Attention‐deficit hyperactivity disorder (ADHD) has consistently been associated with substance use, but the nature of this association is not fully understood. To inform intervention development and public health messages, a vital question is whether there are causal pathways from ADHD to substance use and/or vice versa. We applied bidirectional Mendelian randomization, using summary‐level data from the largest available genome‐wide association studies (GWAS) on ADHD, smoking , alcohol use , cannabis use (initiation), and coffee consumption (cups per day). Genetic variants robustly associated with the “exposure” were selected as instruments and identified in the “outcome” GWAS. Effect estimates from individual genetic variants were combined with inverse‐variance weighted regression and five sensitivity analyses . We found evidence that liability to ADHD increases likelihood of smoking initiation and heaviness of smoking among smokers, decreases likelihood of smoking cessation, and increases likelihood of cannabis initiation. There was weak evidence that liability to ADHD increases alcohol dependence risk but not drinks per week or alcohol problems. In the other direction, there was weak evidence that smoking initiation increases ADHD risk, but follow‐up analyses suggested a high probability of horizontal pleiotropy. There was no clear evidence of causal pathways between ADHD and coffee consumption. Our findings corroborate epidemiological evidence, suggesting causal pathways from liability to ADHD to smoking, cannabis use, and, tentatively, alcohol dependence. Further work is needed to explore the exact mechanisms mediating these causal effects. We applied bidirectional Mendelian randomization  to summary‐level data from the largest available genome‐wide association studies on ADHD, smoking, alcohol use, cannabis use and coffee consumption, to investigate whether there are causal pathways from ADHD to substance use and/or vice versa. We found evidence that liability to ADHD increases likelihood of smoking and cannabis initiation and heaviness of smoking, and decreases likelihood of smoking cessation, as well as weak evidence that liability to ADHD increases alcohol dependence risk This is consistent with research indicating that cognitive deficits such as impaired response inhibition and working memory are important features of both ADHD and substance abuseSeveral explanations have been posited as to why ADHD and substance use are correlated. First, there are risk factors that increase susceptibility to both. These could be environmental factors that have been shown to be risk factors for ADHD and substance use, such as trauma exposure or other adverse early life events,X causes variable Y, it follows that any environmental or genetic risk factor causing variable X will also be associated (indirectly) with variable Y. The current literature has mostly focused on causal pathways from ADHD to substance use, with longitudinal cohort studies showing that externalizing symptoms in early adolescence predict onset of smoking and faster progression to daily smoking, and that ADHD medication reduces early‐onset smoking and alleviates smoking withdrawal.n = 144) suggested reciprocal effects between caffeine consumption and ADHD symptoms during adolescence.While a partial common neurobiological aetiology to ADHD and substance use is therefore likely, environmental and genetic correlations could also (partly) reflect causal effects of one on the other. If variable There is tentative evidence that there may be causal effects in the other direction .It is difficult to fully unravel the nature of the association between ADHD and substance use with observational data because of bias due to (unmeasured) confounding and reverse causality . Mendelian randomization (MR) is a method to infer causality, which has recently gained much popularity. MR uses genetic variants robustly associated with an exposure variable as an instrument to test causal effects on an outcome variable.n = 184 765 instead of n = 32 330). Moreover, potential causal effects in the reverse direction were not adequately tested given that the authors included all ADHD cases instead of just those diagnosed in adulthood.So far, one MR study found evidence for a causal effect of alcohol use on attention problems and aggression in adolescents .We therefore performed bidirectional MR using summary‐level data of the largest available GWAS, investigating causal effects between liability to ADHD and a broad spectrum of substance use phenotypes. We applied five different sensitivity analyses more robust to potential violation of the MR assumptions. Throughout the manuscript, we refer to “liability to” a particular exposure . This is because the exposure estimates and the outcome estimates for our analyses come from separate samples, and it is not possible to determine whether or not the individuals in the outcome sample have actually experienced a particular exposure .22.1P < 5e−08) with the exposure and replicated; (2) are independent of confounding variables; and (3) do not affect the outcome through an independent pathway, other than possible causal effects via the exposure  strongly predict the exposure variable—typically, the selected variants have been genome‐wide significantly associated ( stronglyWe applied MR using summary‐level data (sometimes known as “two‐sample MR”), which uses effect estimates of genetic variants (single‐nucleotide polymorphisms [SNPs]) from large GWAS that have been performed previously. In this approach, the SNP‐exposure association and the SNP‐outcome association estimates are taken from two separate GWAS Figure B. A majo2.2MR is inherently different from other causally informative designs such as twin or family studies. While these methods use a priori knowledge of genetic relatedness between family members to explain variation in a particular phenotype, MR exploits directly measured genotypes in  unrelated individuals. Whereas twin and family studies aim to correct for genetic  differences in order to infer causality, MR exploits the genetic component by using it as an instrument for causal inference. A comprehensive review comparing all methods that use genetic data to strengthen causal inference is available elsewhere.2.3n = 53 293n = 1 232 091; cigarettes per day, n = 337 334; cessation [former versus current smokers], n = 547 219n = 463 003n = 941 280n = 121 604n = 46 568n = 184 765n = 91 462n = 632 783, n = 263 954, n = 312 821, n = 162 082, and n = 537 341, respectively.Summary‐level data of large GWAS were obtained for ADHD  before middle to late adulthood, lifetime smoking was not appropriate to use as an exposure and was only used as an outcome. In addition, cigarettes per day and smoking cessation could not be used as exposures because the GWAS that these are based on were performed in (former) smokers only. To perform an MR analysis with genetic variants for cigarettes per day and smoking cessation as instruments, the outcome GWAS (in this case ADHD) would have to be stratified on smoking status, which was not possible with the summary data we used.n = 15 548) to ensure a plausible temporal sequence of a potential causal effect . This is crucial given that ADHD is generally a child‐onset disorder and the onset of substance use is typically during adolescence or early adulthood. What we aim to test here is whether substance use causes later development of or exacerbating of ADHD symptoms (resulting in a diagnosis at adult age). There was no sample overlap of the ADHD GWAS with the smoking, alcohol, and coffee GWAS. Between the ADHD and the cannabis initiation GWAS, there was very minimal overlap (<3%).When testing causal effects of liability to ADHD on substance use, summary statistics from the complete ADHD GWAS containing child, adolescent, and adult data were used. When testing causal effects of substance use on ADHD, only adult data (ADHD diagnosed > 18 y) were used (2.4P < 5e−08) in the ADHD GWAS to use as genetic instruments. These same SNPs were then identified in the substance use GWAS. To assess causal effects in the other direction, we identified independent genome‐wide significant SNPs in the different substance use GWAS as genetic instruments, and then we identified those different sets of SNPs in the ADHD GWAS. The analyses were conducted in R, using the two‐sample MR package of MR‐Base, a database and analytical platform.To assess causal effects of liability to ADHD on substance use, we identified independent SNPs that reached genome‐wide significance . An I2 value below 0.6 means that MR‐Egger results (even with SIMEX) are unreliable. We report MR‐Egger results when I2 > 0.9, report MR‐Egger SIMEX results when I2 = 0.6‐0.9, and do not report MR‐Egger results when I2 < 0.6.First, we used weighted median regression, which provides an unbiased estimate of the causal effect, even if <50% of the weight of the genetic instrument comes from invalid instruments.Q statistic, which provides an estimate of heterogeneity between the effects of the individual genetic variants,For an additional indication of the robustness of our findings, we inspected the Cochran 2.6We did not explicitly correct for multiple testing to avoid judging the evidence based simply on an arbitrary threshold. Instead, we interpret the evidence by looking at both the effect size and statistical evidence for the main IVW result, combined with how consistent the results of the sensitivity analyses are across multiple MR methods. Because of their stricter assumptions, the sensitivity analyses have lower statistical power to identify a true causal effect. Thus, when the effect sizes of the sensitivity analyses are of similar magnitude and direction, this supports a causal interpretation, even if the statistical evidence for an individual analytical approach is weaker than in the IVW analysis.3P = 1.7e−05), cigarettes smoked per day , smoking cessation , and lifetime smoking . The weighted median and weighted mode sensitivity analyses confirmed these findings, albeit with slightly weaker statistical evidence for the latter . The GSMR could not be performed because there were too few SNPs (<10). Steiger filtering showed that—with the exception of one SNP in the