A Genomic Link From Heart Failure to Atrial Fibrillation Risk: FOG2 Modulates a TBX5/GATA4-Dependent Atrial Gene Regulatory Network.

BACKGROUND: The relationship between heart failure (HF) and atrial fibrillation (AF) is clear, with up to half of patients with HF progressing to AF. The pathophysiological basis of AF in the context of HF is presumed to result from atrial remodeling. Upregulation of the transcription factor FOG2 (friend of GATA2; encoded by ZFPM2) is observed in human ventricles during HF and causes HF in mice. METHODS: FOG2 expression was assessed in human atria. The effect of adult-specific FOG2 overexpression in the mouse heart was evaluated by whole animal electrophysiology, in vivo organ electrophysiology, cellular electrophysiology, calcium flux, mouse genetic interactions, gene expression, and genomic function, including a novel approach for defining functional transcription factor interactions based on overlapping effects on enhancer noncoding transcription. RESULTS: FOG2 is significantly upregulated in the human atria during HF. Adult cardiomyocyte-specific FOG2 overexpression in mice caused primary spontaneous AF before the development of HF or atrial remodeling. FOG2 overexpression generated arrhythmia substrate and trigger in cardiomyocytes, including calcium cycling defects. We found that FOG2 repressed atrial gene expression promoted by TBX5. FOG2 bound a subset of GATA4 and TBX5 co-bound genomic locations, defining a shared atrial gene regulatory network. FOG2 repressed TBX5-dependent transcription from a subset of co-bound enhancers, including a conserved enhancer at the Atp2a2 locus. Atrial rhythm abnormalities in mice caused by Tbx5 haploinsufficiency were rescued by Zfpm2 haploinsufficiency. CONCLUSIONS: Transcriptional changes in the atria observed in human HF directly antagonize the atrial rhythm gene regulatory network, providing a genomic link between HF and AF risk independent of atrial remodeling.

Automated segmentation for early detection of uveal melanoma.

OBJECTIVE: Uveal melanoma is the most common intraocular malignancy in adults. Current screening and triaging methods for melanocytic choroidal tumours face inherent limitations, particularly in regions with limited access to specialized ocular oncologists. This study explores the potential of machine learning to automate tumour segmentation. We develop and evaluate a machine-learning model for lesion segmentation using ultra-wide-field fundus photography. METHOD: A retrospective chart review was conducted of patients diagnosed with uveal melanoma, choroidal nevi, or congenital hypertrophy of the retinal pigmented epithelium at a tertiary academic medical centre. Included patients had a single ultra-wide-field fundus photograph (Optos PLC, Dunfermline, Fife, Scotland) of adequate quality to visualize the lesion of interest, as confirmed by a single ocular oncologist. These images were used to develop and test a machine-learning algorithm for lesion segmentation. RESULTS: A total of 396 images were used to develop a machine-learning algorithm for lesion segmentation. Ninety additional images were used in the testing data set along with images of 30 healthy control individuals. Of the images with successfully detected lesions, the machine-learning segmentation yielded Dice coefficients of 0.86, 0.81, and 0.85 for uveal melanoma, choroidal nevi, and congenital hypertrophy of the retinal pigmented epithelium, respectively. Sensitivities for any lesion detection per image were 1.00, 0.90, and 0.87, respectively. For images without lesions, specificity was 0.93. CONCLUSION: Our study demonstrates a novel machine-learning algorithm's performance, suggesting its potential clinical utility as a widely accessible method of screening choroidal tumours. Additional evaluation methods are necessary to further enhance the model's lesion classification and diagnostic accuracy.

Colour fusion effect on deep learning classification of uveal melanoma.

BACKGROUND: Reliable differentiation of uveal melanoma and choroidal nevi is crucial to guide appropriate treatment, preventing unnecessary procedures for benign lesions and ensuring timely treatment for potentially malignant cases. The purpose of this study is to validate deep learning classification of uveal melanoma and choroidal nevi, and to evaluate the effect of colour fusion options on the classification performance. METHODS: A total of 798 ultra-widefield retinal images of 438 patients were included in this retrospective study, comprising 157 patients diagnosed with UM and 281 patients diagnosed with choroidal naevus. Colour fusion options, including early fusion, intermediate fusion and late fusion, were tested for deep learning image classification with a convolutional neural network (CNN). F1-score, accuracy and the area under the curve (AUC) of a receiver operating characteristic (ROC) were used to evaluate the classification performance. RESULTS: Colour fusion options were observed to affect the deep learning performance significantly. For single-colour learning, the red colour image was observed to have superior performance compared to green and blue channels. For multi-colour learning, the intermediate fusion is better than early and late fusion options. CONCLUSION: Deep learning is a promising approach for automated classification of uveal melanoma and choroidal nevi. Colour fusion options can significantly affect the classification performance.

