While stress in the environment alters sperm function and fertility, the underlying biology is not understood. Our prior studies showed that current stress could significantly change the composition and levels of sperm small noncoding (sncRNA) and impact sperm motility in men. However, the additional influence of adversity experienced earlier in life, designated as adverse childhood experiences (ACE), on sperm sncRNA dynamics remains unclear. To expand our understanding of the important relationship between ACE and stress, we recruited a longitudinal human cohort and modeled differential sncRNA and co-expression networks associated with perceived stress and ACE. Utilizing a repeated measures design, we were able to identify significant time-dependent interactions between ACE and perceived stress that resulted in differential sncRNA expression. Furthermore, network analyses revealed ACE and perceived stress coordinated sncRNA across subtypes, including miRNA that target genes important for offspring development. As these changes were identified widely across sncRNA subtypes, our findings reflect a novel and exciting synchronization strategy by which current and past experiences can impact the next generation. Evolutionarily, this coordination would preserve sncRNA modules essential for species survival while allowing refinement around developmental pathways important for intergenerational fitness.

The regulation of gene expression by microRNAs (miRNAs) is essential during development. However, it is necessary to move beyond individual miRNAs to understand their functions and the intricate interplay as a coordinated system. To address this, we developed and implemented a network approach named miRNA-gene synergic co-regulatory networks. This method builds networks identifying pairs of genes jointly regulated (co-regulation) by multiple miRNAs (synergism). Using these networks, focusing on placental genes, we evaluated the coordinated regulation of miRNAs from different origins (maternal circulating extracellular vesicles (EVs) or placental tissue) and at different times (increased expression in the first or third trimester of pregnancy) using both public and original data. We observed that synergic co-regulatory networks displayed properties compatible with scale-free topology. When comparing the co-regulation networks produced by miRNAs differentially expressed at distinct gestational periods, we identified that miRNAs from the maternal circulation were more coordinated at the first trimester, while the miRNAs from the placental tissue were more coordinated at the third trimester. Regarding maternal circulating miRNAs in the first trimester, we identified a co-expression module that was negatively associated with birth weight. This module, together with other miRNAs expressed at higher levels in the first trimester, regulates transforming growth factor beta (TGF-β) signaling pathways that are important for uteroplacental circulatory development. Overall, the results of this study reinforce the idea that miRNAs, whether associated with EVs in maternal circulation or derived from placental tissue, constitute a regulatory system with dynamic coordination throughout pregnancy.

Abstract Prenatal stress has been implicated in alterations of maternal hypothalamic–pituitary–adrenal (HPA) axis functioning, yet evidence linking psychosocial stressors to long-term cortisol biology remains inconsistent. Hair cortisol concentration (HCC) provides an integrated measure of cumulative glucocorticoid exposure, allowing investigation of whether diverse prenatal stressors shape maternal cortisol trajectories during pregnancy and postpartum. We analyzed data from a prospective population-based birth cohort from São Paulo, Brazil. Pregnant women (n = 185) were assessed in the third trimester for exposure to intimate partner violence (IPV), emotional responses to pregnancy, and anxiety or depression using standardized instruments (WHO-VAW, MINI). Maternal HCC was quantified from 7 cm hair segments representing the last four months of pregnancy and the first two postpartum months. We compared cortisol levels across exposure groups using t-tests and Mann–Whitney tests, and estimated longitudinal associations with linear mixed-effects models. Cortisol levels declined significantly from pregnancy to postpartum (p < 0.001). Most prenatal stressors—including psychological IPV, anxiety, depression, and negative emotional responses to pregnancy—were not associated with HCC at individual time points nor in longitudinal models. Acute prenatal physical/sexual IPV was associated with lower HCC at specific prepartum time points and with lower mean HCC in longitudinal analyses. In this cohort, maternal cortisol biology showed limited sensitivity to most psychosocial stressors during pregnancy. Only acute physical or sexual IPV was associated with reduced cumulative cortisol levels. These findings highlight the specificity of severe interpersonal violence as a biological stressor and underscore the complexity of interpreting cortisol as a biomarker of prenatal stress.

