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Adolescent

Adolescent is a crucial stage of human development, encompassing the years between childhood and aduldhood, typically ranging from 10 to 19 years of age.
This transitional period is marked by significant physical, cognitive, emotional, and social changes.
Adolescents undergo rapid growth and maturation, experience hormonal shifts, and develop a stronger sense of indentity and independence.
This dynamic stage presents unique challenges and opportunities for researchers and clinicians focused on adolescent health and well-being.
Understanding the complexities of adolescent development is essential for designing effective interventions, promoting positive outcomes, and fostering successful transitions into adulthood.
The PubCompare.ai platform can enhance reproducibility and accuracy in adolescent research by providing access to a wealth of relevant protocols, preprints, and patents, while leveraging AI-driven comparisons to identify the most suitable approaches for adolescent-focused studies.

Most cited protocols related to «Adolescent»

1
Carcinogenic Risk Assessment of PM2.5-bound PAHs
Carcinogenic and mutagenic risk assessments15 (link),60 (link)–63 (link),67 (link)–69 (link) induced by inhalation of PM2.5-bound enriched with selected nitro-PAHs (1-NPYR, 2-NPYR, 2-NFLT, 3-NFLT, 2-NBA, and 3-NBA) and PAHs (PYR, FLT, BaP, and BaA) were estimated in the bus station and coastal site samples according to calculations done by Wang et al.60 (link), Nascimento et al.61 (link), and Schneider et al.67 (link) PAH and PAH derivatives risk assessment is done in terms of BaP toxicity, which is well established67 (link)–73 (link). The daily inhalation levels (EI) were calculated as: EI=BaPeq×IR=(∑Ci×TEFi)×IR where EI (ng person−1 day−1) is the daily inhalation exposure, IR (m³ d−1) is the inhalation rate (m³ d−1), BaPeq is the equivalent of benzo[a]pyrene (BaPeq = Σ Ci × TEFi) (in ng m−3), Ci is the PM2.5 concentration level for a target compound i, and TEFi is the toxic equivalent factor of the compound i. TEF values were considered those from Tomaz et al.15 (link), Nisbet and LaGoy69 (link), OEHHA72 , Durant et al.73 (link), and references therein. EI in terms of mutagenicity was calculated using equation (1), just replacing the TEF data by the mutagenic potency factors (MEFs) data, published by Durant et al.73 (link). Individual TEFs and MEFs values and other data used in this study are described in SI, Table S4.
The incremental lifetime cancer risk (ILCR) was used to assess the inhalation risk for the population in the Greater Salvador, where the bus station and the coastal site are located. ILCR is calculated as: ILCR=(EI×SF×ED×cf×EF)/(AT×BW) where SF is the cancer slope factor of BaP, which was 3.14 (mg kg−1 d−1)−1 for inhalation exposure60 (link), EF (day year−1) represents the exposure frequency (365 days year−1), ED (year) represents exposure duration to air particles (year), cf is a conversion factor (1 × 10−6), AT (days) means the lifespan of carcinogens in 70 years (70 × 365 = 25,550 days)70 ,72 , and BW (kg) is the body weight of a subject in a target population71 .
The risk assessment was performed considering four different target groups in the population: adults (>21 years), adolescents (11–16 years), children (1–11 years), and infants (<1 year). The IR for adults, adolescents, children, and infants were 16.4, 21.9, 13.3, 6.8 m3 day−1, respectively. The BW was considered 80 kg for adults, 56.8 kg for adolescents, 26.5 kg for children and 6.8 kg for infants70 .
Santos A.G., da Rocha G.O, & de Andrade J.B. (2019). Occurrence of the potent mutagens 2- nitrobenzanthrone and 3-nitrobenzanthrone in fine airborne particles. Scientific Reports, 9, 1.
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Publication 2019
Adolescent Adult Benzo(a)pyrene Body Weight Carcinogens Child derivatives Factor X Fibrinogen fluoromethyl 2,2-difluoro-1-(trifluoromethyl)vinyl ether Health Risk Assessment Infant Inhalation Inhalation Exposure Malignant Neoplasms Mutagens Polycyclic Hydrocarbons, Aromatic Population at Risk Population Group Respiratory Rate
2
Rigorous Genotypic Data Curation
We excluded SNPs in each individual dataset that had a mean GenCall Score < 0.7, missingness > 5%, a minor allele frequency (MAF) < 0.01 or a Hardy-Weinberg equilibrium (HWE) test P-value < 10−6, using PLINK32 (link). A total of 304,013 SNPs in the adult dataset and 529,379 SNPs in the adolescent dataset passed this process, but only those in the autosomes were included in the analysis (295,400 SNPs for the adult dataset, 516,345 SNPs for the adolescent dataset and an intersect of 294,831 SNPs for the combined dataset).