ADHD risk to smoking initiation analysis—all SNPs were more predictive of the exposure than of the outcome. Cochran Q statistic indicated heterogeneity of the effects of the included variants for the ADHD liability to smoking initiation and ADHD liability to lifetime smoking analyses , while leave‐one‐out analyses gave no indication that the overall causal effect was driven by a particular SNP . Weighted median, weighted mode, and GSMR confirmed this finding, but with (slightly) weaker statistical evidence. MR‐Egger was not reported due to a low I2 value . Leave‐one‐out analyses did not suggest that any individual SNPs were driving the overall effect.There was also considerable evidence that liability to ADHD causally increases risk of cannabis use initiation , this effect was not consistent across the sensitivity analyses. However, when we repeated these analyses using alcohol intake frequency as the outcome measure in UK Biobank only—one of the cohorts included in the much larger GWAS sample the main analyses were based on—there was evidence for a causal effect reflecting increased risk . Weighted median, weighted mode, MR‐Egger, and GSMR sensitivity analyses indicated similarly strong evidence, albeit with smaller effect sizes (Table P = 0.37). However, for this relationship, the I2 value was low—0.60 , and leave‐one‐out analyses did not indicate that the overall effects were driven by a single SNP. As an additional sensitivity test, we repeated the smoking initiation—ADHD analyses using ADHD symptoms in childhood only, with one of the replication samples of the original GWAS paper  ADHD risk. There was no clear evidence of causal effects between liability to ADHD and coffee consumption.Our findings complement and confirm a large body of observational literature suggesting that individuals diagnosed with ADHD are at a higher risk of initiating smoking, transitioning into regular smoking, and being less able to quit.Interestingly, there was also evidence for causal effects of liability to smoking on ADHD risk. This is in line with previous literature indicating that smoking can have detrimental, long‐term effects on attention.n = 46 568), and so it would be good to attempt replication of this finding when bigger samples become available. There was no clear evidence for causal effects between liability to ADHD and coffee consumption, which would indicate that observational correlations are the result of shared risk factors rather than causality.We found some weak evidence for a causal effect of liability to ADHD on alcohol dependence risk (based on a DSM diagnosis) but no clear evidence for causal effects of liability to ADHD on drinks per week or alcohol problems (based on the AUDIT self‐report survey). These findings are of particular interest because current evidence on the mechanisms underlying associations between ADHD and alcohol use is inconclusive.Important strengths of this study include the very large and recent samples that the analyses are based on, the variety of different substance use phenotypes that were included, and the use of multiple sensitivity analyses that each rely on distinctly different assumptions. However, there are also limitations to consider. First, the genetic instruments used in MR may vary in their strength . Stronger instruments are more likely to identify a causal effect, which in theory could explain why there was reasonable evidence for causality for some relationships , but not for others . When looking at the predictive power of the instruments, the differences were modest—for ADHD, all SNPs included in the instrumental variable combined explained 0.5% to 0.7% of the variance, for smoking initiation 2.4%, for cannabis initiation 0.2%, for alcohol drinks per week 1.1%, for alcohol problems 0.3%, for alcohol dependence 0.2%, and for coffee consumption 0.6%  Mendelian randomization analyses from liability to ADHD to substance use risk, and from liability to substance use to ADHD risk.Table S4. I2 statistic for Mendelian randomization analyses from liability to ADHD to substance use risk, and from liability to substance use to ADHD risk.Table S5. Mendelian randomization analysis from liability to ADHD to alcohol intake frequency, in UK Biobank .Table S6. Results of the Mendelian randomization analyses from liability to ADHD to substance use risk and from liability to substance use to ADHD risk, including Inverse Variance Weighted (IVW) estimates and four sensitivity analyses: weighted median, weighted mode, MR‐Egger regression and GSMR  – after Steiger filteringTable S7. Results of the Mendelian randomization analyses using summary level data from liability to smoking initiation to children (<13 years) ADHD symptoms including IVW estimates and four sensitivity analyses: weighted median, weighted mode, MR‐Egger, and GSMR .Table S8. Results of the LCV  modelFigure S1. Leave‐one‐out analyses depicting the results of Inverse Variance Weighted (IVW) Mendelian randomization analyses from liability to ADHD to smoking initiation risk after excluding each of the genetic variants from the analysis, one at the time. This gives an indication of whether or not the overall effect is driven by one particular genetic variant.Figure S2. Leave‐one‐out analyses depicting the results of Inverse Variance Weighted (IVW) Mendelian randomization analyses from liability to ADHD to cigarettes smoked per day after excluding each of the genetic variants from the analysis, one at the time. This gives an indication of whether or not the overall effect is driven by one particular genetic variant.Figure S3. Leave‐one‐out analyses depicting the results of Inverse Variance Weighted (IVW) Mendelian randomization analyses from liability to ADHD to smoking cessation risk after excluding each of the genetic variants from the analysis, one at the time. This gives an indication of whether or not the overall effect is driven by one particular genetic variant.Figure S4. Leave‐one‐out analyses depicting the results of Inverse Variance Weighted (IVW) Mendelian randomization analyses from liability to ADHD to lifetime smoking after excluding each of the genetic variants from the analysis, one at the time. This gives an indication of whether or not the overall effect is driven by one particular genetic variant.Figure S5. Leave‐one‐out analyses depicting the results of Inverse Variance Weighted (IVW) Mendelian randomization analyses from liability to ADHD to alcohol drinks per week after excluding each of the genetic variants from the analysis, one at the time. This gives an indication of whether or not the overall effect is driven by one particular genetic variant.Figure S6. Leave‐one‐out analyses depicting the results of Inverse Variance Weighted (IVW) Mendelian randomization analyses from liability to ADHD to alcohol problems risk after excluding each of the genetic variants from the analysis, one at the time. This gives an indication of whether or not the overall effect is driven by one particular genetic variant.Figure S7. Leave‐one‐out analyses depicting the results of Inverse Variance Weighted (IVW) Mendelian randomization analyses from liability to ADHD to alcohol dependence risk after excluding each of the genetic variants from the analysis, one at the time. This gives an indication of whether or not the overall effect is driven by one particular genetic variant.Figure S8. Leave‐one‐out analyses depicting the results of Inverse Variance Weighted (IVW) Mendelian randomization analyses from liability to ADHD to cannabis initiation risk after excluding each of the genetic variants from the analysis, one at the time. This gives an indication of whether or not the overall effect is driven by one particular genetic variant.Figure S9. Leave‐one‐out analyses depicting the results of Inverse Variance Weighted (IVW) Mendelian randomization analyses from liability to ADHD to cups of coffee per day after excluding each of the genetic variants from the analysis, one at the time. This gives an indication of whether or not the overall effect is driven by one particular genetic variant.Figure S10. Leave‐one‐out analyses depicting the results of Inverse Variance Weighted (IVW) Mendelian randomization analyses from liability to smoking initiation to ADHD risk after excluding each of the genetic variants from the analysis, one at the time. This gives an indication of whether or not the overall effect is driven by one particular genetic variant.Figure S11. Leave‐one‐out analyses depicting the results of Inverse Variance Weighted (IVW) Mendelian randomization analyses from alcohol drinks per week to ADHD risk after excluding each of the genetic variants from the analysis, one at the time. This gives an indication of whether or not the overall effect is driven by one particular genetic variant.Figure S12. Leave‐one‐out analyses depicting the results of Inverse Variance Weighted (IVW) Mendelian randomization analyses from liability to alcohol problems to ADHD risk after excluding each of the genetic variants from the analysis, one at the time. This gives an indication of whether or not the overall effect is driven by one particular genetic variant.Figure S13. Leave‐one‐out analyses depicting the results of Inverse Variance Weighted (IVW) Mendelian randomization analyses from liability to alcohol dependence to ADHD risk after excluding each of the genetic variants from the analysis, one at the time. This gives an indication of whether or not the overall effect is driven by one particular genetic variant.Figure S14. Leave‐one‐out analyses depicting the results of Inverse Variance Weighted (IVW) Mendelian randomization analyses from liability to cannabis initiation to ADHD risk after excluding each of the genetic variants from the analysis, one at the time. This gives an indication of whether or not the overall effect is driven by one particular genetic variant.Figure S15. Leave‐one‐out analyses depicting the results of Inverse Variance Weighted (IVW) Mendelian randomization analyses from cups of coffee per day to ADHD risk after excluding each of the genetic variants from the analysis, one at the time. This gives an indication of whether or not the overall effect is driven by one particular genetic variant.Click here for additional data file.


