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Animal studies are unavoidable in evaluating chemical and drug safety. Generative Adversarial Networks (GANs) can generate synthetic animal data by learning from the legacy animal study results, thus may serve as an alternative approach to assess untested chemicals. AnimalGAN, a GAN method to simulate 38 rat clinical pathology measures, was developed with significant robustness even for the drugs that vary significantly from these used during training, both in terms of chemical structure, drug class, and the year of FDA approval. AnimalGAN showed comparable results in hepatotoxicity assessment as using the real animal data and outperformed 12 conventional quantitative structure-activity relationship approaches. Using AnimalGAN, a virtual experiment of 100,000 rats ranked hepatotoxicity of three structurally similar drugs in a similar trend that has been observed in human population. AnimalGAN represented a significant step with artificial intelligence towards the global effort in replacement, reduction, and refinement (3Rs) of animal use. Generative AI has the potential to transform the way chemical and drug safety research is conducted. Here the authors show AnimalGAN, a model developed using Generative Adversarial Networks, which simulates virtual animal experiments to generate multidimensional rat clinical pathology measurements.

Structural variants (SVs) are a major source of human genetic diversity and have been associated with different diseases and phenotypes. The detection of SVs is difficult, and a diverse range of detection methods and data analysis protocols has been developed. This difficulty and diversity make the detection of SVs for clinical applications challenging and requires a framework to ensure accuracy and reproducibility. Here, we discuss current developments in the diagnosis of SVs and propose a roadmap for the accurate and reproducible detection of SVs that includes case studies provided from the FDA-led SEquencing Quality Control Phase II (SEQC-II) and other consortium efforts.

Many children and adolescents experiencing mental health problems do not receive appropriate care. Strategies to encourage appropriate access to services might be improved by a more detailed understanding of service use determinants within this group. In view of caregivers' key role in young people's pathways to care, this study aimed to advance understanding of caregiver-related characteristics that influence service use among young people. We interviewed 407 primary caregivers of young people aged 9-18 years, recruited from a Greater London (United Kingdom) community sample. Caregivers reported on young people's service use in health care sector and/or education settings, and caregivers' intended stigmatising behaviours, help-seeking attitudes, and personal service use. Logistic regression analyses examined the relationship between these caregiver characteristics and young people's service use, controlling for young people's clinical and socio-demographic factors. Caregivers' intended stigmatising behaviours in particular exerted a strong influence on young people's service use within each service setting. The impact of this characteristic interacted with caregivers' service use in influencing young people's service use across health care and education settings and health care settings specifically. For young people's service use within education settings, caregivers' intended stigmatising behaviours score had a main effect. This study highlights the key role caregivers' attitudes and experiences hold in young people's service use. The findings indicate that strategies aiming to bridge the gap between young people's service needs and utilisation might be improved by targeting stigma amongst caregivers.

Background Parents’ experiences and their relationship to their baby undergo various changes over the course of the first year. This is particularly the case for mothers, who still tend to take on the primary caregiver role in most families in the UK. Better understanding the changes mothers experience in the first year is of importance given the impact of the parent-infant relationship for children’s socio-emotional development. Methods This qualitative longitudinal study explored first-time mothers’ experience of parenting confidence and relationship with their baby from their third trimester of pregnancy to the end of their babies’ first year of life. This study also examined trajectories of relevant consistent parenting factors: perceived social support, relationship with partner, expectations, and coping mechanisms. The sample consisted of ten first-time expectant mothers from a low-risk community urban sample, all White, the majority married or in committed relationships and with higher education. Participants were interviewed at four time periods (prenatal, 1-, 6-, and 12-months). Results The findings indicate that in this homogenous, low-risk sample, most mothers’ parenting confidence improved with time, as did their relationship with their baby. However, most faced many changes in their experiences where, overall, the first six months after birth were the most challenging with many mothers feeling disconnected or having strong shifts in their views of relationship with their baby, feeling unsure about how to parent, having unmet prenatal expectations, and diminished partner support. Conclusion This study demonstrates the complexity of change at multiple levels, both within individuals and within the 15 months of transitioning to motherhood. Consideration of these changes can help inform maternity services and mental health and social care professionals working with expectant parents and those in early parenthood to improve parenting confidence and mother-infant relationships.

