Previous research found that diets high in fruits and vegetables improved symptoms of attention deficit hyperactivity disorder (ADHD). Nevertheless, the relationship between dietary polyphenol intake and the risk of ADHD was not assessed.The purpose of this study was to see if there was a relationship between dietary polyphenol intake and the risk of ADHD in children in preschool and elementary school.A total of 400 children aged 4 to 12 years old participated in this case-control research . The presence of ADHD was diagnosed according to the Diagnostic and Statistical Manual of Mental Disorders-V criteria. To calculate dietary polyphenol intake, a 168-item food frequency questionnaire and the Phenol-Explorer database were used.P < 0.001) in the crude model. This finding was still significant even after adjusting for body mass index, energy intake, socioeconomic status, gender, and age .A significant negative association was observed between one unit increase in dietary polyphenol intake and risk of ADHD (OR: 0.995, 95% CI = 0.994 to 0.996, We found that the increased dietary intake of polyphenols is associated with a lower risk of ADHD in preschool and school children. Prospective studies are needed to corroborate these observations. Attention-deficit hyperactivity disorder (ADHD) is a common childhood psychiatric disorder characterized by inattention, impulsivity, and hyperactivity . It has The exact cause of ADHD is unclear. However, genetic and environmental factors have a role in its etiology . DietaryPolyphenols are secondary plant metabolites that are grouped into five primary classes based on their chemical structure: phenolic acids, flavonoids, lignans, stilbenes, and others . PolypheA case-control study was conducted to compare 200 ADHD patients with 200 non-ADHD controls in Isfahan, Iran in 2017. All participants were 4 to 12 years old. Both groups were matched by sex and age (maximum 6 months age difference). Children with ADHD were recruited from psychotherapy clinics located in the center, north, south, west, and east of Isfahan using a random multistage cluster sampling. Healthy children were enrolled in the study from Isfahan’s preschool and elementary schools. The Diagnostic and Statistical Manual of Mental Disorders-V criteria were used by a psychiatrist to diagnose children with ADHD . The chiWith a non-stretchable tape, the children’s height (cm) was measured to the nearest 0.1 cm. . Bodyweight (kg) was measured minimally clothed without shoes on a calibrated scale accurate to 0.1 kg . The BMI was computed by dividing the weight in kilos by the height in meters squared.Dietary intake was examined using a 168-item semi-quantitative interviewer-administered food frequency questionnaire (FFQ) that included a list of foods usually consumed by Iranian children. Parents were asked to provide information on their children’s food intake listed daily, weekly, monthly, or annual throughout the previous year. The FFQ was validated for the Iranian population [The following formula was used to calculate total polyphenol intake:www.phenol-explorer.eu). All foods that contained no polyphenols, or only traces, were eliminated.The sum of polyphenols in all food sources in FFQ was used to calculate total individual polyphenol consumption. The Phenol-Explorer database provided us with information on polyphenol concentration in foods  and age (years) are important factors related to ADHD. Considering these confounding factors, a pair-match design was used to match both cases and controls. A questionnaire was used to categorize participants into three groups (low- middle – high) according to their socioeconomic status (SES). Factors queried include family size, education level and employment of both parents, home status, car ownership, and international travel.P-value < 0.05 was defined statistically significant. Statistical Package for Social Sciences version 18  was used for all statistical analyses.The normal distribution of the variables was evaluated using the Kolmogorov-Smirnov test. The independent-samples t-test was used to evaluate between-group differences in weight, height, BMI, and age. The chi-square test was used to assess between-group differences in gender and SES. The multivariate analysis of covariance was used to determine energy-adjusted nutrient intake in both cases and controls. Logistic regression analyses were used to examine the risk of ADHD per one unit increase in dietary polyphenol intake. One crude and two adjusted models were defined in logistic regression. The first adjusted model (model I) was controlled for energy intake and the second model (model II) was additionally adjusted for socioeconomic status, age, body mass index, and gender. P = 0.490) or age (P = 0.439), whereas weight (P < 0.001) height (P = 0.003) and BMI (P = 0.001) were significantly higher in healthy subjects compared with the ADHD group. Also, the socioeconomic status of the two groups differed significantly (P < 0.001).The general characteristics of the subjects are reported in Table P < 0.001 for all). In contrast, the intake of carbohydrates was not different between the two groups (P = 0.221).Table P < 0.001). Results remained significant after adjusting for energy intake in model I  and further adjusting for BMI, socioeconomic status, age, and gender in model II .The risk of ADHD per one unit (mg/100 gr) increase in dietary polyphenol intake is reported in Table In this case-control study, it was revealed that one unit increase in polyphenol intake could reduce the risk of ADHD in children. Behavioral problems in ADHD can severely diminish academic performance and impair family living and social functioning . TherefoRelationships between dietary patterns and ADHD have been investigated in previous studies. Healthy dietary patterns characterized by high consumption of fresh fruits and vegetables, whole grains, legumes, vegetable oils, and dairy products would help prevent ADHD or hyperactivity , 19. AlsPolyphenols protect against ADHD through several mechanisms. They can modify membrane fluidity and adrenergic receptors . DietaryIn Iran and most parts of the world, various methods are used to cook vegetables like steaming, boiling, baking, microwaving, or deep frying before eating. These methods influence total polyphenols and their antioxidant capacity . PolypheThis study had some limitations. First, we did not consider factors that affect on polyphenol content of foods such as cooking methods. Second, ADHD subtypes were not specified in this study. It is possible that polyphenols intake has different effect on different types of ADHD. Third, the severity of the disease was not assessed, and we just evaluated association between dietary polyphenol intake and risk of the incidence of ADHD disease. Fourth, it is possible that the association between polyphenol intake and risk of the ADHD was significant because of the large sample size, but they are quite small. However, we examined the risk of ADHD per one-unit increase in dietary polyphenol intake. Therefore, we do not expect to observe a remarkable change in the risk of ADHD. Usually, large changes in the risk of a disease are observed in studies that used tertile, quartile or quintile. Another limitation was using FFQ, a dietary assessment tool based on the memories of participants.We found that increased dietary polyphenol intake was potentially associated with a reduced risk of ADHD. To confirm a causal relationship between dietary polyphenols and the risk of ADHD, clinical trials and cohort studies should be done.


Addressing this challenge requires extricating the potential of such cultivars in terms of agro-morphological and nutritional attributes. In this study, 29 traditional crop landraces of maize (11), paddy (07), finger millet (03), buckwheat (05), and naked barley (03) were characterized and evaluated for target traits of interest. In maize, Chitkanu emerged as an early maturing landrace (107 days) with high concentrations of zinc (Zn), iron (Fe), and potassium (K), and Safed makki showed the highest 100-seed weight (28.20 g). Similarly, Bamkua dhan exhibited high concentrations of K and phosphorus (P), and Lamgudi dhan showed a high protein content (14.86 g/100 g) among paddy landraces. Ogla-I and Phapra-I showed high contents of protein (14.80 g/100 g) and flavonoids (20.50 mg/g) among buckwheat landraces, respectively, followed by Nei-I, which exhibited the highest protein content (15.66 g/100 g) among naked barley landraces. Most of the target traits varied significantly (p < 0.05) among evaluated samples, except those associated with finger millet landraces. The grouping pattern obtained by principal component analysis (PCA) and multidimensional scaling (MDS) was congruent with the geographical relationship among the crop landraces. This study led to the identification of elite crop landraces having useful variations that could be exploited in plant breeding programs and biofortification strategies for future crop improvement. Our endeavor would aid in conserving the depleting Himalayan agro-biodiversity and promoting versatile traditional crops toward mainstream agriculture vis-à-vis future nutritional security.The northwest Indian Himalayas are often regarded as a  The broad spectrum of natural resources, including crop genetic resources, is under the immense burden of extinction mainly in the regions where cropping patterns are shifting at an alarming rate . The croThe substantial diversity in crop landraces makes them a good source of genes and alleles for plant breeding . TherefoSeeds of 29 different traditional landraces belonging to 5 crop groups, namely, maize (11), paddy (07), finger millet (03), buckwheat (05), and naked barley (03) were collected from different geographical locations in northwest Himalayan states of Himachal Pradesh and Uttarakhand during the summers of 2017–2019. These accessions were selected on the basis of their historical significance and maintenance by the farmers in the given agro-ecosystem. Detailed passport information regarding the genetic material has been provided in ° 35′ N, 70° 18′ E, altitude 1,924 m amsl) during summer season of 2018–2019 and 2019–2020. This study was performed in a Randomized Complete Block Design (RCBD) for 5 crop groups with each entry of the corresponding group replicated thrice, and recommended agronomic practices were followed to raise the experimental crops. The observations were recorded against important agro-morphological characters, namely, days to tasseling (DT), days to silking (DS), ear length (EL), ear height (EH), plant height (PH), days to 80% maturity (DM), and 100-seed weight (SW) for maize; days to 50% flowering (DF), leaf length (LL), leaf width (LW), number of tillers (NT), panicle height (PAH), PH, DM, and 100-SW for paddy; DF, finger length (FL), finger width (FW), NT, number