As neonatal care improves in low-resource settings, more preterm or low birth weight (LBW) babies are surviving, but little is known about their long-term outcomes. Globally, preterm and/or LBW babies are at increased risk of mortality, malnutrition, and developmental delay.We aim to describe the differences in development in rural Rwandan children at 24–36 months of age born preterm and/or LBW compared to their peers born term or normal birth weight (term/NBW), and to assess factors associated with poor development.We conducted a cross-sectional study using secondary data analysis from two combined datasets from 2014, using Ages and Stages Questionnaire (ASQ-3) for developmental assessment and anthropometrics for nutritional status (stunting and wasting). Demographic and clinical factors associated with poor developmental outcomes in univariate regression at α = 0.20 were included in a full model; we used backward stepwise penalized multivariable logistic regression to identify a final model at α = 0.05.In total, 445 children were included; 405 term/NBW, and 40 preterm and/or LBW. Half of them  had developmental delay, including 207 (51.1%) among term/NBW and 27 (67.5%) among preterm and/or LBW (p = 0.048). In the final model, term/NBW children with stunting alone had a significant increase in the odds of developmental delay , and children with wasting had a borderline statistically significant increased odds of developmental delay . Being preterm and/or LBW and not stunted completely predicted delay.Half of the children had developmental delay in our sample from rural Rwanda. Preterm and/or LBW infants were more likely to have developmental delay, and the main predictor of developmental delay was stunting, with high rates of stunting observed also in term/NBW infants. Interventions to reduce undernutrition and prevent prematurity and LBW, alongside investments to promote early stimulation for optimal development, are needed if gains in addressing developmental delay are to be made. Among children under five years in low- and middle-income countries (LMICs), an estimated 250 million 44%) are at risk of not reaching their full developmental potential 2. A chil4% are atChildren born with risk factors for developmental delay such as prematurity are a high-risk group 8 whose o8131417Rwanda has worked to reduce neonatal mortality through newborn survival initiatives, including a National Neonatal Care Protocol , and the21Kirk et al. found poor health, nutrition and development outcomes among children born preterm and LBW when they were ages 1 to 3 years in rural Rwanda . To our We conducted a cross-sectional analysis, using secondary data from two studies conducted in rural communities across 11 of 30 districts in Rwanda. Data from the original two datasets were combined and are described below.The first study collected data in 2014 on a random sample of children aged 0–11 and 24–36 months located in rural communities in 10 districts that were pre-selected based on high rates of stunting and poverty to receive the implementation of the Early Childhood Development and Family (ECD&F) intervention by Imbuto Foundation and UNICEF Rwanda (“UNICEF study”) . FollowiThe second study collected data from hospital registers of infants born preterm and/or LBW who were discharged alive from the Neonatal Care Unit at Rwinkwavu District Hospital (RDH) between October 2011 and October 2013 and who received routine community-based care (“Preterm/LBW study”) . RDH is In the combined dataset for this study, we included all children from both databases aged 24–36 months at the time of assessment. We excluded children missing developmental status data which is the primary outcome of the study.The same data collector teams and nutrition and development assessment tools were utilized to collect household data in both studies. Data were merged into one file for analysis; any child with status of preterm and/or LBW was included in the preterm and/or LBW group, whether the child was from the UNICEF study or the preterm/LBW study.Our main outcome, “developmental delay”, was defined using Ages and Stages Questionnaire version 3 (ASQ-3), a parent-reported developmental screening instrument that has been successfully used in Rwanda for providing estimates of the proportion of children whose development may be of concern 2426. The24We defined “preterm” as a dichotomous variable; babies born before 37 weeks of gestational age were defined as preterm . Babies Nutritional status was collected using standard anthropometric procedures and cut-offs from World Health Organization Growth Standards . “Stunti“Ubudehe” is a community-based ranking of relative wealth used in Rwandan national statistics. Ubudehe, at the time of study, was categorized into 6 rankings, with 1 being lowest wealth status and 6 being highest wealth status .We observed an interaction between premature and/or LBW and stunting with respect to developmental delay, so we reported these factors as a combined 4-item categorical variable: “preterm and/or LBW and stunting” equals 0 if neither premature and/or LBW nor stunted, 1 if “not premature and/or LBW but stunted”, 2 if “preterm and/or LBW but not stunted”, and 3 if “both premature and/or LBW and stunted”.We described patient demographics, clinical characteristics, nutritional status and developmental status using frequencies and percentages for categorical data and median and interquartile ranges for continuous data. We reported the proportion of poor developmental outcomes with 95% confidence intervals with a precision of 0.2. We assessed the relationship between patient demographics, clinical characteristics, nutritional status outcomes  and developmental  using Chi-squared test or Fisher’s Exact test for categorical variables and Wilcoxon rank sum or Kruskal Wallis tests for continuous variables.We used logistic regression to identify factors associated with poor developmental outcomes built using backward stepwise procedures for factors associated at α = 0.20 in bivariate analyses. Because we observed small numbers in some cells, we used penalized likelihood logistic regression for the final model to provide robust standard errors, confidence intervals and p-values . All facThe preterm/LBW study received approval from the National Health Research Committee, Rwanda National Ethics Committee (RNEC), the Rwanda Ministry of Health, and the Boston Children’s Hospital Institutional Review Board (IRB). The UNICEF study received approval from RNEC and the Harvard T.H. Chan School of Public Health IRB. Because this is a secondary analysis of deidentified data, additional ethics review was not required; technical review was provided by the PIH/IMB research committee.Among 970 eligible children in the combined dataset, 445 (46%) met inclusion criteria of ages 24–36 months, of which 40 (9.0%) were born preterm and/or LBW and 405 91.0%) were born term/NBW , and significantly more developmental delay among children born preterm and/or LBW (67.5%) compared to term/NBW born children (51.1%) at age 2–3 years. However, we also found the high prevalence of stunting in our population (49.2%) as the major driver of developmental delay in children ages 24–36 months.While we expected preterm and/or LBW children to have a higher prevalence of developmental delay 15 comparWe found significantly higher rates of developmental delay at 2–3 years of age among infants born preterm and/or LBW. This increase in developmental delay was also seen in neighboring Burundi, where 27% of LBW children had some form of developmental impairment based on a screening questionnaire at age two after discharge from a neonatal care unit . In MalaWe expected rates of stunting to be high in our study, given the high prevalence of stunting in rural Rwanda (41%)  and the The prevalence of wasting among the children with a history of term/NBW (1.5%) was similar to that in Rwanda (2%) , but morSugira Muryango (Strengthen the Family)—a home-visiting intervention with a successfully completed pilot [While being preterm and/or LBW was significantly associated with delay in bivariate analysis, it was not a statistically significant independent predictor of poor developmental outcomes in the final model. We suspect that the lack of significance was due to our small sample size of children categorized as preterm and/or LBW, and very high prevalence of stunting, rather than a finding actually contrary to most published literature. All of the children with preterm and/or LBW who were not stunted had developmental delay per the ASQ-3. A 2017 study assessing factors associated with developmental delay among the preterm and/or LBW study population found that children who were SGA and very LBW (birth weight less than 1.5kg) had worse developmental scores than children who were born at a normal size for gestational age or at higher weight . However46ed pilot . ProjectOur study has several limitations. First, there are some differences between the specific indicators collected by the UNICEF dataset of term/NBW infants and the preterm and/or LBW dataset, and so only those indicators available in both datasets are included in this study. However, the data available for the two databases is sufficiently similar to allow comparison of key socio-demographic characteristics; our primary outcomes of child development and nutritional status were measured using the same tools and methods, and the data were collected by the same study team. Second, there is no tool for developmental evaluation that has been developed specifically for Rwandan children, and so estimates of developmental delay in our study are based on standard Western cut-points on the ASQ-3. However, the ASQ-3 has been adapted and translated into Kinyarwanda, the primary language spoken in all regions of Rwanda, and is used widely in sub-Saharan Africa. Third, there were few children in the study with prematurity and LBW; a small sample size likely affected our ability to detect risk differences between our groups. Variables such as prematurity and caregiver education that are relevant in larger studies might be less so in this study due to small sample size. Larger studies in this higher-risk population would be of great benefit to understanding risks specific to Rwanda that may be applicable to other LMICs. In the UNICEF study, mothers may have been unable to report LBW or prematurity accurately due to lack of available information. However, previous studies in similar settings have found no significant difference in parental reporting of birth size with hospital records 2–3 years after birth . FurtherThe alarmingly high rates of developmental delay and stunting among toddlers in the study of both children with a history of preterm and/or LBW and those born term/NBW warrants attention to the nutritional and developmental needs for these children, and country-level strategies that prioritize prevention of prematurity and intrauterine growth restriction as part of stunting reduction efforts. Since stunting and wasting were both independently associated with developmental delay, a special focus of resources on developmental interventions to support children with malnutrition, and children born preterm and/or LBW who are more likely to be malnourished, would be warranted. The interaction between prematurity and/or LBW and stunting with developmental delay demonstrates the close linkages between these factors and strategies, and interventions should focus on all children at risk for these conditions especially for children in poor, rural communities.


