After treatment, 70.8% of adults and 52.8% of children who began in the ASEBA Clinical range, and 80.0% of adults and 63.8% of children who began in the ASEBA Borderline range, were classified in the Normal range. IVA scores also improved after treatment. Changes in HRV and breathing pattern after treatment were consistent with the protocol. QEEG parameters after treatment were closer to the age-based normative mean, which is consistent with effective z-score NFB training.ADHD is a common condition that causes suffering for those affected and economic loss for society at large. The current standard treatment for ADHD includes stimulant medications, which are not effective for all patients, may include side effects, and can be non-medically misused. Z-score neurofeedback (NFB) and heart rate variability (HRV) biofeedback are alternative treatment strategies that have been associated with Attention-Deficit/Hyperactivity Disorder (ADHD) symptom improvement. We utilized a retrospective pre-post study design to quantify the change in clients’ ADHD symptoms after combined NFB + HRV treatment (which included simultaneous z-score training at four sites). We also assessed whether relevant physiological measures changed in accordance with the protocol, which would be consistent with effective NFB + HRV training. Adults (n = 39) and children (n = 100) with Borderline or Clinical ADHD classifications by the Achenbach System of Empirically Based Assessment (ASEBA) received 30 sessions of NFB + HRV training. Measures were compared before and after treatment for the ASEBA, the Integrated Visual and Auditory Continuous Performance Test (IVA), ADHD medication use, HRV and breathing parameters, and quantitative electroencephalogram (QEEG) parameters. Average ASEBA Attention-Deficit/Hyperactive Problems score improved after treatment for adults and children (The online version of this article (10.1007/s10484-019-09439-x) contains supplementary material, which is available to authorized users. Attention-Deficit Hyperactivity Disorder (ADHD), characterized by functional impairment due to persistent inattention and/or hyperactivity and impulsivity ,   is a specialized form of biofeedback in which participants learn to modulate their brain activity through conscious or nonconscious control via operant conditioning. During NFB training sessions, specific components of an individual’s brain activity are continuously presented to him/her as a feedback signal in real time, extracted from electroencephalogram (EEG) recordings from the participant’s scalp. Individuals are rewarded for modifying their brain activity in accordance with a specific NFB protocol Niv .NFB has been used successfully to treat individuals with ADHD . The current study seeks to assess and quantify the changes in clients’ ADHD symptoms after treatment with NFB + HRV. Before and after treatment, clients were evaluated for ADHD symptoms and behavior, ADHD medication use, physiological parameters of HRV, breathing rate, and QEEG. These pre- and post-treatment measurements were compared to evaluate whether, and the extent to which, ADHD symptoms improved after NFB + HRV treatment. We also evaluated whether the physiological parameters were significantly changed after treatment in accordance with the protocol, which would be consistent with effective NFB + HRV training.The NFB + HRV therapy sessions described in this study were performed by a private center that provides EEG NFB and Biofeedback therapies at locations in Michigan and Florida, USA. The center provides NFB and HRV biofeedback therapy for clients presenting with a variety of symptoms, including ADHD, anxiety, depression, memory concerns, migraines, sleep disturbances, and stress. Permission for this study was obtained from the New England Independent Review Board (IRB). This study is purely retrospective, rather than prospective. An IRB Privacy Board Waiver of Consent for retrospective studies was obtained for data analysis of clients who began a thirty-session NFB + HRV treatment program on or after February 15, 2017 that was completed by November 15, 2017, with all personal health information identifiers removed.Both before and after the 30 sessions of NFB + HRV, all clients included in this study took behavioral assessment tests, underwent a full-cap (19-electrode) QEEG assessment, completed an HRV and breathing rate assessment, and listed all current medications (including the frequency and dose taken for each drug). This information was used to compare client ADHD symptoms, performance on an objective attention task, physiological characteristics, and medication use before and after treatment.Two behavioral assessment tools were utilized in this study: the Achenbach System of Empirically Based Assessment (ASEBA) and the Integrated Visual and Auditory Continuous Performance Test (IVA). Each assessment tool was administered to clients both before and after the thirty-session NFB + HRV treatment protocol. These tools were also used to define the sample of clients included in the present study (see Client Demographics section). ADHD medication use was also assessed.The ASEBA symptom checklist was utilized to classify and quantify the severity of symptoms associated with ADHD and comorbid disorders. The Adult Self-Report (ASR) symptom checklist , Borderline (T = 65-69), and Clinical (T ≥ 70). The minimum possible T score as defined by ASEBA is 50 . According to the ASEBA manuals  was conducted to quantify clients’ performance on a continuous performance task  of high-frequency , low-frequency , and very-low-frequency  domains were collected, and they were expressed as percent power of each frequency band over the whole range collected (0.016–0.500 Hz).A blood volume pulse (BVP) sensor  reading from each client’s index finger was utilized to measure heart rate. These data were collected for a total of five minutes to provide for a minimum standard short-term recording  was utilized to measure respiration properties. The belt was placed around the waist of each client at the umbilicus level. The same five-minute interval used to assess HRV measurements was utilized to calculate breaths per minute.EEG activity was collected with a standard protocol using a Neuron-Spectrum-3 amplifier  at 19 electrode locations , according to International 10–20 system standards. Electro-cap surgical style caps  were fitted according to head circumference, and electrode reservoirs were filled with Electro-Gel . EEG data were collected using Neuroguide collection software , using a sampling rate of 500 Hz and keeping impedance at each site under 10 kΩ.Two five-minute EEG recordings were collected for each assessment, one with eyes closed (EC) and one with eyes open (EO), both utilizing a linked-ears reference. The Neuroguide software  was used for post-processing of the raw EEG signal, including artifact removal and conversion of the signal into frequency-based measures of absolute power and relative power in 1 Hz bins from 1–30 Hz at every location. Connectivity measures of coherence and phase lag were calculated among pairwise combinations of electrodes Thatcher .Power and connectivity measurements were compared to the Applied Neuroscience Lifespan EEG Normative Database . Variation from normed database means was expressed in z-score format, or the number of standard deviations away from the mean, at each of, or between, the 19 electrode locations. The z-scores were visually represented via “brain maps” and used to guide NFB training protocols. Although brain maps were created and consulted for both EC and EO conditions, only EC data were used for statistical analysis in the present study. On average, there were 101.5 s of artifact-free data pre-treatment and 116.6 s of data post-treatment for clients in this study.th, 2017, were Symptomatic at baseline based on their AD/H Problems T score on the ASEBA symptom checklist, completed the program on or before November 15th, 2017, and completed an EC QEEG assessment before and after the 30-session program. This initial group of individuals excluded employees and family members of employees, due to potential conflict of interest. Further exclusion criteria for this study were: total time to complete the program was fewer than six weeks or more than 24 weeks  who would score in the Borderline or Clinical range for ADHD on the ASEBA AD/H Problems scale before treatment, do not have a neurodevelopmental disorder, and who would complete the full 30-session NFB + HRV program using 4-channel z-score training. The sample for this study was all clients who scored in the Borderline or Clinical range for ADHD on the ASEBA AD/H Problems scale before treatment, and who completed the full 30-session NFB + HRV program using 4-channel z-score training during a time period that was defined prior to the start of analysis. This included both adults and children. The study initially included 149 clients who started the program on or after February 15n = 100) ranged in age from 6 to 17  and adults (n = 39) ranged in age from 18 to 51 . Potential comorbidities were assessed by the ASEBA for Depressive Disorder, Anxiety, Avoidant Personality Disorder, Antisocial Personality Disorder, Oppositional Defiant Disorder, and Conduct Disorder ; . All LMSWs underwent extensive training and mentoring by study author, LAW, a BCIA-certified professional .4-channel z-score NFB protocols were developed by LMSWs who were either BCIA-certified themselves, or who had been trained, mentored, and supervised by BCIA-certified instructors. Training locations for each client’s protocol were selected by taking into consideration the following: functional dynamics and dysregulation in the EEG/QEEG for both EC and EO conditions; knowledge of neuroanatomy and physiology; and the nature of clients’ symptoms. The Applied Neuroscience Symptom Checklist  was used as a tool for LMSWs to compare symptoms with hypothesized areas of dysregulation, in addition to their knowledge of evidence-based criteria and clinical judgement. The six sites most often utilized for training in this study were, in descending order, F3, F4, P3, P4, FZ, and PZ.At the start of each NFB + HRV session, clients spent three to five minutes on slow, diaphragmatic, paced breathing, with a goal of breathing between six and eight breaths per minute while wearing a finger BVP sensor and respiration belt. The goal of this segment was to teach the client to: 1) increase the amplitude of their RSA, and 2) create an in-phase relationship between their breathing and heart oscillations. Clients observed their breaths on a monitor, along with the fluctuations of their heart rate inter-beat