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Three-dimensional (3D) culture systems have fueled hopes to bring about the next generation of more physiologically relevant high-throughput screens (HTS). However, current protocols yield either complex but highly heterogeneous aggregates (‘organoids’) or 3D structures with less physiological relevance (‘spheroids’). Here, we present a scalable, HTS-compatible workflow for the automated generation, maintenance, and optical analysis of human midbrain organoids in standard 96-well-plates. The resulting organoids possess a highly homogeneous morphology, size, global gene expression, cellular composition, and structure. They present significant features of the human midbrain and display spontaneous aggregate-wide synchronized neural activity. By automating the entire workflow from generation to analysis, we enhance the intra- and inter-batch reproducibility as demonstrated via RNA sequencing and quantitative whole mount high-content imaging. This allows assessing drug effects at the single-cell level within a complex 3D cell environment in a fully automated HTS workflow. In 1907, the American zoologist Ross Granville Harrison developed the first technique to artificially grow animal cells outside the body in a liquid medium. Cells are still grown in much the same way in modern laboratories: a single layer of cells is placed in a warm incubator with nutrient-rich broth. These cell layers are often used to test new drugs, but they cannot recapitulate the complexity of a real organ made from multiple cell types within a living, breathing human body. Growing three-dimensional miniature organs or 'organoids' that behave in a similar way to real organs is the next step towards creating better platforms for drug screening, but there are several difficulties inherent to this process. For one thing, it is hard to recreate the multitude of cell types that make up an organ. For another, the cells that do grow often fail to connect and communicate with each other in biologically realistic ways. It is also tough to grow a large number of organoids that all behave in the same way, making it hard to know whether a particular drug works or whether it is just being tested on a 'good' organoid. Renner et al. have been able to overcome these issues by using robotic technology to create thousands of identical, mid-brain organoids from human cells in the lab. The robots perform a series of precisely controlled tasks – including dispensing the initial cells into wells, feeding organoids as they grow and testing them at different stages of development. These mini-brains, which are the size of the head of a pin, mimic the part of the brain where Parkinson's disease first manifests. They can be used to test new drugs for Parkinson's, and to better understand the biology of the brain. Perhaps more importantly, other types of organoids can be created using the same technique to model diseases that affect other areas of the brain, or other organs altogether. For example, Renner et al. also generated forebrain organoids using an automated approach for both generation and analysis. This research, which shows that organoids can be grown and tested in a fully automated, reproducible and scalable way, creates a platform to quickly, cheaply and easily test thousands of drugs for Parkinson's and other difficult-to-treat diseases in a human setting. This approach has the potential to reduce research waste by increasing the chances that a drug that works in the lab will also ultimately work in a patient; and reduce animal experiments, as drugs that do not work in human tissues will not proceed to animal testing.

In recent years, 3D cell culture has been gaining a more widespread following across many fields of biology. Tissue clearing enables optical analysis of intact 3D samples and investigation of molecular and structural mechanisms by homogenizing the refractive indices of tissues to make them nearly transparent. Here, we describe and quantify that common clearing solutions including benzyl alcohol/benzyl benzoate (BABB), PEG-associated solvent system (PEGASOS), immunolabeling-enabled imaging of solvent-cleared organs (iDISCO), clear, unobstructed brain/body imaging cocktails and computational analysis (CUBIC), and ScaleS4 alter the emission spectra of Alexa Fluor fluorophores and fluorescent dyes. Clearing modifies not only the emitted light intensity but also alters the absorption and emission peaks, at times to several tens of nanometers. The resulting shifts depend on the interplay of solvent, fluorophore, and the presence of cells. For biological applications, this increases the risk for unexpected channel crosstalk, as filter sets are usually not optimized for altered fluorophore emission spectra in clearing solutions. This becomes especially problematic in high throughput/high content campaigns, which often rely on multiband excitation to increase acquisition speed. Consequently, researchers relying on clearing in quantitative multiband excitation experiments should crosscheck their fluorescent signal after clearing in order to inform the proper selection of filter sets and fluorophores for analysis.

Toxicity testing is a crucial step in the development and approval of chemical compounds for human contact and consumption. However, existing model systems often fall short in their prediction of human toxicity in vivo because they may not sufficiently recapitulate human physiology. The complexity of three-dimensional (3D) human organ-like cell culture systems (“organoids”) can generate potentially more relevant models of human physiology and disease, including toxicity predictions. However, so far, the inherent biological heterogeneity and cumbersome generation and analysis of organoids has rendered efficient, unbiased, high throughput evaluation of toxic effects in these systems challenging. Recent advances in both standardization and quantitative fluorescent imaging enabled us to dissect the toxicities of compound exposure to separate cellular subpopulations within human organoids at the single-cell level in a framework that is compatible with high throughput approaches. Screening a library of 84 compounds in standardized human automated midbrain organoids (AMOs) generated from two independent cell lines correctly recognized known nigrostriatal toxicants. This approach further identified the flame retardant 3,3′,5,5′-tetrabromobisphenol A (TBBPA) as a selective toxicant for dopaminergic neurons in the context of human midbrain-like tissues for the first time. Results were verified with high reproducibility in more detailed dose-response experiments. Further, we demonstrate higher sensitivity in 3D AMOs than in 2D cultures to the known neurotoxic effects of the pesticide lindane. Overall, the automated nature of our workflow is freely scalable and demonstrates the feasibility of quantitatively assessing cell-type-specific toxicity in human organoids in vitro .