A previous study  found psychopathological evidence of an additive model of the comorbid group with Chronic Tic Disorders and Attention Deficit Hyperactivity Disorder (CTD+ADHD), which demanded clinical interventions aimed primarily at the factor ADHD. This 14-year follow-up study tested whether this childhood additive model can also be found in young adulthood and whether ADHD remains the most impairing factor.92 patients (22.8% girls) from Roessner et al. (2007) were re-investigated as young adults at the age of 24 years, broken down into four groups: CTD-only (n = 22), CTD+ADHD (n = 23), ADHD-only (n = 24), and controls (n = 23). The Adult Behavior Checklist (ABCL) was used as an equivalent parent-report instrument to the Child Behavior Checklist (CBCL) applied 14 years ago. Statistically, 2x2 factorial design was completed.From the point of view of parents, the factors CTD and ADHD in young adults contributed almost equally to psychopathological problems and showed many interactions, i.e. an interactive model was supported. In addition, the ADHD factor was no longer the leading problem for psychosocial impairment in the adult CTD+ADHD group.The additive model of CTD+ADHD seems to exist no longer in young adults, nor may the childhood predominance of the factor ADHD in comorbid CTD+ADHD. Thus, treatment priority should be decided by clinicians on a case-by-case basis depending on the most impairing disorder of each patient. Chronic Tic Disorders (CTD) and Attention Deficit Hyperactivity Disorder (ADHD) are disabling neuropsychiatric disorders with childhood onset and development into adulthood, where their symptom profile and prevalence rates may have changed. Both disorders show a high rate of comorbidity; about 80% of individuals present with at least one additional behavioral problem , 2.While ADHD co-exists in about 50 percent of patients with CTD, around 20% of patients with ADHD also have CTD. Both of these percentages are well above the level of chance .The phenomenological relationship between CTD and ADHD, especially in the long-term, appears complex. While in CTD most cases show a remitting course , the negn = 112, mean age = 11.1, SD = 2.6 years; CTD+ADHD n = 82, mean age = 10.7, SD = 2.3 years; and ADHD-only n = 129, mean age = 10.5, SD = 2.5 years. A control group  was made up of children from the general outpatient clinic . Patients with CTD and/or ADHD fulfilled the diagnostic criteria for these disorders according to the Diagnostic and Statistical Manual of Mental Disorders DSM-IV-TR  or gender  differences between the CTD and/or ADHD groups.From a descriptive point of view, most mean scores on the ABCL subscales in  were lowThe additive model for CTD+ADHD in adulthood was first tested using information from the parents (ABCL scores). In the 2x2 ANOVA, scores on the eight ABCL syndrome subscales were used as dependent variables, the factors CTD and ADHD as independent variables. The full model resulted in five significant interaction effects between the factors CTD and ADHD on the subscales of aggressive behavior, rule breaking behavior, attention problems, anxious/depressed, and somatic complaints . Contrasn = 92 was found to be sufficiently representative of the original sample of N = 467, despite some bias, namely that the follow-up group in childhood might have been more impaired by the factor ADHD than the original group. The psychopathological profile in young adulthood of the four clinical groups—as originally assessed in childhood, namely CTD-only, ADHD-only, CTD+ADHD, and clinical controls—was assessed using parent (ABCL) ratings on the eight subscales of the ASEBA instruments. In general, the scores on these subscales decreased from childhood (CBCL) to adulthood (ABCL), i.e. after 14 years, patients presented with fewer psychopathological problems. Similar to the pattern found in childhood, the highest levels of psychopathology were found in the comorbid CTD+ADHD group, followed by the ADHD-only group. The CTD-only and control groups reported more or less similar levels. Hence, in descriptive terms, the children in the CTD and/or ADHD groups showed parallel improvement in their psychopathological symptoms in young adulthood, while retaining the same ranking they had in childhood. The higher scores on psychopathology in the comorbid group compared with those with pure CTD or ADHD correspond to previous research [The main objective of this 14-year follow-up psychopathological study was to investigate 1) whether the additive model for comorbid CTD+ADHD found in childhood could also be detected in young adulthood, and 2) whether ADHD was still the most impairing factor. From an original study of 467 patients [research , 20, 21.research . The exiIn view of these results, those parents perceived the comorbidity CTD+ADHD in young adulthood as a single disorder (interactive model), despite their observations in childhood (using the CBCL) supporting an additive model. Possible reasons for this, such as the different perspectives of the respondents or the natural course of the disorder, for example, have still to be elucidated. More longitudinal psychopathological research is needed to clarify this issue, possibly in combination with neurobiological and genetic investigations . In addiDue to the long interval of time, 14 years, between the original investigation and this re-investigation we could only reach 20% of Roessner et al.’s  originalThe lack of information on symptom severity and treatment is another limitation of this study, because the impact of a disorder on general psychopathology largely depends on these factors. Hence, it remains unclear to what extent the shift of an additive model to an interactive model may reflect the natural clinical course of CTD and ADHD or the effect of treatment. It would be of particular value if future research could additionally examine the information on symptom severity and treatment and take their influence into account.In this study we did not conduct a full clinical re-assessment of the patients. Instead, we applied the psychometrically sound broad-band psychopathological questionnaire from ASEBA instruments (ABCL), which was comparable with the childhood information assessed using the CBCL and methodologically sufficient to answer our research question concerning the dimensional modeling of CTD+ADHD. Moreover, we adopted the ABCL fulfilled by the parents instead of self-report from patients to avoid bias from different informants. However, these parent reports need to be interpreted with cautious since the parents are normally not able to observe their children in the same intensity as in the childhood. Four different groups of CTD and/or ADHD patients were investigated according to their assessment at the time of the original study, despite the fact that as young adults some of our patients’ categorization may have changed during development from childhood to young adulthood (e.g. newly onset of tics in former ADHD-only patients). In addition, possible confounding factors such as treatment, severity ratings, and socioeconomic status were not considered. Therefore, interpretations concerning clinical groupings should be treated with caution.This 14-year follow-up study tested whether the additive model of childhood CTD+ADHD can also be found in young adulthood. Parent reports supported an interactive model of CTD+ADHD which suggest they perceived the comorbidity CTD+ADHD as a single disorder. In addition, ADHD was no longer the clearly leading problem for psychosocial impairment in comorbid CTD+ADHD in young adulthood. Therefore, the recommendation to treat ADHD first can no longer be given to young adults. Treatment priority should be decided by clinicians on a case-by-case basis depending on the most impairing disorder of each patient, with severity of disorders being considered in further therapeutic decisions.


Low birth weight continues to be a main cause of child morbidity and mortality. Low birth weight can cause complications in adult life, and is therefore a public health concern. In this study, we determined the maternal factors that contribute to low birth weight (LBW) deliveries in Tshwane District, South Africa.We conducted a case control study of 1073 randomly selected mothers who delivered babies in four hospitals in the district. We reviewed antenatal and maternity registers to obtain information about the mothers and their offspring. We fitted a multiple logistic regression to examine relationships between possible factors associated with LBW.From the total sample of mothers (n = 1073), 77% (n = 824) were adult women, aged 20 to 35 years. Of the adult mothers, 38.54% (n = 412) delivered low birth weight (LBW) infants. The mean gestational age and weight of all infants at birth was 37.16 weeks (SD 2.92) and 2675.48 grams (SD 616.16) respectively. LBW was associated with prematurity, odds ratio (OR) 7.15, 95% confidence interval (CI) 5.18 to 9.89; premature rupture of membranes OR 7.33, 95% CI 2.43 to 22.12 and attending fewer than five antenatal care (ANC) visits OR 1.30, 95% CI 1.06 to 1.61. Male infants were less likely to be LBW, in this population.Women who attended fewer than five ANC visits were predisposed to give birth to low birth weight babies. Mothers should be encouraged to attend ANC visits to detect adverse events like premature rupture of membranes and premature labour timeously. Low birth weight (LBW–a baby born with less than 2500 grams) is a major cause of child morbidity and mortality, especially in sub-Saharan Africa, where most LBW babies are born . AlthougBabies can be born preterm or premature (<37 weeks) or they can be small for their gestational age (≥37 weeks but weigh < 2500 grams). Babies who are born preterm are prone to neonatal problems such as infection, which may require longer hospital stay, increased cost to the family and hospital, the government, and increased mortality rate . Cost anet al (2014) found that maternal anaemia was another risk factor for LBW (72.6% versus 64.5% in term pregnancy) [et al [The main reason for LBW is preterm delivery , but the) [et al  compared) [et al , 10. Mat) [et al .In sub-Saharan Africa, maternal malaria infection is an important predictor of adverse birth outcomes, including LBW or premature birth. In Congo, 94.5% of LBW babies were born to mothers who had malaria . In Malaet al (2016) found that teenage mothers had a higher risk of delivering a LBW infant than adult mothers (16% versus 9%) [Maternal age is another unsettled issue, as it is unclear if young or adolescent mothers are more likely to give birth to LBW babies –15. Somersus 9%) . In contet al 20 found thrsus 9%) . Similaret al 20 found thGlobally, the prevalence of LBW is increasing and remains a challenge despite many interventions aimed at addressing this problem. The United Nations Children’s Fund (UNICEF) reported that in 2013, 22 million LBW infants were born globally, and most died in the neonatal period . The Wor2. It has seven regions, which have 68 clinics, 8 community health centres (CHC), 4 district hospitals, 1 regional hospital, 3 central hospitals and 27 other hospitals [We selected our sample of mothers from four randomly selected hospitals in Tshwane District, South Africa, in 2014. In Gauteng province, Tshwane is one of the 5 districts that are monitored by the health systems trust  . We usedospitals . To seleWe extracted and analysed a total data sample of 1073 mothers and their babies, with a 1:1 ratio of cases and controls. Cases were LBW infants; while controls were normal birth weight infants in this study.All mothers and their babies who had delivered and or were referred to the healthcare centres between January 2013 and December 