Our goal is to promote standardization of procedures and point out that applications of HUC and its byproducts, as well as the resulting advances in regenerative medicine, will depend on rigorous quality control and on more research in this area.The fresh or cryopreserved human umbilical cord (HUC) and its byproducts, such as cells and extracts, have different uses in tissue regeneration. Defining what HUC byproduct is more effective in a particular application is a challenge. Furthermore, the methods of isolation, culture and preservation, may affect cell viability and regenerative properties. In this article, we review the HUC and its byproducts’ applications in research and clinical practice. We present our results of successful use of HUC as a patch to treat gastroschisis and its potential to be applied in other conditions. Our  HUC byproducts, such as cells and extracts, have been studied outcomes . HUC Mesoutcomes . HUC patoutcomes . Howeveroutcomes .in vitro and we propose that different extraction methods might produce differences in healing outcome.In this article, we review previous relevant work regarding the application of HUC in regenerative medicine, we explain details of the HUC structure and its use as a patch, either fresh or cryopreserved. HUC byproducts such as MSCs and extracts have a great potential for clinical application; we describe its characteristics and regenerative properties. In addition, we discuss the potential future applications of HUC and its byproducts. Precise identification of the regenerative properties might lead to specific biosynthesis and pharmaceutical production . AdditioHuman umbilical cord can be easily collected after birth and applied fresh, using the newborn’s own, to treat tissue defects in autologous procedures . It can The initial reports of HUC use in pediatric surgery were published by Heaton, Samii and Jafroudi, Liu, and Komuro, who successfully used it as a patch to treat gastroschisis . The usen = 12, one reported serious adverse event), while no infections were observed and varied levels of improvement were reported. So far, these results imply that standard clinical trials phase II/III are necessary to evaluate efficacy  has shown therapeutic effects in hematopoietic disorders and in cancer treatment . In 1989efficacy .Human umbilical cord, compared to other tissues such as bone marrow or fat, is a better source of mesenchymal stem/stromal cells (MSCs), as it is easy to access and readily available in great quantities after birth . Couto ein vitro. As an example, HUC DWJ was used in a murine calvarial defect, where osteocytes migrated and promoted tissue reconstruction  are needed to fully understand its applications and satisfy the need for innovative and effective therapies.The HUC plays a crucial role in the development of the fetus as it supplies oxygen and nutrient-rich blood to sustain its growth. It has a unique anatomic architecture, allowing the communication between the mother and the fetus through the feto-placentary membrane and through hormones and cytokine interaction . The HUCDuring embryonic development, hematopoiesis takes place in the yolk sac and later in the aorta-gonad-mesenphoros region (AGM). Early hematopoietic and mesenchymal colonies of cells migrate from the newly formed umbilical cord toward the placenta, followed by a second round of migration from the placenta to the fetal liver and finally to the bone marrow . StudiesGastroschisis is a congenital defect, in which the abdominal wall of newborns is not fully closed . The exaSchuster and colleagues used teflon sheets to treat giant omphaloceles . Later, The HUC, and especially its WJ, is a reservoir of stem cells; specifically, WJ-MSCs might be responsible of the immunomodulatory and regenerative effects observed in gastroschisis . The WJ-In Hospital Carlos Andrade Marin in Quito-Ecuador, every year, 4–6 patients with gastroschisis are admitted. Dr. Edwin Ocaña routinely performs a SGC using a HUC patch. In his protocol, under general anesthesia, he performs a manual introduction of the exposed abdominal contents into the abdominal cavity, ligates the umbilical vessels, filets the umbilical cord lengthwise, without disrupting the blood vessels, and applies the exposed WJ the defect and to the reintroduced abdominal contents. He models the patch according to the size of the wall defect and applies a hydrocolloid patch on top. Local care is performed every 5 days, where the HUC is repositioned according to the extent of closure of the wall defect while covered with a new hydrocolloid . This prUmbilical cord patch (UCP) has shown to improve the repair of gastroschisis defects, opening the possibility to apply it in other neonatal and adult defects and wounds . PreviouThe cryopreserved HUC patch has been applied in chronic wounds of foot and ankle , diabetiin vitro differentiation potential in three lineages such as osteocytes, chondrocytes and adipocytes  . These cipocytes . MSCs caipocytes . Regardiipocytes . HUC bloipocytes .in vitro, in vivo and in clinical trials. MSCs from the WJ and the HUCB and decellularized extracts, have been shown to promote tissue repair and healing in patients, potentially becoming “off-the-shelf” therapies , FGF, IGF-I, microvesicles (MVs) and other molecules] that have the potential to be used in regenerative applications as they stimulate cell proliferation and differentiation . The HUC in vivo .in vitro proliferation and migration of fibroblasts, as described in the literature  and at the Hospital Carlos Andrade Marín (HCAM). Ethics Committee approval for the extract assays: Universidad San Francisco de Quito, USFQ, 2018-209IN. HCAM written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin to Dr. Edwin Ocaña. Written informed consent was obtained from the minor(s)’ legal guardian/next of kin for the publication of any potentially identifiable images or data included in this article. No identifiable data or image is presented in the article.in vitro assays of proliferation and wound healing. EO applied the HUC treatment in the gastroschysis patients. EO, JÁ–B, AC, LE, and MG provided intellectual input and supported the development of the HUC extracts and its applications in vitro. LE provided the idea for investigating the HUC and its by-products, in clinical research and translational medicine. FV and AC developed the technique to obtain the HUC extract. FV, VC, EM, MO, LE, MG, and AC wrote the manuscript. LE and AC conceptualized the manuscript.All authors contributed for the development of this article. FV, VC, EM, MO, and AC performed the 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.


Furthermore, the products of the reaction can be isolated into two major components: Oxone®-mediated TEMPO-oxidized cellulose nanomaterials Form I and Form II (OTO-CNM Form I and Form II). This study focuses on the characterization of the properties of OTO-CNMs. Nanoparticle-sized cellulose fibers of 5 and 16 nm, respectively, were confirmed through electron microscopy. Infrared spectroscopy showed that the most carboxylation presented in Form II. Conductometric titration showed a two-fold increase in carboxylation from Form I (800 mmol/kg) to Form II (1600 mmol/kg). OTO-CNMs showed cellulose crystallinity in the range of 64–68% and crystallite sizes of 1.4–3.3 nm, as shown through XRD. OTO-CNMs show controlled variability in hydrophilicity with contact angles ranging from 16 to 32°, within or below the 26–47° reported in the literature for TEMPO-oxidized CNMs. Newly discovered OTO-CNM Form II shows enhanced hydrophilic properties as well as unique crystallinity and chemical functionalization in the field of bio-sourced material and nanocomposites. The 2,2,6,6-tetramethylpiperidin-1-oxyl (TEMPO) oxidation of cellulose, when mediated with Oxone As one of the most abundant resources on Earth, cellulose has been explored for a myriad of applications. Cellulose is a linear polysaccharide chain with repeated β 1–4) glycosidic linkage of D-glucose monomers –4 glycos. While tIn 1995, De Nooy et al. discovered a technique for oxidizing primary and secondary alcohols using the radical 2,2,6,6-tetramethylpiperidin-1-oxyl (TEMPO) oxidation method . Chang a® (KHSO5) is a non-toxic, highly water-soluble, and cost-effective reagent, which when used with TEMPO forms aldehyde-free TEMPO-oxidized cellulose .Solutions of MCC, OTO-CNM Form I, and OTO-CNM Form II were used as the stock solution. After mixing in a graduated flask, 50 mL aliquots of stock solution were separated. Aliquots were then prepared with and without mechanical treatment. Mechanical treatment involved homogenization in a blender for up to one hour, followed by sonication using probe sonication . A NewtoThe preparation of the OTO-CNM thin films was carried out via two methodologies: the casting-through filtration method, and the wet-casting method (drying) ,32. Cast®-mediated TEMPO-oxidized cellulose nanomaterials (OTO-CNMs). The images were analyzed using ImageJ to determine the width of the cellulosic nanomaterials. Microcrystalline cellulose and OTO-CNM Form I and Form II were suspended in distilled water at a concentration of 0.05% by mass. Then, 2.5 microliters of reagent was dropped onto a copper TEM grid. After 24 h of drying, 10 microliters of 1.0% phosphotungstic acid, a negative stain, was used for image clarity of the cellulose fibers [A JEOL JEM-1011, transmission electron microscope Peabody, MA 01960 tunneling electron microscope (TEM) was used to determine the physical properties of the microcrystalline cellulose starting material, and Oxonee fibers . Phospho−1 wavenumber with seven repetitions at a resolution of 4 cm−1 [A PerkinElmer Frontier FTIR-ATR spectrometer from Shelton, CT was used to characterize the chemical differences between the oxidized cellulosic material and traditional microcrystalline cellulose. The FTIR-ATR analysis was done on a single bounce diamond/zinc selenide crystal in the range of 4000–650 cmTo determine the surface charge density of the CNMs, conductometric titrations were carried out. Dispersions of 1 wt% CNMs were corrected to the concentrations of 0.05 M HCl (Sigma Aldrich) and 0.02 M NaCl (Sigma Aldrich). The dispersions were titrated with 0.05 M of NaOH (Sigma Aldrich) at a rate of 0.20 mL/min. Well-distributed dispersions and consistent protonation of the OTO-CNMS in solution were maintained with all samples of OTO-CNM Form I and Form II. Titration conductivity values were determined through plotting against the volume of NaOH added. Since the conductivity is directly proportional to the volume of electrolyte, the measured values of conductivity were corrected using Equation (1) .