This paper reports the methods and preliminary findings of Germina, an ongoing cohort study to identify biomarkers and trajectories of executive functions and language development in the first 3 years of life. 557 mother-infant dyads (mean age of mothers 33.7 years, 65.2% white, 48.7% male infants) have undergone baseline and are currently collecting data for other timepoints. A linear regression was used to predict baseline Bayley-III using scores derived from data-driven sparse partial least squares utilizing a multiple holdout framework of 15 domains. Significant associations were found between socioeconomic/demographic characteristics (B = 0.29), epigenetics (B = 0.11), EEG theta (B = 0.14) and beta activity (B = 0.11), and microbiome functional pathways (B = 0.08) domains, and infant development measured by the Bayley-III at T1, suggesting potential interventions to prevent impairments.

O diagnóstico do Transtorno do Espectro Autista (TEA) requer a atuação de profissionais de saúde altamente treinados, o que limita o acesso ao diagnóstico. O diagnóstico assistido por computador utilizando biomarcadores pode ser uma alternativa para tornar o diagnóstico mais acessível. No entanto, a disponibilidade de bases de dados públicas para apoiar o desenvolvimento de sistemas CAD ainda é um desafio. Este estudo apresenta um algoritmo projetado para identificar falhas na qualidade dos dados em um banco de dados para diagnóstico de TEA, como registros duplicados e dados ausentes, na plataforma Research Electronic Data Capture. A ferramenta automatiza a detecção de erros e gera relatórios estruturados para auxiliar profissionais de saúde na correção dos dados. Uma avaliação qualitativa confirmou sua utilidade e indicou que o tempo necessário para identificar erros pode ser reduzido em cerca de 15 vezes, contribuindo para minimizar o esforço necessário para manter uma base de dados consistente.

Placental exosomes are released into maternal circulation as key mediators of maternal-fetal crosstalk and act as biomarkers of pregnancy adversities, also influencing the regulation of maternal cytokine levels. However, their role in neurodevelopmental outcomes remains poorly explored. Here we analyzed the association between maternal cytokines and placental exosomes with neurodevelopmental outcomes in a Brazilian sample of socially vulnerable pregnant adolescents. Maternal cytokines were measured in 60 plasma samples using the Luminex technology, and placental exosomes isolated from the maternal circulation were analyzed by flow cytometry. Infant neurodevelopmental outcomes at 12 months were assessed using the Bayley Scales of Infant and Toddler Development-III (BSID-III). Multiple linear regression models were applied to investigate the proportion of exosomes (PLAP+ to CD63 + exosomes ratio) and cytokines as predictors of BSID-III. We found a negative association between exosome ratio and infant sex, with the BSID-III cognitive domain. No association was found for the exosomes with BSID-III language and motor domains. Considering the immunomodulatory components when cytokines IL-10 and IL-6 were included in the BSID-III cognition model, a significant association was observed, being positive for IL-10 and negative for IL-6. Finally, when both cytokines IFN-γ and IL-1β were tested with BSID-III cognitive domain, we observed a negative association for IFN-γ and positive association for IL-1β. In both models, exosome ratio, infant sex, and the interaction effect of infant sex on exosome ratio remained significant. Our results underscore the potential contribution of placental exosomes and maternal inflammatory states to the infants’ neurocognitive outcomes.

