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Ischemia-induced retinopathy is a hallmark finding of common visual disorders including diabetic retinopathy (DR) and central retinal artery and vein occlusions. Treatments for ischemic retinopathies fail to improve clinical outcomes and the design of new therapies will depend on understanding the underlying disease mechanisms. Histone deacetylases (HDACs) are an enzyme class that removes acetyl groups from histone and non-histone proteins, thereby regulating gene expression and protein function. HDACs have been implicated in retinal neurovascular injury in preclinical studies in which nonspecific HDAC inhibitors mitigated retinal injury. Histone deacetylase 3 (HDAC3) is a class I histone deacetylase isoform that plays a central role in the macrophage inflammatory response. We recently reported that myeloid cells upregulate HDAC3 in a mouse model of retinal ischemia-reperfusion (IR) injury. However, whether this cellular event is an essential contributor to retinal IR injury is unknown. In this study, we explored the role of myeloid HDAC3 in ischemia-induced retinal neurovascular injury by subjecting myeloid-specific HDAC3 knockout (M-HDAC3 KO) and floxed control mice to retinal IR. The M-HDAC3 KO mice were protected from retinal IR injury as shown by the preservation of inner retinal neurons, vascular integrity, and retinal thickness. Electroretinography confirmed that this neurovascular protection translated to improved retinal function. The retinas of M-HDAC3 KO mice also showed less proliferation and infiltration of myeloid cells after injury. Interestingly, myeloid cells lacking HDAC3 more avidly engulfed apoptotic cells in vitro and after retinal IR injury in vivo compared to wild-type myeloid cells, suggesting that HDAC3 hinders the reparative phagocytosis of dead cells, a process known as efferocytosis. Further mechanistic studies indicated that although HDAC3 KO macrophages upregulate the reparative enzyme arginase 1 (A1) that enhances efferocytosis, the inhibitory effect of HDAC3 on efferocytosis is not solely dependent on A1. Finally, treatment of wild-type mice with the HDAC3 inhibitor RGFP966 ameliorated the retinal neurodegeneration and thinning caused by IR injury. Collectively, our data show that HDAC3 deletion enhances macrophage-mediated efferocytosis and protects against retinal IR injury, suggesting that inhibiting myeloid HDAC3 holds promise as a novel therapeutic strategy for preserving retinal integrity after ischemic insult. [ABSTRACT FROM AUTHOR]

Building upon the concept of mainstreaming social sciences within conservation, we consider their mainstreaming, and so integration, within UK environment policy and practice (EPP) organisations. The paper responds to increasing calls to recognise the essential role of social sciences in addressing global environmental crises across policy, practice and research. An actor-oriented approach was deployed, producing empirical information from a multi-stage, co-designed, collaborative study involving 19 social scientists from a range of EPP organisations, to understand how they experience the mainstreaming of social sciences. The findings contribute to debates about the politics of knowledge in organisational domains other than those focused on research, specifically EPP organisations. Evidence was found of recent positive changes in how social sciences are perceived, resourced and utilised within EPP, as well as examples of positive impact. However, although EPP organisations are recognising the opportunities that social sciences expertise brings, in practice social sciences still face barriers to effective integration. Many of the challenges faced by the social sciences within academic multi-discipline research (e.g., late, narrow, or selective enrolment) were also experienced in EPP organisations, along with some unique challenges. Informed by the findings, the paper proposes a set of integration indicators designed to assess organisational progress toward addressing the observed challenges. It is recommended that these indicators are employed at a strategic level by EPP organisations seeking to better integrate social sciences expertise into their work. [ABSTRACT FROM AUTHOR]

Myeloid cells including microglia and macrophages play crucial roles in retinal homeostasis by clearing cellular debris and regulating inflammation. These cells are activated in several blinding ischemic retinal diseases including diabetic retinopathy, where they may exert both beneficial and detrimental effects on neurovascular function and angiogenesis. Myeloid cells impact the progression of retinal pathologies and recent studies suggest that targeting myeloid cells is a promising therapeutic strategy to mitigate diabetic retinopathy and other ischemic retinal diseases. This review summarizes the recent advances in our understanding of the role of microglia and macrophages in retinal diseases and focuses on the effects of myeloid cells on neurovascular injury and angiogenesis in ischemic retinopathies. We highlight gaps in knowledge and advocate for a more detailed understanding of the role of myeloid cells in retinal ischemic injury to fully unlock the potential of targeting myeloid cells as a therapeutic strategy for retinal ischemia. [ABSTRACT FROM AUTHOR]

Percent necrosis (PN) following chemotherapy is a prognostic factor for survival in osteosarcoma. Pathologists estimate PN by calculating tumor viability over an average of whole‐slide images (WSIs). This non‐standardized, labor‐intensive process requires specialized training and has high interobserver variability. Therefore, we aimed to develop a machine‐learning model capable of calculating PN in osteosarcoma with similar accuracy to that of a musculoskeletal pathologist. In this proof‐of‐concept study, we retrospectively obtained six WSIs from two patients with conventional osteosarcomas. A weakly supervised learning model was trained by using coarse and incomplete annotations of viable tumor, necrotic tumor, and nontumor tissue in WSIs. Weakly supervised learning refers to processes capable of creating predictive models on the basis of partially and imprecisely annotated data. Once "trained," the model segmented areas of tissue and determined PN of the same six WSIs. To assess model fidelity, the pathologist also estimated PN of each WSI, and we compared the estimates using Pearson's correlation and mean absolute error (MAE). MAE was 15% over the six samples, and 6.4% when an outlier was removed, for which the model inaccurately labeled cartilaginous tissue. The model and pathologist estimates were strongly, positively correlated (r = 0.85). Thus, we created and trained a weakly supervised machine learning model to segment viable tumor, necrotic tumor, and nontumor and to calculate PN with accuracy similar to that of a musculoskeletal pathologist. We expect improvement can be achieved by annotating cartilaginous and other mesenchymal tissue for better representation of the histological heterogeneity in osteosarcoma. [ABSTRACT FROM AUTHOR]