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Abstract Antenatal corticosteroids (ACS) are administered where there is risk of preterm birth to promote fetal lung development and improve perinatal survival. However, treatment may be associated with increased risk of developing neurobehavioural disorders. We have recently identified that ACS results in significant changes to DNA methylation patterns in the newborn and juvenile prefrontal cortex (PFC) of exposed guinea pig offspring. Methylation changes at transcription factor binding sites (TFBS) for PLAGL1, TFAP2C, ZNF263, and SP1 were consistently noted at both post-natal stages, suggesting a long-lasting signature of ACS exposure. In this study, we determined if comparable methylation changes are also present in the newborn blood of ACS-exposed guinea pig offspring, as this would determine whether blood methylation patterns may be used as a peripheral biomarker of changes in the brain. Pregnant guinea pigs were treated with saline or betamethasone (1 mg/kg) on gestational days 50/51. gDNA from whole blood of term-born offspring on post-natal day (PND) 1 was used for reduced representation bisulfite sequencing. Overall, 1677 differentially methylated CpG sites (DMCs) were identified in response to ACS. While no specific DMCs identified in the blood overlapped with those previously reported in the PFC of PND1 offspring, significant differential methylation at TFBSs for PLAGL1, TFAP2C, EGR1, ZNF263, and SP1 was persistently observed. Furthermore, re-examination of our previously reported data of DMCs in human neonatal blood following ACS identified the presence of this same TFBS signature in human infants, suggesting the potential for clinical translation of our epigenomic data.

Abstract Antenatal corticosteroids (ACS) are used to treat women at risk of preterm birth to improve neonatal survival. Though affected children may be at long-term risk of neurobehavioural disorders, the driving mechanisms remain unknown. Animal studies have shown that ACS exposure can lead to overlapping changes in DNA methylation between the blood and the brain, identifying gene pathways for neurodevelopment, which highlights the potential to examine peripheral blood as a surrogate for inaccessible human brain tissue. We hypothesized that differential methylation will be identified in blood of term-born neonates following ACS. Mother-infant dyads that received ACS were retrospectively identified through the Ontario Birth Study at Sinai Health Complex and matched to untreated controls for maternal age, BMI, parity and foetal sex (n = 14/group). Genome-wide methylation differences were examined at single-nucleotide resolution in DNA extracted from dried bloodspot cards using reduced representative bisulfite sequencing approaches. 505 differentially methylated CpG sites (DMCs) were identified, wherein 231 were hypermethylated and 274 were hypomethylated. These sites were annotated to 219 genes, of which USP48, SH3PXD2A, NTM, CAMK2N2, MAP6D1 were five of the top ten genes with known neurological function. Collectively, the set of hypermethylated genes were enriched for pathways of transcription regulation, while pathways of proteasome activity were enriched among the set of hypomethylated genes. This study is the first to identify DNA methylation changes in human neonatal blood following ACS. Understanding the epigenetic changes that occur in response to ACS will support future investigations to delineate the effects of prenatal glucocorticoid exposure on human development.

The present study aims to evaluate a conceptual framework that explains patients’ attachment to a healthcare establishment by considering the effect of a natural/green environment as nature-based solutions (NBS), mental health value, emotional wellbeing, and satisfaction. An online survey method was utilized to reach Romanian patients. Using this procedure, a total of 386 cases were used for the data analysis. Structural equation modeling was utilized as the data analysis method. Our findings reveal that natural/green environment as NBS significantly influences patients’ mental health value, emotional wellbeing, and satisfaction, which contributes to an increase in their attachment to the healthcare facility. In addition, the mediating role of mental health value, emotional wellbeing, and satisfaction is uncovered. Our results can help healthcare managers and researchers develop strategies to effectively integrate natural/green features as NBS in the interior spaces of healthcare settings.

