Comparing generative and retrieval-based chatbots in answering patient questions regarding age-related macular degeneration and diabetic retinopathy.

BACKGROUND/AIMS: To compare the performance of generative versus retrieval-based chatbots in answering patient inquiries regarding age-related macular degeneration (AMD) and diabetic retinopathy (DR). METHODS: We evaluated four chatbots: generative models (ChatGPT-4, ChatGPT-3.5 and Google Bard) and a retrieval-based model (OcularBERT) in a cross-sectional study. Their response accuracy to 45 questions (15 AMD, 15 DR and 15 others) was evaluated and compared. Three masked retinal specialists graded the responses using a three-point Likert scale: either 2 (good, error-free), 1 (borderline) or 0 (poor with significant inaccuracies). The scores were aggregated, ranging from 0 to 6. Based on majority consensus among the graders, the responses were also classified as 'Good', 'Borderline' or 'Poor' quality. RESULTS: Overall, ChatGPT-4 and ChatGPT-3.5 outperformed the other chatbots, both achieving median scores (IQR) of 6 (1), compared with 4.5 (2) in Google Bard, and 2 (1) in OcularBERT (all p ≤8.4×10-3). Based on the consensus approach, 83.3% of ChatGPT-4's responses and 86.7% of ChatGPT-3.5's were rated as 'Good', surpassing Google Bard (50%) and OcularBERT (10%) (all p ≤1.4×10-2). ChatGPT-4 and ChatGPT-3.5 had no 'Poor' rated responses. Google Bard produced 6.7% Poor responses, and OcularBERT produced 20%. Across question types, ChatGPT-4 outperformed Google Bard only for AMD, and ChatGPT-3.5 outperformed Google Bard for DR and others. CONCLUSION: ChatGPT-4 and ChatGPT-3.5 demonstrated superior performance, followed by Google Bard and OcularBERT. Generative chatbots are potentially capable of answering domain-specific questions outside their original training. Further validation studies are still required prior to real-world implementation.

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. We developed an automated system that processes and enhances a dataset of 4697 images. Our approach involves brightness and contrast enhancement, followed by applying feature extraction, data augmentation and image classification, integrated with convolutional neural networks. These networks utilize layer-wise feature extraction and transfer learning from pre-trained models to accurately represent and analyze medical images. Among the five evaluated models, including ResNet152, Vision Transformer, InceptionResNetV2, RegNet and ConVNext, ResNet152 is the most effective, achieving a testing area under the curve (AUC) score of 96.47% (with a 95% confidence interval (CI) of 0.931-0.974). Additionally, the paper presents visualizations of the model's predictions, including confidence scores and heatmaps that highlight the model's focal points-particularly where lesions due to damage are evident. By streamlining the diagnosis process and providing intricate prediction details without human intervention, our system serves as a pivotal tool for ophthalmologists. This research underscores the compatibility and potential of utilizing ultra-wide-field images in conjunction with deep learning.

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.) and surgery reimbursement codes (lensectomy, phacoemulsification, buckling, vitrectomy, etc.) in the patients with Marfan syndrome were retrieved by complete enumeration of the data. RESULTS: The annual prevalence of Marfan syndrome adjusted by age and sex was gradually increased from 2.44 per 100,000 in 2010 to 4.36 per 100,000 in 2018. The age group of 10-19 years showed the highest prevalence. The prevalence of ectopia lentis was 21.7%, of whom 43.0% underwent surgeries. Surgery for RD was performed in 253 (14.1%) of 2044 patients during the study period. CONCLUSION: Although the most prevalent ophthalmologic manifestation was ectopia lentis, total prevalence rate of RD was more than 10% in the study period; thus, regular fundus examination is recommended for the patients with Marfan syndrome.

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. However, little information is available on its anti-inflammatory effects and mechanisms of action. AIM OF THE STUDY: In this study, we aimed to determine the anti-inflammatory effects of IBC in vitro and in vivo by conducting a mechanistic study using murine macrophages. MATERIALS AND METHODS: We evaluated the modulatory effects of IBC on the production of pro-inflammatory cytokines and mediators in murine macrophages. In addition, we examined whether IBC inhibits lipopolysaccharide (LPS)-induced inflammatory responses in a zebrafish model. Alterations in inflammatory response-associated genes and proteins were determined using quantitative reverse transcriptional polymerase chain reaction (RT-qPCR) and Western blotting analysis. RESULTS: IBC markedly reduced the overproduction of inflammatory mediators, pro-inflammatory cytokines, inducible nitric oxide synthase (iNOS), cyclooxygenase-2 (COX-2), phosphorylation of mitogen-activated protein kinase (MAPK) and nuclear translocation of nuclear factor-kappa B (NF-κB) in macrophages induced by lipopolysaccharides (LPS). In addition, excessive NO, ROS, and neutrophil level induced by LPS, were suppressed by IBC treatment in a zebrafish inflammation model. CONCLUSIONS: Collectively, bioavailable IBC inhibited on the inflammatory responses by LPS via MAPK and NF-κB signaling pathways in vitro and in vivo, suggesting that it may be a potential modulatory agent against inflammatory disorders.