(1)CondAnalyses were performed in triplicate, and equivalence points were obtained from the intersection of the least-squares regression line fit to the data points making up the conductometric titration curve. Blank titrations were used to correct for any free ions.Equation (2) was used to calculate the surface charge density of the CNCs ,25.MCC and OTO-CNM Form I and Form II samples were prepared on glass coverslips with the addition of an adhesive enamel. OTO-CNM samples were subjected to X-ray diffraction analysis from a 5–45° angle (2θ)  A PhilipCrystallite size and lattice strain were investigated using the Williamson and Hall method . This meB) is inversely proportional to crystallite size (L) [B) is line broadening at half the maximum intensity (FWHM), (K) is a dimensionless shape factor (0.94), (L) is the mean size of the ordered crystalline domains, (λ) is the wavelength of the incident light, and (θ) is the angle of incidence. Lattice strain is a measure of the distribution of lattice constants, such as lattice dislocations arising from crystal imperfections [Crystallite size can be characterized using the Scherrer equation (Equation (4)), where peak width (size (L) ,36.(4)Bfections .cX) of a CNM sample can be calculated using the total diffracted X-ray intensity assuming the signal comes from both the crystalline and amorphous regions, as shown in Equation (5). This calculation is based on Ruland’s principles and Rietveld analysis, requiring the total crystalline area (cA) and the total amorphous area (aA) of the deconvoluted XRD pattern after the subtraction of a background spectrum [The crystallinity (spectrum .(5)Xc was used. Cellulose polymer (10 wt%) was added into NMP to complete a 100 g membrane casting solution . ReagentOnce the final combination of film materials was completed, the mixture was sealed in the reagent bottle to avoid the evaporation of the solvent. The bottle was continuously mixed in a bottle roller over 72–120 h at room temperature. All films were generated by non-solvent induced phase separation . Sample ®-mediated TEMPO-oxidation process (OTO-CNM Form I and OTO-CNM Form II). Cellulose structures that fall within the scope of CNMs were identified and were shown to develop using the Oxone®-mediated TEMPO-oxidation process with and without mechanical treatment. The facile synthesis of CNMs in mild conditions without additional treatment allows for advances in the development of industrially safe production of CNM. Unique chemical structures of the two fractions of OTO-CNM qualitatively identified through FTIR showed the carboxylation of the OTO-CNMs, as illustrated in In this study, we investigated the material properties of the two unique forms of CNMs produced via the OxoneA US patent has been submitted with a potential royalty stream to the inventors. International Publication Number WO 2019/023702 A1. International Publication Date: 31 January 2019.


Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental disorder among children resulting in disturbances in their daily functioning. Virtual reality (VR) and machine learning technologies, such as deep learning (DL) application, are promising diagnostic tools for ADHD in the near future because VR provides stimuli to replace real stimuli and recreate experiences with high realism. It also creates a playful virtual environment and reduces stress in children. The DL model is a subset of machine learning that can transform input and output data into diagnostic values using convolutional neural network systems. By using a sensitive and specific ADHD-VR diagnostic tool prototype for children with DL model, ADHD can be diagnosed more easily and accurately, especially in places with few mental health resources or where tele-consultation is possible. To date, several virtual reality-continuous performance test (VR-CPT) diagnostic tools have been developed for ADHD; however, they do not include a machine learning or deep learning application. A diagnostic tool development study needs a trustworthy and applicable study design and conduct to ensure the completeness and transparency of the report of the accuracy of the diagnostic tool. The proposed four-step method is a mixed-method research design that combines qualitative and quantitative approaches to reduce bias and collect essential information to ensure the trustworthiness and relevance of the study findings. Therefore, this study aimed to present a brief review of a ADHD-VR digital game diagnostic tool prototype with a DL model for children and the proposed four-step method for its development. Attention-deficit/hyperactivity disorder (ADHD) is a disorder characterized by excessive hyperactivity and greater impulsive and inattentive behavior than in peers of the same age group, resulting in disturbances in the patients’ daily functioning, such as studying and interacting with the environment. Moreover, children with ADHD are known to be targets of bullying and become scapegoats for several unwanted situations, which may eventually cause feelings of isolation and trigger other mental disorders, such as behavior disorders, mood disorders, or Internet addiction disorders, in the future . PreviouIn the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition DSM-5), ADHD is categorized as a neurodevelopmental disorder highlighted by deviations of behavior and emotional, cognitive, and psychosocial development . Studies, ADHD isChiatric interviews, observation of the child, and interviewing children and parents based on the diagnostic criteria of the DSM-5 or International Classification of Diseases-10/11 (ICD-10/11). During the mental state examination, several symptoms might or might not be exhibited, depending on the children’s familiarity with and adaptability to the interview and observation setting. Thus, the results of psychiatric examinations to establish diagnosis of ADHD mostly come from the parents. Furthermore, ADHD can co-exist with other neurodevelopmental disorders, such as intellectual disability, autism spectrum disorder, or other specific learning disability and mood disorder like depression or pediatric bipolar disorder. Therefore, it is necessary to develop an ADHD diagnostic tool that is highly sensitive and specific to ADHD clinical symptoms, not only to justify the ADHD diagnosis but also to create a non-threatening environment for children. Several studies have shown that virtual reality (VR) can be used as a diagnostic tool for ADHD because of its ability to create a virtual environment that is more enjoyable and able to collect better behavioral, inattention, and distractibility data than ordinary neuropsychological tests . Most stDue to advancements in technology, diagnostic tools for children with ADHD that is sensitive and specific to ADHD clinical symptoms can be improved by merging a child’s needs and modern technologies, such as VR, with a deep learning (DL) model. In recent years, DL has become quite valuable in the machine learning used in many routine applications, such as VR digital games. DL is a subcategory of machine learning that processes data through many layers of artificial neurons  between the input and output data. These processes are said to be similar to those of the human brain . When suDiagnostic tools are essential in modern medical practices in child and adolescent psychiatry, as they can be used to confirm a diagnosis, provide more evidence to eliminate one diagnosis versus another, provide more evidence for parents, or rule out a differential diagnostic. Although psychiatric examination or other self-rating questionnaires are the main approaches used in ADHD diagnostic procedures, laboratory testing or imaging examinations are used as well, especially to rule out a general medical condition or to identify any medical conditions that mimic ADHD. Nowadays, advancements in biological psychiatry have suggested several hypotheses based on the biological markers for ADHD; however, this area of investigation is still developing. Meanwhile, although there is no device that offers the desired diagnostic accuracy for ADHD clinical symptoms, several devices have been developed to investigate the neuropsychology and electrophysiology of the brain or to perform imaging examinations –22.Nevertheless, the concept of games for health is another important area that should be considered. Under this concept, a game is a system where players engage in conflicts that they need to solve. Hence, a game can be enjoyed by children because it is challenging, consists of rules that stimulate creativity, contains specific goals that need to be completed by the players, provides a feedback system telling them how are they doing, and yields quantifiable outcomes , 24. VR In the last decade, new model of CPTs have been developed using VR technology. VR, one of the most cutting-edge technologies used in the computer science field, was originally developed by Ivan Sutherland and colleagues in 1960 and has grown rapidly since the last decade. Since its beginnings and translations into various areas, several definitions have been proposed for VR, such as real-time interactive graphics with 3D models combined with a display technology that immerses the user in the model world while allowing its direct manipulation. On the other hand, VR has also been defined as the illusion of participation in a synthetic environment rather than external observation of such an environment. VR relies on three-dimensional, stereoscopic head-tracker displays, hand/body tracking, and binaural sound. VR is an immersive, multi-sensory experience. Lastly, VR was described by Cruz-Neira in 1993 as “Virtual reality refers to immersive, interactive, multi-sensory, viewer-centered, 3D computer generated environments and the combination of such technologies that are required to build the objective of a virtual world” , 26.Compared to other media, VR requires greatly heightened immersive techniques; they increase productivity and retention and are easily understood, especially for coding, development, and training. Similarly, VR enables greater spontaneity in users, which strengthens the effect