Revision Medial Patellofemoral Ligament Reconstruction for Recurrent Instability After Patellofemoral Arthroplasty.

Recurrent patellar instability is a rare complication after patellofemoral arthroplasty (PFA) and usually involves a traumatic injury. Medial patellofemoral ligament (MPFL) reconstruction after arthroplasty is a complicated and technically challenging surgical procedure because the lack of patellar bone stock due to resurfacing significantly increases the risk of patellar fracture. We present our surgical technique for revision MPFL reconstruction for recurrent instability after PFA. This technical note describes the use of 1.8-mm all-suture anchors for revision MPFL reconstruction in patients with decreased patellar bone stock after PFA. This technique reduces the risk of patellar fracture without compromising the integrity of the MPFL graft.

Color Fusion Effect on Deep Learning Classification of Uveal Melanoma.

Background: Reliable differentiation of uveal melanoma and choroidal nevi is crucial to guide appropriate treatment, preventing unnecessary procedures for benign lesions and ensuring timely treatment for potentially malignant cases. The purpose of this study is to validate deep learning classification of uveal melanoma and choroidal nevi, and to evaluate the effect of color fusion options on the classification performance. Methods: A total of 798 ultra-widefield retinal images of 438 patients were included in this retrospective study, comprising 157 patients diagnosed with UM and 281 patients diagnosed with choroidal nevus. Color fusion options, including early fusion, intermediate fusion and late fusion, were tested for deep learning image classification with a convolutional neural network (CNN). Specificity, sensitivity, F1-score, accuracy, and the area under the curve (AUC) of a receiver operating characteristic (ROC) were used to evaluate the classification performance. The saliency map visualization technique was used to understand the areas in the image that had the most influence on classification decisions of the CNN. Results: Color fusion options were observed to affect the deep learning performance significantly. For single-color learning, the red color image was observed to have superior performance compared to green and blue channels. For multi-color learning, the intermediate fusion is better than early and late fusion options. Conclusion: Deep learning is a promising approach for automated classification of uveal melanoma and choroidal nevi, and color fusion options can significantly affect the classification performance.

Predicting Malignant Transformation of Choroidal Nevi Using Machine Learning.

Objective: This study aims to assess a machine learning (ML) algorithm using multimodal imaging to accurately identify risk factors for uveal melanoma (UM) and aid in the diagnosis of melanocytic choroidal tumors. Subjects and Methods: This study included 223 eyes from 221 patients with melanocytic choroidal lesions seen at the eye clinic of the University of Illinois at Chicago between 01/2010 and 07/2022. An ML algorithm was developed and trained on ultra-widefield fundus imaging and B-scan ultrasonography to detect risk factors of malignant transformation of choroidal lesions into UM. The risk factors were verified using all multimodal imaging available from the time of diagnosis. We also explore classification of lesions into UM and choroidal nevi using the ML algorithm. Results: The ML algorithm assessed features of ultra-widefield fundus imaging and B-scan ultrasonography to determine the presence of the following risk factors for malignant transformation: lesion thickness, subretinal fluid, orange pigment, proximity to optic nerve, ultrasound hollowness, and drusen. The algorithm also provided classification of lesions into UM and choroidal nevi. A total of 115 patients with choroidal nevi and 108 patients with UM were included. The mean lesion thickness for choroidal nevi was 1.6 mm and for UM was 5.9 mm. Eleven ML models were implemented and achieved high accuracy, with an area under the curve of 0.982 for thickness prediction and 0.964 for subretinal fluid prediction. Sensitivity/specificity values ranged from 0.900/0.818 to 1.000/0.727 for different features. The ML algorithm demonstrated high accuracy in identifying risk factors and differentiating lesions based on the analyzed imaging data. Conclusions: This study provides proof of concept that ML can accurately identify risk factors for malignant transformation in melanocytic choroidal tumors based on a single ultra-widefield fundus image or B-scan ultrasound at the time of initial presentation. By leveraging the efficiency and availability of ML, this study has the potential to provide a non-invasive tool that helps to prevent unnecessary treatment, improve our ability to predict malignant transformation, reduce the risk of metastasis, and potentially save patient lives.