In the slow-rate task condition, compared with the TD group, the ADHD-H group manifested higher delta and lower beta power in the central region, while the ADHD-L group manifested higher parietal delta power. In the fast-rate task condition, in the parietal region, ADHD-L manifested higher delta power than those in the other two groups (ADHD-H and TD); additionally, ADHD-L manifested higher theta as well as lower alpha and beta power than those with ADHD-H. Unlike those in the TD group, the delta power of both ADHD groups was enhanced in shifting from rest to task conditions. These findings suggest that task-rate-related neural dynamics contain specific neural biomarkers to assist clinical planning for ADHD in preschoolers with heterogeneous cognitive proficiency. The novel wireless EEG system used was convenient and highly suitable for clinical application.This study used a wireless EEG system to investigate neural dynamics in preschoolers with ADHD who exhibited varying cognitive proficiency pertaining to working memory and processing speed abilities. Preschoolers with ADHD exhibiting high cognitive proficiency (ADHD-H,  Attention deficit hyperactivity disorder (ADHD) is the most prevalent childhood-onset neuropsychiatric disorder, the symptoms typically emerging early (at preschool age) and persisting into adulthood ,2,3. In Regarding the evaluation of a brain-based biomarker in neurodevelopmental disorders, several criteria should be considered in terms of method, such as practicability, time-efficiency of data acquisition, cost-effectiveness, and neurobiological interpretability, which indicates the neural markers should be grounded in pathophysiological mechanisms of atypical development . EEG is The cognitive proficiency index (CPI) in the Wechsler Preschool and Primary Scale of Intelligence–Fourth Edition (WPPSI-IV) measures the child’s abilities in working memory and processing speed, and provides an estimate of the efficiency with which cognitive information is processed in learning, problem-solving, and higher-order reasoning . SeveralDiagnostic and Statistical Manual of Mental Disorders, 4th Edition, or EEG profile clusters for grouping, computing, and comparing the resting EEG band power of subpopulations of children with ADHD [With regard to ADHD heterogeneity, most studies have used the subtypes outlined in the ith ADHD ,40,41,42Most EEG studies on children with ADHD have used wet-electrode systems with a stretch cap. In the present study, a wireless wearable EEG headset was used to quantify brain dynamics during CPT. Subsequently, we investigated EEG profiles under rest, slow-rate, and fast-rate task conditions among preschoolers with ADHD exhibiting high cognitive proficiency (ADHD-H), those with ADHD exhibiting low cognitive proficiency (ADHD-L), and those with typical development (TD). We hypothesized that EEG profiles during different conditions not only differentiated ADHD from TD but also reflected ADHD heterogeneity, specifically the varying levels of cognitive proficiency. Furthermore, the EEG profiles in the present study may contain specific neural biomarkers that can assist clinical planning for preschoolers with ADHD. For instance, in some cases without complete neurocognitive evaluating data, the neural dynamics measured by the easy-to-use system can provide clinicians with more information in diagnosis and treatment strategy.The participants comprised 73 preschoolers (aged 5–7 years), 42 of whom were diagnosed as having ADHD (34 boys and eight girls) and 31 of whom had TD (23 boys and eight girls). All the ADHD diagnoses were determined and confirmed in the clinical setting by the senior child and adolescent psychiatrists, based on the criteria of the DSM 5th Edition [n = 24) and ADHD-L (n = 18) groups, respectively. Two versions of the Disruptive Behavior Disorders Rating Scale (DBDRS) for parents and teachers [Cognitive function was evaluated using the Taiwanese version of the Wechsler Preschool and Primary Scale of Intelligence–Fourth Edition (WPPSI-IV) , which wteachers  were useIn this study, we used the 7.5-min Conners Kiddie Continuous Performance Test 2nd Edition (K-CPT2)  to assesThe nine main standardized scores generated for each participant help assessors interpret the nature of their attention problems (through different aspects). Standardized T scores were defined as having a mean of 50 with a standard deviation (SD) of 10. Higher scores generally imply worse performance, except in special cases, such as when the hit reaction time (HRT) is measured. In these indexes, detectability (d’) indicates the ability of a participating child to discriminate nontargets from targets. Omissions refer to missed targets, and commissions signify incorrect responses to targets. Perseverations denote responses that are made in less than 100 ms following the stimulus. HRT measures the mean response speed of all nonperseverative responses over the entire test. The consistency of response speed was demonstrated by HRT SD and variability. The HRT block change computes the slope of the change in reaction time across five blocks of administration. The HRT ISI change refers to the slope of the change in reaction time between two ISIs; these nine main scores are all defined clearly in the K-CPT2 manual .The apparatus used was a novel eight-channel wearable and portable EEG system  that conWe used MATLAB software  to preprocess and process the EEG data. We extracted the data on rest- and task-related power for analysis. For artifact removal, first, 0.5 Hz high-pass and 50 Hz low-pass basic finite impulse response filters were applied to improve EEG signal quality. Second, EEG experts and the clinician identified and eliminated the components corresponding to muscle artifacts related to the participants’ restless movements during the experiment by independent component analysis (ICA) ,49, and The demographic and neuropsychological test data of the three groups were compared using a one-way analysis of variance (ANOVA). Comparison analysis of categorical variables  was conducted using Fisher exact tests.t-test and a one-way ANOVA. To control type I error of possible false-positive results from multiple comparisons and to prevent type II error or false-negative error, we reduced the number of comparisons and spectral power was averaged by region as follows: prefrontal , central , and occipital . The power of specific frequency bands was compared in regions of interest . When at least 1 group showed statistically significant differences, pairwise comparisons were conducted. The false discovery rate correction was used for these post hoc analyses, in which the p-values were multiplied by the number of comparisons.Regarding EEG data analysis, the PSD under separate independent conditions  of two groups  as well as three groups (ADHD-H versus ADHD-L versus TD) were also compared using an independent Additionally, to evaluate the group differences in alteration patterns in PSD under different conditions, we performed a generalized estimating equation, with the condition  as a within-subject factor and the group  as a between-subject factor. We computed the average power of specific frequency bands in all recording regions of the brain . All the analyses were performed using IBM SPSS Statistics for Windows, version 22 .p = 0.019, p = 0.006, respectively); however, the verbal comprehensive index did not differ among the groups, which confirmed that all the participants completely comprehended the test instructions. CPI differed significantly between groups; specifically, the ADHD-L group performed poorly compared with the TD and ADHD-H groups (p < 0.001 for both).p = 0.001, p = 0.037, respectively) and teachers . The participants with ADHD-H were given higher scores than those in the group with TD on the hyperactive dimension by parents (p = 0.005) and on the inattentive and hyperactive dimension by teachers . Regarding the K-CPT2, the ADHD-L group performed poorly compared with both the TD and ADHD-H groups in detectability , omission , HRT (p < 0.001 in both), HRT SD , and HRT ISI changes   .Resting relative spectral power did not differ between the groups .p = 0.008, p = 0.018, respectively). The ADHD-L group had significantly higher delta values than the TD group on the Pz electrode (p = 0.006).The PSD on five brain regions in the slow-rate task condition of the two groups  as well as the three groups (ADHD-H versus ADHD-L versus TD) were compared . Table 2p = 0.011, p = 0.039, respectively). Notably, the ADHD-L group had a higher value of delta and theta as well as lower values of alpha and beta on Pz than the ADHD-H group , which indicated apparent ADHD heterogeneity.The PSD on five brain regions in the fast-rate task condition of the two groups  as well as the three groups (ADHD-H versus ADHD-L versus TD) were compared . Table 3p = 0.027), and the post hoc test indicated that the ADHD-L group experienced an enhancement in delta power during the shift from the rest to the fast-rate task condition (p = 0.001). An enhancement in delta power was observed in the ADHD-H group from rest to the slow-rate task conditions (p =0.002). The aforementioned results were distinct from those obtained for the TD group, which manifested no obvious differences in the alteration patterns. Notably, the condition and group had a significant interaction effect  from TD, which can facilitate clinical diagnosis. Subsequently, fast-rate task-related neural dynamics reflect the heterogeneity in the neurobehavioral functions  of children with ADHD. The total average delta power alteration patterns of the three groups were distinct. The research results contribute to the understanding of task-related neural dynamics, particularly concerning preschool ADHD. The EEG profiles in the present study may involve specific neural biomarkers that can assist clinical planning for preschoolers with ADHD. As displayed in the Notably, differences in centroparietal delta power under the slow-rate task conditions were observed between the two ADHD groups and the TD group, which is partially inconsistent with the findings of previous studies that have indicated that the resting frontocentral theta power and TBR of children with ADHD are different from those of their TD peers ,12,14,16In the fast-rate task condition, compared with the ADHD-H group, the ADHD-L group manifested a higher parietal delta and theta power as well as lower alpha and beta power. These results imply ADHD heterogeneity and can be explained by the fact that children with ADHD with low cognitive proficiency are less able to process rapidly presented stimuli than children with ADHD with high cognitive proficiency. On the basis of the hypothesis that ADHD results from neurodevelopmental or neurocognitive deficits, various early interventions have been developed to target these presumed core deficits . SpecialIn this study, both ADHD groups manifested an overall enhancement in delta power in the shift from rest to task conditions. These results are in line with those of a study by Rommel et al. , which rIn this present study, no significant differences in K-CPT and fast-rate EEG PSD values were observed between the group with ADHD-H and TD. These findings are compatible with Sjöwall’s finding , which iThe wearable wireless semidry-electrode EEG recording system used in the present study has none of the drawbacks of conventional wet-electrode EEG systems. Specialists typically find it challenging to monitor the attention status of young children during task performance due to poor compliance and unnatural situations. In our experiences using the semidry-electrode system, children did not need to endure the lengthy preparation procedure involved in wet-electrode systems (which exacerbates impatient and fidgety behavior in children). Notably, the participants noted that wearing the system was comfortable, similar to wearing stylish headphones. They also mentioned that the wire-less design made task performance feel easy and free; thus, the adopted system promoted the cooperation of the participants in the measurements. Most importantly, the data collected using the adopted system are equivalent in quality to that produced by wet-electrode systems. In summary, the adopted system is highly suitable for clinical use in young children.The relatively small sample size used is a limitation of this study. Nevertheless, we believe that our findings are clinically relevant to the understanding of the underlying mechanisms of ADHD, particularly preschool ADHD, and can serve as a reference for clinicians and researchers with regard to the potential neural biomarkers.The EEG profiles in the present study suggest a stepwise approach; EEG dynamics in the slow-rate task condition and in the shift from rest to the task conditions were disparate in children with ADHD of both high and low cognitive proficiency, which assisted in distinguishing ADHD from TD. EEG dynamics under the fast-rate task conditions were different between children with ADHD with high and low cognitive proficiency, which revealed neurobehavioral heterogeneity in ADHD. The acquisition of EEG profiles by employing the novel wireless system is convenient and efficient for clinical use in children. These profiles can provide clinicians and researchers with information on potential neural markers that aid in the planning of early interventions for preschool ADHD, including individual training and educational programs. Future studies should attempt to use the aforementioned system to measure brain activity and provide real-time neurofeedback during game therapy according to the selected neural biomarkers. This system can also be applied to the various attention-related tasks in which children engage, such as reading and writing.


