Metastasis to distant organs is the major cause of cancer-related deaths, and bone is the most frequent destination of metastasis in many cancers. Recent studies have shown that the bone microenvironment, not only permits, but augments cancer cells’ invasion and spread within tissue. Recent evidence also suggests that NG2+ cells participate in the initiation of bone metastasis and enhance proliferation and migration via cell-to-cell interactions and often co-localize with early disseminated tumor cells (DTCs). Hence, we developed an imageomics framework to measure and model three-dimensional (3D) spatial relationships within the bone microenvironment using whole slide confocal imaging. To investigate the progression of DTCs, we delivered Lewis Lung Carcinoma (LLC1) GFP+ cancer cells to hind limb bones through intra-iliac artery injection. We did not observe immunogenic rejection of the cells or loss of markers during tumor progression. We performed whole slide confocal imaging to reconstruct high, single-cell resolution, 3D images of femur bones using NG2-creER;ROSA26-LoxP-TdTomato mice. This allowed us to detect and localize single spontaneous DTCs and NG2+ cells within the overall structure of the bone using anti-RFP and anti-GFP fluorescent markers. To quantify 3D spatial relationships in the bone microenvironment, cell positions were labeled within the image by setting a channel-specific minimum intensity threshold to remove background noise and weakly-stained objects, followed by removing all objects measuring <10 μm in more than one dimension. The remaining markers for DTCs were evaluated manually. DTCs were identified based on 3D shape, size, heterogeneity of stain markers, and absence of non-specific staining, while the coordinates (x,y,z) of each DTC were recorded. NG2+ cell positions were determined automatically; a black-hat transformation is used to remove background noise and enhance contrast, followed by thresholding to segment NG2+ cells. For segmented objects within the volume range of an expected cell (5-20 μm radius), the centroid coordinates of the resulting objects were recorded. To evaluate the spatial relationships in the stationary point patterns (SPPs) generated from the DTC and NG2+ staining, we implemented a 3D Ripley’s cross-K function to indicate spatial clustering or dispersion within and between SPPs to quantitatively evaluate spatial relationships at various distances and statistically compare against random distributions. We have developed an imageomics framework for 3D spatial relationships of different cells within the bone microenvironment using whole slide confocal imaging. Our framework can accurately assess the relationship between the two groups and evaluate additional elements within the bone microenvironment, and potentially other cancer-affected organ systems. Citation Format: Daniel S. Kermany, Weijie Zhang, Jianting Sheng, Matthew Vasquez, Xiang Zhang, Stephen T. Wong. Three-dimensional spatial phenotyping of cellular landscapes in the bone microenvironment of spontaneous metastases [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2382.

Abstract Microbiome dysfunction is considered to contribute to the pathogenesis of many neurodegenerative diseases. However, the relationship between the gut microbiome and glaucoma, a neurodegenerative disorder remains largely unknown. Here, we identified that the gut microbiome of glaucoma patients were rich in Dysgonamonadaceae, along with a lower level of Barnesiellaceae by metagenomic sequencing. This microbiome pattern is shown to increased the production of short chain fatty acids (SCFAs) in the fecal and blood samples. Reducing these glaucoma‐specific gut microbiome bacteria significantly reduced retinal ganglion cell (RGC) loss by alleviating the activation of retinal microglia cells and overproduction of inflammatory cytokines in acute glaucoma mouse model, whereas SCFAs treatment aggravated microglia activation. Mechanistically, reducing the glaucoma‐specific gut microbiome bacteria decreased the retinal microRNA profile including MiR‐122‐5p, which led to neuroprotection through inhibition of retinal inflammation. We validated our results by showing that fecal microbiome transplantation from glaucoma patients significantly exacerbated retinal microglia activation and increased retinal inflammation. Our findings indicate that the change of gut microbiome is associated with glaucoma, by activation of retinal microglia and changing expression of retinal microRNAs, leading to retinal inflammation reaction and RGC loss. The gut microbiome may be a new target for the neuroprotection in glaucoma.