ABSTRACT Introduction Interpersonal violence against women is a major global health problem that may have intergenerational effects. This study investigated associations between maternal experiences of interpersonal violence and other traumatic events and maternal and infant salivary diurnal cortisol in a cohort of adolescent mothers in São Paulo, Brazil. Method Adolescent mothers (14–19 years) participating in a home‐visiting intervention were interviewed retrospectively about lifetime and pregnancy violence and trauma exposure. Mothers collected saliva at waking and before bedtime from themselves ( n = 23) and their infants ( n = 32) at 12 months postpartum. Multivariable regression models were used to examine associations between trauma history variables and salivary diurnal cortisol. Results Adjusting for the intervention group, infant sex, maternal age, non‐supplement medication use, and sample collection time, we found that higher‐than‐average lifetime trauma exposure was associated with maternal evening cortisol ( b = 0.472, p ‐value = 0.028). Lifetime assaultive violence exposure was also associated with maternal evening cortisol ( b = 0.196, p ‐value = 0.02). Maternal exposure to traumatic events in pregnancy was positively associated with bedtime cortisol levels of infants ( b = 0.21, p = 0.01). Trauma variables were not associated with maternal or infant morning cortisol levels. Conclusion Results suggest that maternal trauma history influences both maternal and infant postnatal cortisol regulation as indexed by evening cortisol levels. These results are consistent with models of fetal programming; however, future studies should investigate potential postnatal psychobiological pathways. Lifetime trauma exposure may also become embedded in the maternal hypothalamic–adrenal–pituitary axis regulation. Future studies are needed to consider other biological pathways in the intergenerational transmission of trauma.

We aimed to develop and validate a standardized, qualitative-quantitative protocol for digital IHC analysis to assess neurodevelopmental biomarkers in placental tissue. Placental tissues from 60 births were obtained from the Western Region Birth Cohort (ROC), and IHC staining was performed using NovolinkTM Polymer System. The primary antibody against 11βHSD2 protein was used for protocol development, and ANXA1 was employed for validation. Slides were digitized using the Aperio ScanScope XT, and image analysis was conducted using the Positive Pixel Count V9 algorithm. Protein expression levels were calculated using the IHC Index formula. Protocol steps included combined optical and digital evaluation, representative fields per slide, intra- and interobserver validation, and assessment of reproducibility. Digital analysis of three random fields (scale bar: 300 µm) showed strong concordance with optical microscopy assessments for 11βHSD2 placental expression. Intraobserver validation showed a strong correlation (τ: 0.70, P < .001) and a substantial concordance (kw: 0.67; P-value < .001), while interobserver comparisons also yielded substantial agreement (kw: 0.61, P < .001), confirming the protocol's reliability. Validation using ANXA1 expression revealed moderate intra- and interobserver concordance (kw: 0.50 and kw: 0.48, respectively; both P < .001), reinforcing the protocol's applicability across different proteins. In conclusion, we established a reproducible digital IHC analysis protocol that enhances reliability in exploratory research. This approach optimizes image quantification, minimizes observer bias, and contributes to advances in developmental biology research and digital pathology focused on placental neurodevelopment biomarkers.

Obsessive-compulsive disorder (OCD) affects ~1% of children and adults and is partly caused by genetic factors. We conducted a genome-wide association study (GWAS) meta-analysis combining 53,660 OCD cases and 2,044,417 controls and identified 30 independent genome-wide significant loci. Gene-based approaches identified 249 potential effector genes for OCD, with 25 of these classified as the most likely causal candidates, including WDR6, DALRD3 and CTNND1 and multiple genes in the major histocompatibility complex (MHC) region. We estimated that ~11,500 genetic variants explained 90% of OCD genetic heritability. OCD genetic risk was associated with excitatory neurons in the hippocampus and the cortex, along with D1 and D2 type dopamine receptor-containing medium spiny neurons. OCD genetic risk was shared with 65 of 112 additional phenotypes, including all the psychiatric disorders we examined. In particular, OCD shared genetic risk with anxiety, depression, anorexia nervosa and Tourette syndrome and was negatively associated with inflammatory bowel diseases, educational attainment and body mass index.

