Polystyrene nanoplastics-induced intestinal barrier disruption via inflammation and apoptosis in zebrafish larvae (Danio Rerio).

Plastics are one of the most pervasive materials on Earth, to which humans are exposed daily. Polystyrene (PS) is a common plastic packaging material. However, the impact of PS on human health remains poorly understood.

FQ-UWF: Unpaired Generative Image Enhancement for Fundus Quality Ultra-Widefield Retinal Images.

Ultra-widefield (UWF) retinal imaging stands as a pivotal modality for detecting major eye diseases such as diabetic retinopathy and retinal detachment. However, UWF exhibits a well-documented limitation in terms of low resolution and artifacts in the macular area, thereby constraining its clinical diagnostic accuracy, particularly for macular diseases like age-related macular degeneration. Conventional supervised super-resolution techniques aim to address this limitation by enhancing the resolution of the macular region through the utilization of meticulously paired and aligned fundus image ground truths.

Artificial Intelligence Applications in Diabetic Retinopathy: What We Have Now and What to Expect in the Future.

Diabetic retinopathy (DR) is a major complication of diabetes mellitus and is a leading cause of vision loss globally. A prompt and accurate diagnosis is crucial for ensuring favorable visual outcomes, highlighting the need for increased access to medical care. The recent remarkable advancements in artificial intelligence (AI) have raised high expectations for its role in disease diagnosis and prognosis prediction across various medical fields.

Retinal Disease Diagnosis Using Deep Learning on Ultra-Wide-Field Fundus Images.

Ultra-wide-field fundus imaging (UFI) provides comprehensive visualization of crucial eye components, including the optic disk, fovea, and macula. This in-depth view facilitates doctors in accurately diagnosing diseases and recommending suitable treatments. This study investigated the application of various deep learning models for detecting eye diseases using UFI.

Low-density lipoprotein receptor-related protein 6 ablation in macrophages differentially inhibits lung injury-mediated inflammation and metastasis.

Low-density lipoprotein receptor-related protein 6 (LRP6) is a receptor protein for Wnt ligands. Yet, their role in immune cell regulation remains elusive. Here we demonstrated that genetic deletion of LRP6 in macrophages using LysM-cre Lrp6 (Lrp6) mice showed differential inhibition of inflammation in the bleomycin (BLM)-induced lung injury model and B16F10 melanoma lung metastasis model.

Thrombocyte-derived Dickkopf1 promotes macrophage polarization in the Bleomycin-induced lung injury model.

Immune responses are crucial to maintaining tissue homeostasis upon tissue injury. Upon various types of challenges, macrophages play a central role in regulating inflammation and tissue repair processes. While an immunomodulatory role of Wnt antagonist Dickkopf1 (DKK1) has been implicated, the role of Wnt antagonist DKK1 in regulating macrophage polarization in inflammation and the tissue repair process remains elusive.

Isobavachin, a main bioavailable compound in Psoralea corylifolia, alleviates lipopolysaccharide-induced inflammatory responses in macrophages and zebrafish by suppressing the MAPK and NF-κB signaling pathways.

Ethnopharmacological Relevance: Psoralea corylifolia L. (PC) is widely used in traditional medicines to treat inflammatory and infectious diseases. Isobavachin (IBC) is a bioavailable prenylated flavonoid derived from PC that has various biological properties.

Multi-Scale Learning with Sparse Residual Network for Explainable Multi-Disease Diagnosis in OCT Images.

In recent decades, medical imaging techniques have revolutionized the field of disease diagnosis, enabling healthcare professionals to noninvasively observe the internal structures of the human body. Among these techniques, optical coherence tomography (OCT) has emerged as a powerful and versatile tool that allows high-resolution, non-invasive, and real-time imaging of biological tissues. Deep learning algorithms have been successfully employed to detect and classify various retinal diseases in OCT images, enabling early diagnosis and treatment planning.

Association Between Three Atopic Triad and Retinal Vein Occlusion Risk: A Nationwide Population-Based Study.

Purpose: To evaluate the association between three allergic diseases (allergic dermatitis, allergic rhinitis, and asthma) and the development of retinal vein occlusion (RVO), a major retinal disease that causes visual impairment.

Method: This study used data obtained from the Korean National Health Insurance Claims database between 2009 and 2018. The association between the three atopic triads (allergic dermatitis, allergic rhinitis, and asthma) and the occurrence of sight-threatening RVO, as determined by diagnostic and treatment codes, were analyzed.

Self-FI: Self-Supervised Learning for Disease Diagnosis in Fundus Images.

Self-supervised learning has been successful in computer vision, and its application to medical imaging has shown great promise. This study proposes a novel self-supervised learning method for medical image classification, specifically targeting ultra-wide-field fundus images (UFI). The proposed method utilizes contrastive learning to pre-train a deep learning model and then fine-tune it with a small set of labeled images.

