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Human skin has the capacity to metabolise foreign chemicals (xenobiotics), but knowledge of the various enzymes involved is incomplete. A broad-based unbiased proteomics approach was used to describe the profile of xenobiotic metabolising enzymes present in human skin and hence indicate principal routes of metabolism of xenobiotic compounds. Several in vitro models of human skin have been developed for the purpose of safety assessment of chemicals. The suitability of these epidermal models for studies involving biotransformation was assessed by comparing their profiles of xenobiotic metabolising enzymes with those of human skin. Label-free proteomic analysis of whole human skin (10 donors) was applied and analysed using custom-built PROTSIFT software. The results showed the presence of enzymes with a capacity for the metabolism of alcohols through dehydrogenation, aldehydes through dehydrogenation and oxidation, amines through oxidation, carbonyls through reduction, epoxides and carboxylesters through hydrolysis and, of many compounds, by conjugation to glutathione. Whereas protein levels of these enzymes in skin were mostly just 4-10 fold lower than those in liver and sufficient to support metabolism, the levels of cytochrome P450 enzymes were at least 300-fold lower indicating they play no significant role. Four epidermal models of human skin had profiles very similar to one another and these overlapped substantially with that of whole skin. The proteomics profiling approach was successful in producing a comprehensive analysis of the biotransformation characteristics of whole human skin and various in vitro skin models. The results show that skin contains a range of defined enzymes capable of metabolising different classes of chemicals. The degree of similarity of the profiles of the in vitro models indicates their suitability for epidermal toxicity testing. Overall, these results provide a rational basis for explaining the fate of xenobiotics in skin and will aid chemical safety testing programmes.

A study into the effects of amorphous nano-SiO2 particles on A549 lung epithelial cells was undertaken using proteomics to understand the interactions that occur and the biological consequences of exposure of lung to nanoparticles. Suitable conditions for treatment, where A549 cells remained viable for the exposure period, were established by following changes in cell morphology, flow cytometry, and MTT reduction. Label-free proteomics was used to estimate the relative level of proteins from their component tryptic peptides detected by mass spectrometry. It was found that A549 cells tolerated treatment with 100 µg/ml nano-SiO2 in the presence of 1.25% serum for at least 4 h. After this time detrimental changes in cell morphology, flow cytometry, and MTT reduction were evident. Proteomics performed after 4 h indicated changes in the expression of 47 proteins. Most of the proteins affected fell into four functional groups, indicating that the most prominent cellular changes were those that affected apoptosis regulation (e.g. UCP2 and calpain-12), structural reorganisation and regulation of actin cytoskeleton (e.g. PHACTR1), the unfolded protein response (e.g. HSP 90), and proteins involved in protein synthesis (e.g. ribosomal proteins). Treatment with just 10 µg/ml nano-SiO2 particles in serum-free medium resulted in a rapid deterioration of the cells and in medium containing 10% serum the cells were resistant to up to 1000 µg/ml nano-SiO2 particles, suggesting interaction of serum components with the nanoparticles. A variety of serum proteins were found which bound to nano-SiO2 particles, the most prominent of which were albumin, apolipoprotein A-I, hemoglobin, vitronectin and fibronectin. The use of a proteomics platform, with appropriately designed experimental conditions, enabled the early biological perturbations induced by nano-SiO2 in a model target cell system to be identified. The approach facilitates the design of more focused test systems for use in tiered evaluations of nanomaterials.

The Developing Human Connectome Project has created a large open science resource which provides researchers with data for investigating typical and atypical brain development across the perinatal period. It has collected 1228 multimodal magnetic resonance images of fetal and/or neonatal brain from 1173 participants, together with collateral demographic, clinical, family, neurocognitive and genomic data. All subjects were studied in utero and/or soon after birth on a single MRI scanner using specially developed scanning sequences which included novel motion-tolerant imaging methods. Imaging data are complemented by rich demographic, clinical, neurodevelopmental, clinical, and genomic information. The project is now releasing a large set of neonatal data; fetal data will be described and released separately. This release includes scans from 783 infants of whom: 583 were healthy infants born at term; as well as preterm infants; and infants at high risk of atypical neurocognitive development. Many infants were imaged more than once to provide longitudinal data, and the total number of datasets being released is 887. We now describe the dHCP image acquisition and processing protocols, summarize the available imaging and collateral data, and provide information on how the data can be accessed.