We estimated the genetic relationships among all of the 4,259 individuals in the combined dataset by equation [6]. The estimated relationships (off-diagonal elements of the relationship matrix) ranged from −0.024 to 0.585, suggesting that some close relatives still remained. The mean of genetic relationships of “unrelated” individuals should be close to zero, so the lower-bound of the range can be roughly regarded as the maximum deviation of an estimate from the mean. We estimated the two-tailed 95% confidence interval of relationships (adjusted for multiple tests by Bonferroni correction) to be from about −0.027 to 0.027. Therefore, to avoid having any close relatives in the data, we chose a cut-off value of 0.025 and selectively excluded one of any pair of individuals with an estimated relationship > 0.025 to maximize the remaining sample size. We excluded 287 individuals from the adult dataset and 47 individuals from the adolescent dataset. A total of 3,248 “unrelated” adults and 677 “unrelated” adolescents, with a combined dataset of 3,925 “unrelated” individuals, was retained for analysis.
The phenotypes were corrected for age and sex, and standardized to z-scores in each adult and adolescent dataset separately. We used a two-tailed 90% Winsorisation33 to adjust the z-scores of four individuals in the adult dataset with absolute values greater than 4.17, the (100 – 5/3248)th percentile of the standard normal distribution based on Bonferroni correction, and combined the z-scores in both adult and adolescent datasets for the combined dataset of height (Supplementary Fig. 1e).
Yang J., Benyamin B., McEvoy B.P., Gordon S., Henders A.K., Nyholt D.R., Madden P.A., Heath A.C., Martin N.G., Montgomery G.W., Goddard M.E, & Visscher P.M. (2010). Common SNPs explain a large proportion of heritability for human height. Nature genetics, 42(7), 565-569.
Publication 2010
Adolescent Adult Phenotype Reproduction Single Nucleotide Polymorphism
3
Meta-analysis of Genetic Associations for Height and BMI
The GIANT Consortium performed a meta-analysis of GWAS data in a discovery set with 133,653 and 123,865 individuals of recent European ancestry from 46 studies for height3 (link) and BMI4 (link), respectively. In each of the participating studies, genotype data were imputed to ~2.8 million SNPs present in the HapMap Phase 2 European-American reference panel26 (link), and the standard errors of all SNPs were adjusted by the genomic control method20 (link). We calculated the effective sample size for each SNP and excluded SNPs with effective sample sizes of >2 s.d. from the mean. We also excluded SNPs with MAF of <0.01, retaining ~2.5 million SNPs for both height and BMI.
We also obtained access to the individual-level genotype and phenotype data of the ARIC cohort, a population-based study of Americans28 (link), and the QIMR cohort, a twin study of Australians29 (link). The ARIC samples were genotyped by Affymetrix 6.0 SNP array, and the QIMR samples were genotyped by Illumina 610K or 370K array. After quality control filtering of SNPs, 593,521 and 274,604 genotyped SNPs were retained in the ARIC (excluding SNPs with missingness of >2%, MAF of <0.01 or Hardy-Weinberg equilibrium (HWE) P value of <1 × 10–3) and QIMR cohorts (excluding SNPs with missingness of >5%, MAF of <0.01 and HWE P value of <1 × 10–6), respectively. After sample quality control analysis, 8,682 and 11,742 individuals of European ancestry in the ARIC and QIMR cohorts, respectively, were included for further analysis. The quality control protocol has been detailed previously for the ARIC cohort18 (link),30 (link) and for the QIMR cohort14 (link),29 (link). We then estimated pairwise genetic relationships between individuals14 (link) and removed one of each pair of individuals with an estimated relatedness of >0.025. After these quality control steps, 6,654 and 3,924 unrelated individuals were retained in the ARIC and QIMR cohorts, respectively. All the ARIC samples were from adults and the QIMR samples were from 3,247 adults and 677 16-year-old adolescents. The SNP data for both ARIC and QIMR cohorts were imputed to the HapMap Phase 2 CEU panel by MACH31 (link). We used the best guess genotypes of the imputed SNPs and excluded imputed SNPs with HWE P value of <1 × 10–6, imputation R2 of <0.3 or MAF of <0.01 and retained 2,406,652 and 2,410,957 SNPs in the ARIC and QIMR cohorts, respectively. The ARIC cohort is part of the discovery sample of the GIANT meta-analysis, whereas the QIMR cohort is not. In the prediction analyses, the height and BMI phenotypes in the ARIC and QIMR cohorts were adjusted for age and sex effects and standardized to z scores14 (link),18 (link). In the QIMR cohort, only samples from 3,247 adults were used in the prediction analysis for BMI.
Yang J., Ferreira T., Morris A.P., Medland S.E., Madden P.A., Heath A.C., Martin N.G., Montgomery G.W., Weedon M.N., Loos R.J., Frayling T.M., McCarthy M.I., Hirschhorn J.N., Goddard M.E, & Visscher P.M. (2012). Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nature genetics, 44(4), 369-S3.
Publication 2012
865-123 Adolescent Adult Birth Europeans Genome Genome-Wide Association Study Genotype Gigantism HapMap Phenotype Single Nucleotide Polymorphism
4
Adolescent Mental Health Trajectories