interval as they interacted in real time. The technicians instructed clients to adjust their breath such that the inhalation/exhalation and the variability of the heart’s inter-beat interval began to align with each other in phase. This method of breathing is intended to increase the percentage of power in the LF band of HRV  and continued respiratory biofeedback throughout the remainder of the training session. The finger BVP sensor was removed, but the respiration belt remained in place, at the start of the movie. Respiratory biofeedback was presented to clients as a graph displaying their breathing at the bottom of the training screen to aid in maintaining a smooth, consistent breathing pattern. When clients exceeded 8.75 breaths per minute, or their breathing pace fluctuations exceeded 35% variation between breaths, the movie screen would shrink, using a transition time of 10 seconds. Average breaths per minute and %LF (for a surrogate measure of HRV) were collected at each NFB + HRV session.To prepare for 4-channel z-score NFB, technicians measured the client’s scalp for site placements using the standard International 10-20 system and cleaned those areas with NuPrep gel  to remove any oils and dead skin. They attached gold cup electrodes using Ten20 conductive paste (Weaver), placing the ground electrode along the midline, and the linked reference electrodes to the ears.Each client received a 30- to 40-minute z-score NFB session using customized site placements based on the results of their QEEG. The ProComp Infiniti (Thought Technology) and Biograph software were used to process and feed back the HRV/respiration as well as EEG signals via Neuroguide’s Dynamic Link Library  using a joint time–frequency analysis (JTFA) algorithm, allowing for instantaneous feedback.NFB training was performed simultaneously with the HRV/respiratory biofeedback mechanism described in the previous section. For NFB, the reward was presented in the form of the movie playing when a set percentage of 248 variables, being measured from the 4 channels of raw EEG, were within the targeted upper and lower z-score index thresholds, described further below. The metrics monitored at the four 10-20 EEG sites were absolute power, relative power, amplitude asymmetry, coherence, phase lag, and power ratios for the following frequency bands: delta (1–4 Hz), theta (4–8 Hz), alpha (8–12 Hz), beta (12–25), beta 1 (12–15 Hz), beta 2 (15–18 Hz), beta 3 (18–25 Hz), and high beta (25–30 Hz).The NFB training protocol required the client to keep 95% of the above 248 measurements within an adjustable range of z-score index boundaries. An 80% (± 5%) reward rate was used as an initial starting point for setting the upper and lower z-score index thresholds. Throughout the session, the client’s reward rate was monitored in real time using Biograph software. When the protocol became too “easy” (the client maintained 95% of the measurements within the set z-score threshold more than 85% of the time) or too “difficult” (the client maintained 95% of the measurements within the set z-score threshold less than 75% of the time), the targeted z-score index threshold was adjusted by trained technicians, accordingly. This reward-level guideline was augmented by therapist-monitoring of the client during the session to ensure the client was not getting bored or frustrated. Clients’ NFB progress throughout the program was monitored within the Biograph software. Average z-score values across the four channels trained were saved after every session: z-score index mean, z-score powers index mean, z-score power ratio mean, and z-score connectivity index mean. An in-depth assessment of progress was measured after 20 sessions at the four sites trained using the Biograph software. If insufficient progress was being made, the full QEEG was repeated and the NFB protocol was modified as appropriate. The ASEBA assessment was repeated at this time for roughly half of the clients to gauge behavioral improvement.T score, the primary outcome measure. This is described in more detail in the Statistical Analysis section below.In addition to NFB and HRV training, clients also underwent psychoeducation. The topics covered included exercise, diet, sleep hygiene, and deep breathing (among other coping skills) contained within the center’s curriculum book entitled the “Brain Optimization Book” (unpublished). Of the clients included in this study, 13 adults and 27 children received this psychoeducation during formal meetings with a staff social worker (before or after each session) lasting approximately 20 min each. The other clients included in the study also received psychoeducation on these topics, but not in the same format of formal meetings. The psychoeducation variable  was not included in analyses in this study, because non-significant regression models (one for adults and another for children) containing this variable as a potential confounder were not useful in prediction of change in AD/H Problems ® Enterprise Guide, Version 7.1. Copyright, SAS® Institute Inc. The line graphs were made using the software Graphpad Prism (version 5), and the stacked bar chart was made using Microsoft Excel (version 16.13.1). The box plot in Supplemental Fig. 1 was created in SAS®.The parametric statistical analyses utilized in this study were all performed with SAST scores .Although individuals of any age can have ADHD, the disorder is currently both understudied and undertreated in adults ; HRV measures (K = 4); and QEEG measures (K = 21). The Bonferroni-corrected significance level for ADHD symptom outcome measures was P-values in this study were assessed using an experiment-wise error rate of All t-tests performed for this analysis were two-sided. For all analyses, the assumption of independence is met, yet it should be noted that a negligible proportion of clients might have been biologically related. This is a retrospective sample of all clients meeting the inclusion/exclusion criteria within the specified time range. Thus, convenience sampling was utilized.T score from pre-treatment to post-treatment. The mean changes in Standard Quotient scores for the IVA were assessed using paired t-tests for those who passed both validity checks on the IVA. Mean changes in HRV parameters and breathing rate were evaluated with paired t-tests. Clients were separated by age group (child or adult). For all t-tests the normality assumption of the paired differences was satisfied by assessing sample sizes, box plots, and histograms.Paired t-tests were used to assess the mean changes in AD/H Problems T score from pre-treatment to post-treatment was defined as an improvement of at least three points. This value was determined in the following manner: the MCID for ASEBA score change is defined for two age ranges within each gender . The MCID is the Standard Error of Measure (SE Meas), calculated using ASEBA’s gender- and age-normed population statistics. To arrive at this value, the standard deviation is multiplied by the square root of one minus the test re-test reliability, The Minimal Clinically Important Difference (MCID) for improvement in AD/H Problems x would indicate that the z-scores move closer to zero by x z-scores on average, indicating normalization.Twenty-one z-score measurements taken during the 19-electrode QEEG assessment were compared before treatment versus after treatment, including absolute power, relative power, coherence, and phase lag for each frequency band: delta (1–4 Hz), theta (4–8 Hz), alpha (8–12 Hz), beta (12–25 Hz), and high beta (25–30 Hz). The theta/beta ratio (power of theta (4–8 Hz)/power of beta (12–25 Hz)) was also considered as a separate measure, despite the fact that the theta and beta bands were also considered individually. The theta/beta ratio is widely considered to be important for ADHD NFB training , and children experienced a mean decrease of 8.4  after NFB + HRV treatment. The effect sizes for these changes were large; dz = −1.21 for adults and dz = −1.17 for children. For both adults and children, the average T score after treatment was in the Normal range . 87.2% of adults and 80.0% of children improved by at least the MCID after NFB + HRV treatment. Therefore, 12.8% of adults  and 20.0% of children  did not experience improvement in ASEBA T score of clinical importance after NFB + HRV treatment. Of these 5 adults, 4  experienced no clinically important change in T score (the client’s decrease/increase in T score from pre- to post-treatment was < 3 points), and 1  experienced T score decline of clinical importance (the client’s increase in T score from pre- to post-treatment was ≥ 3 points). Of these 20 children, 15  experienced no clinically important change in T score, and 5  experienced T score decline of clinical importance.Table T score. More specifically, 70.8% of adults and 52.8% of children who began in the Clinical range were classified in the Normal range after treatment. 80.0% of adults and 63.8% of children who began in the Borderline range were classified in the Normal range after treatment. Not all clients experienced an improvement in ASEBA ADHD category after treatment: 16.7% of the adults and 22.6% of children who began in the Clinical range remained in the Clinical range after treatment  are listed in Table All clients in this study also completed the IVA Continuous Performance Test both before and after 30 sessions of NFB + HRV training. Unlike the ASEBA, which relies on either self-evaluation (ASR) or parental/guardian-evaluation of behavior (CBCL), the IVA is an objective measure of task performance. The IVA was used to assess clients’ performance on two quotients relevant to attention and response control  before and after treatment. The normative mean value for these quotients is 100, and scores between 85 and 115 are within 1.0 standard deviation of this mean. One adult and 20 children in this study failed validity checks for the IVA, suggesting that they were responding randomly to test stimuli. Therefore, these 21 clients were not included in the statistical analyses on the two IVA quotient scores presented in this section.n = 38, M = 13.2, SD = 26.0, p = 0.0035, 95% CI = ). FRCQ scores were also improved after treatment, although this was not statistically significant at the n = 38, M = 7.3, SD = 20.1, p = 0.0315, 95% CI = ). For children, there was a statistically significant increase (improvement) in mean IVA FRCQ . There was also improvement in FAQ , but this was not statistically significant  in the mean IVA FAQ  were on ADHD medication. Post-treatment, four of these individuals were no longer on ADHD medication, one reduced their daily dosage of ADHD