confirmed in an independent and large group of ADHD. 181 levels were significantly higher in the patient group compared to the controls. While there was no correlation between Tau and p-Tau181 levels in the patient group, we found a negative correlation in the control group.We found that serum p-Tau181 protein reflects neuronal damage and leaks into CSF and serum after axonal damage [14]. The increase in p-Tau181 concentration in ADHD can be explained by axonal damage. It is possible for Tau protein to undergo abnormally increased phosphorylation in ADHD, as it has been detected in neurological diseases. The results of our study appear to support that microtubule pathology may play a role in ADHD pathogenesis. It is known that p-Tau181 in the control group can be interpreted as that the biochemistry of Tau may be more complex, and that the level of Tau attempts to balance changes in p-Tau181 levels within certain limits.However, the negative correlation between serum Tau and p-Tau181 is a promising potential serum biomarker for the detection of ADHD pathology. Validation of the data obtained with this small-scale study will be possible by supporting larger-scale and well-controlled studies.As it was mentioned above, identifying protein markers in blood has also a few benefits over CSF as it is easily obtained from children patients in the clinic. Therefore, the concept of blood-based biomarkers for ADHD is attractive and can be put into use in many areas, including screening, diagnosis, risk assessment, and supporting drug development in clinical trials. Until now, these two proteins have been evaluated mainly as CSF-based markers in clinical studies [15–19]. These results provide valuable information that serum p-Tau181 levels were not explored. The uncovered ranges in the serum Tau and p-Tau181 protein level could contain kinetic alterations among different patients. Nonetheless, this work shows the elementary proof that the serum p-Tau181 level could operate as a supplementary marker to support the early diagnosis of ADHD and the proper estimation of the consequence of ADHD. Importantly, due to the lack of equidistant studies in the literature, it is hard to measure or note the similarities or dissimilarities of our study.There are a few restrictions on this work. Firstly, the patient number was low; therefore, there was not enough statistical force in the investigation. Secondly, the subjects consisted of only male children, female children were not evaluated. Thirdly, kinetic serum Tau and p-Tau181 had a natural aptitude or skill for separation between patients and controls from ADHD. Therefore, serum p-Tau181 may serve as a predictive protein marker for ADHD patients and may also be used as a prognostic marker during follow-up of patients in the future. In conclusion, the serum p-TauThe ethical and methodological aspects of this investigation were approved by the Institutional Review Board of Erciyes University Faculty of Medicine. Written informed agreements were supplied by the subscribers to participate in this study.


Rationale: Both attention deficit-/hyperactivity disorder (ADHD) and alcohol use disorder (AUD) are accompanied by deficits in response inhibition. Furthermore, the prevalence of comorbidity of ADHD and AUD is high. However, there is a lack of research on whether the same neuronal subprocesses of inhibition  exhibit deficits in both psychiatric disorders.Methods: We examined these three neural subprocesses of response inhibition in patient groups and healthy controls: non-medicated individuals with ADHD , recently detoxified and abstinent individuals with alcohol use disorder , and healthy controls . A hybrid response inhibition task covering interference inhibition, action withholding, and action cancellation was applied using a 3T functional magnetic resonance imaging (fMRI).Results: Individuals with ADHD showed an overall stronger hypoactivation in attention related brain areas compared to AUD or HC during action withholding. Further, this hypoactivation was more accentuated during action cancellation. Individuals with AUD recruited a broader network, including the striatum, compared to HC during action withholding. During action cancellation, however, they showed hypoactivation in motor regions. Additionally, specific neural activation profiles regarding group and subprocess became apparent.Conclusions: Even though deficits in response inhibition are related to both ADHD and AUD, neural activation and recruited networks during response inhibition differ regarding both neuronal subprocesses and examined groups. While a replication of this study is needed in a larger sample, the results suggest that tasks have to be carefully selected when examining neural activation patterns of response inhibition either in research on various psychiatric disorders or transdiagnostic questions. Impulsivity can be seen as a failure to withhold or stop a response while being aware of negative consequences . In ADHD, impaired response inhibition has been observed in all three subprocesses. Hypoactivation during a