Barley is the fifth most important food crop in Ethiopia. The genetic relationship and population structure studies of barley are limited to gene bank collections. Therefore, this study fills a gap by investigating the selection, consumption, economic value, genetic diversity, and population structure of farm-collected barley from the Gumer district of the Gurage Zone, which has received little attention. The information on the use of barley in the study area was collected using semi-structured interviews and questionnaires. 124 households of 11 kebeles, the smallest community unit, were interviewed. Barley landraces collected were compared with those collected from Japan, the United States (USA), and other Ethiopian locations. Illumina iSelect (50K genotyping platform) was used to identify single nucleotide polymorphisms (SNP) . Thirty landraces were found in Gumer. Burdaenadenber had the highest on-farm Shannon index estimate (2.0), followed by Aselecha (1.97) and Enjefo (1.95). Aselecha and Fetazer had the highest (44%) and the lowest (29%) richness values, respectively. High and low Simpson index values were found in Aselecha (84%) and Wulbaragenateretero (79%), respectively. The neighbor-joining tree revealed that Gumer landraces formed a separate subcluster with a common ancestral node; a sister subcluster contained barley landraces from Japan. According to the population structure analysis, barley landraces from Gumer differed from Japan and the United States. The principal component analysis revealed that US barley was the most distant group from Gumer barley. The markers’ allele frequencies ranged from 0.10 to 0.50, with an average value of 0.28. The mean values of Nei’s gene diversity (0.38) and the polymorphic information content (0.30) indicated the presence of high genetic diversity in the samples. The clustering of accessions was not based on geographic origin. Significant genetic diversity calls for additional research and analysis of local barley diversity because the selection and use of barley in Ethiopia would have been affected by the preference of ethnic groups. Hordeum vulgare ssp. Vulgare) was domesticated from its wild ancestor H. vulgare ssp. Spontanum [Barley  [Bmy1) at positions 115, 165, 233, 347, and 430, which allows adaptation and selection based on the preference for climate, and food [Recent studies suggest that barley landraces from multiple locations differentiated into genetically defined populations . AlthougPpd-H1) –20. Six and food –23. Furtand food –16.Single nucleotide polymorphism (SNP) of Illumina iSelect genotyping platforms used to examine barley landraces from the Gumer district, Ethiopia. The objectives of this study were 1) to determine the on-farm diversity of barley in the Gumer district. 2) to determine the genetic diversity and population structure of barley landraces from the Gumer district using SNP markers on the nuclear genome. 3) to compare the pattern of genetic diversity from the United States, Japan, and other Ethiopian barley accessions.Ensete spp., potatoes, kale, garlic, and barley; with the latter being the major cash crop for farmers in this area. The annual rainfall pattern is bimodal, and mean annual temperatures range from 10°C to 21°C [The Gumer highlands in the Gurage mountain range  rise out to 21°C . The stuThe study area is ethnically and linguistically homogeneous, representing the Gurage people and language. The ethnic group has several clans, which are all active in managing society. There is an accessible asphalt road, but most farmers in the study area have little or no road access. Small-scale subsistence agriculture dominates the landscape. The smallest unit of the community is called a ‘kebele’ and comprises 100–500 farmers.Eleven kebeles representing the highlands of Gumer were selected for interviews and collection of their barley landraces. The kebeles were selected based on their potential barley cultivation of landraces from the Gumer highlands. Eleven to 12 farmers were randomly selected from each kebeles for a total of 124 farmers .There is no rule for obtaining ethical approval for plant-related research projects in Ethiopia. To collect barley seed samples and interview farmers, a letter of support was obtained from the Hawassa University, Research and Technology Transfer Vice President Office, which was addressed to the Gumer district in the Gurage Zone, and a copy was provided to the administration of each of the eleven kebeles. The vice president of Hawassa University’s Research and Technology Transfer Office granted permission to interview and collect barley seeds from the farmer . ConsentA cross-sectional research design focused on selected kebeles within a district was identified after following a rapid preliminary informal survey and discussions with the zonal administration. Data were collected from farmers who cultivate barley landraces. Key information and personal observations were obtained through questionnaires, household interviews, and focus group discussions.A questionnaire was used to collect information from a wide range of sources (respondents) on indigenous knowledge and practices involved in barley landraces and utilization in the study areas. The questionnaire was written in English and translated into the local language “Guragigna". The head of the family was chosen based on the preliminary survey and documents obtained from district agricultural offices.Furthermore, all required age groups and sexes, including the heads of household of elder women, were purposefully included to ensure adequate coverage of the requirements. The mentioned landraces, with their names, use-value, and seeds, were collected during interviews. Each of the 124 barley sample communities was sampled during the main barley growing season in 2020. The total land planted with each crop was recorded for each farm. The SNP diversity was compared between barley from Gumer and collections from Japan, the US, and other locations in Ethiopia  that werIndividual barley samples  from eacSNP genotyping was performed with the Illumina iSelect 50K barley SNP array . Before To understand the diversity among the 11 kebeles, the Shannon-Wiener index, Pielou’s index, Pielou’s evenness, Simpson’s index, and abundance analysis were calculated using the vegan package (version 2.5–7)  with theH’ is the Shannon-Wiener index, J’ is the Pielou index, pi is the proportion of barley landraces, S is the number of barley landraces and λ is Simpson’s index.Where H is the Shannon index, Genetic diversity was assessed for the Gumer collection and compared on the unbiased expected heterozygosity (He) from Ethiopian, Japanese, and USA populations chosen from the world barley panel. Genetic differentiation between populations was estimated with F statistics using the snpReady package (version 0.9.0) . The snpThe spatial genetic structure of landraces was studied with principal component analysis (PCA) performed with the adegenet package (version 1.3–1)  using R The common names assigned to genetic resources based on indigenous knowledge can be indicators of genetic distinctiveness . Names aSeed color, spike length, number of rows, stress tolerance, yield, end-use characteristics, and other significant agronomic features distinguish the landraces. Some landraces are still being cultivated, with varying coverage in various locations. In contrast, Keleme is severely marginalized and on the point of extinction, while Shemeya has already vanished, as indicated by its presence only in one family. Similar findings have emerged from northern and Central Ethiopia, suggesting the rapid extinction of the major historical landrace.P> 0.05, The inter-landrace diversity or landrace richness (R), calculated using the Menhinick index, is summarized in We also found that the diversity estimate (Pilou evenness (J)) was high, except for the Aselecha kebele, which scored 0.36 . The culFarmers in the study districts intentionally preserve landraces to meet their needs . These rGender plays a role in selecting which landrace to plant. Women favor Keleme for its sweet taste, easy dehulling, and medicinal value. But men do not prefer it due to its susceptibility to drought, disease, and low market value . All farFarmers cultivate a diverse range of landraces, each with its maturity, yield potential, stress tolerance, end-use quality, and other agronomic characteristics . This diFifteen land races planted in the study areas are identified, named, and described by Gumer farmers. Since SNP markers differentiated the landraces, vernacular names and farmer descriptions of landraces could be linked to formal scientific classifications. The landraces have been identified as a distinct type using the scientific taxonomic classification system using morphological traits .The selection and decision-making process was based on sets of criteria. This value has an impact on the successful management of the landraces for their cultural and environmental needs. Complexity is expressed in their description of landraces for adaptation, stress tolerance, and yield . SelectiIn the present Gumer barley panel, we identified 20,367 replicable SNP calls of the 44,040 SNP probes available on the array . Of thesWe compared the Gumer barley SNP markers to Japanese, USA, and other Ethiopian landraces. The PIC value range was 0.17 to 0.38, with an average value of 0.30. Nei’s gene diversity score ranged from 0.15 to 0.50, with a mean of 0.38. The MAF ranges from 0.10 to 0.50 with an average value of 0.28, indicating the presence of a great diversity among the barley