of grains per spikelet (NGPS), PH, and DM for finger millet; DF, LL, LW, cyme length (CL), petiole length (PEL), PH, DM, and 1,000-SW for buckwheat; and days to emergence (DE), days to 50% heading (DH), NGPS, PH, DM and 1,000-SW for naked barley landraces, as per minimal descriptors developed by ICAR-NBPGR for individual crops under study -National Bureau of Plant Genetic Resources (NBPGR) Regional Station, Shimla .An amount of 1 g of each sample was weighed in triplicates into stainless steel boxes. These were placed in a hot air oven at 60°C overnight for drying. Samples were taken out the next day and placed in the desiccators. The records were taken of the sample boxes after cooling to room temperature. The moisture content was calculated using the below-mentioned formula:Each sample was weighed in triplicates into a pre-weighed crucible. The crucibles along with samples were placed in a muffle furnace at 550°C overnight until white ash was obtained. The samples were taken out the next day and placed in the desiccator. The records of the samples were taken after cooling to room temperature. The weight of the residue amount of total ash per 100 g of the sample was calculated as follows:Proteins of maize, paddy, and millet landraces were estimated using the Kjeldahl method (gravimetric and titration) according to the AOAC protocol . For bucThe presence of carbohydrates in maize landraces was assessed following the AOAC method .1Amylose and amylopectin contents in paddy landraces were analyzed enzymatically using the Megazyme Kit  following the procedure mentioned in the provided booklet.2. The absorbance of the colored solution was recorded at 655 nm using a spectrophotometer.In paddy landraces, the phytate content was determined using the enzymatic method using the Megazyme Kit  following the procedure mentioned in the bookletIn buckwheat and naked barley landraces, starch activity was measured as per the method described by Flavonoids were quantified by reverse phase-high performance liquid chromatography (RP-HPLC) in buckwheat and naked barley landraces. The samples were analyzed with the Waters 515 HPLC system equipped with a model 515 solvent pump, an ASI-100 autosampler, a PDA-Waters 2,996 detector, Waters In-line degasser AF, and Empower Pro software. Flavonoids were detected at 350 nm by comparison of their retention times with those of pure standards and individual quantification. Quantification was performed using the linear calibration curves of standard compounds.Minerals were quantified using the Inductively Coupled Plasma-Optical Emission Spectrometry (ICP-OES) iCAP™ 7200 ICP-OES Analyzer (Thermo Scientific). In brief, samples were digested by acid hydrolysis using nitric acid followed by microwave digestion. After appropriate dilution, minerals comprising trace and heavy metals, including zinc (Zn), copper (Cu), iron (Fe), manganese (Mn), magnesium (Mg), potassium (K), phosphorus (P), chromium (Cr), nickel (Ni), cobalt (Co), selenium (Se), and cadmium (Cd), were evaluated in all landraces.p < 0.05 using the multivariate multiple regression. Furthermore, it was analyzed by principal component analysis (PCA) and multidimensional scaling (MDS) using XLSTAT version 2016.02 .The study data were assessed with a combination of descriptive techniques and were tested for significant levels at The quantitative data were determined using range, mean, standard error, and coefficient of variation for different agro-morphological characters and nutritional profiles assessed among traditional crop landraces .p < 0.05) in terms of EL (18.34–29.89 cm), EH (63.41–149.76 cm), PH (158.26–275.50 cm), and 100-SW (17.41–28.20 g). A high coefficient of variation was recorded for EH (25.09%) followed by EL (12.28%), PH (17.19%), and 100-SW (11.76%), which showed moderate variability. Inexplicably, DM showed low variability (5.32%) where Chitkanu (107 days) and Talaw makki (127 days) emerged as early and late maturing landraces, respectively. The maximum 100-SW was found in Safed makki (28.20 g). In contrast, paddy landraces revealed significant variations (p < 0.05) for DF (44-102), LL (35.80–57.50 cm), NT (7-13), PH (75.10–157.68 cm), DM (73–153), and 100-SW (1.62–2.85 g) with moderate variations for DF (23.13%), LL (15.33%), NT (18.18%), PH (20.80%), DM (21.00%), and 100-seed (17.78%). Safed phulpatas exhibited early maturity (73 days) while Kaonoi dhan matured late (153 days) as compared with others. Similarly, buckwheat landraces showed a comparable pattern for LL (7.11–10.45 cm), LW (8.31–12.05 cm), and CL (2.75–4.33 cm) with moderate variability of 15.84, 17.58, and 20.99% for respective traits. Interestingly, these also exhibited low variation (7.95%) for DM. The early and late maturity was observed in Ogla-III (90 days) and Ogla-II (112 days), respectively. Notably, less significant variations (p < 0.05) for the aforementioned characters were observed in finger millet and naked barley landraces, except for DE in the latter, where a high level of variation (26.46%) was recorded. Furthermore, the data were projected into first and second principal components (PC1 and PC2) to illustrate intra- and inter-landrace variations. It was observed that PC1 interacted with PC2 in a similar pattern among all crop landraces, and the contribution of PC1 was more than PC2. The total variability of 100% indicated successful variance of uncorrelated components without loss of any information. PCA suggested that different traits contributed significantly to the total variation assessed in all crop landraces except for finger millet, namely, DT, DS, EL, EH, and DM in maize; DF, LL, LW, NT, PH, DM, and 100-SW in paddy; DF and PEL in buckwheat; and DE, DE, and DM in naked barley landraces  among the evaluated samples of maize landraces. The highest and lowest ash contents were observed in Talaw makki (5.95 g/100 g) and Ganga challi (1.21 g/100 g), respectively, with very high variability (55.32%). Fat content ranged from 3.93 g/100 g in Meethi challi to 7.79 g/100 g in Chitri makki with moderate variation (15.66%). Among paddy landraces, moderate variability was recorded for ash (14.26%), fat (12.19%), and moisture (15.41%) contents. Protein content differed significantly (p < 0.05) and ranged from 9.27 g/100 g in Safed phulpatas to as high as 14.86 g/100 g in Lamgudi dhan with moderate variability (15.21%). Amylose content varied significantly (p < 0.05) and was observed between 16.45% in Batesu dhan to 36.74% in Lamgudi dhan (32.08%). Similarly, phytic acid content showed a high variation (21.77%) and significantly differed (p < 0.05) from 0.67 g/100 g in Kaonoi dhan to 1.20 g/100 g in Batesu dhan. Furthermore, among buckwheat landraces, fat (16.02%) and starch (12.55%) contents showed moderate variability. Protein content ranged from 10.11 g/100 g in Phapra-I to 14.80 g/100 g in Ogla-I with moderate variation (16.36%). Interestingly, a wide range of significant variation (p < 0.05) was observed for the content of flavonoids, which extended from 2.99 mg/g in Ogla-II to the highest 20.50 mg/g in Phapra-I with maximum variation (92.70%). Among naked barley landraces, protein content varied significantly (p < 0.05) and ranged from 12.35 g/100 g in Nanga jau to 15.66 g/100 g in Nei-I with moderate variation (12.73%). Moreover, these also exhibited high variability percentages for ash, fat, moisture, and flavonoid contents. In contrast, significant variations (p < 0.05) for nutritional traits were found to be least among finger millet landraces. Interestingly, PCA indicated substantial variance among contributing traits for all crop landraces, namely, ash and fat in maize; protein in paddy; ash and fat in finger millet; fat, starch, and flavonoids in buckwheat; and ash, fat, and moisture in naked barley landraces  where Chitkanu (3.13 mg/100 g) and Peeli kukdi (1.88 mg/100 g) showed the highest and the lowest Zn contents, respectively. Similarly, Fe content varied widely among the evaluated samples and ranged from 0.61 mg/100 g in Hacchi to as high as 2.74 mg/100 g in Chitkanu. K content varied significantly (p < 0.05) among the maize landraces and ranged between 299.96 and 529.02 mg/100 g. The lowest and the highest K contents were observed in Hacchi and Chitkanu, respectively. Moreover, P content varied significantly (p < 0.05) with the highest and the lowest found in Peeli kukdi (407.61 mg/100 g) and Hacchi (135.40 mg/100 g), respectively. Furthermore, significant differences (p < 0.05) for K and P contents were observed in paddy landraces as compared with Zn and Fe contents. Bamkua dhan exhibited the highest K (467.77 mg/100 g) and P (249.96 mg/100 g) contents along with Batesu dhan (310.65 mg/100 g) and Lal dhan (112.50 mg/100 g) which had the lowest K and P contents, respectively. On the contrary, fewer variations were observed for selected trace elements among finger millet, buckwheat, and naked barley landraces, although these showed adequate concentrations of target minerals.A wide range of variability was observed for different minerals in all crop landraces . HoweverThe ever-increasing reliance on major crops has typical agronomic, ecological, nutritional, and economic risks and is possibly unsustainable in the long run, especially in view of nutritional security. Traditional crop landraces constitute the base of diversity in indigenous communities of a developing country and offer greater system resilience as per futuristic needs. The variability of quantitative traits in any crop is highly influenced by genetic and environmental factors as well as interactions among them, whereas uniformity of individuals and stability of quantitative traits are major requirements for the development of improved cultigens . In thisMoreover, all crop landraces showed comparable variability for the majority of nutritional traits. Among maize landraces, ash and fat were found to be the greatest variable traits. On the contrary, hidden hunger, has been affecting lives across the world. The conventional strategies to improve future nutritional security include dietary diversification (Likewise, the ever-increasing problem of micronutrient malnutrition, commonly known as fication  and bioffication . Our stufication . Reportsfication .Furthermore, the relative contribution of each trait among crop landraces was inferred by PCA and MDS. PCA was used to visualize the association between variables and determined the non-correlated factors that are linear combinations of initial variables. Bi-plots were generated for PCA analysis of all the observations as described in This study demonstrates a unique pattern of phenotypic diversity and nutritional features among crop landraces in the northwest Himalayas of India. The genetic relationships revealed by PCA as well as MDS were largely in agreement with their geographical distribution and provided a more comprehensive