Go-/No-Go task was observed in frontal, parietal and subcortical areas . While pIn AUD, subprocesses of response inhibition are impaired as well. While performing a Go-/No-Go task, hypoactivation in frontal, parietal and subcortical areas were observed , 40. In via increased recruitment of brain regions (4a) in subcortical regions for AUD, and (4b) in visual and dorsal attention regions for ADHD.The current study aims to identify differences in clinical populations (ADHD and AUD) and compared to healthy controls (HC) regarding task-related neural activation during three subcomponents of response inhibition using the HRI task. (1) We expected a poorer behavioral performance in individuals with AUD or ADHD in contrast to HC. (2) We aimed to replicate previous findings in HC on common but also distinct neural activation patterns for all three subprocesses. (3) A differential neural response, such as hypoactivation in frontal, parietal or subcortical brain regions, in patients (AUD and ADHD) compared to HC was expected for (3a) all subprocesses of response inhibition but (3b) to be more pronounced for action withholding and action cancellation. (4) Comparing individuals with AUD to those with ADHD, we expected compensatory regulation From October 2014 to June 2017, 57 individuals participated in this study. All patients were recruited at the Central Institute of Mental Health . Healthy controls answered to public announcements or were contacted using a list containing participants from previous studies that gave written consent about being contacted for further studies. In accordance with the Declaration of Helsinki, written informed consent was provided by all participants. The Ethics Committee of the University of Heidelberg approved the study (No. 2013-530N-MA).https://www.awmf.org/leitlinien/detail/ll/028-045.html). Besides structured interviews, school records and informant's ratings were used for the diagnostic procedure, if available. Individuals with ADHD were included, if they were medication free  prior to study participation. A diagnosis of ADHD in the AUD group and vice versa was ruled out following the same procedure. After group allocation, sociodemographic information was collected, and all participants filled out ADHD- and AUD-related and other questionnaires and took part in the fMRI assessment. For questionnaires, in- and exclusion criteria, see All volunteers were screened for study in- and exclusion criteria. Regardless of the later group allocation, all individuals were excluded for any mental disorders within 1 year prior to participation, such an anxiety, depressive disorders, or a lifetime diagnosis of delusional disorders, schizophrenia, or bipolar disorder irrespectively of their group allocation. A possible history of oppositional defiant disorder, conduct disorder and other diagnoses during childhood were not examined in our adult study sample. Intake of other psychotropic medication  led to an exclusion of the study. Current and steady use of selective serotonin reuptake inhibitors and the diagnosis of a mild depressive episode were tolerated for patients (AUD or ADHD). All volunteers underwent a questionnaire-based screening for ADHD and AUD. Depending on the suspected diagnosis, individuals followed a subsequent diagnostic procedure: Individuals with AUD were diagnosed by trained masters- or medical-degreed personnel of the Department of Addictive Behavior and Addiction Medicine according to the International Classification of Diseases . ParticThe HRI fMRI task implemented in this study was already used and validated before . The com*-weighted echo-planar images were acquired per run in 8:48 min . Additionally, a 5:21 min anatomical scan was performed to acquire a T1-weighted 3D MPRAGE dataset . Images were presented to participants using MRI Audio/Video Systems goggles  with integrated lenses if necessary. Task presentation and data recording were realized using Presentation® software .Functional MRI data was acquired using a 3T whole-body tomograph . For the HRI task, 217 T2The fMRI data was preprocessed and analyzed using SPM8 . Field maps were used to control for magnetic field inhomogeneity. To ensure a steady state longitudinal magnetization, the first five volumes of each run were excluded. The remaining images (212 images for each HRI-run) were spatially realigned to the first image of the first HRI-run to correct for head motion over the course of the three runs. Further, slice-time correction was performed. The anatomical image was normalized to a MNI  EPI template. Images were smoothed with an isotropic Gaussian kernel . Images from all three runs were used for the following first- and subsequent second-level analyses, e.g., for creating contrasts according to our study aim.Statistical analysis on the first level (single subject) was performed by fitting a linear regression model  on a voxel-to-voxel basis. Realignment parameters were included as regressors of no interest. Stick functions at stimulus onset were used to model all events. Further, a convolution with the canonical hemodynamic