landraces .The PIC values were 0.25, 0.30, 0.30, and 0.30 for Gumer, Ethiopia, Japan, and the US, respectively. The average gene diversity (GD) was 0.31, 0.37, 0.37, and 0.39 for Gumer, Ethiopia, Japan, and the United States, respectively . SimilarP < 0.001), indicating a population of self-pollinating and inbreed in Gumer.The genetically effective size of populations was determined using SNP data . The JapWe determined the most likely population structure for the landraces using two methods. The first technique was a genetic structure-based clustering strategy, which was inferred using the second-order rate of change of the likelihood K . We founWe found that Bayesian model-based stratification and PCA clustering techniques did not group barley collections based on their geographic origin. For example, a variety of Gumer (GM12) clustered along the USA. A higher number of polymorphic SNPs were found in the United States and Japan landraces compared to those in Gumer.A PCA analysis revealed that location on a continental scale is not the most predictor of genetic structure. The geographic origin of the landraces did not confirm the clustering in the first components (PC I), which together explain 26.7% of the variance. PC1 distinguishes Gumer and some Ethiopian barley from the US barley, while PC2 distinguishes the USA and Japan barley from Gumer and some Ethiopian barley .DAPC analysis Figs  and 4 reThe clusters were composed of Gumer, Ethiopia, Japan, and USA samples. Populations were divided according to their collection origin; thus, continuous genetic flow between populations is limited or absent (in the case of Gumer). This was confirmed by the unique structure in the Gumer population, shown by the Structure, DAPC, and PCA analysis. Simultaneously, the use of PCA, Structure, and DAPC in clustering the barley landraces from different geographic regions yielded comparable results, indicating a strong genetic uniqueness of Gumer barley compared to USA and Japan barley Figs –4. The sThe results of AMOVA revealed that the variation accounted for 71%, 50%, and 58% of total genetic variability for Ethiopia, Japan, and the United States, respectively . By contST value was 0.41 (P 0.001). However, the value of FST between Gumer and Ethiopia, Gumer and Japan, Gumer and USA, Ethiopia and USA, and Japan and USA was 0.40, 0.68, 0.74, 0.11, 0.2, and 0.16, respectively  and the geographic origins  in this study. These findings indicate the need for a specific collection site and use of the value of barley to unravel the different human practices in domesticating barley for different use values. When comparing Gumer vs. Ethiopia, FST was 0.40, and Nm was 0.38. Nm between Japan and USA was high, and genetic differentiation based on FST values was significant between the landraces among the different populations, which confirmed the dynamic genetic variation among the collected landraces , 54. TheThe clustering analysis revealed that the SNP data did not classify landraces based on their geographical background, and landraces were jumbled together. For example, cluster I comprised 29 Gumer landraces and 18 landraces from Ethiopia, a landrace from the USA, and Japan. Cluster II comprised 17 landraces from Japan and 10 landraces from the USA, and 4 landraces from Ethiopia. Cluster III included 6 Japanese, 4 American, 1 Gumer, and 18 Ethiopian landraces. Cluster IV contained landraces from the United States, with only six Ethiopian and two Japanese landraces. Cluster V was made up of landraces from the United States, with three from Japan and four from Ethiopia. Cluster VI is composed of 1, and 2 Japanese and Ethiopian landraces .We discovered that the Ethiopian landraces were closer to the Japanese landraces and far from the Gumer landraces, rather than the two groups being closer to each other, using Fst values between populations . The JapSome Ethiopian landraces clustered with Japanese and US landraces, which implies that they share a genetic background . This grGenetic diversity is essential to create a robust food security system capable of responding to repeated biotic and abiotic stresses . In planOur interviews with the farmers showed that most landraces are used for food. These findings provide insight into the observed patterns in the Gumer barley genetic structure. We found little difference in abundance among the kebeles’ collections, likely due to the exchange of landraces among the members in different Kebele. The community uses barley as a food source, animal feed, and a source of income. As a result, the farmers select landraces with higher yields due to their increased adaptation to local farming conditions. These practices, and the importance of barley to the Gumer communities, shed light on the low diversity and genetic structure of barley in Gumer.The 50K barley SNP array is designed to capture the most reliable gene-associated SNP markers available in the barley genome (cv. Morex), but it is not sufficient to discover novel loci from Gumer landraces. The fact that we found fewer replicable SNPs on the array than the original Morex allele panel suggests the presence of new alleles. Subsequent research would benefit from hybridization and sequencing techniques to offer a more comprehensive description of the Ethiopian barley genome.in situ landrace populations must not have changed systematically over time. Alternatively, the genetic diversity in Gumer landraces could arise from factors such as adaptation to wider agroecology, natural crossings caused by cultivating different genotypes in a field, and the use of diversified agricultural methods.Our findings demonstrated that the barley landraces gathered from Gumer had lower diversity indices than those gathered from other regions in Ethiopia. This suggests that the occurrence of variability between the Ethiopian and Gumer landraces may have been caused by the usage of farmer landraces that are stored and employed for every season of farming. This study’s genetic diversity and structure could be attributable to genetic drift that occurred during the rejuvenation cycles. Several conditions must be met for this to be a persuasive explanation. For instance, the initial samples used to create the early landraces must have been large enough to capture a significant amount of the genetic diversity prevalent in today’s landrace populations in Gumer. In addition, the genetic diversity of We identified two barley subpopulations using a genetic stratification approach based on a Bayesian clustering model. A discriminant analysis using the BIC suggested that fewer than six clusters would be preferable. Both methods showed successful clustering of Gumer barely separate from USA, Japan, and other Ethiopia landraces.Another group used barley from gene banks and found , 60 geogHowever, our study found that there was limited admixture between the current barley collection from Gumer and barley from Japan, the USA, and other regions of Ethiopia. Instead, Gumer barley formed a separate structure and cluster, at least from Japan, and USA landraces, demonstrating a unique genetic makeup; therefore, the landraces now represent a unique genome structure of barley.We collected 30 barley landraces from Gumer in this work and compared them to landraces from Ethiopia, Japan, and the USA, using a high-density 50K barley SNP array. The research assesses the genetic diversity and population structure within landraces. Our results revealed a moderate polymorphism in the investigated Gumer barely. The molecular analysis in the tested landraces from Ethiopia, Japan, and the USA showed a high level of SNP variability. Based on the results of AMOVA, genetic diversity observed within Gumer is less than that found among samples from Ethiopia, Japan, and the USA. The result suggests that local landraces have less genetic differentiation. In addition, based on obtained results, the SNP array can distinguish the Gumer barley from Ethiopia, Japan, and the USA. However, many SNP lines were not recognized from the Gumer genome, indicating that a reference genome for Ethiopian barley germplasm is an alternative method to unravel the domestication process of Ethiopian barley. This study highlights the value of farmer-based agroecology in efforts to quantify diversity in barley. As a result, DNA genotyping of barley collection using a large number of landraces and SNP markers tightly linked to known genes associated with plant adaptation to different environments can help breeding programs based on the deployment of specifically selected DNA markers spread across the entire genome. The creation of sets for these chosen SNP markers of adaptation-related genes and their use in breeding programs could speed up the production of new competitive barley cultivars.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Tableo: heterozygosity within a population; Hs: genetic diversity within a population; Ht: overall gene diversity; Http: corrected Ht; Dst: gene diversity among samples; Dstp: corrected Dst; Fst: fixation index; Fstp: corrected Fst; Fis: inbreeding coefficient per overall loci; Dest: a measure of population differentiation. Significant deviation from Hardy–Weinberg equilibrium at *P < 0.05, **P < 0.01, and ***P < 0.001, respectively.Summary statistics of genetic variation existing in barley based on 20368 SNP among 182 landraces. H(XLSX)Click here for additional data file.S4 TableThe results are based on allele frequencies for 20368 SNP markers in 182 barley landraces.(DOCX)Click here for additional data file.S1 Text(PDF)Click here for additional data file.


