Shirley Greenbaum

Bio: Shirley Greenbaum is an academic researcher from Stanford University. The author has contributed to research in topics: Pregnancy & Offspring. The author has an hindex of 7, co-authored 14 publications receiving 324 citations. Previous affiliations of Shirley Greenbaum include Ben-Gurion University of the Negev & Hebrew University of Jerusalem.

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

Abstract: Cerebral palsy (CP) is the most common motor disability in childhood. This syndrome is the manifestation of intrauterine pathologies, intrapartum complications and post-natal 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 like magnesium sulfate and post-natal modalities such as cooling may prevent or reduce the prevalence of this syndrome. Moreover, animal model demonstrated that post-natal treatment with anti-inflammatory drugs coupled with nanoparticles may affect the course of the disease in pups with neuroinflammation. The current review would 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 disease.

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

Abstract: A principal challenge in the analysis of tissue imaging data is cell segmentation—the task of identifying the precise boundary of every cell in an image. To address this problem we constructed TissueNet, a dataset for training segmentation models that contains more than 1 million manually labeled cells, an order of magnitude more than all previously published segmentation training datasets. We used TissueNet to train Mesmer, a deep-learning-enabled segmentation algorithm. We demonstrated that Mesmer is more accurate than previous methods, generalizes to the full diversity of tissue types and imaging platforms in TissueNet, and achieves human-level performance. Mesmer enabled the automated extraction of key cellular features, such as subcellular localization of protein signal, which was challenging with previous approaches. We then adapted Mesmer to harness cell lineage information in highly multiplexed datasets and used this enhanced version to quantify cell morphology changes during human gestation. All code, data and models are released as a community resource. Deep learning algorithms perform as well as humans in identifying cells in tissue images.

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

Abstract: 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.

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

Abstract: 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.

A causal mechanism for childhood acute lymphoblastic leukaemia.

Abstract: In this Review, I present evidence supporting a multifactorial causation of childhood acute lymphoblastic leukaemia (ALL), a major subtype of paediatric cancer. ALL evolves in two discrete steps. First, in utero initiation by fusion gene formation or hyperdiploidy generates a covert, pre-leukaemic clone. Second, in a small fraction of these cases, the postnatal acquisition of secondary genetic changes (primarily V(D)J recombination-activating protein (RAG) and activation-induced cytidine deaminase (AID)-driven copy number alterations in the case of ETS translocation variant 6 (ETV6)–runt-related transcription factor 1 (RUNX1)+ ALL) drives conversion to overt leukaemia. Epidemiological and modelling studies endorse a dual role for common infections. Microbial exposures earlier in life are protective but, in their absence, later infections trigger the critical secondary mutations. Risk is further modified by inherited genetics, chance and, probably, diet. Childhood ALL can be viewed as a paradoxical consequence of progress in modern societies, where behavioural changes have restrained early microbial exposure. This engenders an evolutionary mismatch between historical adaptations of the immune system and contemporary lifestyles. Childhood ALL may be a preventable cancer.

Extinction The Causes And Consequences Of The Disappearance Of Species

Abstract: Thank you for downloading extinction the causes and consequences of the disappearance of species. As you may know, people have search hundreds times for their favorite readings like this extinction the causes and consequences of the disappearance of species, but end up in infectious downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they are facing with some infectious bugs inside their computer.

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

Abstract: A principal challenge in the analysis of tissue imaging data is cell segmentation—the task of identifying the precise boundary of every cell in an image. To address this problem we constructed TissueNet, a dataset for training segmentation models that contains more than 1 million manually labeled cells, an order of magnitude more than all previously published segmentation training datasets. We used TissueNet to train Mesmer, a deep-learning-enabled segmentation algorithm. We demonstrated that Mesmer is more accurate than previous methods, generalizes to the full diversity of tissue types and imaging platforms in TissueNet, and achieves human-level performance. Mesmer enabled the automated extraction of key cellular features, such as subcellular localization of protein signal, which was challenging with previous approaches. We then adapted Mesmer to harness cell lineage information in highly multiplexed datasets and used this enhanced version to quantify cell morphology changes during human gestation. All code, data and models are released as a community resource. Deep learning algorithms perform as well as humans in identifying cells in tissue images.

Cerebral palsy in children: a clinical overview

Abstract: Cerebral palsy (CP) is a disorder characterized by abnormal tone, posture and movement and clinically classified based on the predominant motor syndrome-spastic hemiplegia, spastic diplegia, spastic quadriplegia, and extrapyramidal or dyskinetic. The incidence of CP is 2-3 per 1,000 live births. Prematurity and low birthweight are important risk factors for CP; however, multiple other factors have been associated with an increased risk for CP, including maternal infections, and multiple gestation. In most cases of CP the initial injury to the brain occurs during early fetal brain development; intracerebral hemorrhage and periventricular leukomalacia are the main pathologic findings found in preterm infants who develop CP. The diagnosis of CP is primarily based on clinical findings. Early diagnosis is possible based on a combination of clinical history, use of standardized neuromotor assessment and findings on magnetic resonance imaging (MRI); however, in most clinical settings CP is more reliably recognized by 2 years of age. MRI scan is indicated to delineate the extent of brain lesions and to identify congenital brain malformations. Genetic tests and tests for inborn errors of metabolism are indicated based on clinical findings to identify specific disorders. Because CP is associated with multiple associated and secondary medical conditions, its management requires a multidisciplinary team approach. Most children with CP grow up to be productive adults.