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Publication 2018
Adolescent Child Mental Nerves
5
Preventing Adolescent Suicide Reattempts
The Treatment of Adolescent Suicide Attempters study was a National
Institute of Mental Health multisite feasibility study designed to develop
and evaluate treatments to prevent suicide reattempts in adolescents.
Participants were 124 male and female patients 12–18 years of age
with a suicide attempt or interrupted attempt during the 90 days before
enrollment (34 (link)–36 (link)). Participants were evaluated at
baseline and at treatment weeks 6, 12, 18, and 24, as well as during
intervening unscheduled visits. Evaluations included the C-SSRS, the
Columbia Suicide History Form, the Scale for Suicide Ideation, and
Beck’s Lethality Scale. All instruments were administered by
independent evaluators, who were Ph.D.-, R.N.-, or master’s-level
clinicians. Assessment of participants also included the self-report Beck
Depression Inventory (BDI) at the same visits, as well as ratings by the
treating psychopharmacologist (who was not the independent evaluator) on the
Montgomery-Åsberg Depression Rating Scale (MADRS). Any potential
suicidal events in the study were rated by the suicide evaluation board,
which was an independent panel of suicidology experts uninvolved in the
day-to-day management of the trial. The board, which was blind to original
event classifications, treatment status, and other potentially biasing
information, rated narratives according to predetermined criteria and
definitions of potential suicidal events. Unanimous consensus was reached in
cases where there was any initial disagreement.
Most participants (N=96, 77.4%) were assessed at
week 12; 87 (70.2%) were evaluated at week 18, and 83
(66.9%) at week 24. Attrition between the study visits was due to
participants refusing to continue study treatment or assessments.
Participants who refused treatment but continued with assessments were
included in the analyses. There was one death by suicide in the study during
the follow-up period. As previously reported (36 (link)), participants who remained in the study for
longer than the median duration were similar to those who were followed for
less than the median duration on all baseline predictors of suicidal events
except income.
Posner K., Brown G.K., Stanley B., Brent D.A., Yershova K.V., Oquendo M.A., Currier G.W., Melvin G.A., Greenhill L., Shen S, & Mann J.J. (2011). The Columbia–Suicide Severity Rating Scale: Initial Validity and Internal Consistency Findings From Three Multisite Studies With Adolescents and Adults. The American journal of psychiatry, 168(12), 1266-1277.
Publication 2011
Adolescent Males Mental Health Patients Suicide Attempt Tooth Attrition Visually Impaired Persons Woman