G12D, to investigate KRAS signaling integrators. We observed that the AP1 family member FRA1 is tightly linked to the KRAS signal and expressed in pre-malignant lesions and the basal-like subtype of pancreatic cancer. However, genetic-loss-of-function experiments revealed that FRA1 is dispensable for KrasG12D-induced pancreatic cancer development in mice. Using FRA1 gain- and loss-of-function models in an unbiased drug screen, we observed that FRA1 is a modulator of the responsiveness of pancreatic cancer to inhibitors of the RAF–MEK–ERK cascade. Mechanistically, context-dependent FRA1-associated adaptive rewiring of oncogenic ERK signaling was observed and correlated with sensitivity to inhibitors of canonical KRAS signaling. Furthermore, pharmacological-induced degradation of FRA1 synergizes with MEK inhibitors. Our studies establish FRA1 as a part of the molecular machinery controlling sensitivity to MAPK cascade inhibition allowing the development of mechanism-based therapies.Targeting KRAS downstream signaling remains an important therapeutic approach in pancreatic cancer. We used primary pancreatic ductal epithelial cells and mouse models allowing the conditional expression of oncogenic KrasThe online version contains supplementary material available at 10.1007/s00018-022-04638-y. KRAS oncogene is mutated in 90–95% of pancreatic ductal adenocarcinomas (PDACs), and copy number gains were recently connected to the aggressive basal-like/mesenchymal subtype of the disease and the metastatic cascade  supplemented with protease and phosphatase inhibitors . Lysates were normalized for protein concentration, heated at 95 °C for 5 min in protein loading buffer , and loaded onto 10% SDS–polyacrylamide gels. Proteins were transferred to Immobilon-FL  or nitrocellulose  membranes by tank/wet electroblotting. Membranes were blocked in PBS supplemented with 5% skim milk and 0.1% Tween and incubated at 4 °C overnight with the following antibodies: pan-ERK ; Phospho-p44/42 MAPK (Erk1/2) (Thr202/Tyr204) ; Phospho-Akt (Ser473) ; Akt antibody ; Phospho-FRA1 (Ser265) ; Fra1 (N-17) ; HA-Tag (C29F4) ; GFP antibody ; HSP 90alpha/beta (F-8) , β-actin , and α-tubulin . All antibodies were diluted in 5% skim milk in PBS with 0.1% Tween. Fra1 (N-17) was used at a concentration of 1:500. The GFP antibody as well as all primary antibodies from Cell Signaling Technology was used at a 1:1000 dilution. The loading controls actin, tubulin, and HSP90 were used at 1:5000–1:10,000 dilutions. After overnight incubation with primary antibody, membranes were washed three times with PBS-Tween, incubated with anti-mouse or anti-rabbit secondary antibodies conjugated with DyLight™ 680  or DyLight™ 800   for 1 h at room temperature, and imaged with the Odyssey Infrared Imaging System . Licor Image Studio Lite  was used for quantification of phospho- and total-ERK (pan-ERK) and phsopho-AKT and pan-AKT levels.For histopathology, pancreatic tissues were fixed in 4% formaldehyde , embedded in paraffin, and sectioned (3 μm thick). Haematoxylin and eosin staining was described , 39. ForcypA 5´-A T G G T C A A C C C C A C C G T G T-3´ / 5´-T T C T G C T G T C T T T G G A A C T T T G T C-3´; Fra1 (E1-E2) 5´- C G C A A G C T C A G G C A C A G A-3´ / 5´-A A T G A G G C T G C A C C A T C C A-3´; Fra1 (E3-E4) 5´-C G G C C A G G A G T C A T A C G A G-3´ / 5´-C T T C C A G C A C C A G C T C A A G-3’; p19ARF (p19-TM-for) 5´-T C G C A G G T T C T T G G T C A C T GT-3´ / p19ARF (p19-TM-rev) 5´-G A A C T T C A C C A A G A A A A C C C T C T C T-3´.Total RNA was isolated using the RNeasy Mini-Kit (Qiagen) and cDNA synthesized with TaqMan® Reverse Transcription Reagents (Thermo Fisher Scientific). Quantitative analysis by qPCR was performed as previously described  . Primer Fra1-deficient KCF cell lines SDF287, SDF419, and SDF716—transduced with control vector pLenti RFP, reconstituted with pLenti FRA1, or pInducer-FRA1—was isolated using the Maxwell® 16 LEV simplyRNA Tissue Kit and instrument (Promega) following the manufacturer`s instructions . RNA sequencing was performed at the Sequencing Core Unit at the TranslaTUM, Technical University Munich (TUM). Library preparation for bulk sequencing of poly(A)-RNA was done as described previously [n (n = number of Samples) were removed, and remaining counts were normalized and transformed using regularized log transformation (rlog) implemented in the DEseq2 package.RNA for the RNA-seq of the eviously . Referenhttp://www.broadinstitute.org/gsea/) [Fra1-deficient cell lines (SDF287 RFP + SDF716 RFP) were compared with FRA1 reconstituted cell lines (SDF287 FRA1 + SDF716 FRA1) using the Gene Ontology_biological process (GO_BP) terms. To find pathways activated after the expression of KrasG12D in PDECs, we analyzed recently published expression profiles (EMBL-EBI ArrayExpress Accession number: E-MTAB-2592). Microarray data of untreated and 4-OHT treated PDECs were collapsed to max. probe using the GSEA app 4.1.0. Collapsed data were analyzed with a GSEA (GSEA app 4.2.3) using default settings (permutation type: gene_set) using the MSigDB (c3.tft.v7.5.1) transcription factor signatures. The signatures with a FDR q < 0.05 were depicted.The rlog normalized matrix was used to perform a gene set enrichment analysis using the GSEA tool from the Broad Institute  . To idenhttp://www.cbioportal.org/) (12/2018). Normal tissues, non-PDAC`s, or samples with low cellularity were excluded according to Peran et al. [The PAAD dataset of the TCGA (RNA-seq V2) was downloaded via the cBioPortal platform  RNA-seq analysis was performed using the SCANPY toolkit . Data fohttps://string-db.org/cgi/) [https://depmap.org) [The STRING analysis was performed via the STRING web tool (version 11.5) (rg/cgi/) . The mulmap.org) .t-test or one-way/two-way ANOVA was used to investigate statistical significance. P-values were calculated with GraphPad Prism  and corrected for multiple testing.Unless otherwise indicated, all data were determined from at least three independent experiments and are presented as mean ± standard deviation (SD). A two-sided Student`s R26-CreERT2;LSL-KrasG12D/+ mice allow for the temporally controlled expression of oncogenic KrasG12D driven from the endogenous promoter in the pancreatic context by treatment with 4-hydroxytamoxifen (4-OHT) [Kras locus  . In thisferation , 25. In feration –57. AnalPtf1aCre/+;LSL-KrasG12D/+ mice (KC afterwards) to murine PDAC cell lines . Please note that a pure KC model is used in all experiments of the manuscript. To demonstrate the pre-neoplastic character of murine PDECs, we measured p19Arf mRNA expression, encoded by the Cdkn2a locus, which is deleted in the majority of murine PDACs [p19ARF mRNA expression is induced in PDECs isolated from pre-malignant stages from KC mice, no mRNA expression was detected in murine PDAC cancer cell lines , demonstrating the important tumor suppressive function of Cdkn2a for murine PDAC development and showing that the Cdkn2a barrier is not conquered in PDECs isolated at pre-malignant stages. Concordantly with the ex vivo models, gradual upregulation of FRA1 protein and mRNA was also observed in vivo during carcinogenesis of KC mice . In immunohistochemistry (IHC), we detected FRA1 expression in the nucleus of cells of ADM lesions, in the stromal cells surrounding the pre-neoplastic lesions  and in murine PDAC cells . These data demonstrate that KrasG12D induces FRA1 expression during the carcinogenesis in the murine pancreas. Hereby, FRA1 is expressed in KrasG12D-induced epithelial and PDAC cells as well as cells of the microenvironment.To investigate expression of FRA1 in established murine PDACs, we used IHC and Western blots. We observed a gradual upregulation of FRA1 protein expression starting from normal murine PDECs, over pre-neoplastic PDECs isolated from 3 to 6 months old Fra1 alleles  [Fra1 (Exon 3 and 4) are flanked by loxP sites. Upon Cre-mediated inactivation of Fra1, a GFP reporter gene is spliced to the N-terminus of Fra1 [Ptf1aCre line, Fra1 was specifically deleted in the pancreas  resulting in expression of the GFP-fusion protein in the whole pancreas . No obvious phenotype was observed in Ptf1aCre/+;Fra1lox/lox mice . When crossed with the KC mice, Ptf1aCre/+;LSL-KrasG12D/+;Fra1lox/lox mice (KCF afterwards) developed ADM and PanIN lesions . At 6 months, more low-grade murine PanIN 1A/1B lesions were detected in KCF mice . Like the KC mice, KCF mice progressed to murine PDACs with a median survival of 489 days , which is similar to the survival data for our published KC cohort (466 days) [KC and KCF PDACs was not different. Together, these data demonstrate that FRA1 is dispensable for PanIN progression and PDAC formation in a murine model driven solely by KrasG12D.To decipher the role of FRA1 in the epithelial compartment in vivo, we used mice with floxed ig. S2a) . In thes of Fra1 . In the 66 days) . MorpholFRA1 mRNA expression with a GSEA using the HALLMARK signatures via the GeneTrail3 platform [FRA1 mRNA expression . Again, we observed a connection between FRA1 mRNA expression and basal-like cell identity . To further underscore the relevance of FRA1 in PDAC, we accessed data from a recent CRISPR/Cas-drop out screen [In human PDAC, FRA1 was connected to a worse overall survival . Consistplatform . In a Veion Fig. c, d. Theion Fig. e. These ers Fig. f. In addt screen  for the t screen , but alsFra1-deficient murine PDAC cells ex vivo to investigate cell-autonomous roles of FRA1. We were able to isolate three murine PDAC cell lines from KCF mice. All three investigated Fra1-deficient PDAC cell lines revealed complete recombination of the Fra1 locus  and expressed the FRA1-GFP fusion protein . A Fra1 exon 3 and 4 specific qPCR demonstrated the complete loss of Fra1 expression in these cells compared to the proficient controls . Fra1 exon 1 and 2 specific qPCR showed upregulation of the remaining exons , suggesting that oncogenic signaling in these PDAC cells induces a compensatory regulatory circuit.Since the FRA1 expression data in human and murine PDAC argue for a relevant function of FRA1, we used Fra1-proficient murine PDAC cell lines, growth of the three investigated Fra1-deficient lines was significantly decreased 96 h after seeding . To decipher the role of FRA1 in regulation of growth in more detail, we reconstituted FRA1 expression in the Fra1-deficient KCF cells by transduction with a constitutive FRA1 expression construct (pLenti-FRA1) or their respective control vector expressing an RFP reporter (pLenti-RFP)  expression construct or an FKBP12F36V-GFP (dTAG-GFP afterward) control vector. Treatment with the dTAG13 degrader led to rapid degradation of the dTAG-FRA1 protein chimera within 2 h that lasted for at least 72 h  and the dTAG-FRA1 construct. Treatment with doxycycline for 8 days deleted both Fra1 alleles . To validate these findings, we performed viability assays with the inhibitors RO5126766, ulixertinib, and trametinib . Together, these data indicate that even acute depletion of FRA1 can also sensitize PDAC cells to MAPK inhibition.Fra1-deficient and FRA1-reconstituted cells. We used gene set enrichment analysis and GO-terms to define pathways regulated by FRA1. We found that FRA1 represses signatures linked to immune regulation . However, the induction of FRA1 in these lines over four days did not change their growth . We additionally investigated the responsiveness of these lines to inhibitors of MAPK signaling in the presence and absence of doxycycline. Although the GI50 was increased in all models and for all inhibitors tested in the FRA1 “on” setting, the effects were again highly context dependent and ranged from a small twofold to a rather distinct 11–26-fold increase in the GI50 values . In addition, we investigated RNA-seq data of doxycycline-treated cells 24 h after inducing FRA1 expression. Overall, the changes of mRNAs were again context dependent and rather mild. Although the line with the strongest depletion of the GO-term signatures linked to ERK signaling upon FRA1-induction revealed the greatest change in MAPK inhibitor GI50 values, all q-values were > 0.05 . Furthermore, the