of interactivity. Therefore, VR can be used to perform tasks more easily and with greater comprehension. There are two core components of VR functional aspect: immersion and interactivity. Furthermore, use of a DL model for machine learning can allow more vivid VR experiences as diagnostic tools with more accurate prediction . The expAn ADHD-VR digital game diagnostic tool prototype using a DL model can be designed simplistically with minimal distractions. For example, the child can control virtual hands to grab, release, throw, and place objects. Furthermore, the child can point a laser from his/her index finger for item selection. The environment can be also limited, limiting physical movement, so that children only need a small physical space to operate the VR digital game, allowing them to perform actions while standing or sitting. The more minimized  the VR, the less motion sickness they might experience, helping the child maintain a constant reference to the activity. Furthermore, a VR digital game with a DL model interface would surround the child with various tools having diagnostic value. Children can grab layers from a toolbox to their left or right and place them within a “working space,” a defined region in front of the child containing several sequences of layers within which children can insert, remove, and rearrange layers. After the child grabs a layer from the toolbox, a new layer of the same type will spawn to take its place. In this manner, more than one layer of a given type can be present within a DL model. The child can define the toolbox from left to right and train it by pressing or selecting a button to the left or right of the “working space”. These operations allow for the simple construction and modification of a functional ConvNet. While computing, a display in front of the child reports the status of playing , 27. UpoIn recent years, several studies of ADHD-VR diagnostic tools have been conducted showing that VR is a promising diagnostic tool for ADHD despite not including machine learning or DL applications, and CPT could be of benefit as standardized testing. Neguț et al.  developeDiagnostic tool development is at risk of bias, especially with respect to accuracy. Generally, major sources of bias originate in unclear methodological design, such as research subject recruitment, data collection, the execution or interpretation of the test, or data analysis. As a result, the estimates of sensitivity and specificity of the tool being compared against the reference standard can be flawed, deviating systematically from what would be obtained under ideal circumstances . Biased Therefore, the proposed four-step method is meant to reduce these biases and collect essential information, including the study design and research subjects, to judge the trustworthiness and relevance of the study findings. Furthermore, the proposed four-step method also serves as a mixed-method study that combines qualitative and quantitative methods to aid the development of diagnostic tools in psychiatry . The strIn first step, the qualitative part uses the Delphi technique with a focus group discussion (FGD) method to collect information to build a theoretical concept of an ADHD-VR digital game diagnostic tool for children using a DL model. The FGD would include several mental health professionals, such as child psychiatrists, psychiatrists, developmental psychologists, teachers, parents, computer science experts, and game designers. The first round of the FGD will discuss the clinical symptoms of ADHD based on the DSM 5, ICD-10/11, and their own perspectives. The next discussion would concern how to translate these symptoms into the VR digital game and use machine learning or a DL conceptual framework to transform the input and output data into a single ADHD diagnostic value to support ADHD diagnosis. The second round of the FGD will discuss the concept of a VR digital game diagnostic tool prototype for children using a DL model based on the results of the previous round. The first and second round of FGD may be conducted several times to reach agreement within the group. In addition, the agreement provides a replication concept by either the same or different collaborating groups. From the first and second FGD results, computer science experts and VR digital game designers can begin work to establish the prototype of an ADHD-VR digital game diagnostic tool using a DL model. In this step, the third-round FGD  must include other independent experts and children under the age of 12 years. In this final round, the goals are not only to reach agreement on an ADHD-VR digital game diagnostic tool prototype using a DL model for children but also to confirm whether the diagnostic tool is suitable for use with children. In this way, content validation is achieved.The second step is a feasibility study to lay the foundation of the plan and to reduce or eliminate problems that limit the successful delivery of the accuracy study of the ADHD-VR digital game diagnostic tool prototype using a DL model in a clinical setting (the third step). Based on the United Kingdom’s National Institute for Health Research Evaluation, Trials and Studies Coordination Centre , a feasiThe third step is a diagnostic tool accuracy study  to identify the performance of the ADHD-VR digital game diagnostic tool prototype using a DL model for children. In this step, the results are sensitivity, specificity, likelihood ratio, and positive and negative predictive values. Therefore, the research subjects are children with ADHD and a control group of healthy children; if possible, comparisons can be made with other children whose diagnoses commonly mimic ADHD clinical symptoms, such as autism spectrum disorder, intellectual disability, disruptive behavior disorder, and depressive disorder. The gold standard or standardized testing is the ADHD diagnosis based on the agreement of a number of experts using an ADHD standardized clinical symptom scale or standardized psychiatric examination guidelines for ADHD based on DSM 5 or ICD-10/11 criteria. Although the nature of the behavioral and emotional symptoms of ADHD is heterogeneous, it would be expected that the ADHD-VR digital game diagnostic tool with DL model would be sensitive and specific enough to identify the respective patterns and produce an accurate diagnostic value. Therefore, the number of the research subjects should be adequate for these purposes. In a diagnostic study, the pre-determined value for both sensitivity and specificity must be at least 0.70, and it must be indicative that the probability of the tool detecting a true-positive or a true-negative is at least 70%. Based on the recommendations by Bujang and Adnan , if we eThe fourth step is multicenter clinical trials of the ADHD-VR digital game diagnostic tool prototype for children using a DL model. Multicenter clinical trials should pave the way toward standardization of the diagnostic tool procedures for cost effectiveness and impact on both short-term and long-term clinical uses. While the earlier steps included smaller samples of comparison subjects that are usually locally formed, Step 4 needs to develop larger normative databases that can eventually be used to examine an individual’s data. The development of such databases can be challenging and will require collaboration among research groups concerned with the specific tool that is being developed. The results of this step will be used to re-evaluate the usefulness of the diagnostic tool prototype before it is generalized for clinical use.There are several issues that still need to be carefully examined whenever applying the proposed four-step method diagnostic tool development, such as the fact that qualitative information can be judgmental and biased; therefore, the qualitative data should be collected from many sources until it is condensed. Moreover, the proposed four-step method is originally designed for mixed-method studies. Thus, it needs a careful plan especially to describe all aspects of research, including the research subjects for the qualitative and quantitative parts; the timing ; and the plan for integrating data. However, it affords us the opportunity to ensure that the study findings are grounded in participants’ experiences, providing more and more varied information than can be obtained from strictly quantitative research, and is able to support multidisciplinary team research by encouraging the interaction of quantitative, qualitative, and mixed-methods scholars.ADHD is one of the prevalent neurodevelopmental disorders in a clinical setting. VR and machine learning, such as DL technologies, hold great promise for application in human health diagnostic tools in the near future, especially in areas with low health and mental health resources, such as general practitioners, child psychiatrists, general psychiatrists, psychologists, and behavior pediatricians. Therefore, by utilizing an ADHD-VR digital game diagnostic tool prototype for children with a DL model in standard health services or tele-psychiatry consultation, ADHD can be diagnosed and early management can be delivered, thereby reducing the impact of the illness. In addition, it can provide parents with much clearer evidence for an ADHD diagnosis. In addition, the proposed four-step method, which is based on mixed-method research concepts, holds promise as an approach to develop an optimal diagnostic tool because it is designed to improve the completeness and transparency of reports of diagnostic tool accuracy studies.The datasets generated for this study are available on request to the corresponding author.The studies involving human participants were reviewed and approved by The Ethic Committee of the Faculty of Medicine University of Indonesia, number: KET-503/UN2.FK/Etik/PPM.00.02/2019. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.TW contributed to the design of the manuscript, literature review, manuscript draft and preparation, and writing the manuscript. NW contributed to the literature review and manuscript preparation. RI, FK, RB, and BD conceptualized and reviewed the manuscript.This paper was funded by a “Hibah Q1Q2” grant from Universitas Indonesia Research Funding 2019. The funder had no role in study design, data collection, and data analysis, decision to publish, or manuscript preparation.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.