medication, one changed their type of ADHD medication, and three had no change in their ADHD medication. No adults who were taking ADHD medication before treatment increased their daily intake after treatment. Further, no adults who did Of the children in the study, 44 (44.0%) were on ADHD medication(s) at baseline. After completing the program, ten of these individuals were no longer on ADHD medication, five reduced the daily dosage of their ADHD medication, six changed the type of their ADHD medication, 21 had no change in ADHD medication, and two increased the daily dosage of their ADHD medication. One child who was not on ADHD medication before treatment was taking ADHD medication after completion of NFB + HRV treatment.Physiological parameters for HRV and breathing rate were evaluated before and after treatment using four measures: %VLF band of the heart rate inter-beat interval power spectrum, %LF band, %HF band, and respiration rate (breaths per minute (BPM); Table SD = 12.9, p = 0.0010, 95% CI = ), the %LF band increased by 29.8 , the %HF band decreased by 18.2 , and the respiration rate decreased by 6.7 BPM to an average post-treatment value of 6.5 BPM . For children after treatment, on average, the %VLF band decreased by 4.7 , the %LF band increased by 18.8 , the %HF band decreased by 11.8 , and the respiration rate decreased by 4.7 BPM, to an average post-treatment value of 9.3 BPM . For both adults and children, all HRV and respiration rate physiological measures examined were significantly changed after treatment, and these changes were in the direction expected based on the specific HRV training protocol. These results are consistent with effective biofeedback training.Mean measures for all parameters studied were significantly changed after treatment. For adults after treatment, on average, the %VLF band decreased by 7.3 .For all 21 QEEG parameters examined, there was a consistent trend toward the normative mean (a reduction in absolute z-score) after NFB + HRV treatment Table . For 18 In this study, we evaluated a novel treatment strategy combining z-score NFB with HRV biofeedback to treat clients with symptoms of ADHD. After 30 sessions of NFB + HRV, adults and children in this study experienced both statistically significant and clinically meaningful improvement in ADHD symptoms, as evaluated by the ASEBA DSM-oriented AD/H Problems scale. The magnitudes of these changes were large, with effect sizes of −1.21 for adults and −1.17 for children. 87.2% of adults experienced clinically meaningful improvements, as did 80.0% of children. Adults and children also experienced statistically significant improvements on the IVA continuous performance test, which represents an objective indicator of a client’s ability to pay attention and inhibit unwanted responses. Adults experienced statistically significant improvements in the IVA FAQ, a broad composite scale that includes measures of speed, focus, and vigilance of auditory as well as visual input Sandford . Childrenot trained during NFB + HRV training is presented in Supplemental Table 4. As with the ‘sites trained’ QEEG results, the mean of differences was a negative value for every parameter examined, indicating a trend toward the normative mean. What is more, effect sizes (Cohen’s dz) were medium or large for all parameters and ranged from dz = −0.48 for theta/beta ratio, to dz = −2.20 for Phase Lag: Delta frequency band.We have shown that clients in this study experienced physiological changes in HRV parameters, breathing rate, and QEEG parameters after treatment that are consistent with the NFB + HRV protocol utilized. Specifically, the protocol rewarded a respiration rate of six to eight breaths per minute, thus increasing the %LF component of HRV, and used 4-channel z-score NFB to train brain oscillation parameters toward the age-based normative mean from an EEG normative database  than the overall mean reported in our analysis, while the mean improvement for those who did not invalidate was higher  than that reported in our analysis. Note, however, that mean improvement for both of these groups was well above the MCID of three points. As our target population included all individuals aged 6-59 who would be Symptomatic for ADHD , and children who may invalidate the IVA are included in this population, the authors felt it appropriate to analyze the total group of 100 children as a whole. Although these children were similar to the “included” children on most baseline measures, due to the age distribution of these two groups in our sample, caution should be used when generalizing IVA findings from this study to younger children.The present study is a detailed pre-post analysis of past client outcomes, and it therefore has the limitations associated with this type of study. The experimental design was retrospective, the sample was not random, and there was no sham-control placebo group. Because there was only one treatment condition, we also could not compare the relative importance of NFB versus HRV biofeedback. Further, 20 children were excluded from the IVA pre-post analysis due to invalid test results. Supplemental descriptive statistics including baseline similarities and differences between the 20 children who invalidated the IVA (at either timepoint) versus those who did not invalidate are shown in Supplemental Table 3. Many baseline measures were similar between these two groups of children, including comorbidities, symptom severity, and ADHD medication use. For the children in our sample, the age of those who invalidated the IVA was notably younger than those who did not invalidate. This was examined by comparing the three descriptive measures of center , Q1 and Q2 , and boxplots of the distributions of age for the groups. For all three measures of center, and both quartiles, age was lower for the group of children that invalidated the IVA. Side-by-side boxplots of age distribution can be viewed in Supplemental Fig. 1. We also observed  that mean ASEBA AD/H Problems Despite these limitations, this study utilized both ASEBA  and IVA (an objective performance test) neuropsychological assessment tools, considered changes in medication use, assessed physiological changes in HRV and QEEG parameters, and included a very large sample size for studies of this kind Supplementary material 2 (TIFF 56 kb)Below is the link to the electronic supplementary material.


An experiment on the adsorption of uranium (VI) by chitosan was conducted to investigate the efficiency of chitosan as an adsorbent for U(VI). The adsorption potential of U(VI) by chitosan was investigated with ICP-MS by varying the experimental conditions such as the pH in order to obtain the optimum conditions. Adsorption dependence on the pH was confirmed, and the highest uptake of U(VI) was observed at pH 5. In addition, to scrutinize the experimental results, quantum chemistry calculations were performed. The results, taking into account the experimental conditions, show that the adsorption efficiency increases as the total charge of the adsorbent and adsorbate species decreases if both of them are positively charged. It was also found that a slight change in the adsorption geometric configuration controls the adsorption efficiency. It is well known that uranium (U) is one of the resources required to generate nuclear power and that it is ubiquitous in nature . AlthougOn the other hand, due to the recent rapid advancement of computer facilities, it has become more and more realistic for quantum chemistry calculations to reproduce experimental results with sufficient accuracy. In fact, if we limit ourselves to the quantum chemistry calculations on U(VI) that were deemed to be difficult to deal with several years ago, we witness several numerical studies on uranium published so far, as follows.2(OH)+, UO2(OH)2, NpO2(OH), and PuO2(OH)+ complexes in both first and second hydration spheres 2+ and [UO2(OH)(H2O)4]+) and chitosan dimers were fully optimized separately. The sum of energies of the uranium species and chitosan dimers are defined as the dissociation limit (the reactant).First, the uranium species 5]2+ and [(UO2)2(OH)2(H2O)6]2+ on chitosan dimers in the acidic region [2(H2O)5]2+ and [UO2(OH)(H2O)4]+ on chitosan dimers in the neutral region in view of the figure. The figure indicates that [(UO2)3(H2O)5]+ and [(UO2)4(H2O)7]+ are good candidates for studying the adsorption of uranyl ions in the neutral region. However, [(UO2)2(OH)2(H2O)6]2+, [(UO2)3(H2O)5]+, and [(UO2)4(H2O)7]+ are complicated and large, and may obscure the deduction of essential reaction mechanisms. Therefore, in order for the calculations to be self-contained, we assumed that it is possible to study the adsorption of [UO2(H2O)5]2+ and [UO2(OH)(H2O)4]+ on chitosan dimers in both the acidic and neutral regions.Finally, it is realistic to study the adsorptions of + is more stable than the one with [UO2(H2O)5]2+ in the low pH regions , except for 2(H2O)5]2+ is more stable than the one with [UO2(OH)(H2O)4]+. The former cases  corresponds to 0.60 kcal/mol, it is extremely difficult for all of the cases shown in Finally, as shown by Equation (8) in , the chaAn experiment on the adsorption of uranium (VI) by chitosan was conducted to investigate the efficiency of chitosan as an adsorbent for U(VI). The effect of pH on the adsorption of U(VI) by chitosan was investigated, and the highest uptake of U(VI) was observed at pH 5.In addition, the quantum chemistry calculations revealed that the total charge numbers of the adsorbent and the adsorbate and their relative molecular geometries are crucial in determining the adsorption efficiency.Combining the experimental and theoretical results, we propose that one strategy to develop a new, highly efficient adsorbent-adsorbate combination is to maximize the difference between the total charge of the adsorbent and the adsorbate.In the case of a pH smaller than 7, if the adsorbate is known to be positively charged in the liquid, it is better for the adsorbent to have as few hydroxy-, carboxyl-, or amino-groups attached by hydrogen ions as possible. This is because fewer hydrogen ions in the liquid are attached to these groups, which makes the adsorbent less positively charged. Therefore, the difference between the total charges of the adsorbent and adsorbate will be maximized. On the other hand, in the case of a pH larger than 7, the situation is quite different because there will be no well-known adsorbent negatively charged by the hydroxide ion. In this case, it may be better to choose or design the adsorbent and adsorbate that have the greatest difference of charges in the pH range larger than 7.