Urban-living individuals are exposed to many environmental factors that may combine and interact to influence mental health. While individual factors of an urban environment have been investigated in isolation, no attempt has been made to model how complex, real-life exposure to living in the city relates to brain and mental health, and how this is moderated by genetic factors. Using the data of 156,075 participants from the UK Biobank, we carried out sparse canonical correlation analyses to investigate the relationships between urban environments and psychiatric symptoms. We found an environmental profile of social deprivation, air pollution, street network and urban land-use density that was positively correlated with an affective symptom group ( r  = 0.22, P perm  < 0.001), mediated by brain volume differences consistent with reward processing, and moderated by genes enriched for stress response, including CRHR1 , explaining 2.01% of the variance in brain volume differences. Protective factors such as greenness and generous destination accessibility were negatively correlated with an anxiety symptom group ( r  = 0.10, P perm  < 0.001), mediated by brain regions necessary for emotion regulation and moderated by EXD3 , explaining 1.65% of the variance. The third urban environmental profile was correlated with an emotional instability symptom group ( r  = 0.03, P perm  < 0.001). Our findings suggest that different environmental profiles of urban living may influence specific psychiatric symptom groups through distinct neurobiological pathways. Analyses of data from the UK Biobank reveal different urban living environments that are associated with affective, anxiety and emotional instability symptom groups and mediated by distinct neurological and genetic pathways in adults.

Background The aim of this study was to develop and psychometrically evaluate a parent-rated parenting assessment scale including positive and negative dimensions of parenting. Factorial validity, reliability, measurement invariance, latent mean differences and construct validity of the Assessment Scale of Positive and Negative Parenting Behavior (FPNE) were tested in a pooled sample of five studies of 1,879 school-aged children (6.00 to 12.11 years). Methods Exploratory factor analysis (EFA) was performed on a first randomized split-half sample, and confirmatory factor analysis (CFA) and exploratory structural equation modeling (ESEM) were conducted in the second half of the sample. Measurement invariance tests were conducted to assess factor structure equivalence across gender and age. Results The EFA results supported a two-factor structure and the CFA results revealed a model with two correlated factors (Positive Parenting, Negative Parenting), which included 23 items and showed acceptable model fit and good psychometric properties. ESEM did not yield a model with significantly better model fit. Internal consistencies were acceptable. Adequate concurrent validity was demonstrated by low to moderate correlations between the FPNE and similar constructs. The factor structure was invariant (configural, metric, scalar) across different age groups and gender. Tests of latent mean differences revealed that older children scored significantly higher on negative parenting than younger children, while boys showed lower levels of positive parenting and higher levels of negative parenting compared to girls. All effect sizes were small. Conclusions The results suggest that the FPNE is a reliable and valid instrument for the assessment of parenting.

To enhance the understanding of how symptoms of attention-deficit/hyperactivity disorder (ADHD) and disruptive behavior disorders such as oppositional defiant disorder (ODD), conduct disorder (CD), including callous-unemotional (CU) traits, differentially relate to functional impairment (FI). Participants were 474 German school-age children (age: M = 8.90, SD = 1.49, 81% male) registered for participation in the ESCAschool trial (ESCAschool: Evidence-based, Stepped Care of ADHD in school-aged children). Clinicians assessed the severity of individual symptoms and five FI domains specifically associated with ADHD symptoms or ODD/CD/CU symptoms using a semi-structured clinical interview. We conducted two multiple linear regression analyses, combined with relative importance analyses, to determine the impact of individual symptoms on global FI associated with ADHD and ODD/CD/CU symptoms. Next, we estimated two networks and identified the strongest associations of ADHD symptoms or ODD/CD/CU symptoms with the five FI domains. Symptoms varied substantially in their associations with global FI. The ADHD symptom Easily Distracted (15%) and ODD symptom Argues with Adults (10%) contributed most strongly to the total explained variance. FI related to academic performance, home life and family members, and psychological strain were most strongly associated with ADHD inattention symptoms, whereas FI related to relationships with adults and relationships with children and recreational activities were most strongly associated with hyperactivity-impulsivity symptoms. By comparison, the ODD/CD/CU symptoms most closely linked to FI domains originated from the ODD and CD dimensions. Our findings contribute to a growing body of literature on the importance of analyzing individual symptoms and highlight that symptom-based approaches can be clinically useful.