influence of FRA1-induction on ERK phosphorylation was not clearly observed in the doxycycline-inducible model . Collectively, these data show highly context-dependent functions of FRA1 in the regulation of PDAC growth and a contribution to the control of sensitivity toward inhibitors of the MAPK signaling pathway.To decipher the molecular functions that determine the role of FRA1 in mediating resistance to MAPK inhibition, we performed RNA-seq of ion Fig. g. Furtheion Fig. g. Since ity Fig. h. As expity Fig. h, i. Theent Fig. h, i. HowERK Fig. h, i, whiERK Fig. g. Fra1-dion Fig. h, indicaTo find transcription factors linked to oncogenic KRAS signal transduction in pancreatic cancer, we used primary pancreatic ductal epithelial cells and identified the AP1 transcription factor programs to be activated by oncogenic KRAS. The AP1 complex recognizes specific sequences in promoters and enhancers of target genes and controls critical aspects of tumorigenesis, like cell proliferation or survival/apoptosis .G12D-induced PanIN formation and PDAC development. The important role and functions of FRA1 in pancreatic cancer carcinogenesis as well as in tumor progression have been highlighted recently. In FRA1 gain-of-function models, the impact of the transcription factor to promote acinar-to-ductal metaplasia development, an early step in pancreatic carcinogenesis, was demonstrated [Here, we focused on the AP1-family member FRA1 , 57 and nstrated . Consistnstrated , we founnstrated , 60, 65.nstrated . ConsistIn addition to the epithelial compartment, we detected FRA1 expression also in stromal cells of the tumor microenvironment. Up to 90% of PDAC is stroma, composed of an extracellular matrix hosting stellate cells, tumor-associated fibroblasts, immune cells, nerves, and vessels. This argues for important functions of FRA1 in cells like cancer-associated fibroblasts, which should be deciphered in future work.Fra1 significantly delays tumor development [Cdkn2a, Trp53, Smad4, and Fra1, to the pancreas of LSL-KrasG12D;LSL-Cas9-eGFP mice, the impact of FRA1 to early steps in the carcinogenesis was demonstrated [G12D-driven PDAC in vivo model, FRA1 is dispensable for PanIN progression and PDAC formation. We observed no difference in ADM formation between KC and KCF mice and Fra1-deficient mice developed PDAC with a comparable survival rate to KC mice. Such data demonstrate the relevance of the oncogenic context for the KRAS-driven carcinogenesis [Recent work showed that FRA1 contributes early to the KRAS-driven carcinogenesis by regulating and maintaining chromatin accessibility at genetic loci needed for carcinogenesis , 67. In elopment , 68, 69.ogenesis . WhetherFra1-deficient PDAC cells established from KCF mice displayed variable growth defects in vitro. Furthermore, several genetic gain- and loss-of-function models showed that—to different degrees and dependent on the mode of FRA1 re-expression—FRA1 can augment the growth of PDAC cells. These observations are consistent with a function of FRA1 in the regulation of the cell cycle and effects toward the G1-phase or the G2/M-phases of the cycle have been described [Nevertheless, escribed , 69, 71.Fra1-deficient cells are more sensitive to inhibitors of the canonical KRAS signaling pathway, including RAF-, MEK-, and ERK-inhibitors. We detected compensatory rewiring of oncogenic signaling pathways that might at least partially explain the drug screening hits. In particular, we observed a transcriptional and post-translational upregulation of the MAPK pathway in constitutive FRA1-knockout cells as well as upon small-molecule-induced degradation of FRA1. Re-expression of FRA1 renders KRAS-driven PDACs cells more independent of canonical signaling, underpinning the note that FRA1 may maintain major parts of the transcriptional output of oncogenic KRAS. Such insights are consistent with data elaborated in a switchable KrasG12D model, where FRA1 transfers tumor-forming capabilities in the KRAS “off” setting, demonstrating that the transcription factor can substitute signaling by the oncogene [FRA1 knock-out as a sensitizer to MEK inhibition in 2 out of 5 pancreatic cancer cell lines, but none of the 5 lung cancer cells [FRA1 knock-out as a trametinib sensitizer [G12C inhibitors are approved for the treatment of lung cancers [G12C by ARS-1620 renders the human PDAC cell line MiaPaCa-2 dependent on FRA1 [50 values. Therefore, also with respect to the modulation of drug sensitivity, the function of FRA1 is context dependent and it will demand additional efforts to decipher the relevant molecular determinants.In cancer cells driven by oncogenic KRAS, genes relevant for G2/M-phases of the cell cycle, like Aurora A kinase, were found to be linked to FRA1  and expooncogene . Furtherer cells . An in vnsitizer . In addi cancers . Unbiase on FRA1 . ConsistIt is important to note that the described molecular circuits were not observed in all models. Although the modulation of the sensitivity toward MAPK pathway inhibitors was consistently observed also in the doxycycline-inducible models, the connection to ERK signatures and phosphorylation was not. Whether a longer time period of constant FRA1 expression is needed to explain the discrepant effects in the investigated models needs further investigation.Our findings underscore the complex multi-layered buffering of oncogenic signaling in PDAC, where the loss of one signaling node is quickly compensated by the upregulation of another node. These rapid adaptations make mono-targeted therapies challenging and point to the need to decipher the molecular mechanisms behind these relevant therapeutic processes, including a permissive context, in more detail to develop suitable combination therapies.G12D-driven PDAC model. Although this redundancy observed in the in vivo model might argue against the development of a specific FRA1 inhibitor, pharmacological FRA1 inhibition, which we mimic by a chimeric dTAG-FRA1, can render PDAC cells sensitive to RAF-, MEK-, ERK-inhibition, offering opportunities for the development of rational and mechanism-based combination therapies.Taken together, our data indicate a complex and context-dependent role of FRA1 in PDAC. Even though FRA1 is expressed in pre-neoplastic lesions and PDACs compared to the normal pancreas, it seems to be dispensable for PDAC oncogenesis in a murine KrasSupplementary file1 (PDF 11467 KB)Below is the link to the electronic supplementary material.


Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by lack of self-regulation and deficits in organizing behaviors in response to emotional stimuli. Methylphenidate (MPH) is one of the most effective psychostimulant drugs for ADHD, however, a possible predictive utility of brain hemodynamic data related to MPH administration and its relation to clinical symptomatology is still not clear. To address these questions, we used Near Infrared Spectroscopy (NIRS) technology, a non-invasive optical technique that allows to investigate the effect of psychopharmacological treatment on cortical hemodynamics.Twenty children with ADHD underwent a three-waves study and 25 healthy controls were recruited at W1. At W2 children with ADHD received first MPH administration and at W3 they reached the titration phase. At each phase children performed - during NIRS recording - an emotional continuous performance task with visual stimuli of different emotional content. Clinical data were also collected at W1 and W3. We investigated the relationship among the difference between NIRS activation at W2 and W1 (Delta1) and W3 and W2 (Delta2), for each subject, task condition and brain region. Lastly, we investigated correlations between the Delta1 and clinical symptomatology indexes at W1 and between Delta2 and clinical data at W3.Our study results suggest that hemodynamic changes in right prefrontal region probably induced by first MPH administration could predict hemodynamic changes related to MPH titration phase. These biological indexes could be associated to clinical evidences related not only to core ADHD symptoms but also to affective correlates.No significant relationships.


Ensete ventricosum, Musaceae) is a neglected traditional multipurpose crop plant critical for Ethiopian food security. It has drawn a lot of attention in the last few years. This study was undertaken on the morphological diversity among the enset landraces and their cultural use for the livelihood of the people in Southern Ethiopia. The study was administered in four purposively selected kebeles of the Mareka District. A total of 145 individuals were interviewed using semistructured interviews, and field observation has also occurred. The descriptors for enset developed by the International Board for Plant Genetic Resources were used to measure the morphological features. This study found twenty-two enset landraces. Landraces were categorized into five groups based on their morphological trait variability. The highest mean was in cluster five, while the lowest was in cluster three. The highest landrace diversity was found in Ocha (n = 2.28) and the lowest in Guta (n = 2.17). This study confirmed that the study area has a diverse range of ecosystems. However, a reduction in production and the loss of some landraces were observed. As a result, the protection and preservation of enset landraces must be a priority for all responsible entities.Enset ( Ensete ventricosum (Welw.) Cheesman) is one of the root crops, which are perennial herbaceous and monocotyledonous crops that belong to the Musaceae family and flower just once in their life cycle depending on the climate and landrace type [Enset  of the Mareka woreda in the Dawuro zone, SNNPRS. Its geographical position is between 37° 0° and 37° 1° E latitude and 7° 0° and 7° 1° longitude . It is sThe study sites were selected to support the areas with high production of enset and individual enset landraces that play economic and cultural roles. The areas were selected by referring to literature sources, the survey made by CSA , the proN = the total population studiedn = required sample sizee = the precision level, which is (0.08%) where the confidence level is 95% at p = ±5 (maximum variability)The sample sizes of households were determined by using Yamane's  formula.Ethnobotanical data was gathered in order to better understand farmers' indigenous knowledge of enset plants. Different qualitative and quantitative ethnobotanical data collection methods, like field observation, guided field walks, semistructured interviews, and market surveys, were used to get the participants' needed information. The following ethnobotanical information was gathered using a semistructured interview: the local name of the crop and landrace; the time of cultivation and harvesting; traditional management practices; the cropping system, uses, and market value of the crop; landraces that persist in drought, disease, and pest and have short maturity times; the planting material exchange system; production constraints; and farmers' perceptions of the crop.To characterize the agromorphological traits of the landraces, all landraces found in the study area were measured by their quantitative and qualitative meanings. The IBPGR  approachThere were different morphological and agronomic characteristics that farmers used to identify their landraces in the study area. Some of them are the color of the pseudo-stem, midrib, leaf, petiole, time of maturity, disease resistance, yield, leaf dimensions (width and length), and pseudostem length.Descriptive statistics were used to analyze data obtained through interviews and guided field walks.In preference ranking, 20 key informants were selected from four kebeles and were asked to rearrange a gaggle of things consistent with a given criterion like personal preference or the importance of a species. Each item was then assigned a value, with the most important or preferred item being ranked with the highest value, while the least preferred item was ranked with the lowest value. As a result, in this investigation, distinct enset species utilization values were short-listed and ranked by informants using