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Polycystic Ovary Syndrome (PCOS) is a widespread endocrinological dysfunction impacting women of reproductive age, categorized by excess androgens and a variety of associated syndromes, consisting of acne, alopecia, and hirsutism. It involves the presence of multiple immature follicles in the ovaries, which can disrupt normal ovulation and lead to hormonal imbalances and associated health complications. Routine diagnostic methods rely on manual interpretation of ultrasound (US) images and clinical assessments, which are time-consuming and prone to errors. Therefore, implementing an automated system is essential for streamlining the diagnostic process and enhancing accuracy. By automatically analyzing follicle characteristics and other relevant features, this research aims to facilitate timely intervention and reduce the burden on healthcare professionals. The present study proposes an advanced automated system for detecting and classifying PCOS from ultrasound images. Leveraging Artificial Intelligence (AI) based techniques, the system examines affected and unaffected cases to enhance diagnostic accuracy. The pre-processing of input images incorporates techniques such as image resizing, normalization, augmentation, Watershed technique, multilevel thresholding, etc. approaches for precise image segmentation. Feature extraction is facilitated by the proposed CystNet technique, followed by PCOS classification utilizing both fully connected layers with 5-fold cross-validation and traditional machine learning classifiers. The performance of the model is rigorously evaluated using a comprehensive range of metrics, incorporating AUC score, accuracy, specificity, precision, F1-score, recall, and loss, along with a detailed confusion matrix analysis. The model demonstrated a commendable accuracy of [Formula: see text] when utilizing a fully connected classification layer, as determined by a thorough 5-fold cross-validation process. Additionally, it has achieved an accuracy of [Formula: see text] when employing an ensemble ML classifier. This proposed approach could be suggested for predicting PCOS or similar diseases using datasets that exhibit multimodal characteristics., (© 2024. The Author(s).)

Despite advances in sequencing technologies, a molecular diagnosis remains elusive in many patients with Mendelian disease. Current short-read clinical sequencing approaches cannot provide chromosomal phase information or epigenetic information without further sample processing, which is not routinely done and can result in an incomplete molecular diagnosis in patients. The ability to provide phased genetic and epigenetic information from a single sequencing run would improve the diagnostic rate of Mendelian conditions. Here, we describe targeted long-read sequencing of Mendelian disease genes (TaLon-SeqMD) using a real-time adaptive sequencing approach. Optimization of bioinformatic targeting enabled selective enrichment of multiple disease-causing regions of the human genome. Haplotype-resolved variant calling and simultaneous resolution of epigenetic base modification could be achieved in a single sequencing run. The TaLon-SeqMD approach was validated in a cohort of 18 individuals with previous genetic testing targeting 373 inherited retinal disease (IRD) genes, yielding the complete molecular diagnosis in each case. This approach was then applied in 2 IRD cases with inconclusive testing, which uncovered noncoding and structural variants that were difficult to characterize by standard short-read sequencing. Overall, these results demonstrate TaLon-SeqMD as an approach to provide rapid phased-variant calling to provide the molecular basis of Mendelian diseases.

Purpose: Retinitis pigmentosa (RP), the most common inherited retinal disease, is characterized by progressive photoreceptor degeneration. It remains unknown to what extent surviving photoreceptors transduce light and support vision in RP. To address this, we correlated structure and functional measures using adaptive optics scanning laser ophthalmoscopy (AOSLO), adaptive optics microperimetry, and adaptive optics optical coherence tomography (AO-OCT)-based optoretinograms (ORGs)., Methods: Four patients with RP were imaged with AOSLO across the visual field covering the transition zone (TZ) of normal to diseased retina. Cone density was estimated in discrete regions spanning the TZ. Visual sensitivity was assessed by measuring increment thresholds for a 3-arcmin stimulus targeted via active eye tracking in AOSLO. ORGs were measured at the same locations using AO-OCT to assess the cones' functional response to a 528 ± 20-nm stimulus. Individual cone outer segment (COS) lengths were measured from AO-OCT in each subject., Results: Cone density was significantly reduced in patients with RP. Density reduction correlated with TZ location in 3 patients with RP, while a fourth had patches of reduced density throughout the retina. ORG amplitude was reduced in regions of normal and reduced cone density in all patients with RP. ORG response and COS length were positively correlated in controls but not in patients with RP. Despite deficits in cone density and ORG, visual sensitivity remained comparable to controls in three of four patients with RP., Conclusions: ORG-based measures of retinal dysfunction may precede deficits in cone structure and visual sensitivity. ORG is a sensitive measure of RP disease status and has significant potential to provide insight into disease progression and treatment efficacy.

Oral ingestion of fluorescein can be done in ambulatory pediatric clinics. We show that oral ultra-widefield fluorescein angiography is a non-invasive approach to rapidly diagnose and manage a diverse set of pediatric retinal vascular diseases., Competing Interests: Conflict of Interest: No conflicting relationship exists for any author.

The underlying immune state of inherited retinal degenerations (IRDs) and retinitis pigmentosa (RP) has been an emerging area of interest, wherein the consequences have never been greater given the widespread recognition of gene therapy-associated uveitis (GTU) in gene therapy clinical trials. Whereas some evidence suggests that the adaptive immune system may play a role, the majority of studies indicate that the innate immune system is likely the primary driver of neuroinflammation in RP. During retinal degeneration, discrete mechanisms activate resident microglia and promote infiltrating macrophages that can either be protective or detrimental to photoreceptor cell death. This persistent stimulation of innate immunity, overlaid by the introduction of viral antigens as part of gene therapy, has the potential to trigger a complex microglia/macrophage-driven proinflammatory state. A better understanding of the immune pathophysiology in IRD and GTU will be necessary to improve the success of developing novel treatments for IRDs., (Copyright © 2024 Cold Spring Harbor Laboratory Press; all rights reserved.)