Negative affect (NA) is a central dimension of infant temperament and an early marker of risk for later psychopathology. While maternal mental health has been associated with increased infant NA, few studies have explored how maternal mental health symptoms relate to the specific subdomains of NA throughout infancy. This study examined longitudinal associations between maternal mental health and infant NA, comparing the general domain with its specific subdomains. We analyzed data from 557 mother-infant dyads enrolled in the Germina cohort in São Paulo, Brazil. Maternal symptoms of depression, anxiety, and stress, along with infant NA and its subdomains-sadness, fear, distress to limitations, and falling reactivity-were assessed at 3, 5-9, and 10-16 months postpartum. Longitudinal associations were examined using linear mixed-effects models with successive-differences contrasts, adjusting for sociodemographic covariates. Maternal stress consistently predicted higher NA and its subdomains-sadness, fear, and distress-across infancy, and was linked to reduced falling reactivity. Depression was associated with increased NA, distress, and decreased reactivity throughout infancy. Anxiety exhibited a time-varying association with distress, increasing from 3 to 9 months before declining, but showed no link with overall NA. Subdomain-specific analyses uncovered maternal mental health associations not evident in general NA models. Examining NA subdomains provides a more detailed understanding of their evolving, dynamic relationships with maternal mental health across infancy. These insights highlight the importance of integrating NA subdomains into screening and intervention strategies to more effectively support at-risk children.

Early identification of autism spectrum disorder through cost-effective screening is crucial in low- and middle-income countries. The Child Behavior Checklist 1.5-5, using the Autism Spectrum Problems and Withdrawn Syndrome subscales, has potential as a level 1 autism spectrum disorder screening tool, though its construct validity in low- and middle-income countries remains underexplored. We aimed to validate the Child Behavior Checklist 1.5-5 for autism spectrum disorder screening in a representative sample of 1292 Brazilian children aged 3-5 years and 70 autism spectrum disorder children aged 1-5 years. Confirmatory factor analysis evaluated model fit indices and correlation strength between Child Behavior Checklist items and autism spectrum disorder diagnoses. Receiver operating characteristic curves assessed the optimal cutoff score. The Autism Spectrum Problems model demonstrated good fit and reliability (comparative fit index = 0.96, root mean square error of approximation = 0.037, ω = 0.869), as did the Withdrawn Syndrome model (comparative fit index = 0.974, root mean square error of approximation = 0.034, ω = 0.776), with one item per model showing low factor loadings. A cutoff score of 6 on the Autism Spectrum Problems yielded 82.5% sensitivity and 83.4% specificity, while a cutoff of 4 on the Withdrawn scale resulted in 87.9% sensitivity and 82.2% specificity. The Child Behavior Checklist 1.5-5 Autism Spectrum Problems and Withdrawn scales are reliable level 1 autism spectrum disorder screeners for Brazilian children, with good internal consistency and construct validity.Lay abstractEarly identification of Autism Spectrum Disorder is very important, especially in low and middle-income countries, where access to resources is often limited. The Child Behavior Checklist 1.5-5 is a tool that has been used to help identify children with autism spectrum disorder through specific behavior patterns. However, its effectiveness in low- and middle-income country settings has not been thoroughly studied. This research focused on evaluating the Child Behavior Checklist 1.5-5 as a screening tool for autism spectrum disorder among Brazilian children. The study involved 1292 children aged 3-5 years from the general population and 70 children with autism spectrum disorder aged 1-5 years. Using advanced statistical methods, the study tested how well the Child Behavior Checklist identified children with autism spectrum disorder and how reliable it was in this context. The findings showed that the Child Behavior Checklist 1.5-5 performed well in identifying autism spectrum disorder, with high reliability and consistency in the results. Although one item in each of the autism spectrum problems and withdrawn syndrome subscales did not perform as strongly, the overall tool was effective. In summary, the Child Behavior Checklist 1.5-5 proves to be a reliable and valid tool for early autism spectrum disorder screening in Brazilian children. This can help ensure that more children in low- and middle-income country settings are identified early and receive the necessary support and interventions to help them thrive. Future research should continue to test this tool in different contexts to confirm its usefulness across various populations.