response function was performed and a high-pass filter (cut-off at 128 s) was used. The following contrasts were used for further analyses: incongruent go > congruent go (interference inhibition), no-go > congruent go (action withholding) and stop > congruent go (action cancellation), correct reactions each. Additionally, the contrasts action withholding > interference inhibition, action cancellation > interference inhibition, action cancellation > action withholding were modeled. A quality check was performed, and all datasets were screened for successful preprocessing steps. Individuals with artifacts or excessive head movement (> 3 mm/3°) were excluded from the subsequent analysis. Please see t-test, including HC only, was performed for all three contrasts to assess the task effects. A full factorial model, including all three participant groups, was chosen for each contrast of interest separately, to test for group effects . Regarding differences between subprocesses and groups, three additional full factorial models were estimated, using the contrasts action withholding > interference inhibition, action cancellation > interference inhibition, action cancellation > action withholding. On significant, anatomical region per contrast and group comparison was chosen and anatomical brain masks were created using the WFU_Pick_Atlas  implemented in SPM. In order to demonstrate group differences eigenvariates were extracted within these masks and ANOVA and corresponding post-hoc tests were used to confirm significant group differences. Results are displayed using bar graphs. In order to examine brain-behavior relations, we performed a multiple regression analysis including the imaging data of the overall sample. Following the significant group differences on the behavioral level, the rate of commission errors was chosen as a covariate. Correspondingly, we used imaging data from the first level contrast for action withholding. Relevant frontal, motor and subcortical regions were identified and anatomical brain masks were created, again using the WFU_Pick_Atlas  implemented in SPM. In order to demonstrate the relation between neural activation during action withholding and the rate of commission errors, eigenvariates from the first level results from all individuals were extracted within these masks. Results are displayed using scatter plots.A one-sample www.afni.nimh.nih.gov/) a cluster-defining primary threshold  in combination with a cluster-extend threshold of k ≥ 460 (group comparisons) or k ≥ 452 (HC only) was applied. The average smoothness in each direction was extracted from the SPM residuals using the individual SPM.mat files. A Gaussian-shaped spatial auto-correlation was modeled with estimated smoothness specified from each dimension  separately. The spatial resolution used for the GLM was 2 × 2 × 2. A whole brain mask as a union of the SPM templates “EPI.nii” and “brainmask.nii” was used. Results were visualized using MRIcro  was set to 0.05. Using 10,000 Monte Carlo Simulations and an automatic estimation of smoothness in AFNI's 3dClustSim  ensuring the quality of our diagnostic and group allocation procedure. Groups did not differ in their number of days of abstinence (P > 0.05) and regarding gender, marital status, number of children, education, employment (P > 0.05). However, each group included more men than women. Individuals with ADHD were significantly younger than AUD . Sample characteristics  and corresponding statistics are displayed in P > 0.05; see F = 3.77, P = 0.038; see post-hoc tests did show significant differences between ADHD and HC. In direct comparison between ADHD and HC, ADHD showed a higher stop-signal reaction time . No significant differences were observed regarding all variables for the comparison between patients  and HC, besides patients exhibiting a higher stop-signal reaction time  might contribute to not observing the exact same regions as reported previously. In direct comparison to a study using the same combined task (Regarding hypothesis (1), we observed a shift in activation from motor to visual to striatal areas regarding interference inhibition, action withholding and action cancellation. hibition . Regardifindings . Furtherfindings . In addined task , our resned task . OverallInterference inhibition did not lead to significant differences between ADHD or AUD and HC, although previous studies reported hypoactivation in networks related to attention, such as the anterior cingulate cortex, sensory and parietal regions (Contrary to our hypothesis (3a), we did not observe differences between patient groups and healthy individuals regarding all subprocesses of response inhibition.  