To make progress in science, we often build abstract representations of physical systems that meaningfully encode information about the systems. Such representations ignore redundant features and treat parameters such as velocity and position separately because they can be useful for making statements about different experimental settings. Here, we capture this notion by formally defining the concept of operationally meaningful representations. We present an autoencoder architecture with attention mechanism that can generate such representations and demonstrate it on examples involving both classical and quantum physics. For instance, our architecture finds a compact representation of an arbitrary two-qubit system that separates local parameters from parameters describing quantum correlations.

IntroductionPsychosis, characterised by chronic symptoms often emerging in youth, imposes a substantial burden on individuals and healthcare systems. While early detection and intervention can mitigate this burden, there is limited evidence on the cost-effectiveness of such approaches. To address this lack of evidence, this study protocol outlines the health economic implications of an artificial intelligence (AI)-based intervention, the Computer-Assisted Risk-Evaluation (CARE), designed to prevent psychosis. The intervention uses AI technologies to enhance the diagnosis and treatment quality for individuals at high risk of psychosis.Methods and analysisThe health economic evaluation has been designed alongside a 12-month multicentre randomised controlled trial comparing CARE with treatment as usual from both payer and societal perspectives. An implementation cost analysis will complement the evaluation, and long-term consequences beyond the trial will be explored descriptively. Based on a literature review, an initial economic logic model will guide subsequent analyses by depicting CARE’s programme theory.The cost-effectiveness assessment will include averted cases of manifest psychosis and quality-adjusted life-years using the EuroQol 5-Dimensions 3-Level instrument. Other effectiveness outcomes will also be incorporated into a cost–consequence analysis. Cost-effectiveness acceptability curves reflecting statistical uncertainty will be constructed, incorporating various payer and societal willingness-to-pay values. The implementation cost analysis will follow a mixed-methods approach to capture facility-specific costs.A dark logic model, emphasising negative outcomes, will be developed to investigate long-term consequences. Further, the initial economic logic model will be refined using trial data and expert interviews. This comprehensive approach aims to provide decision-makers not only with evidence on the cost-effectiveness of CARE, but also with a broader understanding of the implications of the intervention.Ethics and disseminationThe study has received ethical approval and plans to disseminate its findings through publication in a peer-reviewed journal and conference presentations.Trial registration numberNCT05813080.

Electroencephalography (EEG) represents a widely established method for assessing altered and typically developing brain function. However, systematic studies on EEG data quality, its correlates, and consequences are scarce. To address this research gap, the current study focused on the percentage of artifact-free segments after standard EEG pre-processing as a data quality index. We analyzed participant-related and methodological influences, and validity by replicating landmark EEG effects. Further, effects of data quality on spectral power analyses beyond participant-related characteristics were explored. EEG data from a multicenter ADHD-cohort (age range 6 to 45 years), and a non-ADHD school-age control group were analyzed (ntotal = 305). Resting-state data during eyes open, and eyes closed conditions, and task-related data during a cued Continuous Performance Task (CPT) were collected. After pre-processing, general linear models, and stepwise regression models were fitted to the data. We found that EEG data quality was strongly related to demographic characteristics, but not to methodological factors. We were able to replicate maturational, task, and ADHD effects reported in the EEG literature, establishing a link with EEG-landmark effects. Furthermore, we showed that poor data quality significantly increases spectral power beyond effects of maturation and symptom severity. Taken together, the current results indicate that with a careful design and systematic quality control, informative large-scale multicenter trials characterizing neurophysiological mechanisms in neurodevelopmental disorders across the lifespan are feasible. Nevertheless, results are restricted to the limitations reported. Future work will clarify predictive value.

Although numerous studies over the past decade have highlighted the influence of environmental factors on mental health, globally applicable data on physical surroundings such as land cover and urbanicity are still limited. The urban environment is complex and composed of many interacting factors. To understand how urban living affects mental health, simultaneous measures of multiple environmental factors need to be related to symptoms of mental illness, while considering the underlying brain structure and function. So far, most studies have assessed individual urban environmental factors, such as greenness, in isolation and related them to individual symptoms of mental illness. We have refined the satellite-based ‘Urban Satellite’ (UrbanSat) measures, consisting of 11 satellite-data-derived environmental indicators, and linked them through residential addresses with participants of the Adolescent Brain Cognitive Development (ABCD) Study. The ABCD Study is the largest ongoing longitudinal and observational study exploring brain development and child health, involving 11,800 children, assessed at 9–16 years of age, from 21 sites across the USA. Here we describe linking of the ABCD Study data with UrbanSat variables, including each subject’s residential address at their baseline visit, including land cover and land use, nighttime lights and population characteristics. We also highlight and discuss important links of the satellite-data variables to the default mode network clustering coefficient and cognition. This comprehensive dataset provides an important tool for advancing neurobehavioral research on urbanicity during the critical developmental periods of childhood and adolescence. In this Perspective, the authors present a model of assessing urban environmental factors’ impact on mental health by using UrbanSat measures and data from adolescents in the ABCD Study.