Attention-deficit/hyperactivity disorder (ADHD) has been associated with increased risk for physical comorbidity. This study used a twin cohort to investigate the association between physical diseases and phenotypic variations of ADHD. A twin cohort enriched for ADHD and other neurodevelopmental conditions were analysed. The Attention Problems subscale of the Child Behavior Checklist/Adult Behavior Checklist (CBCL/ABCL-AP) was used to measure the participants’ severity of ADHD symptoms. Physical health issues were obtained with a validated questionnaire and were tested in relation to ADHD symptom severity in a co-twin control model. Neurological problems were significantly associated with a diagnosis of ADHD. A conditional model for the analysis of within-twin pair effects revealed an inverse association between digestive problems and the severity of ADHD symptoms, after adjusting for co-existing autism spectrum disorder and ADHD medications. Our findings suggest that individuals with ADHD are susceptible to neurological problems, why a thorough neurological check-up is indicated in clinical practice for this population. In addition, health conditions of digestive system could be considered as a non-shared environmental factor for behavioral phenotypes in ADHD. It supports the possible role of gut-brain axis in the underpinnings of ADHD symptoms, at least for a subgroup of individuals with certain genetic predisposition. ADHD is a complex heritable condition and evidence indicates that the physiology underlying ADHD involves alterations of brain monoaminergic neurotransmitter systems2 and reduced connectivity of brain neural networks3 leading to behavioural phenotypes characterized by a range of cognitive challenges in executive functioning and reward processing. However, while an interplay of polygenic liability and environmental factors during crucial time windows of brain development is assumed, there is still insufficient evidence to support clear causal pathways to ADHD4. Environmental factors of ADHD have received considerable research interest in spite of the high heritability of the condition4. Recent studies indicate that environmental risks might not only account for up to 40% of the variability of ADHD symptomatology5, but could also contribute largely to its heritability through gene-environment interactions or correlations4. Among the potential environmental exposures, the effect of health conditions in physical systems on the variation of ADHD symptoms has not yet been thoroughly investigated, albeit for instance, dietary interventions having shown some positive influence on core symptomatology in children with ADHD via digestive system6.Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental condition, defined by impairing symptoms of inattention, impulsivity, and hyperactivity7. Childhood seizure also appear to increase the risk for ADHD up to five times compared to those without seizures8. In addition, an association between migraine and ADHD has been supported by a recent meta-analysis of epidemiological studies9. Other common physical comorbidities among the ADHD population are immunological dysregulation, including asthma, allergic rhinitis, and atopic eczema11, obesity and overweight12, as well as altered gut microbiome functions13. The higher rates of comorbid ADHD and physical health issues might imply the possibility of shared genetic susceptibility and/or environmental adversities which affect multiple systems increasing the likelihood of the emergence of overlaps between ADHD and physical illness. The probability that biological pathways of a specific physical problem are partly involved in the etiological mechanisms of ADHD may increase, if ADHD phenotypes covary with the presence of physical illness. Based on these assumptions, individuals with ADHD and co-occurring physical conditions could be considered to qualify as stratification subgroups with specific etiological pathways involved, where targeted biological intervention might be meaningful and clinically feasible14.There have been several physical health conditions reported to be associated with ADHD. For example, it was found that children with ADHD are at 2.5-fold risk to develop unprovoked seizure17, no age of onset, type of epilepsy, or interictal EEG change seems predictive of ADHD or its symptoms18. In addition, despite the potential role of neuroinflammation in ADHD etiology, interventions for immune dysregulation in ADHD still lack evidence19. Another physical system which has more recently been implicated in ADHD is the gastrointestinal (GI) tract, which communicates with brain bidirectionally via the gut-brain axis in which nervous, endocrine, and immunological pathways are involved20. One reason is the association between gut microbiota composition and behaviour in animal models, including motor activity21, and another are potentially promising, albeit still rather emerging and experimental, dietary interventions for ADHD6. While a couple of studies showed that individuals with ADHD were more likely to exhibit GI problems compared to those without23, it remains unclear whether there are characteristics of GI tract health conditions which could be candidate biomarkers to identify those who are at risk for ADHD or the potential responders to dietary interventions.The current knowledge on the relationship between physical comorbidity and the severity of ADHD symptoms is still scarce, and the results of previous studies are inconsistent. For example, although studies on epilepsy in ADHD reported that children with higher seizure frequency and poorer seizure control may show increased levels of inattention and hyperactivity symptoms24. Second, in addition to genetic stability, evidence supports that genetic innovations are also associated with ADHD symptoms throughout the brain development25. Hence, studies investigating monozygotic twins discordant for ADHD diagnosis or dimensional symptom variations are particularly powerful to unravel the role of environmental factors in the emergence of ADHD symptoms27. Twin studies enable maximal control of genetic confounding and other bias, such as age, gender, shared environment, and early family experiences. Thus, findings from such study designs are more informative to support causal inferences that are not biased by uncontrolled confounders.The nature of ADHD genetics might complicate the elucidation of environmental contributions to the condition, and its variable phenotypes, such as the role of co-occurring physical conditions. First, a combination of common variants mainly constitutes ADHD etiology indicating clinical diagnosis of ADHD is an extreme expression of continuous heritable traits29, we included ASD diagnosis and IQ as covariates in our analyses.The objective of the present study was to apply twin design to contrast twins differing in the severity of ADHD symptoms to disentangle the role of comorbid physical health problems for the underpinnings of ADHD phenotypes across the full range of inattentive and hyperactive-impulsive behavior manifestations. So far, to the best of our knowledge, this is the first study to address physical health in ADHD using this informative approach. We sought to analyze a twin cohort enriched for ADHD, autism spectrum disorder (ASD), and other neurodevelopmental disorders (NDDs) to explore the distribution of physical health conditions in twin pairs who are qualitatively (for ADHD diagnosis) discordant or quantitatively  differing for ADHD phenotypes. Additionally, we examined the association between physical health and the severity of ADHD symptoms using a co-twin control design to account for genetic influence and other possible shared confounds on their relation. Since ASD and intellectual disability are themselves often associated with physical problems30. Participants in RATSS are referred from the Child and Adolescent Twin Study in Sweden (CATSS), (a) Swedish nationwide population-based twin study focusing on children’s somatic and mental health since 199431 (42.0%); (b) the National Swedish Patient Registry by the Swedish Board of Health and Welfare, (c) clinical departments in Region Stockholm  pediatric units); (d) summons in social and print media by national interest organizations for neurodevelopmental conditions, and twin organizations. RATSS enrols twin participants who are discordant or concordant for ASD, ADHD, and other NDDs, as well as concordant typically developing (TD) twin controls. In the present study, 382 twins from 191 pairs were included in the analysis, 107 monozygotic (MZ) and 84 dizygotic (DZ) pairs . Children (< 18 years) accounted for the majority of pairs . Zygosity was determined by standard methods of DNA testing with saliva or whole-blood samples as described previously33. Within our sample, there were 49 twin pairs discordant for a clinical diagnosis of ADHD (14 MZ pairs and 35 DZ pairs), and 27 ADHD concordant twin pairs (14 MZ pairs and 13 DZ pairs). Additionally, 105 twin pairs differed dimensionally for ADHD symptoms (44 MZ pairs and 61 DZ pairs), as defined by an intra-pair difference on the score of the Attention Problems subscale in the Child Behavior Checklist/Adult Behavior Checklist (CBCL/ABCL-AP) of at least 2 points, corresponding to 1 standard error of measurement in our sample . With this definition, the mean scores of CBCL-AP for dimensionally differing twins were 9.44 (twins with higher scores) and 3.74 (the co-twin with lower scores), and the mean scores of ABCL-AP were 8.93 (twins with higher scores) and 2.79 (the co-twin with lower scores). The distributions of CBCL/ABCL-AP scores in groups with different age strata and zygosity are displayed in Table Sample characteristics and composition regarding ADHD concordance within twin pairs are presented in Fig. All procedures performed in this study were approved by the Regional Swedish Ethical Review Board in Stockholm (ref: 2016/1452-31), and were in accordance with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Informed consents were obtained from all participants and/or their legal guardians.30. Clinical diagnoses were based on