insight into the inherent association among target traits. These findings led to the identification of elite genotypes having desired traits of interest, which could be used in future crop improvement programs to develop new crop varieties. Besides, the landraces possessing high variation in concentrations of vital trace elements may be utilized to broaden the genetic base of existing cultigens. Finally, promoting the use of traditional crop landraces and revisiting the diverse areas harboring them would help in the conservation and long-term sustainability of otherwise forgotten traditional agriculture system, which will also aid in realizing futuristic goals of nutritional security.The original contributions presented in this study are included in the article/MS conceptualized the study, contributed to writing, reviewing, and editing the manuscript, and performed funding acquisition. NM contributed to investigation, formal analysis, and writing the original draft. PS and HS contributed to methodology, data curation, and resources. RC and MA contributed to methodology and resources. JR contributed to writing, reviewing, and editing the manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Parents completed the SCT, ADHD inattention (ADHD-IN), ADHD-hyperactivity/impulsivity (ADHD-HI), oppositional defiant disorder (ODD), limited prosocial emotions  traits), anxiety, depression, social impairment, and academic impairment scales of the Child and Adolescent Behavior Inventory (CABI). Parents also completed four dimensions of the Strengths and Difficulties Questionnaire , and five dimensions of the Kidscreen questionnaire . Results: SCT symptoms demonstrated strong discriminant validity from the ADHD-IN symptoms. SCT showed stronger first-order and unique associations than ADHD-IN with anxiety, depression, and ODD, whereas ADHD-IN showed stronger first-order and unique associations than SCT with ADHD-HI, CU, and social and academic impairment. Further, SCT showed stronger first-order and unique associations than ADHD-IN with more emotional problems, peer problems, and with lower prosocial behavior, as assessed with the SDQ. Higher scores for SCT were associated with lower psychological well-being, autonomy and parental relations, and lower peer and social support, as assessed with the Kidscreen. Higher ADHD-IN scores were associated with lower peer and social support, and a lower school environment. Conclusions: The Farsi version of the CABI–Parent Version has very good psychometric properties for assessing SCT and other dimensions of psychopathology/impairment and replicates the findings from similar studies with children and adolescents from South Korea, Spain, Turkey, and the United States. Accordingly, the present study provides further support of the transcultural validity of the sluggish cognitive tempo construct.Background: Sluggish cognitive tempo (SCT), recently renamed cognitive disengagement syndrome (CDS), is a set of behavioral symptoms that includes excessive daydreaming, mental confusion and fogginess, being lost in one’s thoughts, and slowed behavior and thinking. Symptoms of SCT show overlap with a broad range of other symptoms such as attention-deficit/hyperactivity disorder inattention, anxiety, and depression, or oppositional defiant disorder (ODD). To measure SCT, one of the optimal measures is the Child and Adolescent Behavior Inventory (CABI). Here, we report the psychometric properties of the Farsi version of the CABI Parent Version, including the CABI SCT scale and its subscales. Methods: The participants were the parents of 209 children and adolescents (53.9% girls; ages 8–19 years; M Within the last two decades, the construct of sluggish cognitive tempo (SCT) has gained increasing interest among both the scientific community and practitioners caring for children and adolescents with a broad variety of attention-deficit/hyperactivity disorder (ADHD) and ADHD-like behavior ,2,3,4. PWhile it appears that there is the risk to over-pathologize behavioral traits that might at times be experienced by a significant proportion of children and adolescents , some ofFor scientists, the construct of SCT is important because studies have shown that SCT is distinct from ADHD inattentive symptoms (ADHD-IN) and may not identify individuals with a “pure” ADHD inattentive presentation ,6,12,13.For practitioners, the concept of SCT is important to further distinguishing attention problems such as daytime sleepiness and internalizing problems from typical ADHD ,15,16. IThe SCT construct is not related to a specific cultural framework, and measures to assess SCT are available in several languages. More specifically, the Child and Adolescent Behavior Inventory–Parent Version (CABI)  is availn = 53, 2.58%), ADHD-only , SCT + ADHD , and comparison  groups. Compared to children with only symptoms of ADHD, children with only SCT symptoms also reported higher scores for depression, anxiety, conflicted shyness, and sleep difficulties. In contrast, compared to children with only symptoms of SCT, children with only ADHD had greater executive function deficits and higher scores for oppositional defiant disorder (ODD). Children with SCT-only and ADHD-symptoms had similar scores for friendship, social impairment, and academic impairment.To identify children with SCT , 2056 USTo test the psychometric properties of the Korean version  of the CTo test the psychometric properties of the Turkish version  of the CSáez et al.  analyzedThe main aims of the current study were to translate the CABI–Parent Version  into FarThe three hypotheses and research question were as follows: First, based on previous studies ,6,23,25 age = 14.23 years, SDage = 2.72). The community sample was obtained from public elementary, secondary, and high schools (2nd graders to 12th graders) in Kermanshah (Iran).The participants were the parents of 209 children and adolescents  approved the study (IR.KUMS.REC.1398.016), which was performed in accordance with the seventh and current revision of the Declaration of Helsinki. In the present study, we report data of the CABI–Parent Version.Parents reported on their children’s age (years), and gender at birth .To translate the English version of the CABI into Farsi, we followed the algorithms of Brislin  and BeatParents completed the Child and Adolescent Behavior Inventory (CABI ). As desIn the current sample, Cronbach’s alpha for the SCT, anxiety, depression, ADHD-IN, ADHD-HI, ODD, callous-unemotional behavior, social impairment, and academic impairment were 0.89, 0.77, 0.84, 0.89, 0.87, 0.86, 0.83, 0.90, and 0.92. Skewness for the nine CABI scales varied from −0.87 (academic impairment) to 2.02 (ADHD-HI) with kurtosis values varying from −1.02  to 5.84 (ADHD-HI).https://www.sdqinfo.org/py/sdqinfo/b3.py?language=Farsi, accessed on 1 June 2018). Example items are: “Nervous or clingy in new situations” , “often fights with other children” (conduct problems), “rather solitary, tends to play alone” (peer problems), “shares readily with other children” . Answers are given on a three-point Likert scale ranging from 0 (=not true) to 1 (=somewhat true) to 2 (=certainly true). Higher scores reflect a more pronounced dimension. Note that a higher score for prosocial behavior reflects better prosocial behavior. The Farsi version [Parents completed the dimensions of emotional problems, conduct problems, peer problems, and prosocial behavior  of the Farsi SDQ . Dimensions were: Physical well-being (5 items): e.g., “In general, how would your child rate her/his health?”; 0 = poor; 4 = excellent. Psychological well-being (7 items): e.g., “Has your child felt that life was enjoyable?”; 0 = not at all; 4 = always. Autonomy and parent relation (7 items): e.g., “Has your child been able to talk to her/his parent(s) when she/he wanted to?”; 0 = never; 4 = always. Social support and peers (4 items): e.g., “Has your child been able to rely on her/his friends?”; 0 = never; 4 = always. School (4 items): e.g., “Has your child got on well at school?”; 0 = not at all; 4 = extremely. Higher sum scores reflect more pronounced physical well-being, psychological well-being, autonomy and parent relation, social support and peer quality relationship, well-being, and performance at school. The Farsi version [Parents completed the Farsi 27-item version of the Quality of Life questionnaire (Kidscreen) for children and adolescents , the SDQ scales , and the Kidscreen scales . For these analyses, the SCT and ADHD-IN items continued to be treated as categorical indicators with the other scales treated as manifest variables . The purpose of these analyses was to determine the correlations of the SCT and ADHD-IN factors with the other scales.The third series of analyses regressed the CABI, SDQ, and Kidscreen scales (see above paragraph) on the SCT and ADHD-IN factors. For these analyses as well, the SCT and ADHD-IN items continued to be treated as categorical indicators with the other scales treated as manifest variables . The purpose of these analyses was to determine the unique associations of the SCT and ADHD-IN factors with the other scales.®, version 28.0  for Apple Mac®.The level of significance was set at alpha < 0.05. Some statistical analyses were performed with SPSS2 (229) = 385, p < 0.001, CFI = 0.971, RMSEA = 0.057 , and SRMR = 0.066. The loadings of the 15 SCT items on the SCT factor were moderate to substantial  with 11 of the 15 SCT items having non-significant cross-loadings on the ADHD-IN factor . Only two SCT items (lost in a fog and loses train of thought) had relatively lower loadings on the SCT factor  in conjunction with cross-loadings on the ADHD-IN factor of approximately 36. All SCT items thus had higher loadings on the SCT factor than the ADHD-IN factor. The nine ADHD-IN items had moderate to substantial loadings on the ADHD-IN factor . Six of the nine ADHD-IN items had non-significant cross-loadings on the SCT factor . An a priori SCT and ADHD-IN two factor-model with cross-loadings yielded an acceptable fit Xps < 0.001). Higher scores on the SCT and ADHD-IN factors were associated with higher scores on the SDQ emotional problems, conduct problems, and peer problems scales, and lower scores on the SDQ prosocial behaviors scale (ps < 0.001). For the Kidscreen scales, the higher scores on the SCT factor were associated with lower scores on the psychological well-being, autonomy/parent relationships, and social support/quality of peer relations (ps < 0.001) with higher scores on the ADHD-IN factor associated with lower scores on the psychological well-being and social support/quality of peer relations (ps < 0.01).ps < 0.01) unique associations with higher scores on the anxiety, depression, ADHD-HI, and ODD scales while higher scores on the SCT factor did not have significant unique associations with scores on the callous-unemotional behavior, social