Social communication skills, especially eye contact and joint attention, are frequently impaired in autism spectrum disorder (ASD) and predict functional outcomes. Applied behavior analysis is one of the most common evidence-based treatments for ASD, but it is not accessible to most families in low- and middle-income countries (LMICs) as it is an expensive and intensive treatment and needs to be delivered by highly specialized professionals. Parental training has emerged as an effective alternative. This is an exploratory study to assess a parental intervention group via video modeling to acquire eye contact and joint attention. Four graded measures of eye contact and joint attention (full physical prompt, partial physical prompt, gestural prompt, and independent) were assessed in 34 children with ASD and intellectual disability (ID). There was a progressive reduction in the level of prompting required over time to acquire eye contact and joint attention, as well as a positive correlation between the time of exposure to the intervention and the acquisition of abilities. This kind of parent training using video modeling to teach eye contact and joint attention skills to children with ASD and ID is a low-cost intervention that can be applied in low-resource settings.

Problem: Diagnosing Autism Spectrum Disorder (ASD) remains a significant challenge, especially in regions where access to specialists is limited. Computer-based approaches offer a promising solution to make diagnosis more accessible. Eye tracking has emerged as a valuable technique in aiding the diagnosis of ASD. Typically, individuals’ gaze patterns are monitored while they view videos designed according to established paradigms. In a previous study, we developed a method to classify individuals as having ASD or Typical Development (TD) by processing eye-tracking data using Random Forest ensembles, with a focus on a paradigm known as joint attention. Aim: This article aims to enhance our previous work by evaluating alternative algorithms and ensemble strategies, with a particular emphasis on the role of anticipation features in diagnosis. Methods: Utilizing stimuli based on joint attention and the concept of “floating regions of interest” from our earlier research, we identified features that indicate gaze anticipation or delay. We then tested seven class balancing strategies, applied seven dimensionality reduction algorithms, and combined them with five different classifier induction algorithms. Finally, we employed the stacking technique to construct an ensemble model. Results: Our findings showed a significant improvement, achieving an F1-score of 95.5%, compared to the 82% F1-score from our previous work, through the use of a heterogeneous stacking meta-classifier composed of diverse induction algorithms. Conclusion: While there remains an opportunity to explore new algorithms and features, the approach proposed in this article has the potential to be applied in clinical practice, contributing to increased accessibility to ASD diagnosis.

ABSTRACTPolygenic risk scores (PRS) for breast cancer (BC) have a clear clinical utility in risk prediction. PRS transferability across populations and ancestry groups is hampered by population-specific factors, ultimately leading to differences in variant effects, such as linkage disequilibrium (LD) and differences in variant frequency (AF-diff). Thus, locally-sourced population-based phenotypic and genomic datasets are essential to assess the validity of PRS derived from signals detected across populations. Here, assess the transferability of a BC PRS composed of 313 risk variants (313-PRS) in two Brazilian tri-hybrid admixed ancestries (European, African and Native American) whole-genome sequenced cohorts. We computed 313-PRS in both cohorts (n=753 and n=853) versus the UK Biobank (UKBB, n=264,307) as reference. We show that although the Brazilian cohorts have a high European (EA) component, with AF-diff and to a lesser extent LD patterns like those found in EA populations, the 313-PRS distribution is inflated when compared to that of the UKBB, leading to potential overestimation of PRS-based risk if EA is taken as a standard. Interestingly, we find that case-controls lead to equivalent predictive power when compared to UKBB-EA samples with AUROC values of 0.66-0.62 compared to 0.63 for UKBB.