Is Kidney Function Associated with Age-Related Macular Degeneration?: Findings from the Asian Eye Epidemiology Consortium.

PURPOSE: Chronic kidney disease (CKD) may elevate susceptibility to age-related macular degeneration (AMD) because of shared risk factors, pathogenic mechanisms, and genetic polymorphisms. Given the inconclusive findings in prior studies, we investigated this association using extensive datasets in the Asian Eye Epidemiology Consortium. DESIGN: Cross-sectional study. PARTICIPANTS: Fifty-one thousand two hundred fifty-three participants from 10 distinct population-based Asian studies. METHODS: Age-related macular degeneration was defined using the Wisconsin Age-Related Maculopathy Grading System, the International Age-Related Maculopathy Epidemiological Study Group Classification, or the Beckman Clinical Classification. Chronic kidney disease was defined as estimated glomerular filtration rate (eGFR) of less than 60 ml/min per 1.73 m2. A pooled analysis using individual-level participant data was performed to examine the associations between CKD and eGFR with AMD (early and late), adjusting for age, sex, hypertension, diabetes, body mass index, smoking status, total cholesterol, and study groups. MAIN OUTCOME MEASURES: Odds ratio (OR) of early and late AMD. RESULTS: Among 51 253 participants (mean age, 54.1 ± 14.5 years), 5079 had CKD (9.9%). The prevalence of early AMD was 9.0%, and that of late AMD was 0.71%. After adjusting for confounders, individuals with CKD were associated with higher odds of late AMD (OR, 1.46; 95% confidence interval [CI], 1.11-1.93; P = 0.008). Similarly, poorer kidney function (per 10-unit eGFR decrease) was associated with late AMD (OR, 1.12; 95% CI, 1.05-1.19; P = 0.001). Nevertheless, CKD and eGFR were not associated significantly with early AMD (all P ≥ 0.149). CONCLUSIONS: Pooled analysis from 10 distinct Asian population-based studies revealed that CKD and compromised kidney function are associated significantly with late AMD. This finding further underscores the importance of ocular examinations in patients with CKD. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

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. However, existing deep learning algorithms are primarily designed for single-disease diagnosis, which limits their practical application in clinical settings where OCT images often contain symptoms of multiple diseases. In this paper, we propose an effective approach for multi-disease diagnosis in OCT images using a multi-scale learning (MSL) method and a sparse residual network (SRN). Specifically, the MSL method extracts and fuses useful features from images of different sizes to enhance the discriminative capability of a classifier and make the disease predictions interpretable. The SRN is a minimal residual network, where convolutional layers with large kernel sizes are replaced with multiple convolutional layers that have smaller kernel sizes, thereby reducing model complexity while achieving a performance similar to that of existing convolutional neural networks. The proposed multi-scale sparse residual network significantly outperforms existing methods, exhibiting 97.40% accuracy, 95.38% sensitivity, and 98.25% specificity. Experimental results show the potential of our method to improve explainable diagnosis systems for various eye diseases via visual discrimination.

Large language models and their impact in ophthalmology.

The advent of generative artificial intelligence and large language models has ushered in transformative applications within medicine. Specifically in ophthalmology, large language models offer unique opportunities to revolutionise digital eye care, address clinical workflow inefficiencies, and enhance patient experiences across diverse global eye care landscapes. Yet alongside these prospects lie tangible and ethical challenges, encompassing data privacy, security, and the intricacies of embedding large language models into clinical routines. This Viewpoint highlights the promising applications of large language models in ophthalmology, while weighing up the practical and ethical barriers towards their real-world implementation. This Viewpoint seeks to stimulate broader discourse on the potential of large language models in ophthalmology and to galvanise both clinicians and researchers into tackling the prevailing challenges and optimising the benefits of large language models while curtailing the associated risks.