2014 were eligible for inclusion. We excluded mothers younger than 13 years and older than 35 years; mothers who had delivered more than 5 times (grand multipara); and mothers with twin babies since they may have affected the outcome of interest .We determined the gestational age of the baby at birth from clinical records documented by the midwife. These records are usually obtained through self-reports of the mother’s last menstrual period, which the midwife and obstetrician use to estimate clinical gestational age. In South Africa, being a developing country, ultrasound is not readily available in most public health institutions.http://www.epidata.dk).We entered data, including maternal biographic, reproductive, medical and obstetrical information including the baby’s birth outcome using EpiData Software  and p – values. A p-value less than 0.05, was considered statistically significant.The variables that were used were birth weight  and dependent variables were hypertension–clinically recorded by nurse or doctor in the case file during antenatal visit; preeclampsia–current hypertension with proteinuria (protein in urine) as clinically recorded; PROM– this included all forms–preterm rupture of membranes before 37 weeks and prolonged rupture of membranes (PrlROM) before 37 weeks (did not include PrlROM after term gestation). Infection–urinary tract infection and uncategorised type as recorded in the case files. Anaemia–current low haemoglobin and or as stated in clinical records. Marital status–married–couples who were legally married, single–not legally married and not living with partner, stable relationship–living with partner in a stable relationship but not legally married. Residence–rural–living in squatter camps and or without basic amenities like water and electricity, semi-urban–living in location with basic amenities but in overcrowded locations, urban–living in suburb or within town. All these variables were derived from the clinical records as documented by health care workers.The study was approved by the University of Pretoria ethics committee (Ethics reference number 214/2016) and Department of Health Research Committee for Gauteng Province, South Africa (Project number 28/2016) prior to the study commencement. Facility managers of the four study sites gave written consent for the review of records. Confidentiality was ensured by anonymous data entry, and followed the Helsinki declaration for data management of retrospective study.We included 1073 women who delivered at various study sites in Tshwane District. The mean age of the women was 24.18 (SD 5.13) years, 77% of whom were 20 years and older. The mean gestational age at first antenatal visit was 22.78 weeks see , while tWe analysed continuous variables to determine the relationship between low birth weight (LBW) and normal birth weight (NBW). LBW babies had a mean gestation age of 35.60 weeks, compared to mean gestational age of 37.90 weeks for NBW. The infant length and head circumference of LBW babies was less than that of NBW infants .Sociodemographic factors associated with LBW were maternal age, race of the mother and the residence of the mother. Women older than 20 years  were at risk of LBW delivery . Black African women had an OR 2.67 (CI 1.23 to 5.81) of delivering a LBW infant, compared to white mothers, while the risk of LBW for Asian women was OR 5.55, CI 1.77 to 17.37 compared to white mothers. Marital status was not associated with LBW, and the place where the women delivered was not associated with LBW .Prenatal factors associated with LBW were ANC attendance, the number of ANC visits, not having a syphillis test and a positive HIV status. Almost five percent (4.80%) of mothers who did not attend ANC delivered a LBW infant. Mothers who did not attend ANC had increased risk  of delivering a LBW baby compared to those who attended ANC. Women who did not attend ANC, or had missing information on the ANC visits had increased risk of delivering a LBW infant  compared to women who attended more than 5 ANC visits. Women who attended ANC 1 to 4 times, had an increased risk (OR 1.72 CI 1.22 to 2.43) of delivering LBW babies compared to women who attended more than 5 times. Eight percent of women tested positive  or had unknown syphillis status. Women who tested positive for syphyilis or had unkown syphilis status had a greater risk of delivering a LBW baby  compared to women who tested negative for syphillis. About 11.36% of women tested positive for HIV, and had an increased risk of delivering a LBW infant (OR 1.44 CI 1.07 to 1.94) compared to women who tested negative for HIV. While women who had unknown HIV status had an increased risk of delivering a LBW infant, (OR 2.00 CI 0.90 to 4.43), the association was not statistically significant, see Obstetric risk factors associated with LBW included women with preeclampsia  and premature rupture of membranes (PROM) . Anaemia, hypertension and infections were not significant predictors of LBW deliveries. Most obstetric risk factors had missing values, and were excluded from the analysis.Low birth weight babies had a greater risk of mortality (OR 5.89 CI 1.70 to 20.34) than NBW babies. For infants with congenital anomalies, the risk of LBW was OR 3.30 (CI 1.19 to 9.17). Babies who were born premature had a higher risk of LBW  than babies born at term. Male infants were less likely to be LBW  .In adjusted analysis, maternal age was associated with LBW  for older women compared to younger women. Women who had fewer than 5 ANC visits also remained significantly associated with LBW  compared to those who attended 5 or more visits. Additionally, PROM remained significantly associated with LBW . Preterm delivery also remained associated with LBW  and male infants remained less likely to be LBW . See We conducted a case control study to identify the factors associated with giving birth to a LBW baby in Tshwane, South Africa. In our sample of 1073 mothers, 77% were between the 20 and 35 years old. Young mothers (<20 years) did not have an increased risk of delivering a LBW baby. In this study, LBW was associated with older maternal age; preterm birth; inadequate prenatal care; maternal HIV and syphilis infections; premature rupture of membranes and preeclampsia; and infant sex.In our study, older maternal age was a risk factor for LBW. In contrast to our study, some studies have reported that teenage mothers are more likely to deliver a LBW infant compared to adult mothers , 21. Ngoet al [et al [et al [In our study, women who attended 5 or more ANC visits had a reduced risk of giving birth to a LBW baby. Antenatal care needs to be encouraged, especially in poor socio-economic settings , 24. In et al  did not l [et al  and Mcdil [et al , 14. Of In our study, we identified HIV as a possible risk factor for LBW, similar to some studies , 10. SouWhile our adjusted analysis did not find an association between syphilis infection and LBW, our crude analysis highlighted a potential influence, similar to previous studies . In thisPremature rupture of membranes is associated with both preterm delivery , 13, 32,We found that male babies were less likely to be born with LBW, which is consistent with some studies –34. HoweOur study was limited by the use of self-reported LMP to determine gestational age. Ultrasound scans would estimate gestational age more accurately , but theLow birth weight continues to affect the health and wellbeing of infants. By recognising the risk factors that contribute to LBW, interventions can be tailored to address those factors. Low birth weight is a public health concern of unknown magnitude in Africa, and particularly in Tshwane District, South Africa . Therefo


This paper presents a simple and economical method for preparing durable anti-static functionalized inkjet prints by using P[St-BA-F6]—novel antistatic agents synthesized by an oxidative polymerization of styrene, butyl acrylate, and allyl alcohol polyether F6. The P[St-BA-F6] was characterized by gel permeation chromatography and Fourier transformation infrared spectroscopy. One bath pretreatment solution containing P[St-BA-F6] and pentaerythritol tetraacrylate (PETA) were applied to polyester fabrics before inkjet printing, in order to enhance the color yield and the anti-static properties. The pretreatment conditions, including the concentrations of P[St-BA-F6], curing temperature, and time, were optimized based on inkjet printed polyester fabrics. SEM (scanning electron microscope), XPS (X-ray photoelectron spectroscopy), XRD (X-ray diffractometer), TG (thermogravimetric), and DSC  examined the fabrics. The results showed that the treated PET fabrics exhibited good applied performances, such as higher color yield, better dry rubbing fastness, lower electrostatic voltage, and durable anti-static properties, even after washing 10 times. These results can be attributed to alcohol polythene group (F6) and allyl group (PETA). PETA can be cross-linked with P[St-BA-F6] and PET fiber. The thermal stability of the treated fabric was lower than that of the untreated fabric, owing to the presence of resin film on the fiber surface. Inkjet printing technology shows many environmental advantages over the conventional dyeing and printing process, which enables the cost-effective short-run for production. The pretreatment process is very important for inkjet printing process. It is because that the diffusion behavior of disperse dye ink and the weave density on untreated polyester ) fabric exerts significant impact on the printing accuracy and quality ,2.2 plasma surface-treatment to improve the color strength and the pigment adhesion of the treated surfaces  composite was prepared by an oxidative polymerization of butyl acrylate (BA), styrene (St), and allyl alcohol polyether F6 (F6) to expand its potential applications in pretreatment before inkjet printing for antistatic PET fabric. In addition, a cross-linking agent of pentaerythritol tetraacrylate (PETA) was applied in pretreatment solution to improve the durability of P[St-BA-F6] resin film.2) was purchased in the market. Commercial samples of disperse red ink (D2551 Red) and CYMK inks  were purchased from DuPont Co., Wilmington, DE, USA. Commercial grade BA, St, and F6 were purchased from Jiangsu Haian Petroleum Chemical Co., Haian, China. Commercial grade emulsifying agent AES , emulsifying agent TO-7 , NaOH, and ammonium persulfate were purchased in the market. Commercial grade Sodium carboxymethyl cellulose (CMC) was supplied by Group Chemical Reagent Co., Shanghai, China. Pentaerythritol tetraacrylate (PETA) was purchased from Suzhou Changchunteng Import and Export Co., Suzhou, China.A scoured PET fabric , emulsifier TO-7 (1.0 g), ammonium persulfate (1.0 g), BA (10.0 g), St (3.0 g), and deionized water (33.0 g) were mixed by mechanical stirrer, namely emulsion solutions A (50.0 g). The requisite amounts of emulsion solutions A (33.0 g), F6 (20.0 g) were mixed by a mechanical stirrer, namely emulsion solutions B (53.0 g). The requisite amounts of deionized water (29.0 g) and ammonium persulfate (1.0 g) were mixed in a four-necked round-bottomed flask. The emulsion solutions A (17.0 g) that was placed in a dropping funnel was slowly released into the system at a temperature between 75–80 °C over a period of 30–40 min. Afterwards, emulsion solution B (53.0 g) was slowly dropped into the system at a temperature of 80–85 °C over a period of 90–100 min, with a mixture of shell monomers consisting of BA, St, and F6. Subsequently, the mixture was allowed to react for further 2 h. The core-shell latex particles of P[St-BA-F6] emulsion was obtained . The coaThe latex particles size data of latex particles were obtained from dynamic light scattering on a Malvern particle size .GPC (gel permeation chromatography) analysis was performed in THF (tetrahydrofuran)  while using a Viscotek TDA302  with a WL. M GPC solvent/sample module.