Background: Currently, there is a need for approaches to understand and manage the multidimensional autism spectrum and quantify its heterogeneity. The diagnosis is based on behaviors observed in two key dimensions, social communication and repetitive, restricted behaviors, alongside the identification of required support levels. However, it is now recognized that additional modifiers, such as language abilities, IQ, and comorbidities, are essential for a more comprehensive assessment of the complex clinical presentations and clinical trajectories in autistic individuals. Different approaches have been used to identify autism subgroups based on the genetic and clinical heterogeneity, recognizing the importance of autistic behaviors and the assessment of modifiers. While valuable, these methods are limited in their ability to evaluate a specific individual in relation to a normative reference sample of autistic individuals. A quantitative score based on axes of phenotypic variability could be useful to compare individuals, evaluate the homogeneity of subgroups, and follow trajectories of an individual or a specific group. Here we propose an approach by (i) combining measures of phenotype variability that contribute to clinical presentation and could impact different trajectories in autistic persons and (ii) using it with normative modeling to assess the clinical heterogeneity of a specific individual. Methods: Using phenotypic data available in a comprehensive reference sample, the Simons Simplex Collection (n = 2744 individuals), we performed principal component analysis (PCA) to find components of phenotypic variability. Features that contribute to clinical heterogeneity and could impact trajectories in autistic people were assessed by the Autism Diagnostic Interview-Revised (ADI-R), Vineland Adaptive Behavior Scales (VABS) and the Child Behavior Checklist (CBCL). Cognitive assessment was estimated by the Total Intelligence Quotient (IQ). Results: Three PCs embedded 72% of the normative sample variance. PCA-projected dimensions supported normative modeling where a multivariate normal distribution was used to calculate percentiles. A Multidimensional General Functionality Score (MGFS) to evaluate new prospective single subjects was developed based on percentiles. Conclusions: Our approach proposes a basis for comparing individuals, or one individual at two or more times and evaluating homogeneity in phenotypic clinical presentation and possibly guides research sample selection for clinical trials.

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition that combines genetic and environmental factors. The human microbiota is colonized by permanent or transitory microorganisms, depending on the host and the external factors controlling their permanence. The composition of the gut microbiota (GM) in ASD individuals is notably different from that in controls, which may contribute to the clinical conditions observed in these individuals. This study aimed to indirectly investigate the influence of GM on the gut-brain axis in individuals with ASD and controls by analyzing environmental factors that contribute to the microbiota composition. Two questionnaires were designed to collect data, one for the ASD Group (ASDG) and the other one for the Control Group (CG). The raw data from both questionnaires were collected from 2772 respondents. After triage, answers from 1687 ASD individuals, along with 466 respondents from the CG, were analyzed, resulting in a total of 2237 respondents. Our results showed that gastrointestinal problems (GP) escalate as individuals age and become more prominent in ASD individuals. In contrast, feeding problems (FP) did not appear to escalate in either group as individuals aged, even though the FP decreased in the CG. ANOVA revealed significant differences in breastfeeding status compared to GPs among preterm control individuals born via cesarean section (p-value = 0.027). The mean values of GP for breastfed and nonbreastfed individuals, for ASDG (0.257; 0.268) and CG (0.105; 0.248), highlighted the differences in breastfeeding effects on GP for the study groups. The use of antibiotics during pregnancy seemed to be significant for GPs in the ASDG only for breastfed individuals (p-value <0.001), but not in the CG group. In conclusion, variables such as mode of delivery, FPs, type of birth, and length of breastfeeding do not seem to be determining factors for GP in the ASDG but are relevant for the CG. However, for ASDG individuals whose mothers took antibiotics during pregnancy, breastfeeding may act as a protective factor, as maternal antibiotic administration during pregnancy seems to aggravate GP-values across the ages of the participants. Considering GP as a proxy for GM and recognizing the importance of GM composition for central nervous system (CNS) function, it appears that in individuals with ASD, GM seems to be more dependent on other factors, which might be linked to the genetic background of each one. These findings suggest that future studies of the gut-brain axis in individuals with ASD might consider the individual's genetic background, environmental factors, and GM.