the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5)1, endorsed by results from standardized diagnostic instruments, including the Kiddie Schedule for Affective Disorders and Schizophrenia-Present and Lifetime Version (K-SADS-PL)34, and the Diagnostic Interview for ADHD in Adults (DIVA 2.0)35, the Autism Diagnostic Interview-Revised (ADI-R)36, and the Autism Diagnostic Observation Schedule Second Edition (ADOS-2)37. Full-scale IQ was estimated with the Wechsler Intelligence Scales for Children or Adults, Fourth Editions39. Dimensional ADHD symptoms were quantified with the CBCL/ABCL-AP, consisting of 10/17 items, assessing behaviour symptoms related to attention problems within the past 6 months. The CBCL (6- to 18-year-olds version)40 was completed by the parents, and the ABCL (for ages 18–59)41 was completed by the participant’s next of kin (mostly parents or spouse). Both the CBCL and ABCL are broadband screeners and part of the Achenbach System of Empirically Based Assessment (ASEBA), a family of tools evaluating problem behaviours and competencies from a wide range of perspectives, including internalizing and externalizing symptoms, as well as social problems and somatic complaints. Items on CBCL/ABCL are rated on a 3-point Likert scale (ranging from 0 to 2), with higher scores indicating more severe symptoms.Participating twins were evaluated using a comprehensive psychodiagnostic assessment administered by experienced clinicians in the RATSS team42. The questionnaire consists of one open question “Has the child (Have you) been seriously ill during his/her (your) childhood?” and 33 closed questions inquiring about whether the participant had ever had a specific physical health diagnosis or problem. The full list of physical conditions and the distribution of these physical health issues in our sample are summarized in Table A parent-/self-report questionnaire validated against medical registry data and designed to obtain information on medical history and present physical health issues is used in RATSS43 and BayesFactor package44 in R version 3.5.1. Student’s t test was used to compare the frequency of physical comorbidity between the individuals with ADHD and without ADHD in the whole sample. To examine if the amount of physical problems differed within twins discordant for ADHD diagnosis or ADHD symptoms, Wilcoxon sign-rank test was used in consideration of the sample size of those twin pairs. Owing to the overall exploratory nature of the research approach, Bayes factors  were also calculated for these comparisons. For the co-occurring physical health conditions which were identified with significantly higher frequency in ADHD in the whole sample, the association with ADHD phenotypes  was tested. Conditional multivariate logistic and linear regression analysis with twin pairs clustered was used to explore the adjusted associations between physical health and ADHD diagnosis as well as dimensional ADHD symptoms, after adjustment for potential confounding variables. To determine the within pair effect of physical comorbidity on the variation of ADHD symptoms among quantitatively differing twins, we used conditional generalized estimating equations (CGEE), a multiply adjusted  linear regression model, to eliminate the influence of pair-consistent confounders43. Child (< 18 years) and adult (≥ 18 years) participants were analysed separately in addition to the whole sample which included participants of all ages, except for the adult discordant twins due to limited sample sizes. All tests were two-tailed and p-values of 0.05 or less were considered statistically significant. A Bonferroni correction was applied for multiple comparisons in all the analyses , the drgee packageIn the total twin sample of all ages, participants with ADHD diagnosis had significantly more neurological problems  compared to those without ADHD , such as age, gender, a comorbid diagnosis of ASD, and level of IQ 12. More studies on physical comorbidity in adult ADHD are needed to further enhance our understanding of whether the association between ADHD and physical health issues persists into adulthood.The reported odds ratios of neurological, immunological, infectious and digestive problems in ADHD compared to non-ADHD are probably low and less than 1.564, the found association might suggest that children with ADHD are more vulnerable to neurological health issues subsequently, as prospective associations found in previous studies indicate65. Mechanisms contributing to the higher comorbidity rate between ADHD and specific neurological problems have been proposed. From a biological perspective, predisposing genes67, disturbances of the norepinephrine and dopamine systems68, and altered brain functional networks69 have been postulated to be involved in the co-occurrence of ADHD, epilepsy, and migraine. The association between ADHD and headaches could be mediated by other psychiatric problems, such as sleep disorders and affective disorders70. Moreover, headache has been commonly reported as one of more frequent side effects of ADHD medications71.Our results showed that neurological problems are associated with ADHD diagnosis in childhood. Although alterations in attentional capacity can be secondary to frequent seizure attacks, headache episodes, and antiepileptic medication18. Regarding the association between neurological conditions and the severity of ADHD symptoms, we did not find a significant association. Since the individuals with ADHD in our sample were not drug-naïve, possible the treatment and side effects of ADHD medications could not be ruled out as a moderator influencing the outcomes. Further investigations with medication naïve subjects would be helpful to explore the impact of neurological health issues on ADHD symptoms.Given the possible disabling consequences of neurological complications, it is imperative for practitioners working with ADHD to be attentive and to provide adequate management for the comorbid nervous system conditions in this population. Likewise, ADHD should also be screened for in children with neurological problems for early identification and intervention73. Their caregivers and relatives might also pay more attention to their diet preparation, including nutritional balance and elimination of food with artificial additives74. The diet alteration may change the gut microbiota75, which had been proposed to link with the potential pathophysiology of ADHD symptoms through vagus nerve, neuro-metabolites, and neuroinflammation pathways20. In addition, the association between possible dietary change and improvement of ADHD symptoms might reflect the effectiveness of diet intervention in previous double-blind placebo-controlled trials in children with ADHD76. However, our results did not demonstrate similar effects of existence of digestive problems among dizygotic twins, whose genetic makeup differs. This may indicate that the contribution of GI tract health to the variation of ADHD symptoms could be synergistic under certain circumstances, such as a genetic predisposition towards ADHD. This is consistent with the findings from trials of diet treatment, in that only a subgroup of children with ADHD responded to the administration of dietary change76. Therefore, it is recommended for future research to focus on the predictors of recognizing those children who would benefit from diet intervention, as well as the mechanisms underlying the association of diet, digestive problems, and ADHD symptoms. Still, other alternative explanations for the inverse association between GI problems and ADHD symptoms are needed to be considered. For instance, children’s gastrointestinal illness might increase parents’ tolerance for their children’s behavioral problems.Based on our analysis of within pair effects of physical comorbidity, GI system health could be considered a non-shared environmental factor to the severity of ADHD symptoms among monozygotic twin children. Among the digestive problems presented in the twins with less severe ADHD symptoms, lactose intolerance accounted for the majority of those . The others were irritable bowel syndrome, abdominal pain, intestinal polyps, diarrhea, and gastroenteritis. We speculate that those children with GI tract health issues were more likely to have diet adjustment to avoid food possibly inducing GI symptoms31, some twins participating in RATSS were also recruited from via other sources and not sampled randomly, why the overall limited representativeness of our study sample and the generalizability of the results must be kept in mind regarding the frequency of physical problems. Moreover, our findings from a twin sample should be interpreted with caution when extending to singleton samples. Twin pregnancy has increased risk for perinatal morbidity77, which is associated with neurodevelopmental conditions and neurological complications78. Second, the sample size of adult ADHD in our study was limited, making results for association between physical comorbidity and ADHD among children and adults harder to compare. In addition, we did not have enough pairs of twins discordant for ADHD diagnosis to explore the within pair effect of physical problems on the clinical phenotypes of ADHD. Third, the CBCL/ABCL-AP is not a symptom scale derived from DSM-5, making analysis for categorical ADHD and dimensional ADHD symptom less comparable. Despite the validity of CBCL/ABCL-AP for identifying ADHD79, instruments designed for quantifying ADHD symptoms such as the Conners Rating Scale-Revised (CRS-R)80, the Swanson, Nolan, and Pelham Questionnaire (SNAP-IV)81, and adult ADHD self-report scale (ASRS)82 should also be considered in future studies to measure the core symptoms of ADHD more directly. Fourth, information on physical comorbidity and medical history of infectious diseases was reported by either parents of children with ADHD or adult participants with ADHD via questionnaires. Thus a risk of reporting and recall bias which might lead to an overestimation of the found associations cannot be ruled out. Fifth, the contribution of each physical problem to the association with ADHD symptoms may not be equal, which