Regular screening for the early detection of common chronic diseases might benefit from the use of deep-learning approaches, particularly in resource-poor or remote settings. Here we show that deep-learning models can be used to identify chronic kidney disease and type 2 diabetes solely from fundus images or in combination with clinical metadata (age, sex, height, weight, body-mass index and blood pressure) with areas under the receiver operating characteristic curve of 0.85–0.93. The models were trained and validated with a total of 115,344 retinal fundus photographs from 57,672 patients and can also be used to predict estimated glomerulal filtration rates and blood-glucose levels, with mean absolute errors of 11.1–13.4 ml min−1 per 1.73 m2 and 0.65–1.1 mmol l−1, and to stratify patients according to disease-progression risk. We evaluated the generalizability of the models for the identification of chronic kidney disease and type 2 diabetes with population-based external validation cohorts and via a prospective study with fundus images captured with smartphones, and assessed the feasibility of predicting disease progression in a longitudinal cohort. Deep-learning models trained on retinal fundus images can be used to identify chronic kidney disease and type 2 diabetes and to predict the risk of the progression of these diseases.

Artificial intelligence (AI)-based methods have emerged as powerful tools to transform medical care. Although machine learning classifiers (MLCs) have already demonstrated strong performance in image-based diagnoses, analysis of diverse and massive electronic health record (EHR) data remains challenging. Here, we show that MLCs can query EHRs in a manner similar to the hypothetico-deductive reasoning used by physicians and unearth associations that previous statistical methods have not found. Our model applies an automated natural language processing system using deep learning techniques to extract clinically relevant information from EHRs. In total, 101.6 million data points from 1,362,559 pediatric patient visits presenting to a major referral center were analyzed to train and validate the framework. Our model demonstrates high diagnostic accuracy across multiple organ systems and is comparable to experienced pediatricians in diagnosing common childhood diseases. Our study provides a proof of concept for implementing an AI-based system as a means to aid physicians in tackling large amounts of data, augmenting diagnostic evaluations, and to provide clinical decision support in cases of diagnostic uncertainty or complexity. Although this impact may be most evident in areas where healthcare providers are in relative shortage, the benefits of such an AI system are likely to be universal.

Kendall W Wannamaker Sarah Kenny Rishi Das Aaron Mendlovitz Jordan M Comstock Edward R Chu Sepehr Bahadorani Nathan J Gresores Kinley D Beck Chelsey J Krambeer Daniel S Kermany Roberto Diaz-Rohena Daniel P Nolan Jeong-Hyeon Sohn Michael A Singer 1Ophthalmology, University of Texas Health Science Center San Antonio, San Antonio, TX, USA; 2Medical Center Ophthalmology Associates, San Antonio, TX, USA Background and objective: The dexamethasone (DEX) implant is known to cause temporary intraocular pressure (IOP) spikes after implantation. The purpose of this study is to determine if IOP spikes after DEX implant cause significant thinning in the retinal nerve fiber layer (RNFL). Study design, patients, and methods: A total of 306 charts were reviewed with 48 and 21 patients meeting inclusion criteria for the cross-sectional and prospective groups, respectively. Cross-sectional inclusion criteria: IOP spike ≥22 mmHg up to 16 weeks after DEX implant, DEX implant in only 1 eye per patient, and spectral-domain optical coherence tomography (OCT) RNFL imaging of both eyes ≥3 months after IOP spike. Prospective inclusion criteria: OCT RNFL performed within 1 year prior to DEX implantation, IOP spike ≥22 mmHg up to 16 weeks after DEX implant, and OCT RNFL performed ≥3 months after IOP spike. The average RNFL thickness in the contralateral eye was used as the control in the cross-sectional group. Institutional review board approval was obtained. Results: In the cross-sectional group, there was no statistically significant difference in the mean RNFL thicknesses in the treated vs untreated eyes (80.4±15.5 μm and 82.6±15.8 μm, respectively; P=0.33) regardless of treatment diagnosis, magnitude of IOP spike, or history of glaucoma. In the prospective group, mean RNFL thicknesses before and after IOP spikes ≥22 mmHg were similar (78.0±14.8 μm and 75.6±13.6 μm, respectively; P=0.13). Conclusion and relevance: Temporary elevation of IOP after DEX implantation when treated with topical IOP lowering drops does not appear to lead to a meaningful change in RNFL thickness.