−1 by KBr pellets technique and a resolution of 4 cm−1 was used to analyze the chemical structure of the dried latex particles.FTIR (Fourier transform infrared spectroscopy) analyses were obtained from Nicolet 5700  within the frequency range of 650–4000 cm65 and 10° observer).The following measurements were made in order to evaluate the aimed the printing PET fabric. Color yield measurements of the printed PET fabric were obtained from spectrophotometer . The spectrophotometer was set to exclude specular reflection and a large aperture .The rubbing fastness test was performed according to the standard (ISO 105-171 X12:2016) using a Model 670 type friction fastness machine .Electrostatic properties measurements of the sample were examined while using the electrostatic attenuation meter . Electrostatic voltage and static half period were obtained from the electrostatic attenuation meter. Fabric samples were conditioned in a standard atmospheric condition (35% ± 5% relative humidity and 20 ± 2 °C) for 24 h prior to the measurement.The PET fabric was washed 10 times with simulated domestic washing according to the AATCC (American Association of Textile Chemists and Colorists) Test method 135 under the condition of normal washing cycle at 27 ± 3 °C, followed by tumble drying process, in order to determine the durability of the anti-static printed PET fabric.Scanning electron microscopy  was used to examine the surface morphology of the PET fabric treated and untreated by P[St-BA-F6]. The magnification of the SEM is 1800 times.X-ray photoelectron spectroscopy  was used to examine surface chemical composition of fabric surface, using Al Ka radiation (hν = 1486.6 eV) operated at 14.0 kV and 200 W.2 atmosphere and a temperature of 50–600 °C at a heating rate of 10 °C/min. and a flow rate of 100 mL/min.Thermogravimetric analysis (TG) was conducted on PET fabric using a thermogravimetric analyzer . The TG tests were performed in N2 atmosphere on PET fabric. The experiments were conducted in a Perkin-Elmer PE8500 DSC calorimeter . DSC tests were performed in a temperature of −10 to +270 °C at a heating rate of 10 °C/min. and flow rate of 100 mL/min. The sample was heated at 270 °C for 5 min, cooled at a rate of 2 °C/min. to −10 °C during the crystallization process.The differential scanning calorimetry (DSC) experiments were conducted in a flowing NThe X-ray diffraction (XRD) experiments were conducted on PET fabric using X-ray diffraction . The samples were recorded with X-ray diffractometer in angle range 0°–60°.−1, which are assigned to the C=C stretching of the benzene ring. The peaks at 702 and 619 cm−1 are the C-H benzene ring stretching of St  composite was successfully fabricated through the oxidative polymerization of BA, St, and F6. ng of St ,27. The ng of BA . The peacohol F6 . The peaWe use experimental parameter, such as curing fixation temperature, curing fixation time, and P[St-BA-F6] concentration, in order to obtain durable anti-static properties of PET fabric using P[St-BA-F6] pretreatment.In comparison with untreated PET fabric , electroIn comparison with the untreated PET fabric , the K/SThe K/S of treated PET fabric using cyan, magenta, yellow, and black inks was clearly higher than that of the untreated PET fabric, it can be calculated that K/S increased by 25.30%, 33.25%, 40.47%, and 7.47%, respectively. The higher dry rubbing fastness grade was achieved with treated PET fabric using cyan, magenta, and black inks than that with untreated PET fabric. The wet rubbing fastness grade found to be the same for all of the CMYK inks on treated and untreated PET fabric.When the DSC scanning form 270 °C to −10 °C, the untreated PET fabrics enthalpy values, re-crystallization temperature were −50.65 J/g, 208.3 °C, respectively. The treated PET fabrics also did not exhibit any significant difference in enthalpy values (−50.14 J/g) and re-crystallization temperature (205.6 °C). This indicates that the initial reaction proceeded of re-crystallization more easily on the treated P[St-BA-F6] PET fabrics, it had lower enthalpy values, onset temperate, and peak temperate. In this study, the nanoscale P[St-BA-F6] core-shell particles that were prepared by way of seeded emulsion polymerization. The addition of P[St-BA-F6] enhanced the color yield and anti-static properties of polyester fabrics. The obtained latex has a mean size of 34 nm and an average of number molecular weight of 1794.5 with narrow size distribution. The FTIR analysis confirmed the successful fabrication of P[St-BA-F6] core-shell particles while using butyl acrylate, styrene, and ally alcohol polyethylene F6. The results showed that one-bath pretreatment of polyester fabrics with a P[St-BA-F6] and pentaerythritol tetracycline (PETA) is effective in improving the K/S value and the anti-static properties of the disperse dye inkjet Prints. The optimal pretreatment conditions are as follows: P[St-BA-F6] concentration is 30 g/L, curing fixation temperature is 190 °C, and treatment time was for 45 s.It was demonstrated that the P[St-BA-F6] treated PET fabrics exhibited higher K/S values, better dry rubbing fastness, and durable anti-static properties, even after 10 times washings. These results can be attributed to the alcohol polythene group (F6) and allyl group (PETA). The SEM measurements indicated that treated polyester fabric was coated with a thin resin film. XPS measurements indicated that PETA can be cross-linked with P[St-BA-F6] and PET fiber. XRD measurements showed the marginal influence on the crystalline structure of PET fabrics. The TG and DSC measurements showed the thermal stability of treated fabric decreased owing to existing resin film. The initial reaction proceeded of crystal melt was more difficult, but the initial reaction proceeded of re-crystallization was more easily compared with the untreated fabrics.


ADGRL3 (LPHN3) gene predispose to ADHD and predict ADHD severity, disruptive behaviors comorbidity, long-term outcome, and response to treatment. In this study, we investigated whether variants within ADGRL3 are associated with SUD, a disorder that is frequently co-morbid with ADHD. Using family-based, case-control, and longitudinal samples from disparate regions of the world (n = 2698), recruited either for clinical, genetic epidemiological or pharmacogenomic studies of ADHD, we assembled recursive-partitioning frameworks  with clinical, demographic, and ADGRL3 genetic information to predict SUD susceptibility. Our results indicate that SUD can be efficiently and robustly predicted in ADHD participants. The genetic models used remained highly efficient in predicting SUD in a large sample of individuals with severe SUD from a psychiatric institution that were not ascertained on the basis of ADHD diagnosis, thus identifying ADGRL3 as a risk gene for SUD. Recursive-partitioning analyses revealed that rs4860437 was the predominant predictive variant. This new methodological approach offers novel insights into higher order predictive interactions and offers a unique opportunity for translational application in the clinical assessment of patients at high risk for SUD.Genetic factors are strongly implicated in the susceptibility to develop externalizing syndromes such as attention-deficit/hyperactivity disorder (ADHD), oppositional defiant disorder, conduct disorder, and substance use disorder (SUD). Variants in the  In 2010, 147.5 million cases of alcohol and drug abuse were reported , and SUD prevalence is expected to increase over time. Genetic factors have been implicated in SUD etiology, with genes involved in the regulation of several neurobiological systems (including dopaminergic and glutamatergic) found to be important . However, limitations intrinsic to most genetic epidemiological studies support the search for additional risk genes.Substance use disorders (SUD) and addiction represent a global public health problem of substantial socioeconomic implications5, is frequently co-morbid with disruptive behaviors such as oppositional defiant disorder (ODD), conduct disorder (CD), and SUD7. The close association between ADHD and disruptive behaviors is summarized by longitudinal observations in ADHD cohorts9. Children diagnosed with ADHD monitored during the transition into adolescence exhibit higher rates of alcohol, tobacco, and psychoactive drug use than control groups of children without ADHD11. It has been estimated that the lifetime risk for SUD is ~50% in subjects with childhood ADHD persisting into adulthood13. Reciprocally, the prevalence of ADHD is high in adolescents with SUD15 and the presence of an ADHD diagnosis affects SUD prognosis, with ADHD being associated with both earlier and more frequent alcohol-related relapses16 and lower likelihood of cannabis-dependence treatment completion17.Attention-deficit/hyperactivity disorder (ADHD), the most common neurodevelopmental behavioral disorder21. During the last 15 years, we have collected families clustering individuals affected with ADHD and disruptive behaviors from disparate regions around the world23. Although the prevalence of ADHD co-morbid with disruptive behaviors is variable across populations, we found a higher frequency of CD, ODD, and SUD  in ADHD individuals than in unaffected relatives24. Using genome-wide data from extended multigenerational families, we found evidence of linkage of ADHD to markers in chromosomes 4q13.2, 5q33.3, 8q11.23, 11q22, and 17p1125, and co-segregation of ADHD and disruptive behaviors with loci at 2p21-22.3, 4q13.2, 5p13.1-p13.3, 8q24, 8q15, 11q22, 12p11.23-13.3, and 14q21.1-22.28. Fine mapping of the 4q13.2 region identified variants in the adhesion G-protein-coupled receptor L3 gene  that predispose to ADHD30.Strong evidence from family, twin, and genome-wide linkage and association studies suggests that genetic factors play a crucial role in shaping the susceptibility to both ADHD and SUDADGRL3 has provided key information to better predict the severity of ADHD, the long-term outcome, the patterns of brain metabolism, and the response to stimulant medication34. To the best of our knowledge, ADGRL3 linkage and association results represent some of the most robustly replicated genetic and pharmacogenetic findings in ADHD genetic research. While ADGRL3 has also shown association with disruptive behaviors in the context of ADHD35, a direct link to SUD has not been systematically investigated. In this manuscript we tested the hypothesis that ADHD risk variants harbored at the ADGRL3 locus interact with clinical, demographic, and environmental variables associated with SUD.Characterization of the association between ADHD and n = 2698) ascertained through patients affected with ADHD co-morbid with disruptive behaviors  or SUD (Spanish and Kentucky samples).We used independent populations from disparate regions of the world , mean age 28 ± 17 years, ascertained from 18 extended multigenerational and 136 nuclear Paisa families inhabiting the Medellin metropolitan area in the State of Antioquia, Colombia. Initial coded pedigrees were obtained through a fixed sampling scheme from a parent or grandparent of an index proband after having collected written informed consent from all subjects or their parent/guardian, as approved by the University of Antioquia and the NIH Ethics Committees, and in accordance with the Helsinki Declaration. Patients were recruited under NHGRI protocol 00-HG-0058 (NCT00046059).This population isolate is unique in that it was used to identify ADHD susceptibility genes by linkage and association