Cerebral Palsy-Trends in Epidemiology and Recent Development in Prenatal Mechanisms of Disease, Treatment, and Prevention.

Cerebral palsy (CP) is the most common motor disability in childhood. This syndrome is the manifestation of intrauterine pathologies, intrapartum complications, and the postnatal sequel, especially among preterm neonates. A double hit model theory is proposed suggesting that an intrauterine condition along with intrapartum or postnatal insult lead to the development of CP. Recent reports demonstrated that treatment during the process of preterm birth such as magnesium sulfate and postnatal modalities such as cooling may prevent or reduce the prevalence of this syndrome. Moreover, animal models demonstrated that postnatal treatment with anti-inflammatory drugs coupled with nanoparticles may affect the course of the disease in pups with neuroinflammation. This review will describe the changes in the epidemiology of this disease, the underlying prenatal mechanisms, and possible treatments that may reduce the prevalence of CP and alter the course of the disease

Combining Multiplexed Ion Beam Imaging (MIBI) with Convolutional Neural Networks to accurately segment cells in human tissue

Background: Multiplexed imaging is a rapidly growing field that promises to substantially increase the number of proteins that can be imaged simultaneously. We have developed Multiplexed Ion Beam Imaging by Time of Flight (MIBI-TOF), which uses elemental reporters conjugated to primary antibodies that are then quantified using a time of flight mass-spectrometer. This technique allows for more than 40 distinct proteins to visualized at once in the same clinical samples. This has already yielded significant insights into the interactions and relationships between the many different immune cell populations present in the tumor microenvironment. However, one of the remaining challenges in analyzing such data is accurately determining target protein expression values for each cell in the image. This requires the precise delineation of boundaries between cells that are often tightly packed next to one another. Current methods to address this challenge largely rely on DNA intensity to make these splits, and are thus mostly limited to nuclear segmentation. Methods: We have developed a novel convolutional neural network to perform whole-cell segmentation from multiplexed imaging data. Rather than relying only on DNA signal, we use a panel of morphological markers. Our method integrates the information from these distinct proteins, allowing it to segment large cancer cells, small lymphocytes, and normal epithelium at the same time without requiring fine-tuning or manual adjustment. Results: By combining our novel imaging platform with new computational tools, we are able to achieve extremely accurate segmentation of whole cells in tissue. Our approach compares favorably with many of the currently used tools for segmentation. We show that our improvements in accuracy come both from our novel imaging approach as well as algorithmic advances. We perform significantly better than traditional machine learning algorithms trained on the same dataset. Additionally, we show that our algorithm can be trained to identify cells across a range of cancer histologies and disease grades. Conclusions: We have developed a robust and accurate approach to whole-cell segmentation in human tissues. We show the superiority over this method over current state of the art algorithms. The accurate segmentation generated by our approach will enable the analysis of complex tissue architectures with highly overlapping cell types, and will help to advance our understanding of the interactions between cell types in the diseased state

Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning

Understanding the spatial organization of tissues is of critical importance for both basic and translational research. While recent advances in tissue imaging are opening an exciting new window into the biology of human tissues, interpreting the data that they create is a significant computational challenge. Cell segmentation, the task of uniquely identifying each cell in an image, remains a substantial barrier for tissue imaging, as existing approaches are inaccurate or require a substantial amount of manual curation to yield useful results. Here, we addressed the problem of cell segmentation in tissue imaging data through large-scale data annotation and deep learning. We constructed TissueNet, an image dataset containing >1 million paired whole-cell and nuclear annotations for tissue images from nine organs and six imaging platforms. We created Mesmer, a deep learning-enabled segmentation algorithm trained on TissueNet that performs nuclear and whole-cell segmentation in tissue imaging data. We demonstrated that Mesmer has better speed and accuracy than previous methods, generalizes to the full diversity of tissue types and imaging platforms in TissueNet, and achieves human-level performance for whole-cell segmentation. Mesmer enabled the automated extraction of key cellular features, such as subcellular localization of protein signal, which was challenging with previous approaches. We further showed that Mesmer could be adapted to harness cell lineage information present in highly multiplexed datasets. We used this enhanced version to quantify cell morphology changes during human gestation. All underlying code and models are released with permissive licenses as a community resource

The Society for Immunotherapy of Cancer statement on best practices for multiplex immunohistochemistry (IHC) and immunofluorescence (IF) staining and validation.

OBJECTIVES: The interaction between the immune system and tumor cells is an important feature for the prognosis and treatment of cancer. Multiplex immunohistochemistry (mIHC) and multiplex immunofluorescence (mIF) analyses are emerging technologies that can be used to help quantify immune cell subsets, their functional state, and their spatial arrangement within the tumor microenvironment. METHODS: The Society for Immunotherapy of Cancer (SITC) convened a task force of pathologists and laboratory leaders from academic centers as well as experts from pharmaceutical and diagnostic companies to develop best practice guidelines for the optimization and validation of mIHC/mIF assays across platforms. RESULTS: Representative outputs and the advantages and disadvantages of mIHC/mIF approaches, such as multiplexed chromogenic IHC, multiplexed immunohistochemical consecutive staining on single slide, mIF (including multispectral approaches), tissue-based mass spectrometry, and digital spatial profiling are discussed. CONCLUSIONS: mIHC/mIF technologies are becoming standard tools for biomarker studies and are likely to enter routine clinical practice in the near future. Careful assay optimization and validation will help ensure outputs are robust and comparable across laboratories as well as potentially across mIHC/mIF platforms. Quantitative image analysis of mIHC/mIF output and data management considerations will be addressed in a complementary manuscript from this task force

The Youngest Victims: Children and Youth Affected by War

In 1989, the United Nation Convention on the Rights of the Child declared, “[state parties] shall take all feasible measures to ensure protection and care of children who are affected by an armed conflict.” In addition to attempting to secure the welfare of children in armed conflict, the Convention went on to ban the recruitment and deployment of children during armed conflict. Despite the vast majority of sovereign nations signing and ratifying this agreement, this treaty, unfortunately, has not prevented children and youth from witnessing, becoming victims of, or participating in political, ethnic, religious, and cultural violence across the past three decades. This chapter offers an “ecological perspective” on the psychosocial consequences of exposure to the trauma of war-related violence and social disruption

Effects of drinking on late-life brain and cognition

Alcohol consumption is common in Western countries and has been increasing in older adults. Latest figures from Great Britain suggest 75% of those over 65 years drink, an increase from 71% 10 years ago. Chronic heavy intake is a well-established cause of brain atrophy and dementia, with a recent long-term prospective study from the USA reporting a doubling of the odds of later severe memory impairment in those with a history of an alcohol use disorder. Drinking of moderate amounts has been reported to be protective for brain health in a number of epidemiological studies, including some claims of possibly reducing dementia risk. Rigorous recent research has questioned this belief, with new evidence of harmful associations in moderate drinkers compared with abstainers. This has raised suspicion that reported protective effects of moderate drinking were due to confounding by socioeconomic class and intelligence. Clinicians should look out for cognitive impairment in heavy drinkers, considering that abstinence may induce a degree of clinical improvement. Discussions with patients regarding moderate drinking should be informed by recent research. Health benefits of moderate drinking at least for cognitive function are questionable, and if they exist are probably limited to one unit of alcohol daily with respect to other body systems