Introduction: Non-adherence to efficacious pharmacotherapy is a major obstacle in the treatment of children suffering from attention deficit hyperactive disorder (ADHD). Some hold the position that pharmacotherapy induces anxiety, and that this is one of the reasons for this non-adherence. Previous studies have pointed to the opposite, a moderating effect of methylphenidate (MPH) on state anxiety in patients with ADHD. This has been shown in continuous treatment in children, but not on a single dose. We hypothesized that a single dose might have a different effect.Method: Twenty children with ADHD were given single doses of MPH in a randomized, controlled, crossover, double blind study. State anxiety using The Spielberger State-Trait Anxiety Inventory (STAI) and a continuous performance test were assessed.Results: As a group, no change was detected in state anxiety with MPH or placebo. However, children who were given MPH during the first session as opposed to those who received placebo first, demonstrated deterioration in baseline state anxiety in the second session .Conclusion: Our findings show a possible delayed anxiety-provoking effect of a single dose of MPH. This may be relevant to the understanding of difficulties in adherence with MPH treatment in children with ADHD.Clinical Trial Registration:www.ClinicalTrials.gov, identifier: NCT01798459 Attention deficit hyperactive disorder (ADHD) is a common worldwide behavioral and neurocognitive disorder with an onset in childhood and substantial negative effects on child and adult life  , discontinued therapy after a single dose, accounting for approximately a third of the total discontinuation rate within the first year (51% of study population discontinued within the first year) . The patients were recruited through advertisement and through the outpatient clinic. The diagnosis of ADHD was based on structured clinical interviews with the child and parents conducted by a child psychiatrist trained in diagnosing childhood ADHD with the aid of the Swanson, Nolan, and Pelham Rating Scale (SNAP-IV) filled by multiple informants. This questionnaire is widely used, as a screening tool, with good sensitivity to detect the core symptoms of attention deficit hyperactivity disorder (ADHD)  and the national review board (20120239). Participants were recruited from our ADHD clinic. The study protocol was explained in detail to the participant and family. Informed consent was provided by participants' parent or legal guardian prior to any study-related activity.The study was registered on clinical trials.gov 0009-12-SHA; NCT01798459.All participants underwent two identical session evaluations. In both sessions, the subjects completed The Spielberger State-Trait Anxiety Inventory (STAI) questionnaire . Forty-five minutes after drug administration, subjects completed STAI—state questionnaire again and performed the cognitive tasks. In the second session, medication (MPH/placebo) was crossed-over.The Swanson, Nolan, and Pelham questionnaire (SNAP-IV)   were administered: The Motor screening Test (MOT), The Rapid Visual Information Processing (RVP), The Spatial Working Memory (SWM), and the Reaction Time (RT) tasks. These tasks were chosen because they measure executive functions relevant in evaluating ADHD and commonly used both in research and in clinical practice of ADHD. The reason for their use was the thought that they will be challenging and thus anxiety provoking for patients suffering from ADHD.The following Subsets from t-test was applied for within subject comparison. Based on previous studies in adults we expected a sample size of 20 participants to suffice , p = 0.367]; nor following administration of placebo (before 46.25 (±5.40) after 45.95 (±4.12) .No difference was found in state anxiety after administration of MPH (before 44.15 (±5.24) after 45.15 (±4.77) .The difference in baseline state anxiety scores for the participants who were given placebo in the first session was a non-significant decline [In the present study, there was no immediate effect of a single dose of MPH on state anxiety in ADHD pediatric patients. However, there was an incline in baseline anxiety at the second treatment visit in patients that received MPH at the first visit.This may be considered to be a type of conditioned phenomenon, related to the experience with the medication, whose importance is in the possible clinical relevance. Seventeen percent of the pediatric patients that start MPH, stop after the first prescription (Wang et al., Non-adherence to efficient pharmacotherapy is a major obstacle in treating ADHD patients. The reasons for non-adherence vary, but a major reason is side effects. While anxiety is suggested as an important side effect (Pozzi et al., Since most studies focused on prolonged therapy, they could not reveal an immediate anxiety provoking effect that is relevant to early medication withdrawal.In a previous study of adult ADHD patients, our group demonstrated an anxiolytic effect of a single dose of MPH (Bloch et al., The finding of this pilot study relates to a delayed possibly conditioned anxiety provoking effect, that has not been studied yet. It is different from the anxiolytic effect of MPH on adherent patients that probably do not suffer from the aversive effect we encounter with some pediatric ADHD patients in the clinic (Kovshoff et al., There are several limitations of the current study. First and most importantly, the sample was small, and with considerable age variation. Larger and more age homogenous studies with imaging evaluations, are needed in order to substantiate these findings and have a wider understanding of their implications.The study was approved by the Shalvata ethics committee. Parents completed the informed consent process and signed the informed consent form. Children and adolescents signed the assent form.All authors have made substantial contributions to the conception or design of the work. MKr and MKo made major contribution to the acquisition of data. All authors contributed to the analysis and interpretation of data for the work, drafting the work or revising it critically for important intellectual content, provide approval for publication of the content, and agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.The 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.


Guanfacine is used for the treatment of attention‐deficit/hyperactivity disorder (ADHD). Using liquid chromatography–tandem mass spectrometry (LC–MS/MS), metabolite profiling of guanfacine was performed in plasma and urine collected from healthy Japanese adults following repeated oral administration of guanfacine extended‐release formulation. Unchanged guanfacine was the most abundant component in both plasma and urine . In plasma, the M3 metabolite (a sulfate of hydroxy‐guanfacine) was the prominent metabolite; the M2 metabolite (a glucuronide of a metabolite formed by monooxidation of guanfacine), 3‐hydroxyguanfacine and several types of glucuronide at different positions on guanfacine were also detected. In urine, the M2 metabolite and 3‐hydroxyguanfacine were the principal metabolites. From metabolite analysis, the proposed main metabolic pathway of guanfacine is monooxidation on the dichlorobenzyl moiety, followed by glucuronidation or sulfation. A minor pathway is glucuronidation at different positions on guanfacine. As the prominent metabolites in plasma were glucuronide and sulfate of hydroxyguanfacine, which have no associated toxicity concerns, further toxicity studies of the metabolites, for example in animals, were not deemed necessary. The second, approved in 2009, is atomoxetine hydrochloride , classified as a non‐psychostimulant drug. Additional medicine approvals for the management of ADHD will provide Japanese patients and clinicians with more treatment options. Guanfacine is a selective α2‐adrenergic receptor agonist  received approval for the treatment of ADHD in the United States in 2009  is a neurobehavioral condition, predominantly identified during childhood and characterized by various degrees of developmentally inappropriate inattention, hyperactivity and impulsiveness .Metabolite profiling and the identification of guanfacine in human subjects has previously been conducted with urine samples Kiechel, . Howeverin vivo metabolites found in humans are shown.Therefore, this Phase 1 study was conducted to elucidate the prominent metabolite(s) in the plasma and urine of healthy adult Japanese subjects after multiple doses of guanfacine XR. Furthermore, possible metabolic pathways of guanfacine based on 22.1Guanfacine XR tablets (4 mg) were supplied by DSM Pharmaceuticals Inc. . Reference standards for metabolite analysis of guanfacine  and 3‐hydroxyguanfacine (purity > 95% by HPLC) were supplied by Shionogi & Co., Ltd. The reagents  were of guaranteed liquid chromatography‐mass spectrometry (LC–MS) grade, and the reagents (ammonium acetate and acetonitrile) were of guaranteed grade.2.2The samples (plasma and urine) for metabolite analysis used in this study were obtained in a Phase 1 pharmacokinetics study of single and multiple doses of S‐877503 in healthy Japanese and Caucasian subjects  to separate the supernatant. Residues were extracted twice with the same volume of extraction solution in the same manner as the first extraction. All obtained supernatants were combined and evaporated to dryness under reduced pressure, and the residues were reconstituted in 300 μl of reconstitution solution, which consisted of 10 mmol/l ammonium acetate in 0.2% formic acid aqueous solution and 0.2% formic acid in methanol . The reconstituted samples were centrifuged , and the supernatants were used as the samples for metabolite analysis.Each sample of plasma and urine was prepared by pooling with the same volume of samples collected from the 11 actively administered subjects at each time point. One ml of