impairment, and academic impairment scales. In contrast, higher scores on the ADHD-IN factor had significant (p < 0.01) unique associations with higher scores on the depression, ADHD-HI, ODD, callous-unemotional behavior, social impairment, and academic impairment scales. The ADHD-IN factor did not have a significant unique association with the anxiety scale.ps < 0.05) unique associations with higher scores on the emotional problems, conduct problems, and peer problems scales, and lower scores on the prosocial behaviors scale. In contrast, the ADHD-IN factor only had a significant (p < 0.05) unique association with the conduct problems scale. For the Kidscreen scales, higher scores on the SCT factor had significant (ps < 0.05) unique associations with lower scores on the psychological well-being, autonomy/parent relationships, and social support/quality of peer relationships scales, while the higher scores on the ADHD-IN factor only had a significant (p < 0.05) unique association with lower scores on the social support/quality of peer relationships scale.For the SDQ scales, higher scores on the SCT factor had significant (The aims of the current study were to translate the Child and Adolescent Behavior Inventory (CABI)–Parent Version  into FarGiven the aforementioned, the present results add to the current literature in four important ways: First, the Farsi version of the CABI is psychometrically sound, and as such, both scientists and practitioners working in Farsi-speaking areas may benefit from this measure. Second, the pattern of results underlines the transdiagnostic and, above all, transcultural value of the SCT construct. Third, and similarly, SCT is clearly distinguishable from ADHD, in general, and from ADHD-IN, more specifically. Fourth, SCT symptoms such as mental confusion, daydreaming, lethargy, slow in completing tasks, lack of initiative and efforts, and trouble staying awake and alert have negative consequences for the individual with SCT and their social environment.With the first hypothesis, we assumed that SCT scores loaded on a factor clearly distinguishable from the factor ADHD-IN, and data did confirm this. Accordingly, the present results are in accord with previous studies ,6,23. WeSimilar to previous studies ,6,23, SCPrevious research showed that SCT traits and ADHD-IN symptoms were both associated with social impairments ,6,23, anAgainst what has been observed so far in the context of SCT traits and ODD ,25,35,36that SCT and social issues were associated, the quality of the data does not answer why this happened. Given that the present study has a cross-sectional design, by nature, causal relationships cannot be drawn. Theoretically and hypothetically, four processes are conceivable: reactive, evocative, interactional, and proactive processes.Next, while the present data showed First, an individual reporting issues with their parents and peers might withdraw from social contacts and pull back into their inner world. Thus, the individual with SCT might be in a reactive state regarding their social environment.Second, an individual’s behavior of excessive daydreaming, mental confusion, fogginess, and slowed behavior and thinking might trigger social distancing, social rejection, and conflicts with peers and parents see . Thus, tThe third assumption (again highly speculative and not possible to test with the current data) considers bi-directional influences over time, and thus an interactional process: To this end, and for want of similar models for children and adolescents with SCT, we rely on three models: First, Patterson et al.’s model for the development and maintenance of the children’s coercive behavior  model clThe fourth assumption highlights a person’s proactive interaction. As children grow older, they move beyond the social environment provided by their parents and begin to select and construct their own social environment. As such, it is conceivable that children and adolescents with SCT choose their peers, who better match with their SCT traits. In sum, while we acknowledge that these theoretical assumptions are clearly hypothetical, future and longitudinal studies should have the power to shed some more light on such reciprocal processes.The strengths of the present study are the large sample size, and the translation of the measure with low disagreement.In addition, the pattern of results should be balanced against the following limitations: First, here, we exclusively reported the psychometric validation of the Parent Version.Second, latent and unassessed confounders might have biased the pattern of results. To illustrate, higher SCT traits were associated with impaired sleep . As suchThird, and relatedly, there are some sparse, though encouraging observational and interventional studies, which showed that increased engagement in physical activity and exercising might improve dimensions of social contacts, at least among adults with psychiatric issues , and emoFourth, interestingly, and unexpectedly, higher SCT and ADHD-IN scores were associated with higher physical health and school environment. The current literature does not provide a reasonable explanation for such a pattern of results. Accordingly, we have re-run all statistical computations once again, and we have carefully checked, if mistakenly, we have misunderstood the anchor points of the questionnaires, though, we could not identify any mistakes. Given this, we were unable to provide any reasonable explanation. We also note that all questionnaires were psychometrically sound. Though, and against expectations, in the present sample, higher scores for SCT were also associated with higher scores for ODD; as such, it appears conceivable that perhaps latent and unassessed  confounders might have biased the present data in the same or opposite directions. Clearly, the present and unexpected findings need to be replicated in the future.Fourth, given that the pattern of results showed that children and adolescents scoring high on SCT and ADHD-IN, as rated by their parents, also scored high on social issues, future studies should assess the quality of peer and family relationships and interactions more accurately.Fifth and last, data from representative samples of youth showed that excessive use of screen media and social network sites (SNSs) might be problematic ,56,57,58The Farsi version of the CABI–Parent Version has sufficient and satisfactory psychometric properties to accurately identify children and adolescents with SCT, in contrast to those with ADHD-IN. Further, SCT is associated with a broad variety of internalizing and social issues. Lastly, SCT appears to be a transdiagnostic and transcultural psychiatric issue.


This indicates no increased risk of ADHD in offspring following in utero exposure to ADs. The secondary analyses revealed no statistically significant associations either. The present study provides further evidence that an association between in utero AD exposure and ADHD in offspring might not exist. This perceived association may be caused  by confounding by indication. The extent to which depression in the pregnant parent could cause mental disorders such as ADHD in offspring, and the mechanisms involved, should be investigated in further studies.Recent studies have reported an association between antidepressant (AD) use during pregnancy and the risk to develop attention-deficit/hyperactivity disorder (ADHD) in the offspring. However, the association might be confounded by risk factors in the pregnant parent. To control for unmeasured factors between pregnancies carried by the same parent, we set up a case-control sibling study using the University of Groningen prescription database IADB.nl. Children receiving medication for ADHD (cases) before the age of 16 years were matched to siblings not receiving such medication (controls). Exposure was defined as at least two prescriptions for any AD during pregnancy, i.e., the period of 39 weeks before the birth date of the offspring. Secondary analyses were performed to assess the effects of the degree of exposure (the amount of Defined Daily Doses) and the type of AD exposed to. Univariate and multivariate logistic regression was used to estimate odds ratios (ORs) with corresponding 95% confidence intervals (CI). In total, 2,833 children  were included in the analysis. Exposure rate to ADs among cases and controls was 2.2% and 2.4%, respectively. After adjusting for the birth date of the child (as a proxy for the date of pregnancy), age of the pregnant parent at birth, use of psychostimulants, opioids, and antiepileptic drugs by the pregnant parent in the 15 months before birth of the child, an adjusted OR of 1.11 (95% CI 0.67–1.83) was found for the risk of ADHD in the offspring when exposed  The rate of depression during pregnancy is approximately 17% according to a meta-analysis on 15 studies from year 2000–2016, with rates ranging between 4.8% and 33.2% . UntreatA descriptive drug utilization study on six European regions found that a weighted average of 2.5% of pregnancies in the period 2004–2010 were exposed to selective serotonin reuptake inhibitors (SSRIs) . Accordiin utero drug exposure and an increased risk of ADHD in offspring is disputable though, as the etiological mechanisms involved in this disorder are still largely unknown  in offspring has been studied . A meta- unknown . Some st unknown , althoug unknown . However unknown . Using a unknown . Since A unknown , it is l unknown . This main utero AD exposure and risk of ADHD in offspring using data from the Dutch IADB.nl prescription database, while adjusting for confounding by indication by employing a sibling design. Secondarily, this study aims to investigate whether the degree of exposure and the specific type of AD exposed to are effect modifiers.The current study aims to investigate the potential association between A case-control study was conducted using data from a pregnancy database as part of the University of Groningen prescription database IADB.nl. The overarching database contains prescription data from 1994 until 2020 from approximately 120 community pharmacies, covering more than 1.1 million patients. In the pregnancy database used in this study, prescription data from 65,251 infants and their childbearing parents who were born between 1995 and 2020 were included. An address code is used to connect the childbearing parent and their child . The chiThe study population was restricted to singleton pregnancies where the offspring could be followed from birth until at least 4 years. Because of this, all births included in the study took place between 1995 and 2016. The childbearing parent had to be in the database for at least 6 months before the pregnancy, which corresponds to 15 months before the birth of the child. The pregnancy period was defined as 273 days (or 39 weeks) before the birth