Most recents protocols related to «Adolescent»

1
Unipolar Depression Treatment Cohort Study
We aim to establish a cohort of 800 patients referred to the Danish treatment packages for unipolar first-episode, non-psychotic depression during 2021–2025. We recruit patients from all six clinics in the region. Each clinic receives approximately 100–250 treatment referrals yearly, and approximately 1100 patients are referred yearly. Approximately 80% of referrals are sent directly to the clinics. Patients are recruited during evaluation at the central diagnostic and referral centre or the first consultation in the clinics. Approximately 88% of referrals result in treatment package initiation.
During 2019–2020, 37% of patients were on an antidepressant (usually the selective serotonin reuptake inhibitor (SSRI) Sertraline from their GP) when starting the treatment package, and 54% of patients ended the treatment package on an antidepressant medication. 13% of patients were transferred to a treatment package for a different primary diagnosis group, e.g., generalised, social anxiety, post-traumatic stress disorder, emotionally unstable personality, avoidant personality disorder, eating disorder or obsessive–compulsive disorder. 20% dropped out of treatment. 5% of patients were hospitalised during their treatment package; hospitalization does not preclude the continuation of the treatment package.
The treatment package is a program with manualised psychotherapy in groups of eight patients as the core treatment module together with psychoeducation for the patient and relative (Sup. Table 1). In brief, a treatment package consists of 15–18 h: 2–3 h of initial workup followed by 6 h of individual therapy or 12 sessions of 2 h group therapy (8 patients per group); 1–2 h of engagement and psychoeducation of relatives; 1–5 h of medication clinic; and 2 h of relapse prevention. The program is designed around group-based CBT, but clinics also offer alternatives to CBT, e.g., psychodynamic and schema therapy, and groups for specific demographics, e.g., men or adolescents, and individual therapy. Medication is available as needed.
The research and assessment at baseline for recruited participants is conducted at the Neurobiology Research Unit (NRU) at the Copenhagen University Hospital Rigshospitalet and followed by clinicians from the Mental Health Centre Copenhagen who are not involved in the patient's treatment.
Jensen K.H., Dam V.H., Ganz M., Fisher P.M., Ip C.T., Sankar A., Marstrand-Joergensen M.R., Ozenne B., Osler M., Penninx B.W., Pinborg L.H., Frokjaer V.G., Knudsen G.M, & Jørgensen M.B. (2023). Deep phenotyping towards precision psychiatry of first-episode depression — the Brain Drugs-Depression cohort. BMC Psychiatry, 23, 151.
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Publication 2023
Adolescent Antidepressive Agents Avoidant Personality Disorder Diagnosis Differential Diagnosis Eating Disorders Group Therapy Hospitalization Mental Disorders Mental Health Obsessive-Compulsive Disorder Patients Pharmaceutical Preparations Post-Traumatic Stress Disorder Psychotherapy Relapse Prevention Schema Therapy Selective Serotonin Reuptake Inhibitors Sertraline Social Anxiety
2
Dissociative Symptoms in Borderline Personality Disorder
Between-group differences in demographic variables and predictor variables were assessed using Student’s t-test for continuous variables and Pearson’s chi-square for binary variables. Raw DES data was log transformed. Between-group comparisons concerning continuous dissociative symptomatology were conducted using linear regression and between-group comparisons concerning binary categories using logistic regression.
Analyses pertaining to comparisons of adolescents and adults with BPD were conducted controlling for sex and race. Linear regression analyses were used in the predictor analyses. All bivariate predictors with a p-level of < 0.05 were then analyzed in a multivariate model using backwards deletion to attain the most parsimonious model. The significance level for this model was set at < 0.01.
Zanarini M.C., Martinho E J.r., Temes C.M., Glass I.V., Aguirre B.A., Goodman M, & Fitzmaurice G.M. (2023). Dissociative experiences of adolescents with borderline personality disorder: description and prediction. Borderline Personality Disorder and Emotion Dysregulation, 10, 9.
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Publication 2023
Adolescent Adult Deletion Mutation Student
3
Prenatal E-cig Exposure Effects on Locomotion
OFT was performed to evaluate the locomotor activity of the prenatally e-cig exposed or control mice both male and female at PD45 (adolescent) and PD90 (adult) following our previously published study [47 (link), 48 (link)]. Versamax software (Accuscan Instruments., Columbus, OH) was used to automatically calculate the total distance traveled by the mice. Briefly, mice were introduced to 16″ × 16″ unobstructed glass chamber and their activities were monitored and recorded for 1 h. The first 10 min of 1 h was excluded as the acclimatization period. All experiments were performed between 8 and 10 am. Fecal boli was counted for each mouse after completing of the OFT to measure stress/anxiety level following published literatures [49 (link), 50 (link)].