strategies. Detailed clinical and demographic information on this sample has been published elsewhere3.Exclusion criteria for ADHD participants were IQ < 80, or any autistic or psychotic disorders. Parents underwent a full psychiatric structured interview regarding their offspring . All adult participants were assessed using the Composite International Diagnostic Interview (CIDI), as well as the Disruptive Behavior Disorders module from the DICA-IV-P modified for retrospective use. The interview was conducted by a “blind” rater  at the Neurosciences Clinic of the University of Antioquia, or during home visits. ADHD status was defined by the best estimate method. Specific information regarding clinical diagnoses and co-morbid disruptive disorders, affective disorders, anxiety, and substance use has been published elsewheren = 472). This was done to avoid kinship relatedness bias and to exclude children and adolescents, as they may have not been exposed to substances of abuse yet. Of these 472 individuals, 17% (n = 79) fulfilled criteria for ADHD, 17% (n = 78) for ODD, 18% (n = 84) for CD, 22% nicotine dependence (n = 102), 27% alcohol dependence (n = 124), 3% drug dependence (n = 12), 37% social/simple phobia (n = 156), 13% any other anxiety disorder (n = 58), and 25% major depressive disorder (n = 117) , recruited and evaluated at the Psychiatry Department of the Hospital Universitari Vall d’Hebron  according to DSM-IV TR criteria. ADHD diagnosis was based on the Spanish version of the Conners Adult ADHD Diagnostic Interview for DSM-IV (CAADID)36. Comorbidity was assessed by Structured Clinical Interview for DSM-IV Axis I and Axis II Disorders (SCID-I and SCID-II). ODD during childhood and adolescence was retrospectively evaluated with the Schedule for Affective Disorders and Schizophrenia (SADS) for School-Age Children, Present and Lifetime Version (K-SADS). Thirty-nine percent of ADHD patients (n = 263) fulfilled diagnostic criteria for SUD, 21% for disruptive behavior disorders , 21% for depression (n = 143), 13% for anxiety (n = 89), and 8% for phobias (n = 50). The level of impairment was measured with the Clinical Global Impression (CGI) included in the CAADID Part II and the Sheehan Disability Inventory. Exclusion criteria for ADHD patients were IQ < 80; pervasive developmental disorders; schizophrenia or other psychotic disorders; presence of mood, anxiety or personality disorders that might explain ADHD symptoms; birth weight ≤ 1.5 kg; and other neurological or systemic disorders that might explain ADHD symptoms.The ADHD sample consisted of 670 adult ADHD patients, mean age 33 ± 10 years, 69% males (n = 376) recruited and evaluated at the Addiction and Dual Diagnosis Unit of the Psychiatry Department at the Hospital Universitari Vall d’Hebron with the Structured Clinical Interview for DSM-IV Axis I Disorders (SCID-I). All patients fulfilled DSM-IV criteria for drug dependence beyond nicotine dependence. None were evaluated for ADHD.The SUD sample consisted of 494 adults  in which DSM-IV lifetime ADHD symptomatology was excluded under the following criteria: (1) not having been diagnosed with ADHD and (2) answering negatively to the lifetime presence of the following DSM-IV ADHD symptoms: (a) often has trouble keeping attention on tasks, (b) often loses things needed for tasks, (c) often fidgets with hands or feet or squirms in seat, and (d) often gets up from seat when remaining in seat is expected. Individuals affected with SUD were excluded from this sample. None of them had self-administered drugs intravenously. It is important to mention that the exposure criterion was not applied; therefore, this set cannot be classified as “pure” controls.All patients and controls were Spanish of Caucasian descent. This study was approved by the ethics committee of the Hospital Universitari Vall d’Hebron and informed consent was obtained from all subjects in accordance with the Helsinki Declaration.43. Substance use was assessed with a child/adolescent-reported questionnaire adapted for the MTA43. The measure included items for lifetime and current (previous 6 months) use of alcohol, cigarettes, tobacco, cannabis, and other recreational drugs. Also included were items for non-prescribed use or misuse of psychoactive medications, including stimulants. The measure was modeled after similar substance use measures in longitudinal or national survey studies of alcohol and other drug use that also rely on confidential youth self-report as the best source of data45. A National Institutes of Health (NIH) Certificate of Confidentiality further strengthened the assurance of privacy. Substance use was coded positive if any of the following behaviors, selected after examining distributions, were endorsed as occurring in the participant’s lifetime up to 8 years post-baseline: (1) alcohol consumption (more than just a sip) more than five times or drunk at least once; (2) cigarette smoking or tobacco chewing more than a few times; (3) cannabis use more than once; or (4) use of inhalants, hallucinogens, cocaine, or any of amphetamines/stimulants, barbiturates/sedatives, and opioids/narcotics without a prescription or misused a prescription (used in greater quantity or more often than prescribed). Each of the four types of substances, as well as daily use of tobacco and the number of substance use classes endorsed , were explored in secondary analyses.The Multimodal Treatment Study of Children with ADHD (MTA) was designed to evaluate the relative efficacy of treatments for childhood ADHD, combined subtype, in a 14-month randomized controlled trial of 579 children assigned to four treatment groups: medication management, behavior modification, their combination, and treatment as usual in community care. After the 14-month treatment-by-protocol phase, the MTA continued as a naturalistic follow-up in which self-selected use of psychoactive medication was monitored. A local normative comparison group of 289 randomly selected classmates group-matched for grade and sex was added when the ADHD participants were between 9–12 years of age. The outcomes in childhood , and adolescence (6 and 8 years after baseline) and into adulthood  have been reported46 at the 6- and 8-year follow-up assessments. The DISC includes both lifetime and past year diagnoses. The Diagnostic Interview Schedule-IV47 was used at the 8-year follow-up for 18 + year-olds (n = 111). SUD was defined as the lifetime presence of any abuse or dependence .DSM-IV abuse or dependence was based on a positive parent or child report with the Diagnostic Interview Schedule for Children version 2.3/3.0 (DISC)10. All patients in this study provided informed written consent as approved by the NIH Ethics Committee.Additional analyses explored SUD for alcohol, tobacco, and cannabis/other drugs  separately48. Patient interviews and medical record information (including urine drug screens and substance abuse counselor notes) were used by the research nurse to assess the Clinician Rating of Alcohol (CRAUD) and Drug Use Disorder (CRDUD)50 that provides a score from 1 = abstinence (not used in the assessed period) to 5 = severe dependence. Scores of 3 and higher are pathological and were considered positive in our analyses. All drugs were combined into one rating48. Descriptions of the training provided to research nurses to assess the CRAUD and CRDUD were published elsewhere51.A sample of 560 inpatients and outpatients with severe SUD from Central Kentucky psychiatric facilities was collected during a pharmacogenetics investigationn = 285) were male, 82% (n = 436) were Caucasian, 16% (n = 87) were African American, and 2% (n = 10) were from other ethnicities. Additional clinical information for this sample has been described elsewhere51 and included: (1) clinical diagnosis obtained from medical records, (2) prior psychiatric history, (3) history of daily smoking, (4) reviews of current and psychiatric medication use, and (5) body mass index . Allelic discrimination real-time PCR reactions were performed in a 384-well plate format for each individual sample according to the manufacturer’s instructions. Briefly, 20 ng of genomic DNA were mixed with 2.5 μL of 2X TaqMan Universal PCR Master Mix and 0.25 μL of 20X SNP Genotyping Assay in a total volume of 5 μL per reaction. Assays were run in an ABI 7900HT Fast Real-Time PCR System (Thermo Fisher Scientific). Allele calling was made by end-point fluorescent signal analysis using the ABI’s SDS2.3 software. In addition, we had previously collected exome genotype data from the MTA sample26 using the Infinium® HumanExome-12 v1.2 BeadChip kit (Illumina), which covers putative functional exonic variants selected from over 12,000 individual exome and whole-genome sequences. Processed and raw intensity signals for the array data can be accessed at GEO (GSE112652). SNP markers harbored at the ADGRL3 gene were filtered in from this dataset and added to those genotyped using TaqMan® assays.DNA was extracted from whole blood  or buccal swabs (Kentucky sample) using standard protocols. The Paisa sample was genotyped using the service provided by Illumina . The Spanish, MTA, and Kentucky samples were genotyped for select variants using pre-designed TaqManP-values < 0.0001), and a genotyping success rate < 90%, were excluded. For the Paisa and Spanish samples, a subset of variants in the ADGRL3 minimal critical region (MCR), 5′UTR and 3′UTR were selected based on a previous ADHD association study30. Because the Paisa sample is a family-based cohort and recursive-partitioning analysis does not correct for kinship relatedness, only founder members from the pedigrees were included in the analyses. For the MTA sample, a total of 8568 markers with a MAF ≥ 1.0 % from the 244,414 markers genotyped with the exome chip were filtered out using linkage disequilibrium (LD) pruning, and variants within ADGRL3 were selected for analyses. For the Kentucky sample, only four ADGRL3 variants were selected for analyses after LD pruning of a list of markers located within the ADGRL3 5′UTR and MCR regions that was available to us. Variants rs7659636 and rs5010235 had been imputed from ADHD genome-wide association data funded through the Genetics Analysis Information Network (GAIN) initiative, a public-private partnership between the NIH and the private sector . ADGRL3 variants used in this study for each cohort are presented in Supplemental Table Genotype data were imported into Golden Helix® SVS 8.3.1  for quality control analysis. Markers with a minor allele frequency (MAF) < 0.01 (rare variants), significant deviation from Hardy–Weinberg equilibrium  and of the type of data distribution 54. Furthermore, results supplied by tree-based analytics are easy to interpret visually and logically53. Therefore, to generate the most comprehensive and parsimonious classificatory model to predict the susceptibility to disruptive behaviors, we applied ARPA using a set of different modules implemented in the Salford Predictive Modeler® (SPM) software, namely, Classification and Regression Trees (CART), Random Forest, and TreeNet . One important advantage of SPM when compared to other available data mining software is its ability to use raw data with sparse or empty cells, a problem frequently encountered in genetic data.ARPA is a tree-based method widely used in predictive analyses because it accounts for non-linear and interaction effects, offers fast solutions to reveal hidden complex substructures and provides truly non-biased statistically significant analyses of high-dimension, seemingly unrelated data55. The aim is to identify, at each partition step, the best predictive variable and its best corresponding splitting value while optimizing a