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. Multivariate adjusted Cox regression analysis was used to determine the hazard ratios (HRs) and 95% confidence intervals for RVO development in the presence of allergic disease. RESULTS: In this population-based study, 2,160,195 (54.6%) individuals were male, 1,794,968 (45.4%) were female, and 620,938 (15.7%) were diagnosed with allergic diseases. Patients with either asthma or allergic rhinitis had a greater risk of RVO (adjusted hazard ratio (aHR) = 1.101, 95% confidence interval [CI] = 1.029-1.178 for asthma; aHR = 1.181, 95% CI = 1.147-1.215 for allergic rhinitis) compared to those without asthma or allergic rhinitis; however, patients with atopic dermatitis did not show a significant association with RVO (aHR = 1.071, 95% CI = 0.889-1.290), after adjusting for other risk factors. CONCLUSION: Our study revealed that allergic rhinitis, asthma, and coexisting multiple allergic conditions were associated with an increased risk of RVO. Thus, it may be advisable to suggest an ophthalmological examination for patients with allergies due to the increased possibility of the occurrence of retinal vascular disease.

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. However, existing works for UFI classification often focus on a single disease or assume each image only contains one disease when tackling multi-disease issues. Furthermore, the distinctive characteristics of each disease are typically not utilized to improve the performance of the classification systems. To address these limitations, we propose a novel approach that leverages disease-specific regions of interest for the multi-label classification of UFI. Our method uses three regions, including the optic disc area, the macula area, and the entire UFI, which serve as the most informative regions for diagnosing one or multiple ocular diseases. Experimental results on a dataset comprising 5930 UFIs with six common ocular diseases showcase that our proposed approach attains exceptional performance, with the area under the receiver operating characteristic curve scores for each class spanning from 95.07% to 99.14%. These results not only surpass existing state-of-the-art methods but also exhibit significant enhancements, with improvements of up to 5.29%. These results demonstrate the potential of our method to provide ophthalmologists with valuable information for early and accurate diagnosis of ocular diseases, ultimately leading to improved patient outcomes.

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. This approach reduces the reliance on labeled data, which is often limited and costly to obtain, and has the potential to improve disease detection in UFI. This method employs two contrastive learning techniques, namely bi-lateral contrastive learning and multi-modality pre-training, to form positive pairs using the data correlation. Bi-lateral learning fuses multiple views of the same patient's images, and multi-modality pre-training leverages the complementary information between UFI and conventional fundus images (CFI) to form positive pairs. The results show that the proposed contrastive learning method achieves state-of-the-art performance with an area under the receiver operating characteristic curve (AUC) score of 86.96, outperforming other approaches. The findings suggest that self-supervised learning is a promising direction for medical image analysis, with potential applications in various clinical settings.

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. Quantitative retinal vascular parameters were measured from digital retinal fundus photographs using a semi-automated computer-assisted program. Multivariate logistic regression analyses were performed to evaluate associations between retinal vascular geometric parameters and the presence of idiopathic ERM after adjusting for systemic cardiovascular risk factors. There was no significant difference in the baseline characteristics of the two groups, except that the ERM group had a higher proportion of females than the control group. In multivariate regression analyses, female sex (odds ratio [OR] 0.402; 95% CI 0.196-0.802; P = 0.011), wider retinal venular caliber (OR 16.852; 95% CI 5.384-58.997; P < 0.001) and decreased total fractal dimension (OR 0.156; 95% CI 0.052-0.440; P = 0.001) were associated with idiopathic ERM. Idiopathic ERM was associated with alterations in global retinal microvascular geometric parameters, wider retinal venules, and less complex vascular branching patterns, independent of cardiovascular risk factors.

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. We collected a dataset, comprising 1068 pairs of high-quality (HQ) and low-quality (LQ) fundus images from the Kangbuk Samsung Hospital's health screening program and ophthalmology department from 2017 to 2019. Then, we used these dataset to develop data augmentation methods to simulate major aspects of retinal image degradation and to propose a customized convolutional neural network (CNN) architecture to enhance LQ images, depending on the nature of the degradation. Peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), r-value (linear index of fuzziness), and proportion of ungradable fundus photographs before and after the enhancement process are calculated to assess the performance of proposed model. A comparative evaluation is conducted on an external database and four different open-source databases. RESULTS: The results of the evaluation on the external test dataset showed an significant increase in PSNR and SSIM compared with the original LQ images. Moreover, PSNR and SSIM increased by over 4 dB and 0.04, respectively compared with the previous state-of-the-art methods (P < 0.05). The proportion of ungradable fundus photographs decreased from 42.6% to 26.4% (P = 0.012). CONCLUSION: Our enhancement process improves LQ fundus images that suffer from complex degradation significantly. Moreover our customized CNN achieved improved performance over the existing state-of-the-art methods. Overall, our framework can have a clinical impact on reducing re-examinations and improving the accuracy of diagnosis.