regions , for ADH regions .per se. Secondly, compared to HC, differences in neural activation were observable not only regarding patient groups and made them distinguishable in their neural activation, but also regarding action withholding and action cancellation.However, regarding (3b), we detected differences in neural activation during action withholding and action cancellation when comparing patient groups to HC. With increasing demand in response inhibition, differences between groups and subprocesses became more apparent. Firstly, this might underline a differentiation between interference inhibition and motor inhibition (action withholding and action cancellation) action withholding in ADHD compared to HC, hypoactivation in parietal regions while successfully withholding a response has been reported previously for individuals with ADHD  or as compensatory mechanisms (when maintaining an adequate level of performance). However, and possibly due to lack of power following the small sample size, we might have missed to observe a subcortical neural compensation or regulation of motor region activity. Also, facing contradictory results and with respect to differences in task administration, further studies are urgently needed.Relating to hypothesis , we obseDirectly contrasting AUD and ADHD, healthy controls are needed to ascertain if differences in neural activation derive from hypo- or hyperactivation in individuals with AUD or ADHD, respectively.interference inhibition, we observed stronger activation when contrasting AUD against ADHD in superior parietal regions. Previously, hypoactivation in parietal regions in individuals with ADHD have been observed during interference inhibition, supporting the hypothesis of a malfunctioning of selective attention networks. Consequently, this leads to impaired response selection—an early subprocess of response inhibition  compared to earlier processes (interference inhibition and action withholding). These group- and subprocess-specific profiles might indicate deficits in regions relevant for attention and cognitive control, as discussed above. Additional research, also due to here present study limitations, is needed to further elaborate and characterize these profiles.Even though all participants were carefully selected, few reported medication or current cannabis intake only afterwards, which led to exclusion from subsequent analyses. Furthermore, some participants did not return the completed questionnaires. In addition, few participants were not able to complete the fMRI experiment or showed heavy movement in the scanner and had to be excluded from the analysis. Taken together, this resulted in a reduced number of participants in the analysis. However, neural findings can still be discussed in case of small sample sizes . The smaADHD in combination with AUD may aggravate inhibitory deficits. Causal explanations of the relation of ADHD and AUD or preclinical impairments in response inhibition preceding or influencing higher rates of comorbidity in AUD and ADHD still have to be addressed. Further studies need to examine comorbid individuals not only including a larger sample and more trials per run in a task—but also following a longitudinal design, assessing current ADHD or markers of impulsivity. Additionally, a replication of the present study in a larger sample is highly recommended.per se be considered, but also the inhibitory deficits through medication or behavioral training should be addressed specifically. It has been previously shown for atomoxetine that such interventions may reduce alcohol consumption and extend the time of abstinence  when examining consequences of impaired response inhibition or administering preclinical screening measures. It was observed in young heavy drinkers that only deficits in action cancellation were related to an increase in binge drinking in females . While iNeural differences were observed with respect to both the subtask of the neuroimaging paradigm and the examined groups. We observed differential neural activation in subprocesses of response inhibition when comparing individuals with ADHD and AUD patients, and healthy controls. While no effects have been shown in interference inhibition, motor inhibition seems to be more fitting for the characterization of functional neural deficits in ADHD and AUD patients. Our experimental design also revealed distinguishable neural activation patterns for individuals with ADHD and AUD even though impaired response inhibition is seen as a common phenotype of ADHD and AUD. Over all subprocesses, individuals with ADHD consistently showed hypoactivation, indicating compensatory and deficient alterations in neural subcomponents of response inhibition. Individuals with AUD, however, showed hyperactivation in earlier and hypoactivation in later subprocesses of response inhibition, suggesting that different neural subprocesses lead to similar impulse control disorders in the two patient groups. Keeping the limitation of the small sample size in mind, our results might indicate rather attention related alterations in individuals with ADHD and rather motor control related alterations in individuals with AUD compared to HC, respectively. This is relevant for the diagnosis and, even more important, for the treatment of the patients. Based on our findings, further studies should characterize these specific effects in these patient groups in more detail while also examining a larger sample.The data that