date of the child.Drug prescription data was used as a proxy for ADHD in offspring, as diagnostic information was not available. Guidelines for the treatment of ADHD recommend to start with behavioral therapy first and supplement with pharmacotherapy, beginning with methylphenidate (MPH) . When reExposure was defined as at least two prescriptions for any AD (ATC starting with: N06A) during pregnancy, with specific interest in SSRIs and tricyclic antidepressants (TCAs). Non-exposure was split into former users and never users. Former users were defined as childbearing parents who receive at least one prescription for any AD in the 6 months preceding pregnancy, but not during pregnancy. Never users are childbearing parents with no AD prescription anywhere during the 15 months before birth.Among the covariates considered for confounding adjustment were sex of the offspring and age of the childbearing parent at delivery. Additionally, the current study takes into account whether other drugs than ADs were used by the childbearing parent in the 15 months before birth Specifically: psychostimulants (ATC: N06B), antipsychotics (ATC: N05A), benzodiazepine derivatives , opioids (ATC: N02A), antiepileptics (ATC: N03A), acid-suppressive drugs (ATC: A02B), drugs for obstructive airway diseases (ATC: R03), and ADHD drugs, i.e., MPH (ATC: N06BA04), dextroamphetamine (ATC: N06BA02) and atomoxetine (ATC: N06BA09). The moment in time the pregnancy took place was also taken into account by using the birth date of the child, as it is relevant for the changing prescription trends over time . In the in utero AD exposure on ADHD in offspring. This data can be found in an extended version of For the primary analysis, crude odds ratios (ORs) with corresponding 95% confidence intervals (95% CI) were determined by applying a logistic regression model using R. All unexposed pregnancies were used as reference, regardless of former user status. ORs were adjusted for confounders in multivariate logistic regression models. Confounders were determined to be the birth date of the child (as proxy for the date of pregnancy), age of the childbearing parent at birth and use of psychostimulants, opioids, and antiepileptic drugs by the childbearing parent in the 15 months before birth. The covariates for inclusion in the models were selected based on literature and on causal diagrams built using DAGitty , 2 Shah. Some faTwo secondary analyses were performed. 1) The role of the degree of exposure was investigated by using the amount of Defined Daily Doses (a continuous variable) as the determinant instead of the binary variable of exposure status. 2) The effect of each AD type was determined by stratification, effectively leading to three separate analyses, one for each type of AD, being TCAs, SSRIs, and other ADs. For example, for the TCA-specific analysis, a subset of the sample data was used where SSRI-exposed pregnancies and pregnancies exposed to other ADs were removed. Note that these removed pregnancies were not classified as unexposed pregnancies. Additionally, an exploratory analysis was performed to investigate sex of the offspring as an effect modifier. Sex of the offspring was added to the adjusted model as well as an interaction term between sex and exposure.n = 59), followed by TCAs (n = 17), and other ADs (n = 11). 44.1% of children in the sample have used respiratory drugs. There was more use of acid-suppressive drugs during pregnancy in the control group when compared to the cases (p < 0.05).From the IADB.nl prescription database, 1,304 cases of ADHD were identified and matched to 1,529 sibling controls without ADHD. The average age at which children with ADHD got their first prescription for ADHD medication was 8.74 years old (SD 2.73), with 1,294 (99.2%) cases starting with MPH and the rest starting with dextroamphetamine . In totain utero exposure to ADs were found. For the exploratory analysis on sex of the offspring as an effect modifier, it was found that the interaction term was not significant: aOR = 0.76 (95% CI 0.24–2.24) with male sex as the reference.In the main analysis of the association between AD exposure during pregnancy and ADHD in the offspring, a crude OR of 0.94 (95% CI 0.57–1.54) was determined . The mulin utero exposure to ADs and the risk of developing ADHD in the offspring. The analyses on degree of exposure and the type of AD did not lead to any statistically significant differences either. The results of this study may imply that the previously observed association between prenatal AD use and increased incidence of ADHD in offspring  than controls (32.8%). According to prior studies, being the firstborn may be a risk factor for developing ADHD . Howeverin utero AD exposure and ADHD in offspring might not exist, but that this perceived association may be caused  by confounding by indication. The extent to which depression in the pregnant parent could cause mental disorders such as ADHD in offspring and the mechanisms involved should be investigated in further studies, preferably using diagnostic data on both depression in the pregnant parent as well as on ADHD in the offspring.The present study provides further evidence that an association between


Low birth weight is one of the primary causes of child mortality and several diseases of future life in developing countries, especially in Southern Asia. The main objective of this study is to determine the risk factors of low birth weight and predict low birth weight babies based on machine learning algorithms.Low birth weight data has been taken from the Bangladesh Demographic and Health Survey, 2017–18, which had 2351 respondents. The risk factors associated with low birth weight were investigated using binary logistic regression. Two machine learning-based classifiers (logistic regression and decision tree) were adopted to characterize and predict low birth weight. The model performances were evaluated by accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and area under the curve.The average percentage of low birth weight in Bangladesh was 16.2%. The respondent’s region, education, wealth index, height, twin child, and alive child were statistically significant risk factors for low birth weight babies. The logistic regression-based classifier performed 87.6% accuracy and 0.59 area under the curve for holdout (90:10) cross-validation, whereas the decision tree performed 85.4% accuracy and 0.55 area under the curve.Logistic regression-based classifier provided the most accurate classification of low birth weight babies and has the highest accuracy. This study’s findings indicate the necessity for an efficient, cost-effective, and integrated complementary approach to reduce and correctly predict low birth weight babies in Bangladesh. Globally, more than 80% of newborn babies die due to LBW [Low birth weight (LBW) is a leading concern for public health in many developing countries, including Bangladesh. LBW refers to babies born weighing less than 2.5 kilograms –4. LBW ie to LBW . Southere to LBW . The pree to LBW , 20% in e to LBW , and 16%e to LBW . That shLBW was the leading cause of neonatal and under-five mortality . UnderstSome important literature related to identifying the most informative risk factors and predicting LBW using various ML algorithms. Eliyati et al.  used thest stage of sampling, 675 enumeration areas (EAs) were chosen, whereas 250 EAs were from urban areas and 425 from rural areas, with a probability proportional to the EA scale. A complete household listing procedure was carried out in all selected EAs to provide a sampling frame for the 2nd stage sorting of households. A systematic sample of 30 homes per EA was chosen in the 2nd stage of sampling to provide statistically accurate estimates of key demographic and health variables for the nation as a whole, rural and urban areas separately, and each of the eight divisions. In data collection, a total of 20,250 residential households were chosen under this plan. Face-to-face interviews using questionnaires were used to obtain data for the survey. Around 20,100 ever-married women aged 15–49 were expected to complete the interviews [The LBW data was extracted from the Bangladesh Demographic and Health Survey (BDHS), conducted in 2017–18, and is freely accessible online . BDHS usterviews . Each reThis study analyzed an existing public domain survey dataset that was freely available online with all identifier information removed. The ethics committee in Bangladesh approved the survey. The authors were permitted to use the data for independent research purposes.The main outcome variable of the present study was birth weight, which was measured in grams. LBW refers to a weight less than 2500 gm at birth, and Normal birth weight (NBW) refers to a weight greater than or equal to 2500 gm. For our study purpose, we defined the outcome variable as “1” for LBW and “0” for NBW .st birth interval, parity, height, weight, child is twin, child’s sex, child is alive, ANC visit, delivery place, delivery by cesarean section (CS), taking pills, and body mass index (BMI). The predictor names, predictor types, predictor descriptions, along with their categorizations, were presented in In the current study, we considered several predictors based on the relevant previous works , 27, 32.Data for nominal and ordinal variables were expressed as a percentage (%), whereas data for continuous variables was mean±SD. We employed a chi-square test for nominal variables and an independent paired t-test for continuous variables to examine the association between different factors and LBW. A p-value<0.05 determines the statistical significance. The significant predictors were used in the LR model to determine the prominent risk factors of LBW. This work utilized Stata version 14.2 for statistical analysis and R studio version 1.3 for ML-based classifiers.To classify and predict LBW, two well-known ML-based classifiers, namely LR and DT, were adopted. A brief description of each of these classifiers is as follows.1, x2, …, x6) is the set of predictor variables. The mathematical form of the logistic regression model isLogistic regression is a category of generalized linear modeling known as a logistic model or a logit model. It is a well-known and simplest supervised ML-based classification technique. This technique is widely applicable in regression and various classification problems like diabetes, LBW, hypertension, and so on –35. It p1, β2,…,β6 be the parameters. The parameters are estimated by using the maximum likelihood estimation method.Equivalently, after taking exponentiation, both sidesDecision tree (DT) is one of the first tree-based supervised ML techniques . The corA holdout approach is a simple form of cross-validation (CV) in which the dataset is partitioned into two sets as a training set, and a test set. In this present work, we