Features of brain asymmetry have been implicated in a broad range of cognitive processes; however, their origins are still poorly understood. Here we investigated cortical asymmetries in 442 healthy term-born neonates using structural and functional magnetic resonance images from the Developing Human Connectome Project. Our results demonstrate that the neonatal cortex is markedly asymmetric in both structure and function. Cortical asymmetries observed in the term cohort were contextualized in two ways: by comparing them against cortical asymmetries observed in 103 preterm neonates scanned at term-equivalent age, and by comparing structural asymmetries against those observed in 1,110 healthy young adults from the Human Connectome Project. While associations with preterm birth and biological sex were minimal, significant differences exist between birth and adulthood. Williams et al. show that structural and functional brain asymmetry is already seen in the newborn brain, but that adult patterns of brain asymmetry are not fully developed.

A new camera - SIRIS (scanning infrared imaging system) - developed at the National Gallery in London, UK allows highresolution images to be made in the near infrared region (900-1700 nm). The camera is based on a commercially available 320 × 256 pixel indium gallium arsenide area array sensor. This relatively small sensor is moved across the focal plane of the camera using two orthogonal translation stages to give images of c. 5000 × 5000 pixels. The main advantages of the SIRIS camera over scanning infrared devices or sequential image capture and mosaic assembly are its comparative portability and rapid image acquisition - making a 5000 × 5000 pixel image takes less than 20 minutes. The SIRIS camera can operate at a range of resolutions, from around 2.5 pixels per millimetre over an area of up to 2 × 2 m to 10 pixels per millimetre when examining an area measuring 0.5 × 0.5 m. The development of the mechanical, optical and electronic components of the camera, including the design of a new lens, is described. The software used to control image capture and to assemble the individual frames into a seamless mosaic image is mentioned. The camera was designed primarily to examine underdrawings in paintings; preliminary results from test targets and paintings imaged in situ are presented and the quality of the images compared with those from other cameras currently used for this application.

A study into the effects of amorphous nano-SiO2 particles on A549 lung epithelial cells was undertaken using proteomics to understand the interactions that occur and the biological consequences of exposure of lung to nanoparticles. Suitable conditions for treatment, where A549 cells remained viable for the exposure period, were established by following changes in cell morphology, flow cytometry, and MTT reduction. Label-free proteomics was used to estimate the relative level of proteins from their component tryptic peptides detected by mass spectrometry. It was found that A549 cells tolerated treatment with 100 mu g/ml nano-SiO2 in the presence of 1.25% serum for at least 4 h. After this time detrimental changes in cell morphology, flow cytometry, and MTT reduction were evident. Proteomics performed after 4 h indicated changes in the expression of 47 proteins. Most of the proteins affected fell into four functional groups, indicating that the most prominent cellular changes were those that affected apoptosis regulation (e.g. UCP2 and calpain-12), structural reorganisation and regulation of actin cytoskeleton (e.g. PHACTR1), the unfolded protein response (e.g. HSP 90), and proteins involved in protein synthesis (e.g. ribosomal proteins). Treatment with just 10 mu g/ml nano-SiO2 particles in serum-free medium resulted in a rapid deterioration of the cells and in medium containing 10% serum the cells were resistant to up to 1000 mu g/ml nano-SiO2 particles, suggesting interaction of serum components with the nanoparticles. A variety of serum proteins were found which bound to nano-SiO2 particles, the most prominent of which were albumin, apolipoprotein A-I, hemoglobin, vitronectin and fibronectin. The use of a proteomics platform, with appropriately designed experimental conditions, enabled the early biological perturbations induced by nano-SiO2 in a model target cell system to be identified. The approach facilitates the design of more focused test systems for use in tiered evaluations of nanomaterials.

Accurate estimation of the age in neonates is essential for measuring neurodevelopmental, medical, and growth outcomes. In this paper, we propose a novel approach to predict the post-menstrual age (PA) at scan, using techniques from geometric deep learning, based on the neonatal white matter cortical surface. We utilize and compare multiple specialized neural network architectures that predict the age using different geometric representations of the cortical surface; we compare MeshCNN, Pointnet++, GraphCNN, and a volumetric benchmark. The dataset is part of the Developing Human Connectome Project (dHCP), and is a cohort of healthy and premature neonates. We evaluate our approach on 650 subjects (727scans) with PA ranging from 27 to 45 weeks. Our results show accurate prediction of the estimated PA, with mean error less than one week.