the Martin , 34 apprDirect matrix ranking was applied in order to answer the question of which landrace was best for which purpose. The chosen informants reported the landraces and their purposes. Then, each key informant was asked to rank the landraces for each of the purposes. The values of every landrace were summed up and ranked for every informant and then finally for the entire informant population.H) [S is the number of phenotypic classes for a character and pi is the relative proportion of the total number of entries (N) in the ith class [H is the Shannon-Wiener Diversity Index, Hmax is ln (N), and N is the total number of landraces.Enset landrace diversity analysis, including the Shannon-Wiener Diversity Index (H) , and theth class . RichnesThe assessment on the size of the land indicated that the majority (55%) of the respondents had 2-4 hectares of their own land that was used for farming purposes, including home gardens, and the maximum amount of land owned by the respondents was 11 hectares of land. The largest recorded land cover of enset was 28% hectares and, on average, 13% hectares of land on a farm. Some of the respondents (15%) reported having 9–13 enset landraces growing in their yards, while most of the respondents (43%) grow only three to six enset landraces.A total of 22 landraces were identified from the four kebeles of the study area . DependiN = 11). Landraces are distinguished by their light green pseudostems, deep green leaves, light green midribs, high-quality fiber, and resistance to drought and disease. These were Yesha Maziya, Hoeya, Amiya, Shasha, Yaka, Bothena, Ontha, Botha Maziya, Erantiya, Ankuwa, and Boza. In cluster two, the landraces provide high-quality bulla, kocho, and fiber. These are Arkiya Budunthuwa, Gena Shododiniya, Mataka, and Aguntha. Cluster three consists solely of Kuruwa and Wosa ayfiya landraces. They have a dark red pseudostem, deep green leaves, medium fiber quality, and are vulnerable to drought and diseases. Cluster four includes Keteriya, Tochinuwa, Chamerotiya, and Udunthiya landraces. It was well defined based on the leaf. They had purple leaves and were resistant to diseases and drought. Cluster five includes landraces having deep red pseudostems, deep red petioles, yellowish-green leaves, and high resistance to disease and drought. These are Shakariya, Adinona, Lochingiya, Kataniya, Koshikoshiya, Babaka, Badaluwa, and Wora Kana Utha landraces  and agronomic characteristics (disease resistance and drought resistance). Cluster one includes the most important number of enset landraces , followed by Nekir Kebele . However, Guta kebele showed the lowest richness . According to the farmer, Ocha has the most landraces on average, followed by Nekir, Madara, and Guta in that order (When the diversity of enset was estimated based on the number of landraces (richness), Ocha Kebele (of the Mareka District) showed the largest richness , evenness (E), Simpson (D), and Shannon (H) indices.The diversity of landraces in the study area was measured by the richness  and is ready to scrape the leaf sheath peduncle and grated corm into a pulp, squeezing liquid containing starch from the pulp, allowing the resultant starch to concentrate into white powder and rehydrating with water. As shown in The preference ranking on the use-value of enset by the key informants showed that enset is primarily used as a source of food by the local people, which is followed by its use for medicinal purposes, and therefore the least used as a means of income generation. Preference ranking based on enset use-value within the study area was indicated in ascending order from 1 (least useful) to 5 (most useful) .The Abyssinian banana is found at the highest frequency among different landraces. According to the informant report, each farmer owns a number of enset landraces on their farmland. The traditional identification mechanism used by the farmers is similar to that used by the Sidama and Wolaita zones , 37. In Both the Botha Maziya and Boza landraces were the most disease-resistant, while the Lochingiya, Badadiya, and Kataniya landraces were the most susceptible. Botha Maziya and Ontha landraces are known for their quality of Kocho. Boza, Hoeya, and Shasha are high-yielding landraces, but they require a low level of Kocho and Bulla. According to the respondents, enset is cultivated by every household; however, its cultivation is declining from time to time. The increasing demand for engaging in the production of other crops might be the reason for the decreasing trend of enset production in the study area. This might also be due to population growth and a shortage of land, which leads them to food shortages as they are forced to grow short-season crops instead of enset. This agrees with the recent reports by Abebe and Eshetu  that werIn this study, 22 enset landraces were identified. Yemataw et al.  and ZebeIn the study area, farmers cultivate enset crops in their home gardens, followed by chat and coffee crops. A similar study by Magule et al.  in the WIn Ethiopia, enset is produced mainly as a source of food for the family's subsistence . In thisThis research confirms that local farmers' roles in the maintenance of their enset landraces are high. Farmers have their own indigenous enset planting material selection, multiplication, and management system. The identified enset landraces have morphological variability. Although some characters are the same throughout the study area, some other characters show some differences. Clustering similar landraces together and knowing morphological variation among landraces helps farmers select and maintain their landraces. Generally, the study indicated that the study area is rich in having different enset landraces, culture, and indigenous knowledge on enset production and maintenance. However, some endemic landraces have been lost in line for different reasons. As a result, the number of landraces will decrease, but some landraces and their habitats will become extinct, resulting in a loss of information, services, and cultural values related to enset crops, which could have long-term consequences.


The Brown Attention-Deficit Disorder Scale (BADDS) was developed as a self-report assessment that was designed to screen for presence of symptoms of attention deficit hyperactivity disorder (ADHD). The objective here was to translate and validate the adult self-report BADDS for use in Brazil.  Cross-cultural validation study conducted in an addiction unit at a public university hospital. This study included a control group (n = 100) and a drug-user group (n = 100). Both groups included subjects aged 18 to 60 years old. The control group had no prior diagnosis of drug addiction and the drug-user group included participants with a diagnosis of addiction. Each participant answered Brazilian Portuguese translations of both the BADDS and the Adult Attention Deficit Hyperactivity Disorder Self-Report Scale (ASRS) questionnaires, in paper-and-pencil format. The drug-user group scored higher than the control group on both scales. The mean scores on ASRS were 27.26  and 25.85 (SD: 8.65) respectively (P > 0.05). The mean scores on BADDS were 79.56 (SD: 29.61) and 79.31 (SD: 18.09), respectively (P > 0.05). Cronbach’s alpha for BADDS was 0.95. BADDS presented fair sensitivity (72% accuracy) and fair specificity (88% accuracy).  This study provides discriminative validity evidence for use of BADDS among Brazilian adults with substance-use disorders. Until recently, it was believed that attention deficit hyperactivity disorder (ADHD) was exclusively a pediatric condition.Currently, there are no biomarkers available for diagnosing ADHD. All diagnoses require careful assessments by clinicians through interviews and appropriate classification criteria.These two diagnostic tools define ADHD as a hyperkinetic disorder, a disorder characterized by inattention, hyperactivity and impulsivity with onset in childhood or adolescence. It is believed that the current diagnostic criteria (both DSM-5 and the ICD-10) are inadequate for evaluation of adults because they focus on early childhood problems and they do not fully account for developmental and maturation changes.Many people who suffer from ADHD may also be at risk of having co-occurring psychiatric disorders or chronic illnesses. It has been estimated that more than 87% of adults with ADHD have some form of comorbidity.In order to enhance and assist the diagnostic process for some psychiatric conditions, standardized instruments are becoming increasingly necessary. Standardized assessment instruments are widely disseminated within research and have increasingly been used as a resource for evaluating different aspects of mental health. In clinical practice, standardized instruments are critical for screening and diagnosing patients. Currently, researchers use self-report questionnaires as a critical part of screening and diagnosing patients with ADHD. Limitations exist because the screening and diagnosis tool is unavailable in other countries.To improve the reach of the Brown Attention-Deficit Disorder Scale (BADDS) in Brazil, the aim of this study was to translate and validate it for use in Portuguese among a Brazilian sample of drug users and among a sample of people with no history of drug use.This was a translation and cross-cultural validation study, conducted at the Federal University of São Paulo . The ethics committee of UNIFESP approved the study  All participants signed an informed consent statement.BADDS is a self-report questionnaire that is used for screening adults with a possible case of ADHD.organizing and prioritizing work and activation for work;focusing on tasks, sustaining this focus and shifting attention to tasks;regulating alertness and sustaining effort, and the ensuing processing speed;managing frustration and modulating emotions; andusing working memory and accessing recall.Each question has a possible score from 1 to 4. The higher the cluster score and overall score are, the higher the risk is that the individual has ADHD.All individuals who complete the BADDS questionnaire are classified into three groups: i) possible, but unlikely to have ADHD, if the score is less than 40; ii) possible, but unconfirmed ADHD, if the score is between 40 and 54; and iii) highly likely but unconfirmed ADHD, if the score is above 55.A 2008 study demonstrated that BADDS was more reliable than were other instruments that were based on the DSM-IV criteria.,,The paper-and-pencil format of BADDS was translated into Portuguese. The translation was conducted using a two-step procedure, known as the back-translation method, as recommended by Brislin (1973) and by Smit (2006).ASRS is currently the most accepted and most widely used self-report questionnaire for screening for ADHD symptoms.ASRS was chosen as the gold standard for this study. The main reason for this choice was that the Addiction Unit did not have enough trained psychiatrists to perform a complete ADHD diagnosis on these 100 subjects. It is important to state that this unit is not specifically designed for research purposes and that this evaluation would imply a significant increase in the psychiatrists’ workload. The validation study sample consisted of two groups. One group (control group) consisted of a convenience sample of 100 students from UNIFESP, in accordance with the following inclusion criteria: i) between the ages of 18 and 60 years; ii) either female or male; iii) literate, independent of education level, socioeconomic level or ethnicity; and iv) no prior diagnosis of psychiatric conditions or drug addiction (according to self-report).The second group (drug users) comprised 100 adults who were currently attending an outpatient facility, the Addiction Unit of UNIFESP . At this facility, patients participate in weekly group therapy sessions and are individually evaluated by a psychiatrist at least once a month. This trained psychiatrist is responsible for making the diagnosis of drug dependence, using the DSM-5 criteria. Individual sessions with a psychologist may form part of the treatment, depending on the needs