split the dataset at the following ratios: 70:30, 75:25, 80:20, 90:10. We fit the predictive model for the training set and predict the class (LBW vs. NBW) based on the test set. The predictive power of the models were measured using accuracy, sensitivity (Se), specificity (Sp), positive predictive value (PPV), negative predictive value (NPV), and area under the curve (AUC) , 39.The baseline and demographic characteristics of the participants are shown in The LR results of the different associated risk factors for LBW are presented in DT and LR-based classifiers were adopted to predict LBW babies based on the significant risk factors (we get from LR). The entire data set is split into two sets as training set and test set using four different sets . Our findings show that the LR-based classifier was achieved an accuracy of 85.0% for Set-1, 85.3% for Set-2, 85.2% for Set-3, 87.6% for Set-4 . So, SetThe other performance evaluation parameters of two ML-based classifiers for set-4 were presented in The current study determined the significant risk factors for LBW and predicted LBW babies using the critical risk factors with ML in Bangladesh. We implemented the LR-based method to determine the most significant risk factors. The LR method demonstrated that region, education, wealth, height, the child is twin, and the child is alive were the significant risk factors of LBW. Our study showed that the prevalence of LBW was 16.2% which was coincided with the prevalence of LBW (16.0%) <2500 g) reported in BDHS, 2017–2018 . Our fin00 g repoMothers from low-income families were more born babies with LBW than mothers from wealthy families. Low-income families cannot provide sufficient nutrition, medicine, and medical facilities to pregnant women, so they mostly give birth to LBW babies. Most of the families depend on only a single earning member due to lower family income. By being self-reliant, family members can contribute to the family income, which might help overcome poverty. The overall GDP of the country also postulates most families’ wealth condition. Increasing GDP can reduce poverty and LBW problems. Previous studies in developing countries, including India and Bangladesh, have shown the association between the wealth index and LBW , 26, 40.The main contribution of this work is as follows: (i) Usage of nationally representative BDHS, 2017–18 dataset with large sample size; (ii) Utilization of sample weight that helped to achieve higher accuracy in the representing country; (iii) Utilization of t-test for continuous and Chi-square tests for categorical factors to show the association between different factors and LBW; (iv) Utilization of LR to detect the high-risk factors of LBW on the basis of OR and p-values ; and (v) Utilization of two ML-based classifiers for the prediction of LBW babies. Although this study offered several contributions, it also had some limitations:First, this study considered only limited factors. Second, the actual birth weight of babies was not reported in BDHS, 2017–18. We defined an LBW-based child size at birth. Third, we did not consider the gestational age of the mothers in our present study. Fourth, Only LR employs to extract the significant risk factors of LBW. Fourth: Usage of only two ML-based classifiers for the prediction of LBW babies.We may use other feature selection methods like the random forest, principle component analysis, multilevel logistic regression, stepwise logistic regression, and so on instead of logistic regression. We may also adopt different classifiers like support vector machine, Gaussian process classification, artificial neural network, AdaBoost, and deep learning for the prediction of LBW. We also want to see the effect of LBW over time.LBW can be a leading cause of different diseases for babies at their adult age. It is sometimes dependent on key variables that we may manage or control. This study determined the informative risk factors and employed ML-based algorithms to predict LBW using the risk factors. However, mothers with an education are less likely to give birth to a child with LBW than no educated mother. Other factors like region, wealth index, mothers height, twin child, and alive child were significantly associated with LBW. Bangladesh’s government and health authorities should take the necessary steps to improve the economic condition of poor people and other significant factors in our country. This problem can be studied using ML-based algorithms. We believe that addressing the major factors that contribute to LBW will aid clinicians in predicting LBW before the child is born in Bangladesh.According to the current study, various demographic features are still significant for producing LBW in babies. It is possible to recommend that the government may create opportunities for women to access higher education and take a necessary step to improve the economic condition of poor people in Bangladesh.S1 Appendix(DOCX)Click here for additional data file.


In seeking to explain the current context exemplified by the sacking of P&O Ferry workers and the recruitment of “foreign agency” crews in March 2022, the study foregrounds 19th- and 20th-century maritime bordering legislation on ships and onshore, focusing on public-/private-bordering partnerships between governments, shipping companies, and unions. Archival research on British Indian seafarers employed by P&O a century ago and analysis of contemporary media and political discourses relating to “foreign agency crews” are drawn on to consider the implications of earlier bordering discourses and practices for 21st-century British citizenship and belonging. Attending to imperial bordering regulations that created the racialized and class-defined labor category of  An immediate question is whether or not seafarers—who spend their working lives in transit at sea or contained at docksides and who are expected to return to their countries of origin between contracts—should be considered “migrants.” The negative answer to this question arises in the context of labor migration theory  made 786 seafarers redundant, stating the necessity of improving business viability through a change to its crewing model from permanent to agency staff, many recruited from the Philippines. The seafarers were mostly British citizens living in the UK but employed under Jersey law working on ferries registered in Cyprus, Bermuda, and Barbados. P&O's strategy was to bypass legally binding consultation with the unions [Section 188 of the Trade Union and Labor Relations (Consolidation) Act 1992]. The employer calculated that they could afford any additional costs resulting from that breach of procedure by offering employees redundancy compensation above the legal requirements and incentivizing dismissed employees from costly disputes of the redundancies in the courts  into an analytical framework informed by the concepts of bordering and everyday bordering to evidence how colonial employment categories and related bordering discourses and immigration practices worked together over four centuries to exclude the seafarers recruited in the British Empire and thus to ensure that others recruited later from elsewhere in the Global South continue to be excluded from the UK.Tlostanova and Mignolo , p. 7 exstate borders through ideology, cultural mediation, discourses, political institutions, attitudes, and everyday forms of transnationalism. In the UK, everyday bordering is integral to the government's “hostile environment” immigration regulations .When P&O amalgamated with its main rival, the British India Steam Navigation, in 1914, the Peninsula and Orient Steam Navigation Company became the world's largest shipping conglomerate playing important roles in transporting food and troops in the First and Second World Wars. A 150th-anniversary publication referred to P&O as a “phenomenal company” that had diversified into enterprises well beyond shipping but remained anchored to traditions of trust, loyalty, service, and pride Jack, . As markFollowing the crew dismissals, political and media debates about the actions of P&O Ferries were constructed through a discourse that drew on related nationalist notions. These were as follows: first, of P&O as a British icon tied to imperial greatness and second, of British exceptionalism as an island, seagoing “race” threatened by globalization. That discourse was evident in journalist Cumming's later comment, “For an island race with sea in our veins, cross channel sailings are more than just transport” and that of travel writer, Adrian Bridges, quoted in the same article, “We are an island nation and going to sea has always been a huge part of our heritage and history. On a ferry we're connecting with who we are as a people” Cumming, .Politicians and media commentators with different positionalities and political perspectives shared this discourse of P&O as a famous icon of the “island nation” in arguing for “decency,” “fair play,” and the need for “British crews.” In a special debate about the sackings Hansard, , the ConSpirit of Britain, Pride of Kent or any other name that attaches it to this country when it does not have British workers would be completely wrong, and I will be calling on P&O to change the name of the ships .To have a ship called The Labor Party shadow minister used the same discourse when drawing on an imagined past of seafaring labor relations that, through the elision of colonial histories of legalized discrimination and conflict, I argue below, has contributed to the continuing exclusion of workers recruited from the Global South:Hansard 21/3/2022).We are an island nation. British seafaring has been and is the envy of the world, and a sense of fair play and decency runs deep in this country: it is part of who we are. The action on Thursday was a straightforward assault on that tradition and on our values, so deeply entwined with our identity and synonymous with our global reputation. . Lascar became a term of racist abuse in the English maritime language, described as the mobile equivalent of coolie  terminus across the west of the subcontinent. In the east of India, The British India Steam Navigation Company (BISN) employed the second-highest number of Indian crewmen whom they recruited inland from networks centered in Calcutta (Kolkata)  Ewald, , p. 278.In the later decades of the British Empire, there were continuing tensions between the “mobility” and “containment” of British Indian subjects who were moving around the empire as indentured laborers, military personnel, and seafarers Ahuja, . The seaserangs (boatswain)—recruited via their own networks. Serangs also controlled the lives of seafarers on board through bonds of debt that reached back to villages. Their own dependence on the white officers and financial obligations meant that it was in their interests to ensure that Indian