means the effect size of each problem in the association analysis could be varying. Therefore, the results of our study might be limited with the unweighted approach. Still, the grouping of physical problems might not be accurate either with regard to underlying mechanisms. Sixth, we were unable to confirm whether twins with more digestive problems compared to their co-twin were administered diet adjustments by their parents or not. Further investigations are needed to clarify the association between dietary change and the variation of ADHD symptoms among twins dimensionally differing for ADHD. Finally, our participants with ADHD were not free from treatment. Hence, our results could also be limited in view of the effects of ADHD medication or non-pharmacological interventions.There are several limitations to this study that need to be addressed. First, although mainly selected from a population-based study75. In addition, we found that neurological problems are associated with ADHD diagnosis among children, in line with previous prevalence studies. Since the emergence of neurological conditions could be either prior to or subsequent to ADHD, it is recommended for clinicians to be aware of the higher rate of comorbidity, and to provide adequate assessment and intervention to improve both physical and psychosocial outcomes of children with ADHD and neurological health issues.In conclusion, controlling for the contribution of complex genetics and other common confounders, our findings suggest that health conditions of digestive system are associated with ADHD symptom presentation among twins, and thus form a non-shared environmental factor for behavioural phenotypes in ADHD. Hence, our results support that the gut-brain axis might play some role in the underpinnings of ADHD symptoms, at least for a subgroup of individuals with certain genetic predisposition. In non-responders and those intolerant to ADHD medications, it could be of clinically valuable to identify those individuals with ADHD who might benefit from diet treatment, which has been believed to alter the gut microbiota composition


Objective: Attention deficit hyperactivity disorder (ADHD) is one of the most common problems in adolescents. Risky behaviors in patients with ADHD are due to impaired impulse control resulting from problems with inhibition of proponent responses, controlling interference, and stopping ongoing responses after feedback on errors. The present study investigated the relationship between ADHD and risky driving behavior and the likelihood of car accident in Arak, Iran, in 2015-16.Method: This case-control study was conducted in the Emergency Department of Vali-Asr hospital in Arak (Iran) on drivers who met the inclusion criteria. The data gathering tools included the Demographic Questionnaire, Manchester Driving Behavior Questionnaire (MDBQ), and Wender Utah Rating Scale (WURS). Statistical analyses were performed using SPSS version 20 software. Results: The mean of ADHD (±SD) was higher among cases (81.64 [26.78]) than in controls . The mean of risky driving behaviors (±SD) was higher among cases (66.41[26.78]) than in controls (36.79 [25.42]). There was a significant relationship between ADHD, risky behavior, lapse errors, slips, deliberate violation, and unintentional violation and car accident (P = 0.000).Conclusion: This study showed that ADHD increases the risk of road crashes and motor vehicle injuries. These drivers tend to drive at unauthorized speed, have less control over the vehicle, drive more carelessly, and are more likely to have an accident. ADHD is known as a risk factor for traffic crashes. In the past, research has shown a relationship between driving and individuals with attention-deficit hyperactivity disorder (ADHD) . This coRisky behaviors in patients with ADHD are due to impaired impulse control because of problems with inhibition of proponent responses, controlling interference, and stopping ongoing responses after feedback on errors -7. AlmosWe performed a case-control study to investigate the relationship between adults’ attention-deficit hyperactivity disorder, risky driving behaviors, and motor vehicles injuries. The study was conducted in the Emergency Department of the Vali-Asr hospital in Arak (Iran), which serves urban and rural populations of West of Iran. Patients were recruited from October 2015 to March 2016. Sample size was calculated based on previous studies  study showed that all 4 factors of this scale have high internal consistency coefficients  and that the Driving Behavior Questionnaire is a reliable and valuable tool which can be used in driving behavior research . QuestioThe Wender Utah Rating Scale (WURS) is a self-report questionnaire used to retrospectively diagnose the occurrence of attention-deficit hyperactivity disorder in childhood in the age group 18 years and above and confirms the occurrence of attention deficiency hyperactivity disorder in childhood . It seemStatistical AnalysisIn this study, descriptive statistics were used to determine mean and standard deviation. Descriptive analysis and bivariate tests of association were used prior to multivariate binomial regression analysis to estimate the odds ratios of ADHD and risky riding behavior scales. Also, P < 0.05 was considered to be statistically significant. Scores on each factor of the MDBQ  and ADHD were compared between case and control groups using independent samples t tests. Statistical analyses were out using SPSS version 20 software. Of the 202 participants enrolled in the study, 102 (50.49%) drivers involved in a car accident, with a mean (±SD) age of 35.67 (12.41) were compared with 100 (49.51%) drivers without car accident, with a mean (±SD) age of 32.89 (12.28). All participants were men and their mean age was 34.26±12.4 years. Of the participants, 67.3% were married and 58.4% were the head of their own family. The present study aimed to investigate the relationship between road Crashes in adults with ADHD and risky driving behavior. The findings of this study showed that the average of ADHD and high-risk driving behaviors were higher in the case group than in the control group. As predicted and in line with previous research, this study showed that ADHD and risky driving behaviors increase the risk of road crashes and motor vehicle injuries. Inattention, careless and imagination that there is no risk cause their accidents. In this study, most of the participants in both case and control groups were 20-40 years old, married, self-employed, heads of their family, and living with their family. Based on the results of Alavi et al (2017) study, older age reduces the occurrence of accidents (positive effect), which means that the odds of accident occurrence is reduced by 0.01-fold per 1 year age increase . The resMost of the participants had cycles, good income, and no medical history, respectively . Some stThe mean of ADHD (±SD) in case and control groups was 81.64 (26.78) and 64(24.28), respectively, which is similar to El Farouki study. El Farouki et al proposed that distraction induced by an external event, distraction induced by an internal thought, and ADHD are associated with responsibility for crashes. Their study showed a higher risk of being responsible for road crashes when exposed to both external distraction and ADHD . In thisThe present study showed that risky driving behavior, including lapse errors, slips, deliberate violation, and unintentional violation are more common in cases, indicating that risky driving behaviors cause more accident and injuries in drivers. Based on this study and previous research, ADHD drivers differed from healthy drivers on a number of indicators of driving performance, specifically, average speed, traveling distance over the speed limit, vehicle control , changing lanes/overtaking safely on the motorway, reactions to sudden events, and the expression of frustration or anger with other road users. Barkley et al (1993) claimed that the risk of crashes in drivers with ADHD was 3 to 4 times higher than in other drivers. Other studies that have rejected the above claim but have shown that drivers who have ADHD disorder along with other disorders, such as ODD  or conduct disorder (CD), have a higher risk of car accidents than drivers who have only ADHD .  The ADHD group also had more driving offences and accidents and scored higher on the lapses and violations based on subscales of the Manchester Driving Behavior Questionnaire. In addition, ADHD drivers made negative comments (anger and swearing) significantly more frequently than healthy drivers , 27-32. The results of this study showed that ADHD and high-risk behavior were more prevalent in the case group who had a traffic accident. Karsazi investigated the role of adult ADHD disorder in high-risk driving behaviors with difficulty mediating emotion regulation. The results indicated that slips and driving errors are directly affected by ADHD symptoms due to their cognitive origins. However, driving disorders, because they are influenced by emotional and motivational factors, are indirectly mediated by difficulty in regulating the emotion regulation of ADHD symptoms.This study had some limitations that must be considered when interpreting the results. One limitation is that the research community only included men. The most important reason for the overwhelming number of male participants in this study was to observe the principle of the gender ratio of drivers. Many traffic injuries are fatal but they were not included in our study. One of the limitations of this study was the use of self-report questionnaires. However, to reduce this limitation, clinical psychologists completed the questionnaires by interviewing the participants.Drivers with ADHD and risky driving behaviors have less control over their cars and drive more violently and faster compared to normal drivers. Also, drivers with ADHD and risky driving behaviors have more chance to cause driving accident. Lack of patients' cooperation was one of the limitations of this study. Also, due to inappropriate physical condition, the patients were not able to fill out the questionnaires.