splitting statistical criterion, so that the dataset can be successfully split into increasingly homogeneous subgroups55. We used a battery of different statistical criteria as splitting rules  to determine the splitting rule, maximally decreasing the relative cost of the tree while increasing the prediction accuracy of target variable categories55. The best split at each dichotomous node was chosen by either a measure of between-node dissimilarity or iterative hypothesis testing of all possible splits to find the most homogeneous split (lowest impurity). Similarly, we used a wide range of empirical probabilities (priors) to model numerous scenarios recreating the distribution of the targeted variable categories in the population55. Following this iterative process, each terminal node was assigned to a class outcome. To avoid finishing with an over-fitted CART predictive model , and to ensure that the final splits were well substantiated, we applied tree pruning. During the procedure, predictor variables that were close competitors  were pruned to eliminate redundant commonalities among variables, so the most parsimonious tree would have the lowest misclassification rate for an individual not included in the original data55.Briefly, CART is a non-parametric approach whereby a series of recursive subdivisions separate the data by dichotomization56. The RF strategy differs from CART in the use of a limited number of variables to derive each node while creating hundreds to thousands of trees. This strategy has proved to be immune to the over fitting generated by CART56. In RF, variables that appeared repeatedly as predictors in the trees were identified. The misclassification rate was recorded for each approach.Additionally, we applied the Random Forest (RF) methodology using a bagging strategy to exactly identify the most important set of variables predicting disruptive behaviors57. The TreeNet algorithm generates thousands of small decision trees built in a sequential error-correcting process converging on an accurate model57. The number of variables considered to derive each node with RF was n is the number of independent variables (either 3 or 4).The TreeNet strategy was used as a complement to the CART and RF strategies because it reaches a level of accuracy that is usually not attainable by single models such as CART or by ensembles such as bagging 55. We used a 10-fold cross-validation as implemented in the SPM software.To derive honest assessments of the derived models and have a better view of their performance on future unseen data, we applied a cross-validation strategy where both training with all the data and then indirectly testing with all the data were performed. To do so, we randomly divided the data into separate partitions (folds) of different sizes. This strategy allowed us to review the stability of results across multiple replicationsA fixed-effects meta-analysis of the overall fraction of correctly classified individuals (accuracy) using the derived models from each of the four samples was applied to derive a general perspective of the SUD predictive capacity of this demographic-clinical-genetic framework.A series of predictive models were built on our data using combinations of the following criteria: (i) the rules of splitting ; (ii) the priors; (iii) the size of the terminal nodes; (iv) the costs; (v) the depth of branching; and (vi) the size of the folds for cross-validation, to maximize the accuracy of the derived classification tree while considering class assignment, tree pruning, testing and cross-validation.A parsimonious and informative reconstructed predictive tree derived from CART for the Paisa sample revealed demographic (age), clinical (CD), and genetic variables (rs5010235 and rs4860437) Fig. . The impIn the case of the Spanish sample, a parsimonious and informative tree was reconstructed with CART revealing demographic (sex), clinical , and genetic variables (rs4860437 and rs1868790) Fig. . The TreAs in the previous cohorts, for the MTA sample we derived a parsimonious and informative predictive tree with CART depicting demographic (site of ascertainment), and genetic variables  Fig. . The TreFinally, for the Kentucky sample, we derived a parsimonious and informative predictive tree with CART involving demographic (sex), clinical (high body mass index (HBMI) and schizophrenia diagnosis), and genetic variables (rs4860437 and rs7659636) Fig. . The TreADGRL3 marker rs4860437 was the most important variant predicting susceptibility to SUD, a commonality suggesting that these networks may be accurate in predicting the development of SUD based on ADGRL3 genotypes.A fixed-effects meta-analysis for overall accuracy returned a value of 0.727 (95% CI = 0.710–0.744) . For instance, in the Spain sample, marker rs2271339 conferred significant risk to nicotine use: the heterozygote genotype A/G confers 43% increased risk of being diagnosed with nicotine use . In the same vein, we found in the Paisa sample that the heterozygote A/T genotype for rs1456862 confers 83% increased risk to nicotine use  than the A/A genotype. Regarding alcohol use, we found in the Paisas that the heterozygote C/T genotype for rs2159140 confers susceptibility, whereas the C/C genotype does not . Supplemental Fig. P < 0.0001, Supplemental Table P < 0.0001, Supplemental Table To determine the significance of improvement of prediction when genetic markers are introduced in the ARPA-based predictive model for SUD, we compared the performance measures  across all cohorts under two disjunctive scenarios: inclusion of genetic markers or not. We found that including genetic markers improved the performance measures of the resulting ARPA-based predictive model of SUD, regardless of cohort  insufficient power to detect significant associations due to small sample size;59 (iii) phenotypic heterogeneity of SUD across samples that may reflect different disease stages or multiple subtypes ; (iv) genetic heterogeneity arising from distinct risk genes sets; (v) ethnicity inconsistencies between discovery and replication samples;61 and (vi) comorbidity with other psychiatric conditions  with shared genetic and environmental architecture63. Consequently, additional studies are required to identify new SUD candidate genes and to help dissect genetic contributions in the context of complex interactions with co-morbid conditions.SUD genetic epidemiological studies across multiple substances have been plagued with inconsistency in the replication of genetic association results. This may be due to reasons such as: (i) small effect size of variants expected to influence the SUD phenotype, as with any complex disease;ADGRL3 marker rs4860437, which is a major predictor variable component in the trees for SUD, is in complete LD with ADHD susceptibility markers rs6551665 and rs1947274 in Caucasians52, suggesting that the phenotype underpinning SUD is under the pleiotropic effect of ADGRL3 variants. Unfortunately, rs4860437 was not included in the exome chip used to genotype the MTA sample and, therefore, could not be included in the analyses for this sample. Given the limited overlap of markers across datasets and possible stratification differences among study populations, a gene- rather than a marker-level approach has been advocated64.In this study, we present a demographic, clinical and genetic framework generated using ARPA that is able to predict the risk of developing SUD. Interestingly, marker rs4860437 showed a differential splitting pattern in the Paisa, Spain, and Kentucky cohorts. For instance, in Fig. ADGRL3 locus confer susceptibility to SUD in populations from disparate regions of the world. These populations are from three different countries and involve different investigators, diverse inclusion criteria, and different clinical assessments, which suggests that our results may replicate in other settings and are likely to be clinically relevant. Of particular interest is the generalization of our findings to a longitudinal study (the MTA sample), where adding genetic information to baseline data predicted the development of SUD at later ages, as determined from information gathered over a period of more than 10 years. Additionally, our results generalized to a sample of patients with severe SUD from Kentucky (U.S.) that were not ascertained on the basis of ADHD diagnosis.Adopting such a perspective, our results suggest that genetic variants harbored in the 65. Marker rs4860437 is not represented in this dataset; however, this study was not aimed at identifying loci shared between ADHD and SUD. In any case, while genome-wide association studies are a useful tool for discovering novel risk variants—as it involves a hypothesis-free interrogation of the entire genome—the lack of genetic association may be a reflection of the polygenic, multifactorial nature of ADHD, with both common and rare variants likely contributing small effects to its etiology68. In addition, an important factor may be the genetic heterogeneity of ADHD subtypes, which may have different underlying genetic mechanisms. Therefore, genome-wide significance may identify loci with larger genetic effects, while others with smaller effects remain undetected for a given population size.The first genome-wide significant ADHD risk loci were published recentlyADGRL3 has been implicated in ADHD in diverse populations69. ADGRL3 is a member of the latrophilin subfamily of G-protein-coupled receptors (GPCR)70 and is most strongly expressed in brain regions implicated in the neurophysiological basis of ADHD71. Mouse and zebrafish knockout models also support ADGRL3 implication in ADHD pathophysiology73. More recently, Martinez et al35. identified a brain-specific transcriptional enhancer within ADGRL3 that contains an ADHD risk haplotype associated with reduced ADGRL3 mRNA expression in the thalamus. This haplotype was associated not only with ADHD, but also with disruptive behaviors, including SUD35. A member of the family of leucine-rich repeat transmembrane (FLRT) proteins has been identified as an endogenous postsynaptic ligand for latrophilins74. Interference with this interaction reduces excitatory synapse density in cultured neurons and decreases afferent input strength and dendritic spine number in dentate granule cells, which implicates ADGRL3 and FLRT3 in glutamatergic synapse development74. Similarly, convergent evidence from a network analysis of a gene set significantly associated and/or linked to ADHD and SUD revealed pathways involved in axon guidance, regulation of synaptic transmission, and regulation of transmission of nerve impulse18. These data altogether suggest that ADGRL3 may be an important SUD susceptibility gene.Variation in 81. More strikingly, ADHD treatment has been shown to reduce the risk of SUD84. Though the neurobiological basis for this association remains unclear, a variety of causal pathways from ADHD to SUD have been proposed that involve conduct problems79. Clinical studies have suggested that the link between SUD and ADHD disappears after controlling for co-morbid CD86. In agreement with these studies, the presence of CD was a major predictor of SUD in the ARPA-based predictive models for SUD in the Paisa and Spanish cohorts (both assessed first for ADHD)  Figs. .87. Some investigators have proposed that patients with ADHD use addictive substances to self-medicate88 and that the differential response to drugs of abuse and atypical behavioral regulation in response to social cues  may fuel substance use89. Others suggest that the poor judgment and impulsivity associated with ADHD contribute to the development of substance dependence79. Clinical variables from childhood have also been associated with SUD in patients with ADHD, such ADHD subtype, temper characteristics , sexual abuse, suspension from school, and a family history of ADHD13.Some researchers implicate genetically mediated personality traits, such as impulsivity and lack of inhibitory control (common to ADHD and disruptive behaviors) as a link between ADHD and SUD resulting from common neurological substratesADGRL3 in modulating drug seeking behavior. Regardless of the type of abused substance, longitudinal studies generally find that the onset of ADHD precedes that of SUD, suggesting that the psychopathology of ADHD is not secondary to SUD in most patients79. Accordingly, it is reasonable to consider that timely diagnosis and treatment of ADHD with stimulant medication may reduce the occurrence and/or severity of SUD. Based on the relationship with medication response30, we speculate that ADGRL3 variants may underlie a differential genetic susceptibility not only to SUD, but also to the long-term protective effects of medication treatment. Confirmation of such hypothesis would have substantial public health implications. Inasmuch as ADGRL3 participates in synaptic formation and function, its involvement in SUD could be mediated by either influencing brain development or moderating drug-induced changes in synaptic strength. Molecular studies are required to elucidate the pathogenic mechanism(s) associated with ADGRL3 dysfunction in SUD.In summary, our results support a possible functional role for Supplemental material