seafarers were kept under surveillance when anchored in docks and caught and punished if they attempted to cross the dockside border by deserting  was obliged to house Indian seafarers in barracks near the ports or privately run boarding houses since they were prohibited from terminating their contracts anywhere outside of British India. Indian, African, Chinese, and Caribbean seafarers were targets of racist attacks and abuses throughout the 19th and early years of the 20th centuries Visram, . In 1816campzenship outlined by Sigona  argued that Chinese and Indian seafarers “lowered the standard of life for white men” struggled for by the unions and threatened that it the “Chinese Invasion” continued, “the workers would have one of the biggest fights that the country has ever known.” In the same year, while in London, NTWF leader Ben Tillett complained that the shipping companies had “engaged all possible Asiatics and foreigners including negroes” forcing white crews out  demanded that Indian and Chinese seafarers should be fluent in English. This was clearly not needed since the labor categories on board steamships, described in the previous section, were well known to be segmented and controlled by the  Visram, , p. 88. The memoirs of Dada Amir Haider Khan, who worked as a seafarer during the First World War, counter the one-dimensional constructions of Indian men who were compelled to stay on board or in approved lodgings when they arrived in British ports. Arriving on the P&O Steamship, the SS Khiva in the Royal Victoria Docks in the winter of 1917–1918 Khan wrote of leaving the ship and docks to visit acquaintances who were living and working onshore. In doing this, Khan and his friends were “illegal border-crossers.” During and after the First World War, Indian seafarers were recruited and illegally employed by businesses such as Tate&Lyle located near the docks. Khan also gives an idea of the P&O accommodation and security arrangements for Indian seafarers:I encountered a former shipmate of my senior brother whom I knew from Bombay. He was residing in a working class locality of London where he was employed in some factory. A few times he took me to his lodging house and other places where the working people lived … After taking some additional men from the reserve which the P&O kept near the docks, and having our photographs taken for identification cards, the S.S. Khiva crawled out of her mooring place in January 1918 Gardezi, , p. 120.Khan deserted the ship at the end of that voyage in New York, quickly obtained naturalization papers, and got recruited onto an American ship with better conditions and freedom to leave the vessel when it docked in Liverpool 2 months later. However, he remained conscious of the risks faced by Indian seafarers crossing the border when he visited the P&O ship he had previously deserted when it returned to New York:serang would not have detained us forcibly in the presence of so many of our friends . Imperial bordering laws were not the impermeable mechanisms of control represented by governments.Khan's memoirs hint at the interactions and negotiations among seafarers, In 1919, riots in port areas of the UK were started by local white populations who attacked people and property of the mixed neighborhoods, blaming African and Asian laborers for the lack of employment during the economic downturn Tabili, . The govDuring this period, onshore accommodation of seafarers was associated with an array of state-bordering practices. The case of the 1922 surprise inspection of official and unofficial lodgings in dock areas of east London illustrates how these practices were aimed at preventing desertion, avoiding racial conflict that would have a negative impact on public opinion in India , protecting profits of the shipping companies, or a combination of all of the above Tabili, , p. 198.I have reason to believe that this accommodation consists of an old hulk in the Royal Albert Docks. It is under the supervision of the Port of London Sanitary Authority and is therefore outside our jurisdiction and cannot be inspected by any member of our staff. I am given to understand therefore that this hulk is an “abomination” and a byword in the Dock neighborhood for filthiness and unsuitability… [godown (a word used for a warehouse in parts of Asia)—suggesting perhaps that its isolation had led to the rumor of a floating “hulk” (Winterton to Peel 4 December 1922 L/E/7/1152). Floating or not, the inspection report confirmed that conditions were “unsatisfactory in every detail,” dirty, badly heated, no proper cooking arrangements, and insufficient space . Not wanting to antagonize the shipping company, the India Office sent P&O private communications about the “disgraceful” quarters, to which its directors responded that they were already planning to demolish them (Communications between Peel and Shaw 1–11 January 1923 L/E/7/1152).P&O gave permission for the party to visit the “hulk,” after which it was referred to as a shed in the resulting report and communication. It was reported that P&O called it a lascar accommodation” following the inspection made suggestions that aimed to develop the bordering roles and partnerships between owners of lodging houses, the LCC, the India Office, and shipping companies. The first suggestion to prevent desertions was that the LCC should introduce regulations to compel lodging housekeepers to report to the India Office within 24 h of the arrival of any Indian seafarer, their name, the name of their ship, and the reason for leaving their ship. Another was to follow Australia and Canada in legislating for shipowners to be fined for every Indian desertion. A further suggestion to avoid “racial disturbances” was to house Indian seafarers separately from others .A conference to discuss the government's response to a forthcoming parliamentary question about “lascar accommodation also worked to exclude and silence the experiences of Indian seafarers in the UK. In responding to the question on lascar accommodation, the Conservative MP, Earl Winterton, said that he had been part of the inspection, but he shared none of the details that would have alerted others to the appalling living conditions of working-class British Indian men. He said that he “had come to the conclusion that there is room for considerable improvement in certain cases” (Hansard, Bordering discourses of Conservative and Labor MPs who took part in the parliamentary discussion on Hansard, . Manny SWill the Department at the same time inquire into the housing accommodation provided for white seamen in various ports of this country? Cannot steps be taken to prevent crews being shipped on vessels to be paid off at British ports so that they shall not be discharged in this country? Hansard, .In the following decade, the situation for racialized seafarers deteriorated further with legislation including the explicitly racist 1925 Colored Alien Seamen Order that required “colored seamen” to register with the police and be deported if “destitute.” African and Caribbean men, Goan Christian seafarers who were not categorized as British Indian and British Indian crew without papers that proved their status as British subjects were deported Ahuja, . HoweverThis study is a partial response to the question of whether seafarers, recruited from the Global South and working between British and European ports but prohibited from settling in the UK, should be considered “migrants.” I have argued that the normalization of the view that they are not “migrants” is due to the accumulation, since the early years of British colonialism, of racially discriminatory maritime legislation, everyday bordering practices, and discourses that forced racialized seafarers to embody the border.lascar were targeted as undesirable migrants. The British border was never impermeable to British Indian working-class seafarers. However, by making it illegal to leave their ships and compelling them to initially live and work without documents, the bordering legislation forced them to hide from officials and private individuals, further making them and their families invisible as migrants. Immigration laws combined with maritime legislation produced and maintained the cultural whiteness of the metropole and settler colonies. In dialog with the legislation, bordering discourses worked to exclude and silence the voices of men recruited from coastal and inland colonized India but whose lives were spent crossing oceans between empire ports and elsewhere. More invisible still were the lives and voices of their families in India, while ideologies of racial purity stigmatized their families in Britain.Throughout the 19th and early 20th century, British imperial legislation aimed to make British borders differentially permeable to British subjects and those categorized as “aliens.” Indian men, racialized and class-defined through the labor category of Bordering legislation and practices from 150 years ago constitute the power relations of coloniality that structure lives today. The proliferation of the differential bordering of bodies continues to be central to national and global operations of neoliberal globalization. In 2022, dominant political and media discourses mostly ignore the everyday lives of agency seafarers recruited from the Global South. However, the contracts they work under construct them, including those working on P&O Ferries in British waters, as potential “illegal migrants.” Empire-era bordering legislation and bordering discourses have normalized the conditions of living unseen and unheard on board for 6-month stretches, on low pay, away from families, and yet constructed as part of the “family” of the DT World conglomerate. As in the case of British Indian seafarers, the racialization of “foreign agency” crew ensures that they embody the border on sea and onshore.In centering racialized bordering discourses and partnerships between government and private companies on ships, at the border-crossing spaces of UK docks and onshore, I am arguing for a deeper awareness of and further sociological research into the histories of marginalization, objectification, and physical containment of racialized maritime laborers. Along with swathes of bordering legislation across the empire, the public/private partnerships between the colonial governments and shipping companies constitute the long history of the UK's so-called “hostile environment” immigration policies whereby everyday bordering discourses and practices that have drawn ordinary citizens into border-guard roles, continue to target racialized working-class men and women (Yuval-Davis et al., https://www.bl.uk/subjects/south-asia.Publicly available datasets were analyzed in this study. This data can be found here: The author confirms being the sole contributor of this work and has approved it for publication.