support the findings of this study are not publicly available due to containing information that would compromise research participant consent. We will provided data upon direct request by research colleagues following current data protection guidelines.The studies involving human participants were reviewed and approved by Ethics Committee of the University of Heidelberg, Germany . The patients/participants provided their written informed consent to participate in this study.SG: investigation, data curation, formal analysis, writing-original draft preparation, and visualization. ML: conceptualization, supervision, project administration, and writing (review and editing). JMB: writing (review and editing). ES: conceptualization, project administration, and writing (review and editing). FM: writing (review and editing). FK: conceptualization, resources, funding acquisition, and project administration. SV-K: conceptualization, resources, project administration, funding acquisition, data curation, formal analysis, and writing (review and editing). All authors revised the manuscript critically for important intellectual content and approved the final version.This project was supported in part by grants from the Deutsche Forschungsgemeinschaft .ML has received honoraria as a speaker and for participation in advisory boards from MEDICE/Arzneimittel Pütter GmbH and Shire/Takeda, and travel costs from Shire/Takeda. ES has received speaker's honoraria from Shire and MEDICE/Arzneimittel Pütter GmbH. FM has received speaker's honoraria from Janssen. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Bartonella infections in small rodents in the Zhongtiao Mountain, China have been explored. In this study, the liver, spleen and kidney tissues of captured rodents were used for Bartonella spp. detection and identification by combination of real-time PCR of transfer-mRNA (ssrA) gene and traditional PCR and sequencing of citrate synthase (gltA) gene. It was shown that 49.52% of the rodents (52/105) were positive for Bartonella spp.. The infection rate in different gender  and tissues  of small rodents did not have statistical difference, but that in different small rodents  and habitats  had statistical difference. And, the sequencing data suggests that Bartonella sequences (n = 31) were identified into three species, including 14 of B. grahamii, 3 of B. queenslandensis and 14 of unknown Bartonella species. Phylogenetic analysis showed that B. grahamii sequences were clustered with the isolates from South Korea and China, and B. queenslandensis sequences were mainly closely related to the isolates from China and Thailand. The genetic diversity analysis showed that B. grahamii and B. queenslandensis sequences exhibited noticeable intraspecies diversity. Taken together our data demonstrates the high prevalence and genetic diversity of Bartonella infections in small rodents in the Zhongtiao Mountain, especially a potential novel Bartonella specie was detected, which could benefit the prevention and control of rodent-Bartonella species in this area.The prevalence and molecular characteristics of  Bartonella species are an emerging family of vector-borne, facultative, gram-negative, haemotrophic bacteria, known to infect mammalian erythrocytes and endothelial cells and might cause human Bartonellosis [Bartonella identified, and 14 of them are known to be zoonotic [nellosis . There azoonotic . They hazoonotic . Humans zoonotic , and atyzoonotic –8.Bartonella was first isolated from HIV patients in 1993 [Bartonella species have been recognized as human pathogens, such as B. bacilliformis [B. quintana [B. henselae [B. elizabethae [B. clarridgeiae [B. koehlerae [B. vinsonii subsp. Arupensis [B. vinsonii subsp. Berkhoffii [B. grahamii [B. rochalimae [B. tamiae [B. ancashensis [B. washoensis [Bartonella have been carried out successively in various countries around the world, including the United States [Bartonella in rodents varies greatly in different countries and regions, making it necessary to investigate the rodents-associated Bartonella infection in different areas.Since liformis , B. quinquintana , B. henshenselae , B. elizzabethae , B. clarridgeiae , B. koehoehlerae , B. vinsrupensis , B. vinsrkhoffii , B. grahgrahamii , 19, B. chalimae , B. tami. tamiae , B. ancaashensis , B. washshoensis , and theshoensis . In reced States , Europe d States , Asia [2d States , 28, Afrd States , Latin Ad States  and Ocead States . These sBartonella infection also increased. However, investigations of Bartonella species in small rodents in this area have not been reported. Given this, this study was designed to explore the prevalence and genetic diversity of Bartonella species in small rodents in the parts of Zhongtiao Mountain, in an effort to provide the necessary scientific information to help support the creation of a plan for the control and prevention of Bartonella infection in humans in this area.The Zhongtiao Mountain, is one of the major mountains, in the southern Shanxi Province of China, with an average elevation of 1200–2300 m, spread across