Archie S.R., Sifat A.E., Zhang Y., Villalba H., Sharma S., Nozohouri S, & Abbruscato T.J. (2023). Maternal e-cigarette use can disrupt postnatal blood-brain barrier (BBB) integrity and deteriorates motor, learning and memory function: influence of sex and age. Fluids and Barriers of the CNS, 20, 17.
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Publication 2023
Acclimatization Adolescent Adult Anxiety Feces Females Locomotion Males Mus
4
Socioeconomic Status and Food Insecurity Survey
Face-to-face and door-to-door interviews were conducted with household heads after completing informed consent forms. Some general information within each household was collected, such as the head of the household’s age, education level (illiterate, primary, secondary, or high school, diploma, and college), employment status (unemployed, employed, self-employed, pensioner), the number of family members. Their socioeconomic status was calculated considering possession of 9 specific items, including home, personal vehicle, washing machine, LCD TV, dishwasher, refrigerator, handmade rug, laptop, and microwave. Based on the number of items possessed by households, the socioeconomic status was categorized into three groups, low (3 items or less), moderate (4 to 6 items), and high (more than 7 items) [25 ]. In addition, they were asked whether they had chronic diseases (at least one of the non-communicable diseases, such as diabetes, cardiovascular disease, kidney disease, and cancer), a vulnerable group member in the household (child under 6, adolescent, disabled member, pregnant, handicapped, and elderly), receive financial help from the charity, the portion of income allocated to food purchase, covid-19-induced poverty (including job loss, reduced income, and reduced food purchase), and marital status. The heads of households completed the validated HFIAS (Household Food Insecurity Access Scale) questionnaire to assess food insecurity [26 (link)]. The FAO Indicator Guide was used to score a nine-item HFIAS questionnaire [27 ]. The results were categorized into mild/moderate and severe to make the results more understandable and more appropriate for interventions for policymakers.
Joulaei H., Keshani P., Foroozanfar Z., Afrashteh S., Hosseinkhani Z., Mohsenpour M.A., Moghimi G, & Homayouni Meymandi A. (2023). Food insecurity status and its contributing factors in slums’ dwellers of southwest Iran, 2021: a cross-sectional study. Archives of Public Health, 81, 38.
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Publication 2023
Adolescent Aged Cardiovascular Diseases Child COVID 19 Diabetes Mellitus Disease, Chronic Face Family Member Food Head of Household Households Kidney Diseases Malignant Neoplasms Microwaves Noncommunicable Diseases
5
Longitudinal Neuroimaging Study of Autism Spectrum Disorder
Sixty-three children and adolescents (38 ASD, 25 TD, 7–14 years) were recruited for this neuroimaging study at the Hospital for Sick Children (SickKids), between 2011 and 2013 (Vogan et al., 2019 (link)). All participants were invited back two years later (9–16 years) for a follow-up study. Of the 63 participants, 18 (12 ASD, 7 TD) did not return for the follow-up study due to relocation, declined to participate, had contraindications for MEG (e.g., braces), or were lost to follow-up. MEG data from 13 additional participants (10 ASD and 3 TD) were excluded from analyses due to a) sex matching; b) <20 clean MEG trials; and c) <55% task accuracy. Thus, the final sample consisted of 64 datasets from 17 children with ASD and 15 age- and sex-matched TD controls. The final sample differed slightly for the 2-back memory load condition due to increased task difficulty (58 datasets: 15 ASD, 14 TD). Importantly, as previously reported by Vogan et al. (2019) (link), the participants that returned at follow-up did not significantly differ from those who did not return in terms of age, sex, and IQ. The study protocol was approved by the Research Ethics Board at SickKids. Written informed consent was obtained by a parent or legal guardian, and informed verbal assent was provided by all children. For TD controls, exclusion criteria included a diagnosis of a learning, language or neurodevelopmental disorder; for both groups exclusion criteria also included history of prematurity, severe neurological damage, uncorrected visual impairment or colour blindness and IQ < 70. For children in the ASD group, a primary diagnosis of ASD was confirmed by the Autism Diagnostic Observation Schedule-Second Edition (ADOS-2; Lord et al., 2012 ) by expert clinicians. A summary of the demographic characteristics is shown in Table 1.