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. Design, Settings, and Participants: This was a prospective decision analytical model conducted at a single tertiary-care hospital. The fundus photographs of participants with PD and participants with non-PD atypical motor abnormalities who visited the neurology department of Kangbuk Samsung Hospital from October 7, 2020, to April 30, 2021, were analyzed in this study. A convolutional neural network was developed to predict both the H-Y scale and UPDRS-III score based on fundus photography findings and participants' demographic characteristics. Main Outcomes and Measures: The area under the receiver operating characteristic curve (AUROC) was calculated for sensitivity and specificity analyses for both the internal and external validation data sets. Results: A total of 615 participants were included in the study: 266 had PD (43.3%; mean [SD] age, 70.8 [8.3] years; 134 male individuals [50.4%]), and 349 had non-PD atypical motor abnormalities (56.7%; mean [SD] age, 70.7 [7.9] years; 236 female individuals [67.6%]). For the internal validation data set, the sensitivity was 83.23% (95% CI, 82.07%-84.38%) and 82.61% (95% CI, 81.38%-83.83%) for the H-Y scale and UPDRS-III score, respectively. The specificity was 66.81% (95% CI, 64.97%-68.65%) and 65.75% (95% CI, 62.56%-68.94%) for the H-Y scale and UPDRS-III score, respectively. For the external validation data set, the sensitivity and specificity were 70.73% (95% CI, 66.30%-75.16%) and 66.66% (95% CI, 50.76%-82.25%), respectively. Lastly, the calculated AUROC and accuracy were 0.67 (95% CI, 0.55-0.79) and 70.45% (95% CI, 66.85%-74.04%), respectively. Conclusions and Relevance: This decision analytical model reveals amalgamative insights into the neurologic dysfunction among PD patients by providing information on how to apply a deep learning method to evaluate the association between the retina and brain. Study data may help clarify recent research findings regarding dopamine pathologic cascades between the retina and brain among patients with PD; however, further research is needed to expand the clinical implication of this algorithm.

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. Incidence, time to onset, and grade 1-2 and grade ≥ 3 irAE risk factors were analyzed from medical records. The association of irAE severity with progression-free survival (PFS) and prognostic factors for PFS were evaluated. RESULTS: Among a total of 431 patients, irAEs occurred in 45.2%, and 9.5% were grade ≥ 3 irAEs. There were no significant differences in the median time to onset based on severity. Risk factors for the development of grade ≥ 3 irAEs were the presence of autoimmune disorders or diabetes mellitus. The median PFS was significantly different at 13.20, 9.00 and 4.17 months for the grade 1-2, grade ≥ 3, and no irAE groups, respectively. An increase in administration cycles was associated with a reduced risk of progression in patients with grade 1-2 and grade ≥ 3 irAEs. CONCLUSIONS: The development of grade ≥ 3 irAEs was affected by comorbidities and associated with improved PFS compared with those without irAEs. Our findings identified the real-world epidemiology, risk factors, and prognostic significance of irAEs, which may guide treatment decisions of PD-1/PD-L1 inhibitors.

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). SLC22A18 downregulation was positively correlated with worse survival in patients with CRC. Low SLC22A18-expressing cells showed relatively lower sensitivity to oxaliplatin than high SLC22A18-expressing cells. In addition, ERK activation was found to be involved in the mechanisms underlying SLC22A18-related oxaliplatin resistance. To confirm ERK pathway dependence, we used an ERK inhibitor and found that combined treatment with oxaliplatin and the ERK inhibitor overcame oxaliplatin resistance in the low SLC22A18-expressing cells. Ex vivo approaches using PDC confirmed the correlation between SLC22A18 expression and oxaliplatin resistance. Results of the in vivo study showed that SLC22A18 expression regulated oxaliplatin efficacy, and that combined treatment with an ERK inhibitor could be a useful therapeutic strategy when SLC22A18 is downregulated. Together, our findings indicate that SLC22A18 could serve as a biomarker for the prediction of oxaliplatin resistance. In cases of oxaliplatin resistance due to low SLC22A18 expression, resistance can be overcome by combined treatment with an ERK inhibitor.