the three cities of Linfen, Yuncheng and Jincheng. Here, we selected Pingquan Village within the Yangcheng County of Jincheng City as the rodents trapping site, as it is located at the east of Zhongtiao Mountain, and 30 km from the Manghe National Nature Reserve. Pingquan Village, as the Red Tourist Spot, with the development of tourism, the direct or indirect contact between humans and rodents increased, and the risk of transmission of rodents-associated This study was approved by the Ethics Committee of Changzhi Medical College (No: DW2021052). All animals were treated according to the Guidelines of Regulations for the Administration of Laboratory Animals  and the Guidelines for Treating Animals Kindly from Ministry of Science and Technology of the People’s Republic of China. All efforts were made to minimize discomfort to the animals.Small rodents were captured using snap traps from Pingquan Village  within Yangcheng County of Shanxi Province during May 2021. The trapped rodents were identified by morphology and DNA barcoding via the Cytochrome C oxidase subunit I (COI) gene. The liver, spleen and kidney tissues were then harvested under sterile conditions from each rodent after euthanasia, and stored at -80 °C until use.Bartonella transfer-mRNA (ssrA) gene. DNA amplification was performed in 20 μL mixtures containing 10 μL HR qPCR Master Mix , 5 μL double-distilled H2O, 0.8 μL (10 μmol/L) of each primer and 0.4 μL (10 μmol/L) probe  [ssrA amplification was performed under the following conditions: one cycle for 5 min at 95 °C; 40 cycles for 15 s at 95 °C, 45 s at 60 °C, and positive and negative control were set.DNA was extracted from approximately 10 mg of each liver, spleen and kidney tissues using the TIANamp Micro DNA Kit (TIANGEN Biotech (Beijing) Co., Ltd., China) as directed by the manufacturer. Real-time PCR was performed to detect the CG-BHQ1)  and 3 μLssrA gene positive samples, Bartonella citrate synthase (gltA) gene amplification was further performed. DNA amplification was performed according to the manufacture’s protocols of TaKaRa PCR Amplification Kit  in 20 μL mixtures containing 2 μL 10 × PCR buffer, 1.6 μL dNTP mix, 0.1 μL Taq, 13.5 μL double-distilled H2O, 0.4 μL (10 μmol/L) of each primer  for sequencing.For AGTAAACA ), and 2 Bartonella species sequences in the GenBank using the BLAST program available from the National Center for Biotechnology Information website (http://blast.ncbi.nlm.nih.gov/Blast.cgi). Besides, the gltA sequences of B. grahamii and B. queenslandensis in GenBank released before July 2021 were collected for traceability analysis of these two Bartonella species in our study. For the sequences isolated of the same strain from the same host in the same laboratory at the same time, we randomly selected a sequence as the reference. Phylogenetic tree was created using the maximum-likelihood method with MEGA version 7.0, and bootstrap values were calculated with 1000 replicates [Brucella abortus was used as the outgroup.The sequences generated in this study were submitted to the GenBank (accession numbers: MZ672181-MZ672211). The nucleotide sequence homology was blasted against reported plicates , 35. Bruk) and the haplotype diversity (Hd), were analyzed using DNASP 5.10 software. We used a sliding window interval of 25 bp to determine which segment of the target gene sequence had the highest nucleotide diversity (π) by analyzing 100 bp at a time across the length of the gene.The polymorphism of nucleotide sequences, including the number of polymorphic sites (S), the number of haplotypes (H), the nucleotide diversity (π), the average number of nucleotide differences . P < 0.05 was considered statistically significant.The positive rates of Apodemus agrarius (32), Eothenomys inez (28), Apodemus draco (19), Mus musculus (14), Niviventer confucianus (9), Apodemus peninsulae (1), Rattus tanezumi (1), and Tscherskia triton (1).In total, 105 small rodents were captured and identified into eight species, including Bartonella infection by qPCR, with an infection rate of 49.52% (52/105). The animals were classified into six species (A. agrarius (26/32), E. inez (10/28), A. draco (5/19), M. musculus (1/14), N. confucianus (9/9), A. peninsulae (1/1)), and the difference of positive rate among these species was shown to be statistically significant    . Unfortu= 0.890) .χ2 = 0.079, P = 0.778). There were 17 small rodents from three species captured in villages, with a Bartonella infection rate of 23.53% (4/17). 