Reactive oxygen species (ROS) are a byproduct of normal metabolism and have roles in cell signaling and homeostasis. Species include oxygen radicals and reactive nonradicals. Mechanisms exist that regulate cellular levels of ROS, as their reactive nature may otherwise cause damage to key cellular components including DNA, protein, and lipid. When the cellular antioxidant capacity is exceeded, oxidative stress can result. Pleiotropic deleterious effects of oxidative stress are observed in numerous disease states and are also implicated in a variety of drug-induced toxicities. In this paper, we examine the nature of ROS-induced damage on key cellular targets of oxidative stress. We also review evidence implicating ROS in clinically relevant, drug-related side effects including doxorubicin-induced cardiac damage, azidothymidine-induced myopathy, and cisplatin-induced ototoxicity.

Drug-induced liver injury (DILI) is one of the most cited reasons for the high drug attrition rate and drug withdrawal from the market. The accumulated large amount of high throughput transcriptomic profiles and advances in deep learning provide an unprecedented opportunity to improve the suboptimal performance of DILI prediction. In this study, we developed an eight-layer Deep Neural Network (DNN) model for DILI prediction using transcriptomic profiles of human cell lines (LINCS L1000 dataset) with the current largest binary DILI annotation data [i.e., DILI severity and toxicity (DILIst)]. The developed models were evaluated by Monte Carlo cross-validation (MCCV), permutation test, and an independent validation (IV) set. The developed DNN model achieved the area under the receiver operating characteristic curve (AUC) of 0.802 and 0.798, and balanced accuracy of 0.741 and 0.721 for training and an IV set, respectively, outperforming the conventional machine learning algorithms, including K -nearest neighbors (KNN), Support Vector Machine (SVM), and Random Forest (RF). Moreover, the developed DNN model provided a more balanced sensitivity of 0.839 and specificity of 0.603. Besides, we found the developed DNN model had a superior predictive performance for oncology drugs. Also, the functional and network analysis of genes driving the predictions revealed their relevance to the underlying mechanisms of DILI. The proposed DNN model could be a promising tool for early detection of DILI potential in the pre-clinical setting.

Background Gene copy number variations (CNVs) contribute to genetic diversity and disease prevalence across populations. Substantial efforts have been made to decipher the relationship between CNVs and pathogenesis but with limited success. Results We have developed a novel computational framework X-CNV ( www.unimd.org/XCNV ), to predict the pathogenicity of CNVs by integrating more than 30 informative features such as allele frequency (AF), CNV length, CNV type, and some deleterious scores. Notably, over 14 million CNVs across various ethnic groups, covering nearly 93% of the human genome, were unified to calculate the AF. X-CNV, which yielded area under curve (AUC) values of 0.96 and 0.94 in training and validation sets, was demonstrated to outperform other available tools in terms of CNV pathogenicity prediction. A meta-voting prediction (MVP) score was developed to quantitively measure the pathogenic effect, which is based on the probabilistic value generated from the XGBoost algorithm. The proposed MVP score demonstrated a high discriminative power in determining pathogenetic CNVs for inherited traits/diseases in different ethnic groups. Conclusions The ability of the X-CNV framework to quantitatively prioritize functional, deleterious, and disease-causing CNV on a genome-wide basis outperformed current CNV-annotation tools and will have broad utility in population genetics, disease-association studies, and diagnostic screening.

Psychological stress is implicated in the development of schizophrenia, but little is known about experiences of stress among children at elevated risk for the disorder. To examine stressor exposure and reactivity in children with different vulnerability profiles for schizophrenia: (a) children presenting multiple antecedents of schizophrenia (ASz group), (b) children with a family history of schizophrenia (FHx group) and (c) typically developing low-risk (TD) children. Ninety-five children (ASz = 29; FHx = 19; ASz+FHx = 5; TD = 42), identified aged 9-12 years using a community-based screening procedure or as relatives of individuals with schizophrenia, completed questionnaires assessing environmental stressors and psychopathology at age 11-14 years. Relative to their typically developing peers, children in the FHx and ASz groups were exposed to a greater number of negative life events and a higher frequency of daily stressors, respectively; and were more distressed by these experiences. Stress exposure and reactivity may constitute useful targets of early intervention for psychosis.