of each patient. For our second group, the same inclusion criteria were used, with the addition that all patients had a diagnosis of substance dependence, which had been assessed and diagnosed by a psychiatrist in accordance with the DSM-5 criteria. All substances except tobacco were included in the assessment.All participants in both groups answered two self-report questionnaires: BADDS and the Adult ADHD Self-Report Scale (ASRS). Aside from age, gender and psychiatric diagnosis, no other sociodemographic or clinical information was collected.The total scores on both scales were tested in order to check for normal distribution, which was confirmed. Chi-square tests were used to analyze categorical data, while t tests were used to analyze parametric continuous variables. The internal consistency of BADDS was measured by means of the Cronbach’s alpha method. Alpha was computed by correlating the score for each scale item with the total score for each individual observation, and then making comparisons with the variance for all individual item scores.The participants’ scores were compared using the means that were obtained through the two questionnaires, to determine criteria for concurrent and discriminant validation measurements for BADDS. Given the scores from each questionnaire, several cutoff points were verified for sensitivity and specificity, in increments of 10 .To analyze cutoff points, the receiver operating characteristic (ROC) curve was used. The statistical significance level was taken to be 0.05. The statistical analysis software Statistical Package for the Social Sciences (SPSS 22.0) for Windows was used.Within the drug-user group, men comprised 87% of the sample, whereas in the control group, men represented 47% of the total sample. The drug-user group scored higher than the control group in both ADHD instruments. The mean score from the ASRS questionnaire in the drug-user group was 27.26 (SD: 11.99), compared with 25.85 (SD: 8.65) in the control group (P > 0.05). The mean scores from the BADDS questionnaire were 79.56 (SD: 29.61) and 79.31 (SD: 18.09), respectively (P > 0.05). Cronbach’s alpha from BADDS was 0.95.This study demonstrated that BADDS, a tool for diagnosing ADHD among adults, has high internal consistency and differentiates possible cases of ADHD among people with concurrent substance use and people without a psychiatric diagnosis. The scale has fair sensitivity and specificity.These results encourage use of BADDS for identifying possible ADHD cases, both in clinical practice and in research. Screening for ADHD using this scale enables greater agility in reaching diagnostic confirmation of ADHD. Moreover, this tool results in the following:higher quality for the service, given that a standard is created for diagnostic investigation;improvement of adherence to treatment, since patients can track their progress through reductions in the scores; andgreater focus of the available resources on individuals who are accurately screened positive, so that they can be evaluated by a psychiatrist or a psychologist.In this context, it is essential to develop and validate these important tools, not only to improve the diagnostic process, but also to allocate resources where needed.Once validated, the BADDS scale can become a screening tool that could improve psychiatric care and provide resources for those who truly are ADHD-positive. The self-report format, the non-medical language and the fact that the questions do not examine only the presence of symptoms make the Brown Attention-Deficit Disorder Scale a valuable instrument for use by any healthcare professional who wants to optimize ADHD healthcare services .Diagnosing psychiatric disorders is complex, and this is even more so when they are associated with other psychiatric comorbidities, as is the case with ADHD. The lack of biomarkers to identify psychiatric conditions makes validation tools necessary, to minimize diagnostic difficulties.Screening instruments are useful and increase the quality of healthcare services. Validated screening tools provide cost-effectiveness strategies, reinforce diagnostic accuracy and allow exploration of different aspects of mental health. In addition, they adds to the body of knowledge of overall mental health examination and care.Since the demand for mental health facilities is greater than the number of care services available in Brazil, validation of the BADDS questionnaire provides the possibility of extrapolating the sphere of psychiatric consultation offices and could be a way to reduce the gap in mental health facilities. It could reduce the burden on mental health facilities, while simultaneously correctly identifying cases of adults living with ADHD.Some limitations of the present study need to be noted. Because a self-report questionnaire was used, rather than psychiatric interviews, the questions were subject to interpretation by the participants and to possible information bias. Moreover, a psychiatric interview should be the gold standard, but this was not possible because of limitations to the capacity of our facility. In addition, this was a cross-sectional survey and therefore associations do not imply causation. Lastly, although sensitivity and specificity are characteristics of each test, positive and negative predictive values depend on the prevalence of the condition studied within a given sample. Therefore, in populations with low prevalence of ADHD, the positive predictive value tends to be low and the negative predictive values tend to be high. This means that, although the findings through an instrument might rule out a diagnosis of ADHD, there is a high chance that any positive results will in fact be false positives.In summary, this study conducted on a Brazilian sample demonstrated that BADDS has discriminative validity for making diagnoses of ADHD. The ROC curve analyses showed the usefulness of BADDS for detecting adults who need ADHD treatment, particularly among those with substance-use disorders.


Migration has been implicated as a risk factor for autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD), but evidence is still limited and inconsistent. We aim to investigate the relationship between migration status and risk of ASD and ADHD.Electronic databases including PubMed, EMBASE, Web of Science, and PsychINFO were searched to identify observational studies on this topic, from inception to February 2021. Random-effects meta-analysis models were used to pool the summary odds ratio (OR) and 95% confidence interval (95% CI), and subgroup analyses were conducted to detect possible discrepancies in associations. Certainty of evidence was assessed as per the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) guidelines.P for Z test = 0.010), but no association was found between migration and ADHD  or hyperactivity . Subgroup analyses further demonstrated that maternal migration was ASD risk factor , and migrant children were more likely to develop ASD with comorbid intellectual disability (ID)  than ASD without ID. After standardized the origin of migrants, European migrant children from Americas were at higher risk of ASD and ADHD (pooled OR were 4.13 and 1.26), and increased ASD risk was also observed in African children (pooled OR: 2.72). The GRADE of evidence was very low.A total of 13 studies  for ASD, five studies  for ADHD, and six studies  for hyperactivity were included. Overall, the pooled results indicated that migration was associated with increased risk of ASD (pooled OR: 1.32; 95% CI: 1.07–1.63; Maternal migration is a risk factor for ASD, and migrant ASD children are more likely comorbid ID. The role of migration on ADHD remains controversial, more studies are needed to assess the association between migration status and ADHD. Health care practitioners should consider screening and providing extra resources for migrant children.The online version contains supplementary material available at 10.1186/s12888-022-04037-4. Over the past two decades, the number of international migrants have grown robustly from 153 million in 1990 to 281 million in 2020, and much of this increase was due to labour, family migration, or humanitarian crises in many parts of the world . With anAutism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) are highly correlated childhood-onset mental disorders, with prevalence of 1.85 and 5.29%, respectively –8. The cA pervasive review of risk factors of autism listed maternal migration as suggestive evidence . PoolingMeasured by the Strengths and Difficulties Questionnaire (SDQ), Derluyn et al.  found miASD and ADHD are the most devastating mental disorders of childhood in terms of prevalence, morbidity, outcome, impact on the family, and cost to society. Investigating the relationship between migration and these disorders can help to provide better mental health service for this population. Therefore, we conducted this systematic review and meta-analysis to evaluate the current epidemiological evidence about migration status and risk of ASD and ADHD.In this systematic review and meta-analysis, we searched PubMed, EMBASE, Web of Science Core Collection, and PsycINFO, for studies published in English between database inception and February, 2021. We included epidemiological studies that measured the association between migration status and risk of ASD  or ADHD in children and adolescents (0–19 years) and provided risk estimates including OR, RR, or hazard ratio (HR) with 95% CIs. Given the limited evidence about ADHD, we further included studies comparing ADHD symptom scores between migrant and non-migrant children as a supplementary analysis. Migrant children were defined as 1) the children who moving away from the place they live, or 2) the children had one or more parent born aboard. We included internal and international children migration, defined as migration within and beyond a country’s borders, respectively. We included children migration for any reason, such as employment (labour migration), armed conflict or disaster (forced migration). The comparator group was non-migrant (native) children. Conference abstracts and review articles were excluded. For overlapping data, study with the largest dataset was reserved. The search strategy and full list of search terms used were provided in the supplementary material  . Study q2 statistic, an I2 ≥ 50% was considered to represent high heterogeneity  which identified significant variations in ASD prevalence by race/ethnicity, and socioeconomic status. Race/ethnicity can be described as differences in nationality, culture, religion, biological factor or language . PremigrMigration status was assessed across multiple studies which achieved relatively strong statistical power detecting underlying associations; however, significant publication bias and high heterogeneity between studies were observed. Given the limitations of included studies themselves, we were unable to comprehensively assess potential factors that might influence these associations . Therefore, the irrelevant relationships we found should be interpreted with caution and further validation is required. Other limitations of present study should also be taken into consideration. First, all eligible studies were conducted in high-income countries. Although most migrants originate from low-income or middle-income countries and relocate in search of employment opportunities , there aMigration status had potential effect on two genetic correlated childhood-onset mental disorders. Comparing to non-migration status, maternal migration was associated with increased risk of offspring ASD. Migrant children were more susceptible to ASD with comorbid intellectual disability. Although overall migration was not associated with the risk of ADHD or hyperactive score, we observed a significant different effect of maternal and paternal migration on ADHD. Given to the children in such position might suffer social injustice and unbalanced medical resources, health care practitioners should consider screening and providing extra resources for migrant children, especially those from Americas. Additional file 1.