88 small rodents from seven species were captured in the returning farmland to forests (RFF), with an infection rate of 54.55% (48/88). Thus, the Bartonella infections rates in the rodents from different habitats were significantly different   in males and 48.33% (29/60) in females, which was not statistically significant (= 0.019) .gltA sequences identified from 31 animals positive for Bartonella, 18 small rodents (58.06%) had the sequences in more than two tissues. The DNA sequence homology and phylogenetic analyses of the gltA gene indicated that three Bartonella species were detected in the liver, spleen and kidney of these small rodents, and the Bartonella species detected in different tissues of each small rodents were consistent. It was shown that 14 sequences were B. grahamii with 96.32–99.68% identity, including nine sequences from A. agrarius, three sequences from N. confucianus, one sequence from A. draco and one sequence from A. peninsulae. Three sequences from N. confucianus were B. queenslandensis with 97.94–99.42% identity. And, 14 sequences from A. agrarius were unknown Bartonella species, which shared 94.49–95.69% nucleotide sequence similarity in their gltA fragment with the nearest species of Bartonella, B. krasnovii, B. gabonensis and B. elizabethae, respectively  obtained from N. confucianus were most closely related to B. queenslandensis from N. confucianus in China and N. fulvescens in Thailand, and one B. queenslandensis sequence (NC59SXYC) creating a unique cluster that did not correspond with any of the existing B. queenslandensis reference strains  of 14 strains of B. grahamii, 3 strains of B. queenslandensis and 14 strains of unknown Bartonella species revealed that the sequences from B. grahamii and B. queenslandensis exhibited noticeable intraspecies genetic diversity (π = 0.018) with the fragment diversity being highest between 275 bp and 288 bp  [Bartonella infection is by PCR. In this study, the real-time PCR targeting ssrA gene was used for Bartonella detection. The ssrA gene has the same species identification function as the traditional gltA gene, indicating that the amplification of ssrA fragment can be used as a fast and accurate method for the detection and classification of Bartonella [Bartonella detection, with multi-tissue detection likely to capture a more accurate positive rate and produce as many sequences as possible for evaluation. We detected Bartonella species in six of the eight small rodent species identified in this study, and determined that their infection rates were significantly different. In addition, the infection rate was significantly different between different habitats, but not between genders, which was similar to the previous study [In the present study, the prevalence and molecular characteristics of  (31.4%) , Fujian  (31.4%) , Yunnan  (31.4%) , and Qin (31.4%) , 42, and (31.4%) . Currentrtonella . Here wegltA gene identified three Bartonella species in this cohort of animals, including B. grahamii, B. queenslandensis and unknown Bartonella species. B. grahamii was detected in A. agrarius, N. confucianus, A. draco and A. peninsulae, which infections are primarily associated with neuroretinitis and cat scratch disease (CSD) [B. grahamii can infect a variety of rodents, and has the ability to cause human disease in this area. In addition, B. queenslandensis was only detected in N. confucianus, and unknown Bartonella was only detected in A. agrarius. Unexpectedly, no Bartonella sequences was obtained from E. inez in this study, might be associated with the low quantity of bacteria in E. inez in this area, which still needs further investigation. These results also indicated that there is some host specificity for Bartonella in rodents, and that A. agrarius may be more susceptible to multiple species of Bartonella, which is similar to the conclusions of several other studies [Bartonella species can be detected in the same rodent species , and one Bartonella specie  can infect a variety of rodents, indicating that this pathogen has a strong adaptability in rodents.DNA sequence homology and phylogenetic analyses of se (CSD) , 19, sug studies –46. In aB. grahamii was mainly clustered into two clusters, one showing close association with the isolates from South Korean A. agrarius and the other with strains from Chinese T. triton samples. Our B. queenslandensis strains were most closely related to the isolates from N. confucianus in China and N. fulvescens in Thailand. In addition, we had one B. queenslandensis sequence that did not cluster with the reported B. queenslandensis reference strains, indicating that it might be a novel genotype. In addition, genetic diversity analysis demonstrated that B. grahamii and B. queenslandensis exhibited noticeable intraspecies genetic diversity, which was similar to the results of a previous study [Traceability analysis showed that us study .Bartonella, B. grahamii, B. queenslandensis and unknown Bartonella in four species of rodents, A. agrarius, N. confucianus, A. draco and A. peninsulae from the Zhongtiao Mountains in China. These results also showed that B. grahamii and one potential novel Bartonella species were dominant in this region and that B. grahamii and B. queenslandensis had the high genetic diversity in this area. And the biological characteristics of the potential novel Bartonella species need to be further investigated. Our study provided a better understanding of the prevalence and molecular characteristics of Bartonella species in small rodents in the Zhongtiao Mountain, which could benefit prevention and control of rodent-Bartonella species in this area.In conclusion, we identified three species of