BackgroundCurrent standards of psychiatric assessment and diagnostic evaluation rely primarily on the clinical subjective interpretation of a patient’s outward manifestations of their internal state. While psychometric tools can help to evaluate these behaviors more systematically, the tools still rely on the clinician’s interpretation of what are frequently nuanced speech and behavior patterns. With advances in computing power, increased availability of clinical data, and improving resolution of recording and sensor hardware (including acoustic, video, accelerometer, infrared, and other modalities), researchers have begun to demonstrate the feasibility of cutting-edge technologies in aiding the assessment of psychiatric disorders. ObjectiveWe present a research protocol that utilizes facial expression, eye gaze, voice and speech, locomotor, heart rate, and electroencephalography monitoring to assess schizophrenia symptoms and to distinguish patients with schizophrenia from those with other psychiatric disorders and control subjects. MethodsWe plan to recruit three outpatient groups: (1) 50 patients with schizophrenia, (2) 50 patients with unipolar major depressive disorder, and (3) 50 individuals with no psychiatric history. Using an internally developed semistructured interview, psychometrically validated clinical outcome measures, and a multimodal sensing system utilizing video, acoustic, actigraphic, heart rate, and electroencephalographic sensors, we aim to evaluate the system’s capacity in classifying subjects (schizophrenia, depression, or control), to evaluate the system’s sensitivity to within-group symptom severity, and to determine if such a system can further classify variations in disorder subtypes. ResultsData collection began in July 2020 and is expected to continue through December 2022. ConclusionsIf successful, this study will help advance current progress in developing state-of-the-art technology to aid clinical psychiatric assessment and treatment. If our findings suggest that these technologies are capable of resolving diagnoses and symptoms to the level of current psychometric testing and clinician judgment, we would be among the first to develop a system that can eventually be used by clinicians to more objectively diagnose and assess schizophrenia and depression with the possibility of less risk of bias. Such a tool has the potential to improve accessibility to care; to aid clinicians in objectively evaluating diagnoses, severity of symptoms, and treatment efficacy through time; and to reduce treatment-related morbidity. International Registered Report Identifier (IRRID)DERR1-10.2196/36417

Background: Current standards of psychiatric assessment and diagnostic evaluation rely primarily on the clinical subjective interpretation of a patient's outward manifestations of their internal state. While psychometric tools can help to evaluate these behaviors more systematically, the tools still rely on the clinician's interpretation of what are frequently nuanced speech and behavior patterns. With advances in computing power, increased availability of clinical data, and improving resolution of recording and sensor hardware (including acoustic, video, accelerometer, infrared, and other modalities), researchers have begun to demonstrate the feasibility of cutting-edge technologies in aiding the assessment of psychiatric disorders. Objective: We present a research protocol that utilizes facial expression, eye gaze, voice and speech, locomotor, heart rate, and electroencephalography monitoring to assess schizophrenia symptoms and to distinguish patients with schizophrenia from those with other psychiatric disorders and control subjects. Methods: We plan to recruit three outpatient groups: (1) 50 patients with schizophrenia, (2) 50 patients with unipolar major depressive disorder, and (3) 50 individuals with no psychiatric history. Using an internally developed semistructured interview, psychometrically validated clinical outcome measures, and a multimodal sensing system utilizing video, acoustic, actigraphic, heart rate, and electroencephalographic sensors, we aim to evaluate the system's capacity in classifying subjects (schizophrenia, depression, or control), to evaluate the system's sensitivity to within-group symptom severity, and to determine if such a system can further classify variations in disorder subtypes. Results: Data collection began in July 2020 and is expected to continue through December 2022. Conclusions: If successful, this study will help advance current progress in developing state-of-the-art technology to aid clinical psychiatric assessment and treatment. If our findings suggest that these technologies are capable of resolving diagnoses and symptoms to the level of current psychometric testing and clinician judgment, we would be among the first to develop a system that can eventually be used by clinicians to more objectively diagnose and assess schizophrenia and depression with the possibility of less risk of bias. Such a tool has the potential to improve accessibility to care; to aid clinicians in objectively evaluating diagnoses, severity of symptoms, and treatment efficacy through time; and to reduce treatment-related morbidity. [ABSTRACT FROM AUTHOR]