In addition, more severe ADHD symptoms were significantly associated with younger appearing brains. Altogether, these results are in line with the proposed delay of individual brain maturation in children with ADHD. However, given the relatively small sample size (N = 144), the findings should be considered preliminary and need to be confirmed in future studies.Attention-deficit hyperactivity disorder (ADHD) is a debilitating disorder with apparent roots in abnormal brain development. Here, we quantified the level of individual brain maturation in children with ADHD using structural neuroimaging and a recently developed machine learning algorithm. More specifically, we compared the BrainAGE index between three groups matched for chronological age (mean ± SD: 11.86 ± 3.25 years): 89 children diagnosed with ADHD, 34 asymptomatic siblings of those children with ADHD, and 21 unrelated healthy control children. Brains of children with ADHD were estimated significantly younger (−0.85 years) than brains of healthy controls (Cohen’s  Attention-Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder that affects approximately 5% of all children and adolescents worldwide, with a higher incidence in males compared to females –5. ADHD To our knowledge, only two other studies , 29 haveTherefore, to shed further light on these questions and contribute to an understudied field of research, here we applied a well-validated brain age estimation algorithm –39 in chp = 0.363). However, the distribution of boys and girls differed significantly between the three groups (p < 0.001), with a significantly higher male-to-female ratio in children with ADHD compared to siblings and controls. All participants provided written informed consent/assent and the study was approved by the UCLA Institutional Review Board. Additional approval for the data analysis was obtained from the University of Auckland Health Research Ethics Committee.A total of 144 participants  were recruited from the community, drafted from other ongoing studies of ADHD at the University of California, Los Angeles (UCLA), or referred to the study by their physicians. All children were evaluated for ADHD and other psychiatric disorders based on an interview with their primary caretaker using the Kiddie-Schedule for Affective Disorders and Schizophrenia–Present and Lifetime (K-SADS-PL) . In addiTR = 1,900 ms, TE = 3.26 ms, flip angle = 9°, 176 axial sections, voxel size = 0.98 mm × 0.98 mm × 1 mm. These T1-weighted images were then processed in Matlab1 using SPM122 and the CAT12 toolbox  but, instead, increased the Akaike Information Criterion suggesting that the model without the interaction term was a better fit. Moreover, the interaction did not reach significance . Thus, the model was conducted without the group-by-sex interaction. Given our hypothesis that children with ADHD as well as their siblings  would present with a delay in brain maturation  compared to control children, one-tailed T-tests were applied. Assumptions for parametric testing were confirmed using a Lilliefors test determining the normal distribution of the residuals and a Bartlett test determining equality of variance.To compare BrainAGE across the three groups, the BrainAGE indices were the dependent variable and group status  was the independent variable. Sex was included as a covariate because the distribution of boys and girls differed between the groups, and “family” was included as a random variable because several of the participants were siblings. In addition, we tested whether there was a significant group-by-sex interaction because the prevalence of ADHD during childhood and adolescence is higher in males than in females . HoweverT-tests were applied. Given the five different models for the five different symptom scores, corrections for multiple comparisons were applied using the false discovery rate (FDR) , sex was a covariate, and “family” was a random variable. Again, the addition of a sex interaction did not improve the models and also did not reach significance for any of the symptom measures see ; it was te (FDR) . AssumptNo significant sex interaction was found, neither in the group comparisons nor the correlation analyses. However, given the higher prevalence of ADHD in males during childhood and adolescence , we condd = −0.23, t (140) = −1.38, p = 0.086], but was significant for children with ADHD compared to controls . The difference in brain maturation between children with ADHD and siblings was not significant .The exploratory analyses investigating group differences and associations with symptom scores in boys and girls separately suggest that effects are primarily found in boys. However, given that group-by-sex interactions and symptom-by-sex interactions in the main analyses were not significant, these findings will solely be provided as a reference for future studies without interpreting them see , 3. WhenOriginally catalyzed by a competition , a numbeIn addition to children with ADHD and control children, the present study also included unaffected siblings of children with ADHD. The difference between siblings and controls did not reach significance, but the brains of siblings were estimated 8.5 months younger than the brains of controls. Given that the brains of children with ADHD were estimated 10.2 months younger than the brains of controls, the unaffected siblings were overall closer to their siblings with ADHD than to the controls. This finding seems to be consistent with the observation that ADHD runs in families, with siblings of children with ADHD having a 5–10-fold increased risk of developing ADHD when compared to the general population , 31. It Since the brain age estimation is a relatively novel approach, our study complements and extends the existing field of research. However, future studies are needed to replicate the present findings and address some study limitations: First of all, the sample size is rather small and would benefit substantially from an increase, particularly in the control group. This might not only enhance statistical power but also allow to stratify analyses by sex and to interpret sex-specific findings with confidence. For example, it is certainly noteworthy that our supplemental analyses revealed a lower BrainAGE index (compared to controls) only in boys with ADHD as well as in unaffected brothers, while no effects were evident in girls with ADHD or unaffected sisters. However, due to the non-significant sex-by-group interaction in the main analysis, which is potentially due to the limited statistical power, any interpretations would be conjecture. Moreover, while the current cross-sectional study captured apparent delays in brain maturation, future longitudinal studies may not only shed light on the causality of the effects but also on the onset as well as the longevity/stability of the effects. For example, ADHD has been reported to manifest in early childhood, middle childhood, adolescence, or even adulthood, where adulthood ADHD may or may not be a continuation of childhood ADHD –51. In tf.kurth@auckland.ac.nz.The data analyzed in this study is subject to the following licenses/restrictions: We do not have permission to share the data. Requests to access these datasets should be directed to FK, The studies involving human participants were reviewed and approved by the UCLA Institutional Review Board and University of Auckland Health Research Ethics Committee. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.EL, FK, and JL planned the present analysis of the dataset, which was conceived, acquired, and curated by JL, SL, JO’N, JA, and KN. CG processed the MRI data and calculated the BrainAGE indices. FK conducted the statistical analyses. FK and EL wrote the manuscript. JO’N contributed to the writing and editing of the manuscript. All authors contributed to the article and approved the final manuscript.


Attention-deficit hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders diagnosed in children of this era. ADHD in children and adults is challenging but highly manageable. Children with ADHD cannot focus, are hyperactive, and appear withdrawn. These symptoms make them endure difficulties in learning and create academic challenges. Methylphenidate (MPH) is one of the psychostimulants used as a first-line therapy for ADHD.In this literature review, we have gathered information that describes the evidence of psychotic symptoms in children and young patients with ADHD as a side effect of MPH. We used articles from the National Library of Medicine (PubMed) and Google Scholar to gather the relevant information. Our findings concluded that MPH can increase the risk of psychosis, particularly when taken in high doses. It is still unclear whether the psychotic range of symptoms occurred due to an increased dopamine (DA) level caused by MPH or as a predominant feature of ADHD or if there was another comorbid feature in the history of the patient that led to psychosis. However, a necessary step for every medical practitioner prescribing psychostimulants is that they enlighten the patient and caregiver that this rare but threatening side effect is a possibility. Attention-deficit hyperactivity disorder (ADHD) has always been a subject of interest for researchers . The estlack of touch with reality [The prefrontal cortex (PFC) is an important area of the brain that mediates cognitive and executive functions, such as working memory, sustained attention, inhibitory response control, and cognitive flexibility . Delayed reality . Psychos reality .PubMed and Google Scholar were utilized to search for English literature articles. MeSH terms such as ADHD, MPH, psychosis, schizophrenia, and psychostimulants were used to search for appropriate articles. Multiple design studies are used. Filters included free full-text articles and those published within the past 10 years (January 2013 to June 2022), with children and young patients aged 5 to 30 years. Out of 1,400 articles, 24 were shortlisted based on relevance to the topic of discussion.The primary objective of this literature review is to examine the relevant data that shows a correlation between the use of MPH and psychosis as a side effect in children and young patients with ADHD. Databases such as Relationship between the new onset of psychosis and MPH in ADHDStimulants such as MPH and amphetamine (AMP) are the most common pharmacological treatments for ADHD . AlthougRamstad et al. evaluated the proof of psychotic signs and symptoms as a detrimental impact of MPH treatment in children and adolescents with ADHD . Their aAnother meta-analysis of the nonrandomized clinical study conducted by Storebø et al. included a total of 2,345 patients who were categorized based on exposure to MPH into control and experimental groups [A cohort study conducted by Moran et al. included patients in the age group of 13 to 25 years, diagnosed with ADHD, and taking MPH between January 1, 2004, and September 30, 2015 . The outAnother cohort study conducted by Moran et al. selected 239 patients who had a history of psychotic disorders . Out of insensitive (rarely develop psychosis after exposure to stimulants) and some are sensitive (highly likely to develop psychosis after taking low doses of stimulants or without exposure to stimulants) [Cortese, in his literature review, suggests that some individuals are mulants) . This stmulants) . Professmulants) . The anamulants) .Walichniewicz and Lew-Starowicz, in their case report, describe the case of a 31-year-old female patient who developed MPH-induced psychosis after taking MPH for almost 15 months . She hadMan et al. conducted a study using electronic medical records on the Clinical Data Analysis and Reporting System 2001-2014) [01-2014 [Management of psychosis after MPH use and treatment of ADHD after an episode of psychosisIn his case report, Güneş describes the case of a 16-year-old boy who had symptoms of ADHD and mild intellectual instability . The patBeckmann et al. described MPH as a cause of psychosis in ADHD patients, particularly when taken in high doses . If a paIn her review article, Hechtman emphasizes the importance of slowly titrating stimulant medications such as MPH and informing the patient about the possibility of developing this side effect . If the In a case report, Gable and Depry outlined a 26-year-old patient with ADHD who required hospital admission after taking the prescribed dose of a stimulant . The repLimitationsThe current conclusions have been drawn from accessible studies available on PubMed and Google Scholar databases published in English literature. The data presented was not representative of all age groups. We have included articles that were published in the past 10 years. Conflicting findings have been presented in various studies. The exact dose of MPH responsible for adverse outcomes has not been clarified.We studied the relationship between psychosis and MPH in children and young patients with ADHD. We have found a considerable amount of evidence that suggests prescribing MPH puts the patient at risk of developing a rare side effect of psychosis, which can lead to hallucinations and delusions, whereas some studies did not obtain the same result. However, physicians should be aware that psychotic symptoms can occur during treatment with MPH. It is their responsibility to educate patients and their caretakers about this rare but serious side effect.Further research in children and young patients with ADHD is needed to identify the side effects of MPH to provide data that is representative of different age groups. A more detailed study with larger samples should be performed that provides statistically significant results. Some questions remain unanswered such as the exact dose or range of doses of MPH that leads to psychosis. The relationship between the family history of psychosis and the development of psychotic features by MPH should also be investigated further.