pooled plasma samples was mixed with 3 ml of 0.1% formic acid in acetonitrile as the extraction solution, shaken (10 min) and centrifuged . The supernatant was then used as the sample for metabolite analysis. The metabolites produced from guanfacine were numbered in order of retention times .An aliquot of 100 μl of pooled urine samples was mixed with 10 μl of 0.1% formic acid and centrifuged  were assumed to be unity (1).2.52), the auxiliary gas was 20 arb (N2), the sweep gas was 3 arb (N2), the collision gas was He, the normalized collision energy was 35% (collision‐induced dissociation [CID]) and the isolation width was 2 u. All data were obtained and processed using Xcalibur software .All analyses were performed using a HPLC system  equipped with a mass spectrometer . An XBridge C18 column  and Security Guard Cartridge C18 column  were used for HPLC with tandem mass spectrometry (LC–MS/MS) analysis. The column temperature was maintained at 40 °C and the mobile phase consisted of a mixture of 10 mmol/l ammonium acetate in 0.2% formic acid aqueous solution (A) and 0.2% formic acid in methanol (B). The solvent gradient conditions were: 0 min, 5% B; 0–30 min, 5–50% B; 30–30.1 min, 50–98% B; 30.1–33 min, 98% B; 33–33.1 min, 98–5% B; and 33.1–45 min, 5% B. The flow rate was set at 0.3 ml/min. Mass spectra were obtained in the positive ion mode and the ionization interface was electrospray ionization (ESI). Optimized parameters were set as follows: the spray voltage was 4.0 kV, the capillary temperature was 350 °C, the tube lens voltage was 45 V, the sheath gas was 50 arb  chromatograms of authentic standards are shown in Figure + of m/z 246  and 159 (204–45)  and 175 (220–45)  are shown in Table 3.4m/z 246 was detected at the same RT as guanfacine in urine at 0–12 and 12–24 hours, and in plasma at 2, 4, 6, 10 and 24 hours  indicated that M9 was a glucuronide. Therefore, the molecular formula of the aglycone of M9 was estimated to be C8H9Cl2NO2. The molecular formula of the aglycone indicated that guanfacine was degraded at the guanidine group to amine, monooxidized and hydrogenated (+2H). The MS2 fragment ion at m/z 205 (−17) indicated that the aglycone of M9 has at least one primary amine group. Moreover, the MS2 fragment ion at m/z 194 was estimated to be a conjugate of glucuronic acid and amine (176 + 17 + 1). The MS2 fragment ion at m/z 159 was estimated to be a dehydrated ion of the aglycone (Table M9 was detected at the RT of approximately 25 min with 3.15The proposed metabolic pathway of guanfacine is monooxidation (3‐hydroxyguanfacine and M6) at different positions on the dichlorobenzyl moiety, followed by glucuronide M2) or sulfate (M3). Another minor pathway is glucuronide  at different positions on guanfacine  at 3 mg, the metabolite in urine was investigated, and unchanged guanfacine accounted for 27.6% of radioactivity in urine samples (0–24 hours). The prominent metabolite in urine was reported to be the 3‐hydroxyguanfacine conjugated as either O‐glucuronide (34.5% of radioactivity in urine sample) or O‐sulfate (7.7%), and the metabolites were identified using HPLC, gas chromatography mass spectrometry and on‐line radioactivity detection  might be more appropriate triggers for testing. Some metabolites are not of toxicological concern (e.g. most glutathione conjugates) and do not warrant testing ICH, . Most gl5Exhaustive metabolite analyses of guanfacine in plasma and urine were conducted using LC–MS/MS after repeated oral administration of guanfacine XR in healthy Japanese adult subjects. Unchanged guanfacine was the abundant component, and the M3 metabolite (a sulfate of hydroxy‐guanfacine) was the prominent metabolite in human plasma. The proposed main metabolic pathway of guanfacine is monooxidation on the dichlorobenzyl moiety, followed by glucuronidation or sulfation. Although there was a possibility that some metabolite levels were greater than 10% of the total drug‐related exposure in plasma, there was no need for further studies in animals to determine the potential toxicity of the metabolites, as the prominent metabolites in plasma were glucuronide and sulfate, in which toxicity is not of concern.Yuji Inoue, Hirotoshi Morita and Takushi Kanazu are employees of Shionogi & Co., Ltd. Kohei Nozawa is an employee of Sekisui Medical Co., Ltd. This study was funded by Shionogi & Co., Ltd., Shire, a Takeda company, and Shire International GmbH, a Takeda company.The clinical trial in this study was not registered as it is not required to register clinical trials of Phase 1 studies  in Clinical Trials Information/JapicCTI.The content has not been submitted for publication elsewhere except as a brief abstract in the proceedings of a scientific meeting.


The analysis of time-varying connectivity by using functional MRI has gained momentum given its ability to complement traditional static methods by capturing additional patterns of variation in human brain function. Attention deficit hyperactivity disorder (ADHD) is a complex, common developmental neuropsychiatric disorder associated with heterogeneous connectivity differences that are challenging to disambiguate. However, dynamic connectivity has not been examined in ADHD, and surprisingly few whole-brain analyses of static functional network connectivity (FNC) using independent component analysis (ICA) exist. We present the first analyses of time-varying connectivity and whole-brain FNC using ICA in ADHD, introducing a novel framework for comparing local and global dynamic connectivity in a 44-network model. We demonstrate that dynamic connectivity analysis captures robust motifs associated with group effects consequent on the diagnosis of ADHD, implicating increased global dynamic range, but reduced fluidity and range localized to the default mode network system. These differentiate ADHD from other major neuropsychiatric disorders of development. In contrast, static FNC based on a whole-brain ICA decomposition revealed solely age effects, without evidence of group differences. Our analysis advances current methods in time-varying connectivity analysis, providing a structured example of integrating static and dynamic connectivity analysis to further investigation into functional brain differences during development. Neuropsychiatric disorders represent a central field of inquiry in network neuroscience, akin to lesional analysis of complex brain system dynamics. The prevalence of these conditions increases from mid-childhood to peak in adolescence and regress thereafter, challenging our ability to disambiguate maturational and connectivity effects. ADHD is a paradigmatic example, and the most common neuropsychiatric disorder of development. Here we present the first whole-brain analysis of time-varying connectivity in ADHD. Building on leading-edge methods in dynamic connectivity, our novel approach analyses time-varying connectivity on both a global brain basis, and within local network systems. This framework demonstrates that analysis of time-varying connectivity offers additional ways to characterize group and maturational effects in ADHD that are extensible to other developmental neuropsychiatric disorders. Concomitantly, secondary measures of dynamism have intuitive biological appeal as potential corollaries of cognitive control and flexibility  data has largely proceeded by computing mean correlations between networks (or nodes) of interest across time courses. This provides an averaged snapshot of “static” functional network connectivity (sFNC), and superordinate approximation of underlying dynamic states  to estimate four, five, and six brain states, identifying group differences in dFNC and measures of dynamic fluidity in this native state space. We removed the effects of gender, IQ-level, motion, and site by using the general linear model, successively including and then removing the effect of age. Finally, we identified four, five, and six prototype connectivity clusters by applying the temporal ICA (tICA) algorithm to wFNC. We discretized these to delineate a “meta-state” space and computed higher dimensional measures of dynamic fluidity and range, again with and without age effects and after removing variance attributable to IQ-level, gender, site, and motion. Five-state solutions are presented, and 4- and 6-state results reported. In addition, replication analyses were performed in a subsample of 444 subjects who were additionally matched for head motion in a 5-state solution.Given evidence of altered DMN function in ADHD, and control network roles in functional configuration switching, we hypothesized that altered dynamism would be most prevalent within the DMN or in dFNC involving DMN subnetworks. We used ICA to estimate a 44-network model  FNC across all functional brain networks detected no significant difference in any pairwise correlation between ADHD and TD youth. Furthermore, there was no significant interaction of group with any other covariate including age and gender. These findings were replicated in the motion-matched sample. To examine the potential effects of comorbid diagnosis, history of prior or current medication usage, and handedness on sFNC, we performed an additional multivariate analysis in ADHD youth. By restricting the subject selection to ADHD youth, we took a conservative approach, reasoning any such effects would not be “diluted” by the presence of TD youth and including all subjects with a history of current or prior medication usage. We did not detect effects of comorbid diagnosis, medication usage, or handedness in sFNC, or interaction of these variables with group or age.To explore the effects of maturation in sFNC, we analyzed age effects in all subjects together and in the ADHD and TD groups separately. Analysis of the effects of increasing age in sFNC in the entire subject group  and in TIn youth with ADHD, we found increasing anticorrelation in fewer (sub)networks, which may be appreciated in We computed five measures of dynamic fluidity across the native  and meta-state . In the When we examined dynamism measures on a local basis (among subnetworks associated with the same neurocognitive function) in the native state space, we discovered significantly reduced dwell time in one state (State 2) in subjects with ADHD versus TD within the DMN system  that wasAcross all meta-state measures there was a generalized finding of reduced fluidity and range in ADHD versus TD within the DMN system, in every case either significant or narrowly missing significance. However, when subjects were motion-matched, no effect approached significance, although directionally there continued to be consistently reduced dynamism in ADHD. Differences in intrasystem dynamic fluidity never approached statistical significance in any other neurocognitive system  in eitheWe identified no significant  differences in any measure of local fluidity or range between the DMN system and other network systems in ADHD . HoweverWe identified a single, robust finding in analysis of FNC within five brain states that replicated in the 4-state and 6-state analysis. In local dFNC between the DMN and subcortical systems, subjects with ADHD displayed significant hyperconnectivity between a posterior cingulate-anchored subnetwork of the DMN (IN14), and the basal ganglia-thalamic network (IN30). This was a transient phenomenon that appeared in one brain state . Of noteWe also identified significant group differences in localized Notwithstanding its interest to the neuroimaging research community, surprisingly few studies have been published in youth with ADHD utilizing ICA to assess intrinsic connectivity between large-scale networks. In intrinsic sFNC, a combination of group and maturational effects have been identified. A trio of studies in the ADHD-200 data by the same group represents the largest body of work to date. These use a grid-based 907-seed parcellation restricted to cortex, in 133 youth with ADHD and 288 TD subjects , performing Using an ICA-based functional parcellation, we could not attribute sFNC differences to group (ADHD) effects as detected by  and a subnetwork of the DMN anchored in the posterior cingulate (IN14). This phenomenon was robust to variance in analytic parameters and provides the first conceptual link between decades of structural and task-based findings in ADHD implicating the striatum, and dynamic connectivity. Furthermore, in the motion-matched subsample even more dFNC effects were detected, the preponderance involving the same basal ganglia-thalamic network. Of note, the effect in the original sample occurred in a distinctive brain state where maximal anticorrelation obtained between the DMN and subcortical system generally, and strong positive connections were observed within the DMN system. In the motion-matched subsample, the largest number of dFNC differences were also in this state. An intriguing conceptual parallel is present in our earlier work in schizophrenia, where we identified that thalamico-cortical dFNC differences with TD existed in states with the strongest cortical-subcortical anticorrelations  spatial regression between each normalized functional image and a group mask constructed from all subjects and (2) root-mean-square difference of volume N to volume N + 1, also known as DVARS  ranging from 73 to 153 with a mean of 110. There was no significant difference in FSIQ between groups. Our sample contains 3:1 male-to-female ratio and is weighted toward children over 11 years old, directionally similar to the distribution of clinical incidence of ADHD in the population. Within the younger age bands, we included a slightly greater proportion of females given our desire to include more younger subjects and the relative paucity of male controls available at this age. Our 504 subjects came from the Peking, KKI, NYU, and Pittsburgh sites in the ADHD-200 repository. Individual variables (regressors) were constructed for each site. A list of included subjects with their ID codes, corresponding to that given in the ADHD-200 repository key, may be inspected in Supporting Information Table S3  with more left-handedness in the ADHD group. Similarly, in the ADHD group, 96 subjects (38%) reported past or present comorbidity with specific learning disorders, oppositional defiant disorder, depression, anxiety disorders, and Tourette’s syndrome, approximately equivalent to the general population with ADHD. No subjects reported autism, schizophrenia or psychotic symptoms. Instruments used to make diagnoses of ADHD and comorbidities varied across site—a limitation—and these may be inspected at http://fcon_1000.projects.nitrc.org/indi/adhd200/. Fifty-nine subjects in our sample with ADHD were reported to not be naïve to psychoactive medications. Of these, three subjects in the Pittsburgh sample were reported to be taking medications at the time of scanning, but otherwise psychostimulants were withheld at least 24 hours prior to scanning. Subjects with ADHD are also known to have higher rates of head motion during MRI scanning and this has been proposed as a genuine trait, and perhaps genetic, difference  that were further matched for head motion, quantified as DVARS score. The subjects that were omitted from the original sample to create this motion-matched subsample are detailed in Supporting Information Table S4 . All methods detailed below were applied similarly to each subject sample, with any exceptions noted.The clinically diagnosed ADHD population is enriched for left-handedness, although its significance is disputed  developed in our group and widely used in ICA of fMRI  gray matter networks and (2) artifactual noise components. All gray matter networks were included in the study, and all noise components such as the effects of blood pulsations and motion were discarded. Sorting was performed using a combination of expert visual inspection and quantitative metrics. For each of the 60 components we computed the spectral metrics of (1) fractional amplitude of low-frequency fluctuations (fALFF) and (2) dynamic range , again following an established pipeline . Third, network correlations with reverse inference maps of regional activations associated with specific neurocognitive functions were inspected in Neurosynth  associated with peak intensities for each of the 44 INs. The top three coordinates were compared with the literature. We found multiple literature-based confirmatory sources that gave specific Talairach or MNI coordinates and associated these with network labels for all networks in the task-positive network group, the DMN, and primary sensorimotor and visual networks  using the MANCOVAN toolbox in GIFT to compare the effects of age with other possible predictors of variance in the same set of network maps for (1) all 504 subjects, (2) the 252 TD subjects, and (3) the 252 subjects with ADHD using an established method  they spent in each of the states once entering that state (dwell time), and the fraction of their total time course spent in each state. We also computed FNC between pairwise INs within each state.We computed four measures of functional dynamism in the native state space, that is, on the stable brain states computed with k means, spatial ICA, and principal component analysis are used  for use in higher dimensional measures of dynamism, we followed a similar method but in this case preferred the use of the tICA algorithm , which subjects may occupy over time. Two metrics describe the fluidity with which subjects traverse the meta-state space: the number of distinct meta-states passed through by each individual and the number of times each subject switches between meta-states. The remaining two metrics describe the high-dimension dynamic range achieved by subjects: the maximal L1 span achieved between occupied meta-states, and the total distance “traveled” by an individual through the state space .We computed four measures of dynamism in the higher dimensional meta-state space by using the same procedure as detailed by Miller and colleagues  the overall group of 44 networks, (2) within each of the six groups of INs when grouped by their dominant neurocognitive attribution , and (3) between the default mode system and each of the other functional system groups .t tests. These were computed at a significance level of α < 0.05, corrected to control FDR where multiple comparisons obtained. We performed this analysis using the general linear model to disambiguate the effects of maturation (age) versus group effect of ADHD > TD. Variance associated with site, DVARS measure, IQ-level, and gender was removed by regression using the general linear model from the correlation values prior to performing clustering. The analysis was then repeated, removing variance associated with site, DVARS measure, IQ-level, gender, and age using the general linear model.Group differences in each measure of dynamic connectivity between TD subjects and subjects with ADHD were calculated using two-sample The authors would like to thank B. Ernesto Johnson for his assistance with figure preparation.Nina de Lacy: Conceptualization; Formal analysis; Investigation; Writing – original draft. Vince Calhoun: Supervision; Writing – review & editing.http://dx.doi.org/10.13039/100006108), Award ID: KL2TR000421 and KL2TR002317. Vince Calhoun, National Institutes of Health (http://dx.doi.org/10.13039/100000002), Award ID: 2R01EB005846. Vince Calhoun, National Institutes of Health (http://dx.doi.org/10.13039/100000002), Award ID: P20GM103472. Vince Calhoun, National Institutes of Health (http://dx.doi.org/10.13039/100000002), Award ID: R01EB020407. Vince Calhoun, National Science Foundation (http://dx.doi.org/10.13039/100000001), Award ID: 153906.Nina de Lacy, National Center for Advancing Translational Sciences (Click here for additional data file.