Discriminative-Region Multi-Label Classification of Ultra-Widefield Fundus Images.

Ultra-widefield fundus image (UFI) has become a crucial tool for ophthalmologists in diagnosing ocular diseases because of its ability to capture a wide field of the retina. Nevertheless, detecting and classifying multiple diseases within this imaging modality continues to pose a significant challenge for ophthalmologists. An automated disease classification system for UFI can support ophthalmologists in making faster and more precise diagnoses.

Ocular Involvement and Treatment Pattern in Korean Patients with Marfan Syndrome: A Population-Based Study.

Purpose: This study aimed to investigate the incidence and prevalence of, and treatment patterns for ocular complications in Korean patients with Marfan syndrome.

Methods: Incidence and prevalence of Marfan syndrome was calculated from 2010 to 2018, based on data from the Korean National Health Insurance Service (KNHIS). Diagnosis codes (for cataract, ectopia lentis, retinal detachment, etc.

Retinal vessel geometry in patients with idiopathic epiretinal membrane.

We investigated the associations between retinal vascular geometric measurements and idiopathic epiretinal membrane (ERM). Whether changes in retinal vascular geometry are independent of systemic cardiovascular risk factors was also evaluated. This retrospective, cross sectional study included 98 patients with idiopathic ERM, and 99 healthy age-matched controls.

A deep learning-based framework for retinal fundus image enhancement.

Problem: Low-quality fundus images with complex degredation can cause costly re-examinations of patients or inaccurate clinical diagnosis.

Aim: This study aims to create an automatic fundus macular image enhancement framework to improve low-quality fundus images and remove complex image degradation.

Method: We propose a new deep learning-based model that automatically enhances low-quality retinal fundus images that suffer from complex degradation.

Neurologic Dysfunction Assessment in Parkinson Disease Based on Fundus Photographs Using Deep Learning.

Importance: Until now, other than complex neurologic tests, there have been no readily accessible and reliable indicators of neurologic dysfunction among patients with Parkinson disease (PD). This study was conducted to determine the role of fundus photography as a noninvasive and readily available tool for assessing neurologic dysfunction among patients with PD using deep learning methods.

Objective: To develop an algorithm that can predict Hoehn and Yahr (H-Y) scale and Unified Parkinson's Disease Rating Scale part III (UPDRS-III) score using fundus photography among patients with PD.

Prognostic Significance of the Severity of Immune-Related Adverse Events in Advanced Cancer Patients Treated with PD-1/PD-L1 Inhibitors: A Real-World Data Analysis.

Background: There is limited evidence regarding immune-related adverse events (irAEs) in Asian cancer patients treated with antibodies directed against programmed cell death-1 (PD-1) or programmed cell death-ligand 1 (PD-L1).

Objective: This study aimed to investigate the clinical patterns and prognostic significance of grade 1-2 and grade ≥ 3 irAEs by PD-1/PD-L1 inhibitors in cancer patients using real-world clinical data.

Patients And Methods: We conducted a retrospective study of cancer patients who received pembrolizumab, nivolumab, or atezolizumab at a tertiary hospital in South Korea.

Expression of SLC22A18 regulates oxaliplatin resistance by modulating the ERK pathway in colorectal cancer.

Although oxaliplatin-based chemotherapy is the current standard adjuvant therapy for colorectal cancer (CRC), the molecular mechanisms underlying oxaliplatin resistance remain unclear. Here, we examined the molecular mechanisms underlying SLC22A18-associated oxaliplatin resistance and strategies for overcoming oxaliplatin resistance. We evaluated the association between SLC22A18 and prognosis in 337 patients with CRC and its functional significance and studied the mechanisms through which SLC22A18 affects oxaliplatin resistance development in CRC cells, using CRC cell lines and patient-derived cells (PDCs).

Generating future fundus images for early age-related macular degeneration based on generative adversarial networks.

Background And Objective: Age-related macular degeneration (AMD) is one of the most common diseases that can lead to blindness worldwide. Recently, various fundus image analyzing studies are done using deep learning methods to classify fundus images to aid diagnosis and monitor AMD disease progression. But until now, to the best of our knowledge, no attempt was made to generate future synthesized fundus images that can predict AMD progression.

Enzyme-Responsive Amphiphilic Peptide Nanoparticles for Biocompatible and Efficient Drug Delivery.

Self-assembled peptide nanostructures recently have gained much attention as drug delivery systems. As biomolecules, peptides have enhanced biocompatibility and biodegradability compared to polymer-based carriers. We introduce a peptide nanoparticle system containing arginine, histidine, and an enzyme-responsive core of repeating GLFG oligopeptides.