Participant demographics.

Time pointASD (n = 17)TD (n = 15)Significance test
M (SD) or countM (SD) or count
Sex (M:F)Baseline15:28:7p = 0.05†
Age (years)BaselineFollow-up11.13 (1.83)13.50 (1.58)10.69 (2.32)12.91 (2.29)t(30) = 0.59, p = 0.56t(30) = 0.85, p = 0.40
ADOS-2BaselineFollow-up6.29 (2.05)7.13 (2.28)––

†A Fisher’s exact test was used to test for differences in the proportion of boys and girls between-groups.

Full-scale IQ (FSIQ) was measured using the two sub-test version of the Wechsler Abbreviated Scale of Intelligence (WASI; Wechsler, 2013 (link)) for all children at both time points. FSIQ scores were estimated based on performance on the Vocabulary and Matrix reasoning sub-tests. To assess working memory ability, two sub-tests of the Working Memory Test Battery for Children (WMTB-C) (Gathercole & Pickering, 2000 (link)) were administered (Digit Recall and Block Recall). Parents also completed questionnaires on executive function abilities and social impairment using the Behavior Rating Inventory of Executive Function (BRIEF; Gioia et al., 2000 ) and the Social Responsiveness Scale, Second Edition (SRS-2; Constantino, 2012 ), respectively.
Sato J., Safar K., Vogan V.M, & Taylor M.J. (2023). Functional connectivity changes during working memory in autism spectrum disorder: A two-year longitudinal MEG study. NeuroImage : Clinical, 37, 103364.
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Publication 2023
Adenosine Adolescent Autistic Disorder Blindness, Color Boys Braces Cardiac Arrest Child Diagnosis Executive Function Fingers Intelligence Tests Legal Guardians Low Vision Memory, Short-Term Memory Disorders Neurodevelopmental Disorders Parent Premature Birth Trauma, Nervous System Woman

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More about "Adolescent"

Adolescence is a critical period of human development, marking the transition from childhood to adulthood.
This dynamic stage, typically ranging from 10 to 19 years of age, is characterized by significant physical, cognitive, emotional, and social changes.
Adolescents undergo rapid growth, hormonal shifts, and the development of a stronger sense of identity and independence.
This unique phase presents both challenges and opportunities for researchers and clinicians focused on adolescent health and well-being.
Understanding the complexities of adolescent development is essential for designing effective interventions, promoting positive outcomes, and fostering successful transitions into adulthood.
The PubCompare.ai platform can enhance reproducibility and accuracy in adolescent research by providing access to a wealth of relevant protocols, preprints, and patents, while leveraging AI-driven comparisons to identify the most suitable approaches for adolescent-focused studies.
Researchers can utilize PubCompare.ai to locate protocols from literature, preprints, and patents, and leverage the platform's AI-driven comparisons to identify the best protocols and products for their adolescent-focused studies.
This optimization of the research workflow can be particularly useful for studies involving adolescent populations, where understanding the unique developmental characteristics is crucial.
Beyond PubCompare.ai, researchers may also find value in statistical software like SAS version 9.4, SPSS version 22.0, and Stata 15, which can provide powerful analytical tools for adolescent research.
Additionally, the C57BL/6J mouse model is often used in adolescent-related studies to investigate physiological and behavioral changes during this transitional period.
By incorporating these resources and techniques, researchers can enhance the reproducibility and accuracy of their adolescent-focused studies, ultimately contributing to a better understanding of this crucial stage of human development.