Background and objective: The dexamethasone (DEX) implant is known to cause temporary intraocular pressure (IOP) spikes after implantation. The purpose of this study is to determine if IOP spikes after DEX implant cause significant thinning in the retinal nerve fiber layer (RNFL). Study design, patients, and methods: A total of 306 charts were reviewed with 48 and 21 patients meeting inclusion criteria for the cross-sectional and prospective groups, respectively. Cross-sectional inclusion criteria: IOP spike ≥22 mmHg up to 16 weeks after DEX implant, DEX implant in only 1 eye per patient, and spectral-domain optical coherence tomography (OCT) RNFL imaging of both eyes ≥3 months after IOP spike. Prospective inclusion criteria: OCT RNFL performed within 1 year prior to DEX implantation, IOP spike ≥22 mmHg up to 16 weeks after DEX implant, and OCT RNFL performed ≥3 months after IOP spike. The average RNFL thickness in the contralateral eye was used as the control in the cross-sectional group. Institutional review board approval was obtained. Results: In the cross-sectional group, there was no statistically significant difference in the mean RNFL thicknesses in the treated vs untreated eyes (80.4±15.5 μm and 82.6±15.8 μm, respectively; P=0.33) regardless of treatment diagnosis, magnitude of IOP spike, or history of glaucoma. In the prospective group, mean RNFL thicknesses before and after IOP spikes ≥22 mmHg were similar (78.0±14.8 μm and 75.6±13.6 μm, respectively; P=0.13). Conclusion and relevance: Temporary elevation of IOP after DEX implantation when treated with topical IOP lowering drops does not appear to lead to a meaningful change in RNFL thickness.

Retinal degenerative diseases are a major cause of blindness. Retinal gene therapy is a trail-blazer in the human gene therapy field, leading to the first FDA approved gene therapy product for a human genetic disease. The application of Clustered Regularly Interspaced Short Palindromic Repeat/Cas9 (CRISPR/Cas9)-mediated gene editing technology is transforming the delivery of gene therapy. We review the history, present, and future prospects of retinal gene therapy.

The implementation of clinical-decision support algorithms for medical imaging faces challenges with reliability and interpretability. Here, we establish a diagnostic tool based on a deep-learning framework for the screening of patients with common treatable blinding retinal diseases. Our framework utilizes transfer learning, which trains a neural network with a fraction of the data of conventional approaches. Applying this approach to a dataset of optical coherence tomography images, we demonstrate performance comparable to that of human experts in classifying age-related macular degeneration and diabetic macular edema. We also provide a more transparent and interpretable diagnosis by highlighting the regions recognized by the neural network. We further demonstrate the general applicability of our AI system for diagnosis of pediatric pneumonia using chest X-ray images. This tool may ultimately aid in expediting the diagnosis and referral of these treatable conditions, thereby facilitating earlier treatment, resulting in improved clinical outcomes. VIDEO ABSTRACT.

BACKGROUND AND OBJECTIVE:The purpose of this study is to compare cancellation and no-show rates in patients with diabetic macular edema (DME) and exudative macular degeneration (wet AMD).PATIENTS AND METHODS:An anonymous survey was sent to 1,726 retina specialists inquiring as to the number of appointments their patients with DME and wet AMD attended, cancelled, or did not show up for in 2014 and 2015.RESULTS:Data were obtained on 109,599 appointments. Patients with DME in the U.S. had a 1.591-times increased odds of cancelling or no-showing to their appointments than patients with wet AMD (P < .0001). Patients with DME in Europe had a 1.918-times increased odds of cancelling or no showing to their appointments than patients with wet AMD (P < .0001).CONCLUSION:Patients with DME in the U.S. and Europe cancelled and no-showed to their appointments significantly more often than patients with wet AMD. These findings can be taken into consideration when establishing treatment plans for patients with DME. [Ophthalmic Surg Lasers Imaging Retina. 2018;49:186-190.].

Diabetic macular edema is a serious visual complication of diabetic retinopathy. This article reviews the history of previous and current therapies, including laser therapy, anti-vascular endothelial growth factor agents, and corticosteroids, that have been used to treat this condition. In addition, it proposes new ways to use them in combination in order to decrease treatment burden and potentially address other causes besides vascular endothelial growth factor for diabetic macular edema.

Diabetic macular edema is a serious visual complication of diabetic retinopathy. This article reviews the history of previous and current therapies, including laser therapy, anti-vascular endothelial growth factor agents, and corticosteroids, that have been used to treat this condition. In addition, it proposes new ways to use them in combination in order to decrease treatment burden and potentially address other causes besides vascular endothelial growth factor for diabetic macular edema.