Background: Recent research has shown an increased risk of accidents and injuries in ADHD patients, which could potentially be reduced by stimulant treatment. Therefore, the first aim of our study was to evaluate the prevalence of adult ADHD in a trauma surgery population. The second aim was to investigate accident mechanisms and circumstances which could be specific to ADHD patients, in comparison to the general population. Methods: We screened 905 accident victims for ADHD using the ASRS 18-item self-report questionnaire. The basic demographic data and circumstances of the accidents were also assessed. Results: Prevalence of adult ADHD was found to be 6.18% in our trauma surgery patient sample. ADHD accident victims reported significantly higher rates of distraction, stress and overconfidence in comparison to non-ADHD accident victims. Overconfidence and being in thoughts as causal mechanisms for the accidents remained significantly higher in ADHD patients after correction for multiple comparison. ADHD patients additionally reported a history of multiple accidents. Conclusion: The majority of ADHD patients in our sample had not previously been diagnosed and were therefore not receiving treatment. The results subsequently suggest that general ADHD screening in trauma surgery patients may be useful in preventing further accidents in ADHD patients. Furthermore, psychoeducation regarding specific causal accident mechanisms could be implemented in ADHD therapy to decrease accident incidence rate. Attention-deficit/hyperactivity disorder (ADHD) is among the most prevalent neurodevelopmental disorders worldwide, with a prevalence of ~5% in children and ~2%–3% in adults ,2,3. TheIt has also been reported that ADHD patients have a higher mortality risk before the age of 40, which is mainly due to accidents . SeveralThe aims of our PRADA study  were to firstly investigate whether ADHD prevalence was higher in a sample of accident victims than the estimated prevalence in the general population. To achieve this, we screened a sample of accident victims for ADHD symptoms using the Adult ADHD Self-Report Scale Version 1.1 (ASRS v1.1) screening questionnaire. Given that adult ADHD at least in Germany is still severely underdiagnosed and under-treated ,20, in tThe sample cohort was recruited in three trauma surgery units; the Occupational Accident Clinic , Frankfurt; the Department of Trauma, Hand and Reconstructive Surgery, University Hospital Frankfurt; and the Department of Oral, Dental and Cosmetic Facial Surgery, University Hospital Frankfurt. All units are based in Frankfurt, Germany. Participants were recruited between November 2016 and May 2018 within the framework of the “Prevalence of Adult Attention Deficit/Hyperactivity Disorder in Accident Victims” (PRADA) study. A preliminary analysis of a subsample of this study has already been published . During Participants that screened positive for ADHD were asked for further information regarding the circumstances of the accident that led to admittance in the trauma unit, using a self-designed questionnaire . We compared a randomly selected subgroup of the non-ADHD accident victims  and accident victims who screened positive for ADHD in the ASRS short form  with regards to sex and age distribution, and all items of the accident questionnaire, by Chi-square Test. If case numbers were too small, the Fisher’s Exact Test was utilized. The student’s t-test was used to compare the age of the tested groups. As the accident questionnaire consisted of 22 different items, we applied Bonferroni-correction for multiple testing, and all p-values reported were defined as being significant at the level of p ≤ 0.0023 (0.05/22). Due to the age difference between the ADHD group and the control group, all items which showed significant results in the Chi-square Test/Fisher’s Exact Test were later included in an additional multivariate logistic regression model.Data analysis was conducted using SPSS , Version V24 , but scored highly in Part B of the ASRS-18. Using only the positively screened participants in the ASRS-SF, there was a prevalence of 6.18 % adult ADHD in our sample of accident victims. A less conservative approach would have demonstrated a prevalence of 8.3%, including the additional participants who screened positively for ADHD in the ASRS-18. For further analyses, we used only the group of patients who screened positive for ADHD in the ASRS-SF. From herein they are referred to as the ADHD group, and the control group consists of accident victims who screened negative in the ASRS short version. n = 11) visited our specialized outpatient clinic to get a full diagnostic assessment. From these participants, seven were confirmed to have adult ADHD , two had confirmed childhood ADHD but did not fulfill the full adulthood criteria , and two had the initial ADHD diagnosis dismissed (18.2%). One of these dismissals was due to the presence of multiple substance abuse disorder, and one due to a previous head trauma, which could have caused the cognitive and behavioral ADHD-like symptoms. Combining participants with persistent and partially persistent ADHD, the positive ASRS screening could be validated in 81.8 % of participants. Adjusting our initial conservative ADHD prevalence of 6.18% based on the assumption that ~80% diagnoses would be confirmed as either persistent or partially persistent ADHD in a full assessment, consequently resulted in a corrected estimated prevalence of 4.88% adult ADHD in our accident victim sample. This ADHD prevalence is approximately 1.6-fold higher than in the general population. From the 75 patients that screened positively for ADHD using both the short and full versions of the ARSR, only 14.7%  than female patients in the total accident victim cohort. In the ADHD group (following the more conservative screening method) there were 28.6% females and 35.3% females in the control group, which was not statistically significant. However, ADHD patients were found to be significantly younger than the control trauma patients . These subgroup findings were comparable with the total accident victim cohort findings. p < 0.0001). A pre-existing, previously confirmed ADHD diagnosis was also significantly more common in our ADHD group  compared to controls, further validating the screening instrument used. However, there were two participants in the control sample who screened negative for ADHD in the ASRS, yet reported having an ADHD diagnosis. Lastly, ADHD patients were significantly more likely to be regularly taking prescribed sedatives and other psychopharmacological medication   see .p = 0.06). However, ADHD patients were significantly more likely to be under the influence of alcohol or illegal drugs when the accident happened . Medication use directly before the accident did not remain statistically significant different after correcting the χ²-test for multiple testing, and no medication was significantly different between ADHD and non-ADHD . ADHD patients were significantly more likely to have been generally distracted before the accident , however this finding was no longer statistically significant after correcting for multiple comparisons. Mind wandering/being in thoughts before the accident was statistically higher in the ADHD group . In the total sample, most accidents happened in road traffic, but after calculating a logistic regression there were no significant differences between ADHD patients and controls regarding the place of accidents or the type of road accident . Self-inflicted accidents were also not significantly higher in the ADHD group based on self-report . Despite this, significantly more ADHD patients reported feeling stressed at the time the accident happened . This result was already reported in our initial study, and has now been validated in the total accident victim cohort, using a greater number of control patients [p = 0.0004). Lack of sleep or tiredness was not statistically significant different between the groups . During recruitment and interview, significantly more ADHD patients reported feeling stressed, however this did not withstand multiple corrections   revealed that only being in thoughts and being overconfident remained significantly different between ADHD and controls see . Lastly,001) see .In recent years, it has been consistently reported that childhood, adolescent and adult ADHD is associated with an increased risk of unintentional injuries and accidents. A recent meta-analysis  concluded that the risk of injuries in ADHD is increased by 1.53 OR and 1.38 HR, respectively . DalsgaaWe performed a cross-sectional study using a cohort of 905 accident victims recruited from three separate trauma surgery units, and screened for adult ADHD using the ASRS-18 item questionnaire as well as the ASRS short form . We founDemographic data showed that ADHD patients had a higher number of psychiatric comorbidities, particularly substance use disorders and chronic pain, consistent with previous findings ,35. ADHDThe mechanisms underlying this increased risk of injuries and accidents in ADHD are currently unknown. Knowledge of such mechanisms could potentially lead to non-pharmacological accident prevention strategies. It has previously been suggested that ADHD patients might drive in a more-risky manner, and also more frequently under substance influence, contributing to car accidents . We coulIn our cohort, ADHD participants more frequently attributed overconfidence to accident causality. Additionally, our results supported the data obtained from our preliminary study , wherebyFrom our results, we conclude that a screening for ADHD in trauma surgery patients should be recommended, as patients may be at increased risk for multiple accidents and adult ADHD. Moreover, adult ADHD is still underdiagnosed and undertreated in Germany, and routine trauma surgery screening may help to improve this. We additionally suggest that ADHD psychoeducation should include information about accident risk, focusing on prevention of substance abuse, avoidance of overconfidence, strategies against mind-wandering and stress reduction management, as a potential non-pharmacological strategy to decrease accident incidence in ADHD patients.