Attention deficit hyperactivity disorder (ADHD) is a well-studied topic in child and adolescent psychiatry. ADHD diagnosis relies on information from an assessment scale used by teachers and parents and psychological assessment by physicians; however, the assessment results can be inconsistent.To construct models that automatically distinguish between children with predominantly inattentive-type ADHD (ADHD-I), with combined-type ADHD (ADHD-C), and without ADHD.k-fold cross-validation was applied to ensure the consistency of the machine learning results.Clinical records with age 6–17 years-old, for January 2011–September 2020 were collected from local general hospitals in northern Taiwan; the data were based on the SNAP-IV scale, the second and third editions of Conners’ Continuous Performance Test (CPT), and various intelligence tests. This study used an artificial neural network to construct the models. In addition, We collected 328 records using CPT-3 and 239 records using CPT-2. With regard to distinguishing between ADHD-I and ADHD-C, a combination of demographic information, SNAP-IV scale results, and CPT-2 results yielded overall accuracies of 88.75 and 85.56% in the training and testing sets, respectively. The replacement of CPT-2 with CPT-3 results in this model yielded an overall accuracy of 90.46% in the training set and 89.44% in the testing set. With regard to distinguishing between ADHD-I, ADHD-C, and the absence of ADHD, a combination of demographic information, SNAP-IV scale results, and CPT-2 results yielded overall accuracies of 86.74 and 77.43% in the training and testing sets, respectively.This proposed model distinguished between the ADHD-I and ADHD-C groups with 85–90% accuracy, and it distinguished between the ADHD-I, ADHD-C, and control groups with 77–86% accuracy. The machine learning model helps clinicians identify patients with ADHD in a timely manner. According to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), ADHD can be divided into three subgroups, combined-type ADHD (ADHD-C), predominantly inattentive-type ADHD (ADHD-I), predominantly hyperactive/impulsive ADHD ADHD-H; . ClinicaIn the past, the diagnosis of ADHD relied heavily on information from teachers, parents, and psychological assessment. When teachers find that students have inattention, hyperactivity and other ADHD-related behavioral problems, they may suggest that the parents take their children to the hospital for evaluation. One study showed that even in a student with strong cognitive ability, teachers were more likely to consider the possibility of ADHD because of inconsistencies between student’s behavioral problems and high cognitive levels . In addiMachine learning, deep learning, and artificial intelligence are widely applied technologies in medical informatics, such as in the diagnosis of dementia and Alzheimer’s disease . One stuA notable study in Taiwan by Cheng et al. used a support vector machine method to identify ADHD based on the CPT-2, the SNAP-IV scale, and Conners’ Parent and Teacher Rating Scales-Revised: Short Form , and theTherefore, from previous studies, we found that machine learning can help distinguish ADHD and control groups, but less attempts have been made to distinguish ADHD-I and ADHD-C, as well as the influence of intellectual factors. Our research hypothesis is that the use of neural networks can help distinguish between ADHD-I and ADHD-C, and the inclusion of intelligence factors may increase the model prediction accuracy. The present study, conducted in Taiwan, collected data based on the SNAP-IV scale, the CPT-2 and CPT-3, and various intelligence tests. We used an artificial neural network to construct predictive models to distinguish between ADHD-I, ADHD-C, and the absence of ADHD.2.2.1.Inclusion Criteria: This study included patients aged 6–17 years who received an ADHD-I or ADHD-C diagnosis. Children with ADHD are often diagnosed with other childhood and adolescent psychiatric disorders. According to a previous study, 66% ADHD patients had at least one comorbid psychiatric disorder, including learning disabilities, anxiety disorder, Tourette’s syndrome, etc.  neurological diseases, including disorders of the brain and central nervous system ; (ii) intellectual disabilities (for patients who had taken an intelligence test); (iii) other serious psychological disorders, such as schizophrenia, bipolar disorder, and major depressive disorder; and (iv) physiological diseases potentially affecting attention and activity level.Diagnostic Procedure and Psychiatric Assessment Questionnaire: Patients were first evaluated by psychiatrists from the Child and Adolescent Psychiatry Division to determine whether they had ADHD-I or ADHD-C, as defined by the DSM-V. In addition, the following psychiatric evaluations were conducted: (i) SNAP-IV scale evaluations by the patients’ teachers and parents to identify attention deficit and hyperactivity in patients; (ii) evaluations using the CPT-2 and the CPT-3; and (iii) intelligence tests, which were based on the Standard Progressive Matrices, the Colored Progressive Matrices, and the Wechsler Intelligence Scale.Grouping of Individuals: According to the assessment results, individuals were divided into ADHD-I, ADHD-C, and control groups. The control group comprised patients who had received attention and activity level assessments in a hospital and for whom psychological assessment and psychiatric evaluation indicated no ADHD-I or ADHD-C.This was a retrospective study that analyzed medical records. The study was approved, and the waiver of informed consent was provided by the Taipei Medical University Joint Institutional Review Board . The participants were patients who were assessed at any period between January 2011 and September 2020 in local hospitals in northern Taiwan.The data were categorized according to the two editions of the CPT, after erroneous or missing data points were removed. The process of grouping the individuals are presented in 2.2.The following five items (30 indicators) were used in the modeling process:Demographic items: sex and age; two indicators.Intelligence test items: the overall score for all scales; one indicator.SNAP-IV scale items: sum of scores for the inattention, hyperactivity, and oppositional subscales of the SNAP-IV scale evaluations by parents and teachers; six indicators.β), perseverations, hit reaction by block (hit RT block change), standard error by block (hit SE block change), reaction time by interstimulus interval (hit RT ISI change), and standard error by interstimulus interval (hit SE ISI change); 12 indicators.CPT-2 items: omissions, commission, overall hit reaction time (hit RT), overall standard error (hit RT standard error), variability of standard error, detectability (d’), response style indicator (CPT-3 items: detectability (d’), omissions, commission, perseverations, overall hit reaction time (hit RT), overall standard error (hit RT standard error), variability of standard error, hit reaction by block (hit RT block change), and reaction time by interstimulus interval (hit RT ISI change); nine indicators.2.3.k-fold cross-validation was applied to ensure the consistency of machine learning results. Number of folds was selected as 5 for distinguishing ADHD-I and ADHD-C group in CPT-2 and CPT-3 set. Due to the data with the intelligence test and the control group is relatively small, number of folds was selected as 3, and also for distinguishing ADHD-I, ADHD-C and Control group in CPT-2 set.This study used an artificial neural network. The data were segmented into training (70%), verification (15%), and testing (15%) data sets. To identify the optimal model, a neural network with perceptron in multiple layers was used to perform classification tasks. The hidden layer used the default setting with the default number of hidden neurons. Moreover, 2.4.After the neural network was trained on the data, the confusion matrix was used to evaluate model performance . By usinRecall: The case was actually positive and then predicted to be positive. The ratio is TP/(TP + FN).Precision: The case was predicted to be positive and then actually positive. The ratio is TP/(TP + FP).Accuracy: The ratio is TP + TN/(TP + TN + FP + FN).3.This study included 328 participants with CPT-2 and 239 participants with CPT-3. Participants were divided into ADHD-I, ADHD-C, and control groups according to the assessment results.n = 4).We also used CPT-2 data to distinguish between participants in the ADHD-I, ADHD-C, and control groups. In the M5 model, the overall accuracy was 86.74% for the training set and 77.43% for the testing set. In the M6 model, which included intelligence, the overall accuracy was 89.08% for the training set and 74.87% for the testing set.Adding intelligence into the M6 model yielded an ADHD-I recall ratio of 86.99%, an ADHD-C recall ratio of 92.97%, and a control ratio of 79.63% for the training set. For the testing set, the ADHD-I recall ratio was 63.64%, the ADHD-C recall ratio was 85.00%, and the control ratio was 66.67%.4.This study observed that the combination of demographic information, SNAP-IV scale results, and CPT-2 results yielded an overall accuracy of 88.75% in the training set and 85.56% in the testing set. By contrast, the combination of demographic information, the SNAP-IV scale, and the CPT-3 results yielded an overall accuracy of 90.46% in the training set and 89.44% in the testing set. The use of the CPT-3 resulted in higher model accuracy for distinguishing between ADHD-I and ADHD-C. However, because the data used in this study were collected between 2011 and 2020, the training results may have been affected by the relatively poor consistency of earlier data. This explains why, despite the comparable overall accuracy of the two training sets (both 88–90%), the accuracy of the CPT-2 testing set decreased.The addition of the results from the intelligence tests into the model increased ADHD-I recall  and slightly increased the overall accuracy in both training sets. However, in the testing set, the change in ADHD-I recall was inconsistent and produced no significant increase in the overall accuracy.Previous studies showed if adults with ADHD and higher IQ (>110) performed significantly better on CPT than those with ADHD and standard IQ . In chilThe patients in this retrospective study were brought to the hospital for assessment after the patient’s parents and teachers observed difficulties with their concentration, activity level, and impulse control. Therefore, the control group was relatively small, and the patients may differ from their counterparts in the general population. They were referred to hospital for evaluation at older age, and the intelligence of the control group is lower than the ADHD group. This constitutes a limitation of the present study. The overall accuracies for CPT-2 were 86.74 and 77.4% in the training and testing sets, respectively. Although the overall accuracy among participants with CPT-2 was acceptable, future studies should include more patients to construct a more stable model.ADHD may be caused by various factors, such as those pertaining to the environment and one’s genetic makeup and personal characteristics, and ADHD is prone to different clinical manifestations between patients because of the inherent uniqueness in the relationship between an individual and their environment. One study assessed brainwave examinations and reported positive and negative predictive powers of 98 and 76%, respectively. This indicates that a very high proportion of brainwave abnormalities are observed in ADHD patients . HoweverBecause this was a retrospective study of patients who presented to the hospital for assessment, patients in the control group were few in number and may differ from the average person in the general population. At the point of evaluation, patients may have other comorbidities, and some patients may have received medication which may also affect the results of the CPT test and the teacher’s and parent’s SNAP-IV scales. In a previous study, it was shown that family education, household income, and social deprivation index may intelligence-independently increase the risk of an ADHD diagnosis. These factors were not available in our study and may need to be explored in the future . Moreove5.Overall, artificial neural networks can be used to integrate complicated clinical data, including those on age, sex, intelligence, SNAP-IV scale results (obtained through parent and teacher observations), and computer-based test results. This study’s method had a 74–89% accuracy in distinguishing between the ADHD-I, ADHD-C, and control groups and an 85–90% accuracy  in distinguishing between the ADHD-I and ADHD-C groups. Therefore, artificial intelligence, machine learning, and deep learning are expected to be useful for ADHD diagnosis in the future. If future studies can obtain a large, accurate clinical data set for machine learning, the accuracy can be improved. The machine learning model can help physicians distinguish between patients with ADHD-I, with ADHD-C, and without ADHD more quickly, making treatment and identification more timely.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Taipei Medical University Joint Institutional Review Board. Written informed consent from the participants’ legal guardian/next of kin was not required to participate in this study in accordance with the national legislation and the institutional requirements.I-CL designed the idea for the manuscript, analyzed the data, and wrote the manuscript. TK and H-WC designed and coordinated the study. I-CL, S-CC, and Y-JH collected and managed the data. H-WC revised the manuscript. All authors contributed to the article and approved the submitted version.The study was supported by the Ministry of Science and Technology, Taiwan, R.O.C., under grant numbers MOST109-2221-E-038-011 and MOST110-2221-E-038-006.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.