Their adsorption and self-aggregation properties are investigated by surface tension, conductivity, dynamic light scattering and transmission electron microscopy (TEM) measurements. The results show that these surfactants reduce the surface tension of water to a minimum value of 26.51 mN m−1 at a concentration of 5.72 × 10−2 mmol l−1. Furthermore, the increased alkyl chain length of the carboxylic counterions leads to the increased critical micelle concentration, the decreased degree of counterion binding (β) and the decreased self-assembly tendency, but the minimum area per surfactant molecule (Amin) adsorbed at the air–aqueous solution are similar. TEM images reveal that these surfactants self-assemble spontaneously into aggregates with vesicle or bilayer structures. It is also found that they have superior antibacterial activity at a concentration of 0.1 g l−1. The high surface activity and high antibacterial activity of the Gemini quaternary ammonium salt surfactants containing different carboxylic counterions bring more possibilities for the application in the field of biomedicine.A group of Gemini quaternary ammonium surfactants with the formula C Among them, anionic surfactants are often used in thickening, emulsification, decontamination and other aspects of research ,2. Thereet al. studied the effect of a Hofmeister series of inorganic counterions and p-toluenesulfonate on the solvation dynamics and rotational relaxation in aqueous micelles of hexadecyltrimethylammonium surfactants, and indicated the positive significance of this research on the physico-chemical properties of surfactants in aqueous solution  are two orders of magnitude higher than that of these Gemini surfactants, although the hydrophobic chain lengths are different  have been reported [−3 mol l−1, the CMC values of 11-2-11-2Y are much lower; especially, the CMC values of 11-2-11-2HCOO− and 11-2-11-2CH3COO− are two orders of magnitude lower. But, the CMC values of 13-2-13-2Y and 15-2-15-2Y are one order of magnitude lower compared with those of 13-2-13 and 15-2-15 (7.5 × 10−4 and 5.0 × 10−4 mol l−1). This indicates that although micellization of Gemini surfactants is affected by both the hydrophilicity of counterions and hydrocarbon parts of the polar headgroup [As seen in high CMC . The CMCifferent . Some Gereported . Compareeadgroup , but witet al. [Amin and Γmax. But from Γmax and Amin. The reason may be that, for gemini quaternary ammonium salts with the short alkyl chain counterions, the dominating factors influencing the Γmax values are the size of hydrophilic head groups and the electrical repulsion between hydrophilic head groups [20 values of these surfactants with the same length of hydrophobic chains increase fractionally with decreasing the alkyl chain length of carboxylic counterions, which indicates that Gemini surfactants with formate counterion have higher efficiency in reducing the surface tension. Because of the negative ΔGads and Gads values.Gao et al.  suggested groups . In addiθ values , it can 3.4.κ) of 11-2-11-2Y, 13-2-13-2Y, 15-2-15-2Y aqueous solution as a function of their concentrations (c). It is noteworthy that some results of conductivity measurements  seem to deviate from linearity at concentrations lower than CMC. This phenomenon could be a consequence of ionic pairing or premicellar associations, which can be analysed by the shape of κ versus c and molar conductivity Λ (κ0 is the conductivity of water) versus c0.5 plots at c< CMC [κ versus c and Λ versus c.50 plots curve toward the concentration. On the other hand, premicellar association of ionic surfactant gives rise to small aggregates of surfactant ions, which results in an increase in the conductivity of the solution; therefore, the corresponding κ versus c plot will show a curvature toward the κ axis. In addition, Λ will increase with c. At higher concentration, where micelles appear in the solution and bind counterions, the value of Λ will start decreasing. Thus, the plot of Λ versus c.50 will present a maximum. So, according to electronic supplementary material, figure S3 A-II, B-II, C-I and C-II, and electronic supplementary material, figure S4, ion pairing may occur in solutions of 11-2-11-2CH3COO−, 13-2-13-2CH3COO− and 15-2-15-2CH3COO−, and premicellar association may be present for 15-2-15-2HCOO−.Electronic supplementary material, figure S3 shows the conductivity values ( c< CMC . Ion paiκ-c curve. The ionization degree of micelle (α) is obtained from the ratio of the slope of the linear portions right of the inflection point at CMC versus left. The relation: β = 1 − α is used to calculate the degree of counterion binding (β) [β and β values of ammonium formate, ammonium acetate and ammonium lactate with the same length of hydrophobic chains is gradually decreasing. The reason is probably that, for the micelles of the same size and structure, the larger the hydration radius of the counterion, the lower the degree of counterion binding [−, CH3COO− and CH3CHOHCOO−, the absolute value of the The CMC of the surfactants can be obtained from the inflection point of the ding (β) . ΔGmθ isequation :3.5ΔGmθ= binding . Simulta3.5.−1), the bacteriostatic efficiency of these Gemini surfactant aqueous solutions was 53.9–80.2% for Gram-positive bacteria and Gram-negative bacteria. However, at the concentration of 1 × 10−3 mol l−1 (about 0.1 g l−1), the antibacterial efficiency of all these surfactant solutions is above 75.3%, most of which are 80–95%. So, the self-assembly behaviour of these Gemini surfactants aqueous solutions at higher concentration (1 × 10−3 mol l−1) was also investigated.The dynamic light scattering (DLS) data and TEM micrographs of surfactant solutions at a concentration of 2 × CMC are frequently used to evaluate their molecular self-assembly behaviour. In the antibacterial activity subsequently discussed, at this concentration , tubular micelle and lamellar micelle. Accordingly, the morphology of aggregates of these Gemini surfactants in aqueous solution with the concentration of 2 × CMC and 1 × 10−3 mol l−1 has been carried out on TEM measurement, which is shown in figures Seen from electronic supplementary material, figure S5, the − and 11-2-11-2CH3COO− (figure 3a(i)(ii)), while at the concentration of 1 × 10−3 mol l−1, MLV with a diameter of about 500 nm are formed (figure 3b(i)(ii)). In addition, at the concentration of 2 × CMC, a mass of hollow slender bilayer micelles are observed in the aqueous solution of 11-2-11-2CH3CHOHCOO− (figure 3a(iii)), while at the concentration of 1 × 10−3 mol l−1, vesicles are formed (figure 3b(iii)). This indicates that the introduction of −OH and the increase in the length of the alkyl carbon chain in the counterions cause the deformation of the aggregates at the concentration of 2 × CMC, but the effect of counterions seems to be insignificant at relatively high concentration. This trend is also present in the series of 13-2-13-2Y. The mixed micelles of vesicles and deformed bilayers structure are observed in figure 4a(i), while uniform short tubular micelles are observed in figure 4a(ii)(iii). However, hollow and longish tubular micelles are all observed among that at a high concentration (figure 4b(i)(ii)(iii)). In spite of that, in the series of 15-2-15-2Y, the counterions have little influence on the self-assembly behaviour of these surfactants whether at a low or high concentration. Seen from −3 mol l−1. The formation of various micelles is mainly due to the self-organization of quaternary ammonium cation driven by entropy. So, one of the major factors leading to the differences may be the distinction of hydrophobic chain length of these systems.Seen from figures 3.6.VH) occupied by the hydrophilic group and the chain length (lc) of the hydrophobic group, the P-values are calculated by the following equation [Amin with n = 2 [VH and lc values [n is the hydrophobic alkyl chain length inserted into the aggregates, lc is approximated to 80% of the calculated value for the fully extended alkyl chains. The values of P, which are between 1/2 and 1, are listed in P and the structures of aggregates [P < 1, the geometry of surfactants turns into an ellipse cone with wide hydrophilic head base area and tiny hydrophobic chain and it forms a flexible and closed bilayer structure more readily. So, it is reasonable for these Gemini surfactants to form vesicles and bilayer aggregates observed by TEM.The hydrophilic head groups of aggregates formed by the surfactant molecules pack towards the water phase and the hydrophobic tail chains are away from the water phase in aqueous solution. Due to the different volumes of head groups and the tail chains, the aggregates have a diverse morphology. According to the space  are shown in electronic supplementary material, figure S6. From electronic supplementary material, figure S6, it can be seen that the OD values of the blank rise over time initially. After 18 h, OD values achieved stability, which indicates that P. aeruginosa enters a stable status. After 30 h, the OD values decrease slightly, which indicates that P. aeruginosa enters the decay period. The same tendencies are observed in the groups containing 11-2-11-2Y, merely their OD values are smaller than that of the blank, suggesting the antibacterial ability of these Gemini surfactants. In addition, the OD values decrease with the increasing concentration of the same surfactant. The bacteriostatic efficiency is calculated according to the following formula:0 and OD1 refer to the OD values of the bacillus without surfactants and with surfactants at the same time, respectively. The values at 15 h are used to evaluate the surfactant inhibition for S. aureus and S. aureus, and the values at 10 and 12 h are, respectively, used to evaluate for E. coli and B. subtilis, which are listed in −1 benzalkonium chloride solution is also listed in As a representation, the plots of the OD values of −1, the antibacterial activities of most of these Gemini surfactants are better than those of 0.1 g l−1 benzalkonium chloride. At the concentration of 0.1 g l−1, all these surfactants have excellent bacteriostatic effect both on Gram-positive bacteria and Gram-negative bacteria. In addition, among these bacteria, the surfactant inhibition for S. aureus is best, followed by the E. coli. 4.−5 to 1.01 × 10−4 mol l−1, and the γCMC values are 26.51–29.09 mN m−1. With an increase of the alkyl chain length of the carboxylic counterions, the CMC values increase, the values of β and pC20 and the absolute values of Amin values show little difference. This suggests that those Gemini surfactants with shorter alkyl chain length of the carboxylic counterions, to adsorb in the air–aqueous interface, form micelles more readily and have higher efficiency on reducing the surface tension, and those surfactants with longer alkyl chain length of the carboxylic counterions have larger hydration radius and weak combination degree. Meanwhile, the DLS results show that the increasing concentration of these surfactant solutions leads to more diverse and larger size aggregates. TEM results show that, in the series of 11-2-11-2Y and 13-2-13-2Y, the transformation of carboxylic counterions from formate to acetate to lactate induces the transition of aggregates from vesicles to slender bilayer micelles at a concentration of 2 × CMC. However, in the series of 15-2-15-2Y, such carboxylic counterions conversion had little effect on the morphology and size of the aggregates whether at a concentration of 2 × CMC or 1 × 10−3 mol l−1. These Gemini surfactants are also found to have superior antibacterial activity at a concentration of 0.1 g l−1. The increasing alkyl chain length of the hydrophobic groups as well as the decreasing alkyl chain length of the carboxylic counterions leads to higher bacteriostatic efficiency. The high surface activity and high antibacterial activity of these Gemini quaternary ammonium salt surfactants containing different carboxylic counterions bring more possibilities for their application in the field of biomedicine.To study the effects of carboxylate counterions on the properties of Gemini quaternary ammonium surfactants, nine surfactants with three carboxylic counterions are synthesized by a counterion conversion process. The current studies have shown that the CMC values of these surfactants are 1.05 × 10


Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental characterized by attention deficit, hyperactivity, emotional impulses and difficulty with cognitive functions. The Das-Naglieri Cognitive Assessment System (DN: CAS), as a theory-driven assessment kit, was explored based on Planning, Attention, Simultaneous, and Successive Theory (PASS). Recent researches have tried to explore the sensitivity and specificity of DN: CAS in diagnosing ADHD; nevertheless, these studies were performed in a small study population. The following study explores the cognitive functions in ADHD by the DN: CAS and to evaluate the DN: CAS’s diagnostic value in ADHD.A total of 135 children with ADHD and 140 healthy controls were enrolled to evaluate cognitive function by the DN: CAS. ROC curve and the area under the curve (AUC) were applied to evaluate the diagnostic value of DN: CAS on ADHD.Compared with healthy controls, children with ADHD had significantly lower scores in Planning, Simultaneous , Attention in the four Subtests of DN: CAS, as well as the total scores. ROC analysis indicated that Planning and Attention of DN: CAS had good classification accuracy in diagnosing ADHD with AUCs of 0.808 and 0.730, respectively.The planning and attention assessment of DN: CAS revealed high sensitivity and specificity in diagnosing ADHD, thus suggesting that DN: CAS might be an effective tool in diagnosing ADHD. In China, approximately 4.31–5.83% of children are affected by ADHD, which is a neurodevelopmental disorder characterized by attention deficit, hyperactivity, emotional impulses and difficulty with cognitive functions . The AmeThe Das-Naglieri cognitive assessment system (DN: CAS), as a theory-driven assessment kit, was explored based on PASS theory by JP. Das, which was applied to assess the cognitive function. PASS theory has reconceptualized intelligence as a process-driven understanding of cognitive abilities based on the following four cognitive processes: Planning, Attention, Simultaneous and Successive processing . This thPASS theory, which is similar to Barkley’s execution function defect theory, proposes that children with ADHD may be more impulsive in cognitive processing, which in turn can influence planning processing. Attention difficulties are expected to negatively affect attention processing. Some studies have demonstrated that children with ADHD assessed by DN: CAS have the lowest performance on Planning and Attention scores, whereas their Simultaneous and Successive processing scores appear normal , 15. DN:ADHD is a multifactorial disorder, and genetic factors might be associated with the risk of ADHD , 18. GivA total of 135 children with ADHD and 140 controls were recruited in this study. The ADHD children were enrolled from People’s hospital of Guangxi Zhuang Autonomous Region between Jan 2015 and Dec 2016. In addition, 140 healthy controls were enrolled from the same communities and schools. All study subjects were interviewed by two well-trained investigators at the Department of Psychiatry, who used K-SADS to conduct interview. ADHD was diagnosed according to American Psychiatric Association (APA) criteria ). Medical history, clinical examination and APA diagnosis criteria evaluated by two independent psychiatrists were used to identify healthy controls as free of ADHD. Children with Intellectual Disability  < 85 by WISC-CR-II), Learning Disorder, Tic Disorders and Autism spectrum disorder were excluded from the study. The FSIQ was included in the present study.The Ethics Committee of People’s hospital of Guangxi Zhuang autonomous Region approved this study, and all study subjects agreed to participate in our research after they were debriefed about the study and after they signed the informed consent; for the children under 16-years of age, the consent was obtained from their guardians.The DN: CAS  was admiPlanning assessment was used to evaluate the efficiency in task solving, including Matching Numbers, Planned Codes and Planned Connections. Matching Numbers requires the test taker to find two identical numbers in the same row . Arrangement in each row is well-designed in order to detect the benefit of the matching strategy during differentiation. Planned Codes require the test taker to code characters arranged in specific order with certain strategy, based on the way they’ve been previously thought . Planned Connections require the subject to connect randomly scattered numbers/numbers and letters in numerical or/and alphabetical order (e.g. 1–2–3-4/1-A-2-B-3-C).Attention mainly assesses the ability to selectively focus on one part of the two-dimensional stimulus while ignoring the other part, including Expressive Attention, Number Detection and Receptive Attention. Expressive Attention refers to the ability to inhibit interfering stimuli in the procedure of expression . Number Detection evaluates selective attention, switching attention and cognitive inhibition to distraction . Receptive Attention requires the subject to circle every pair of characters that look the same or that have the same pronunciation .Simultaneous processing requires the test taker to interconnect components of special items to get the correct answer. It includes Nonverbal Matrices, Verbal-Spatial Relations and Figure Memory. The test taker needs to figure out relation among the segments in the item, then to integrate them with abstract thinking and logical perception ability. There are three types of items in Nonverbal Matrices, i.e. graph filling, analogical reasoning and spatial vision. Each item contains graphs and geometrical elements with relations to either spatial organization or logical organization, and the subject reveals abstract reasoning by observing these relations (e.g. he/ she is asked to make a deduction and find out the absent figure based on the relationship among the five figures presented). In Verbal-Spatial Relations, the subject needs to choose a corresponding picture after he/she is given the description with underlying logical relationship . Figure Memory is a representative subtest of simultaneous processing. It includes two-dimensional or three-dimensional figures that are shown to subject, who is then asked to discriminate the simple figure from a complex one .Successive Processing requires individuals to replicate a particular event or sequence of events. It contains Word Series, Sentence Repetition and Sentence Questions. In this subscale, the subject needs to understand or repeat auditory information that is presented in specific order. The Word Series is made up of monosyllabic words  whose length ranges from 2 to 9 alphabets. The administrator reads them in a speed of 1 s/alphabet and asks the subject to repeat in the same order. The Sentence Repetition requires for a duplication of the auditory sentence that is made up of color items and with no actual meaning (e.g. red is black). The Sentence Questions are used to evaluate the subject’s understanding of the grammatical relations in the sentence by letting the subject to answer a question based on the stated sentence (e.g. ‘White is blue. What is blue?’ answer ‘white’).Calculate raw scores in the 12 subtests separately, then convert them to scale scores. After that, add up the scale scores in each subscale and convert them to standardized scores.p < 0.05.Continuous variables were shown as the mean value ± SD. Normal distribution of data was analyzed using the Kolmogorov–Smirnov normality test. Data with normal distribution were compared by Student’s t test and bonferroni correction. Those with unequal variance or without a normal distribution were analyzed by a Mann–Whitney rank sum test. Items of DN: CAS and FSIQ were compared by Student’s t test, and statistical analyses were carried out using statistical analysis software package SPSS19.0 with the level of significance at ROC curve was used to measure classification performance (sensitivity and specificity) for the DN: CAS and statistical analyses were carried out using statistical analysis software MedCalc15.8 with the level of significance at p < 0.05.The general characteristics of the ADHD and control group are presented in Table T = − 10.260, p < 0.01), Simultaneous , Attention  of DN:CAS, while the total scores were also statically lower in ADHD group compared to controls . Compared with control group, ADHD children had worse performance in Matching Numbers , Planned Codes , Planned Connections  of Planning; Verbal-Spatial Relations  of Simultaneous and Expressive Attention ; and Number Detection , Receptive Attention   and 0.730  respectively. The cutoff point was drawn from the curve for Planning 25-point and for Attention 29-point for diagnostic measures  have been widely used in the diagnosis of ADHD. The CPT is used to assess attention and control ability , 8, 19. Our results demonstrated that the ADHD gender incidence was 4.9 112 boys): 1(23 girls), which was consistent with previous studies 12 boys: , 20, 21.Since the ADHD children appeared to have obvious defects in DN: CAS assessment, especially in planning and attention, ROC analysis was applied to evaluate the sensitivity and specificity of planning and attention assessment in diagnosing ADHD. The AUC of Planning was 0.808 when the cut-off point was set at 25-points, where the sensitivity was 72.6% and specificity was 79.3%; the AUC of Attention was 0.730, when the cut-off point was set at 29-points, where the sensitivity was 78.5% and specificity was 57.9%. The obtained results indicated that the planning and attention assessment of DN: CAS had high sensitivity and specificity in diagnosing ADHD, which suggested that DN: CAS might be an effective tool for diagnosing ADHD, especially in a clinical setting. This study also provided a useful indication for the treatment of ADHD by using remedial programmers; their specific focus will be the amelioration of EF deficit.This study had some limitations. First, selection bias might exist in enrollment; Secondly, we recruited the ADHD cases without any comorbidities in order to reduce the possible bias from study subject’s stratification, we need to enlarge the study population of ADHD in the future; thirdly, larger sample needed to be carried out to verify the diagnostic value of DN:CAS in ADHD diagnosis.The planning and attention assessment of DN: CAS had highly sensitivity and specificity in diagnosing ADHD, which was encouraged that DN: CAS might be an effective tool in diagnosing ADHD.