A total of 88 students took part in this study , aged from 6 to 16 years . The sample was divided into two groups: an ADHD group (n = 50) and a Control group (n = 38). Students in the ADHD group obtained lower scores in working memory and in processing speed, as well as demonstrating poorer performance in Aula Nesplora than did their peers. Working memory, and the number of omissions, were both shown to be reliable predictors of group membership. This study revealed the importance of obtaining data from attentional variables differentiated by modality when considering cognitive variables, in order to better characterize the difficulties experienced by individuals diagnosed with ADHD.In previous studies, children with Attention-Deficit Hyperactivity Disorder (ADHD) have been found to have more difficulties with processing speed, working memory, and attentional tasks. The present study aimed to compare the cognitive variables (working memory and processing speed) and the attentional profiles of a sample of students with and without ADHD, using scales from the WISC-IV, and the virtual reality-based attentional test known as  ADHD  is a common neuropsychiatric disorder in childhood with prevalence rates ranging from 5 to 7% in the school-age population . AccordiGenerally, the characteristic symptoms of ADHD have serious consequences which result in difficulties in scholastic, social and familial contexts , 4. SpecNowadays, some children may be evaluated by clinicians using only a single information source, such as a semi-structured interview with the patient and/or parents. In this sense, comprehensive assessments are generally preferred over single-reports because they allow us to understand the children´s difficulties and offer the opportunity to rule out alternative explanations for the pattern of symptoms. Within the context of these assessments, cognitive and neuropsychological measures facilitate an unbiased evaluation of the core problems .Considering the importance of carrying out an objective assessment, the attentional profile of children with ADHD has been widely studied by means of Continuous Performance Tests (CPTs), which provide quantitative data on different attentional variables of interest and have been shown to be useful in the diagnosis of ADHD . A numbeNonetheless, these types of tests are commonly criticized for their low ecological validity , 11. TheEfforts to find improved assessment methods that offer higher ecological validity, as well as better sensitivity and specificity levels, have led to new techniques for evaluating ADHD that are based on the use of Virtual Reality (VR). Nowadays, new technological developments in the field of VR have generated an innovative and interesting option for carrying out neuropsychological evaluations of many cognitive processes , 14. By These advances present a huge number of advantages over more traditional techniques . For exaConversely, other neuropsychological literature considers that although CPTs  identify impairment in ADHD patients, no one test is sufficient (on its own) in making a diagnosis, considering that other types of variables have also been shown to be affected by ADHD morbidity , 28. SevRegarding processing speed, a large number of researchers support the view that a low score in processing speed is one of the best predictors of ADHD, especially for those with inattentive symptoms , 39. MorOn the other hand, with respect to the Executive Functions, many authors have observed an impairment in working memory in children with ADHD –44. WorkOther researchers have focused on working memory as a key component, analyzing whether academic problems in ADHD are due to ADHD symptoms, working memory deficits, or both ,48. The These findings suggest that both processing speed and working memory are relevant neurocognitive markers of ADHD and other disorders which share common symptoms .Given the high prevalence rates of ADHD and the low ecological validity of the majority of CPTs, virtual reality tasks provide a means of examining the relationship between performance and cognitive profiles in a more realistic environment. For this reason, considering that the field of assessment tools for detecting the ADHD is still in development, there is a need to continue research aimed at better understanding how the combination of cognitive and neuropsychological measures may facilitate an accurate diagnosis of this disorder. While previous studies have focused on processing speed, working memory and executive-function variables, the present study aimed to detect the effectiveness of all three variables in order to predict ‘real-world’ ADHD symptoms. For this reason, the study had two main objectives: 1) To describe and compare cognitive profiles provided by a cognitive scale  to behavioral profiles provided by a VR-CPT (using variables from the Aula Nesplora test) in a sample of students with and without ADHD; and 2) To determine the extent to which the aforementioned variables can predict student group membership, while also controlling for the potential explanatory effects of gender and age.M = 10.20; SD = 2.79). Of the total sample, 50 (56.8%) had a diagnosis of ADHD and 38 (43.2%) were controls. The average IQ (using standard scores) of the total sample was 110.05 (SD = 13.93). Only the following two scales from the WISC-IV were considered for inclusion criteria: Verbal Comprehension , and Perceptual Reasoning ; as the remaining scales (Processing Speed and Working Memory) were the dependent variables in the present study. There were significant differences between the groups . No statistically significant differences were found between the ADHD and Control groups regarding perceptual reasoning  = 2.868, p = .081, ηp2 = .032 ), verbal comprehension  = 3.799, p = .159, ηp2 = .042), and age  = 3.061, p = .806, ηp2 = .001). However, although there were no differences in the age ranges of the samples, age was included as a covariate in subsequent statistical analyses, in order to avoid omitting the explanatory effect of this variable within each diagnostic group.A non-probabilistic sample of 88 students took part in this study, 66 males (75%) and 22 females (25%) between 6 and 16 years of age , the EDAH scale  was compp = .065) between presentation-types measured by EDAH scale As None of the participants with ADHD were receiving pharmacological treatment at the time of evaluation, or during the assessment of their individual symptoms, as it was important to gauge the true (unmasked) severities of ADHD symptoms in order to ascertain their relationship to the resultant effects of ADHD, such as lower performance in processing speed, working memory and executive variables in comparison to children without this disorder.The following instruments and measures were used in the present study:The Wechsler Intelligence Scale for Children-IV (WISC-IV) by Wechsler , while processing speed only correlated significantly  to perceptual reasoning, R2 = .333, p < .01. The correlation between verbal comprehension and perceptual reasoning was .609 (p < .01).These scales correlated significantly with each other, Results from Aula Nesplora general measures (raw scores), as well as for each of the six conditions of the test are presented in In terms of the raw scores of general measures, the Mann-Withney U test indicated the existence of statistically significant differences between groups in all the dependent variables see .Concerning stimulation channels, the Mann-Withney U test indicated the existence of significant differences between groups in both channels see . Effect With regard to the X vs. X-No condition, statistically significant between the groups were obtained in all the dependent variables, with the exception of commissions in the X-NO condition . The var2(10) = 27.033, p = .003; and response time, χ2(10) = 27.159, p = .002.Finally, considering the presence or absence of distractors, the Mann-Withney U test showed the existence of statistically significant differences between students with and without ADHD in all the dependent variables, with the one exception again being commissions in the presence of distractors . While cR2 = -.224, p < .05, and response time, R2 = -.217, p < .05.First of all, correlation analyses revealed the existence of statistically significant associations between cognitive and attentional measures, but only in the case of working memory. This variable correlated negatively with all the Aula Nesplora general measures, being significant in the case of omissions, Considering the first model , only omissions and age were statistically significant predictors of group membership. Omissions showed the highest standardized coefficient, being the most relevant variable identifying subjects with and without ADHD. The statistics indicated that the older the student and the higher the score in omissions, the higher the probability to present ADHD. This model classified 76.1% of the sample correctly .Taking into consideration the different conditions of the task, omission was present in all the models tested, being the strongest predictor of student group membership. This variable was significant in both visual and auditory conditions, as well as in X-no task and under the no distractors paradigm, showing the same relationship to ADHD symptoms as in the previous model. Age was also a significant predictor in most of the models, showing in all of them a positive relationship with the presence of ADHD. Working memory turned was a significant predictor of group membership only in some test conditions , but was not for the general measures, or when the condition Presence vs. Absence of distractors was taken into account. Finally, when the last pair of conditions were analyzed, two new variables were significant (i.e. response time under the distractors condition and commissions under the no distractors condition). These three models classified correctly a similar number of students as the model with general measures: the models with both Visual vs. Auditory channel and X vs. X-no task classified correctly 75% of the sample , while in the case of the Distractors vs. No distractors condition, 76.1% of the students were correctly classified .one model that related to X vs. X-no conditions. In this way, the more parsimonious model was the first model which provided similar sensitivity and specificity values than the other one. On the other hand, the model that best identified students without ADHD was the last model, which included the Distractors vs. No Distractors condition. The current results demonstrated the important roles of omissions, age, and working memory deficit in predicting the probability of a child receiving a diagnosis of ADHD.On the whole, the results indicated that the models that best classified students with ADHD were the models which consisted of the general measures of attention, and the The objectives of this study were: 1) To describe and compare cognitive profiles provided by a cognitive scale  to behavioral profiles provided by a VR CPT (using the variables from the Aula Nesplora test) in a sample comprising students with and without ADHD; and 2) To determine the extent to which the aforementioned variables can accurately predict a student’s pre-determined diagnostic-group association.In line with previous studies, the two groups of children differed significantly on the basis of their cognitive and behavioral profiles , 17, 34.At the behavioral level, students with ADHD showed poorer performance in Aula Nesplora  than the control group. On the other hand, when the results were analyzed according to the type of task, both groups (ADHD and Control) produced more omissions as well as higher motor activity in Go-tasks. Given its design, Aula Nesplora provides the possibility of obtaining different executive profiles, which are also displayed under different contextual and stimulatory conditions. These differences may have important implications for the design and implementation of more adaptable interventions based upon broader-scoped profiles. More specifically, the results from such tests can show if a student concentrates better, and has more inhibitory control, is related to whether the stimuli are presented via auditory or visual channels. This type of information may be very relevant with respect to recommendations about how a study might obtain better results . Similarly, VR CPT also offers the possibility of observing the effect of ‘distractors’ upon student performance. This additional information is also useful for making other types of recommendations, such as the need of some students to be situated in a quiet place to perform tasks optimally.This study’s findings are in agreement with the results obtained by Iriarte et al. , which aPertinently however, although both cognitive and attentional components were important in order to establish a profile in ADHD, the amount of variance explained by attentional variables was larger than that explained by the cognitive variables in the present study, as was indicated by the effect sizes. This means that although cognitive profiles in ADHD can be used to provide a diagnosis of ADHD, they may be insufficient in establishing a definitive diagnosis if utilized as an exclusive diagnostic tool. .Concerning the study’s second objective, the purpose of which was to determine the extent to which attentional and cognitive variables can predict student diagnostic-group associations, the results showed that the specific attentional and cognitive variables analyzed allowed for the classification of a significant proportion of the students into diagnostic groups. In particular, omissions, working memory and age, were revealed as the most significant predictors within the different models analyzed. In this sense, working memory impairments, an increase in age, and the presence of a great number of omissions in Aula Nesplora were significantly related to a higher likelihood of having a diagnosis of ADHD. The models were more useful in discriminating subjects with ADHD than controls, as was expected, given that the nature of the task was aimed at identifying students with the disorder. In this sense, it was observed that, similar to other CPTs, this virtual reality tool has been demonstrated to have better levels of sensitivity than specificity , 12. TheFinally, these findings suggest that, focusing only on one of the test conditions  may have some advantages over the whole test, such as a reduction in data interpretation. However, this study also highlights the necessity of taking into account additional measures  in order to reach a similar discriminant value than that provided by the whole test itself and age, a model that, in turn, results more parsimonious. This would ultimately depend upon professionals´ choice and assessment purposes. Although this study suggests preliminary evidence supporting this hypothesis, additional studies are necessary.Some limitations of the present study should be considered in future investigations. Firstly, additional studies with a wider sample size are needed in order to examine whether the statistical power of the variables analyzed is similar to that obtained in the present study. It is important to verify if these specific findings could be replicated in future researches. In addition, in order to identify the developmental processes of ADHD symptoms, it is important to analyze the statistical power of the variables in the different age groups. Finally, a direct comparison between the Aula Nesplora with other traditional CPTs would test the benefits of the use of ecologically valid tools in the ADHD diagnosis.S1 FigThe 3d virtual glasses allows to obtain a motor activity indicator to value the possible hyperactivity problems.(JPG)Click here for additional data file.S2 FigVirtual classroom environment where the patient does the tasks which are explained by a virtual teacher.(TIF)Click here for additional data file.S3 Fig(TIF)Click here for additional data file.S1 Database(SAV)Click here for additional data file.