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. In this paper, we developed a deep learning model using fundus images for AMD patients with different time elapses to generate synthetic future fundus images. METHOD: We exploit generative adversarial networks (GANs) with additional drusen masks to maintain the pathological information. The dataset included 8196 fundus images from 1263 AMD patients. A proposed GAN-based model, called Multi-Modal GAN (MuMo-GAN), was trained to generate synthetic predicted-future fundus images. RESULTS: The proposed deep learning model indicates that the additional drusen masks can help to learn the AMD progression. Our model can generate future fundus images with appropriate pathological features. The drusen development over time is depicted well. Both qualitative and quantitative experiments show that our model is more efficient to monitor the AMD disease as compared to other studies. CONCLUSION: This study could help individualized risk prediction for AMD patients. Compared to existing methods, the experimental results show a significant improvement in terms of tracking the AMD stage in both image-level and pixel-level.

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. GLFG oligopeptides exhibit specific sensitivity towards the enzyme cathepsin B that helps effective controlled release of cargo molecules in the cytoplasm. Arginine can induce cell penetration, and histidine facilitates lysosomal escape by its buffering capacity. Herein, we propose an enzyme-responsive amphiphilic peptide delivery system (Arg-His-(Gly-Phe-Lue-Gly)3, RH-(GFLG)3). The self-assembled RH-(GFLG)3 globular nanoparticle structure exhibited a positive charge and formulation stability for 35 days. Nile Red-tagged RH-(GFLG)3 nanoparticles showed good cellular uptake compared to the non-enzyme-responsive control groups with d-form peptides (LD (LRH-D(GFLG)3), DL (DRH-L(GFLG)3), and DD (DRH-D(GFLG)3). The RH-(GFLG)3 nanoparticles showed negligible cytotoxicity in HeLa cells and human RBCs. To determine the drug delivery efficacy, we introduced the anticancer drug doxorubicin (Dox) in the RH-(GFLG)3 nanoparticle system. LL-Dox exhibited formulation stability, maintaining the physical properties of the nanostructure, as well as a robust anticancer effect in HeLa cells compared to DD-Dox. These results indicate that the enzyme-sensitive RH-(GFLG)3 peptide nanoparticles are promising candidates as drug delivery carriers for biomedical applications.

Association between body mass index and diabetic retinopathy in Asians: the Asian Eye Epidemiology Consortium (AEEC) study.

BACKGROUND/AIMS: Obesity is a well-known risk factor for diabetes, but its association with diabetic retinopathy (DR) is inconclusive, in particular in Asians. We aimed to assess whether body mass index (BMI) is associated with the presence and severity of DR in Asian populations with diabetes. METHODS: Pooled analysis of individual-level cross-sectional data from 10 010 adults with diabetes who participated in 12 population-based studies conducted in China, India, Japan, Russia (Asian), Singapore and South Korea that were part of the Asian Eye Epidemiology Consortium (AEEC). BMI was calculated as weight in kilograms divided by height in square metres and categorised into normal (<25 kg/m2, reference), overweight (25-29.9 kg/m2) and obese (≥30 kg/m2). Any-DR (n=1669) and vision-threatening DR (VTDR, n=489) were assessed from digital retinal photographs and graded according to standard protocols. Each study was analysed separately using multivariable logistic regression models adjusted for age, sex, haemoglobin A1c%, systolic blood pressure and diabetes duration, and the estimated odds ratios (ORs) and 95% confidence interval (CIs) from all studies were then combined using random-effects models. RESULTS: In multivariable models, obesity showed a significant inverse association with any-DR (pooled OR (95% CI) =0.74 (0.59 to 0.91)) and VTDR (0.75 (0.60 to 0.93)). Similarly, in continuous analysis, BMI showed a significant inverse association with both any-DR (0.93 (0.87 to 0.99)) and VTDR (0.79 (0.68 to 0.92) per SD increase). Overweight did not show a significant association with any-DR. CONCLUSIONS: Among Asian adults with diabetes, both BMI and obesity showed an inverse association with DR. These findings warrant confirmation in further longitudinal studies.

Statin-mediated inhibition of RAS prenylation activates ER stress to enhance the immunogenicity of KRAS mutant cancer.

BACKGROUND: Statins preferentially promote tumor-specific apoptosis by depleting isoprenoid such as farnesyl pyrophosphate and geranylgeranyl pyrophosphate. However, statins have not yet been approved for clinical cancer treatment due, in part, to poor understanding of molecular determinants on statin sensitivity. Here, we investigated the potential of statins to elicit enhanced immunogenicity of KRAS-mutant (KRASmut) tumors. METHODS: The immunogenicity of treated cancer cells was determined by western blot, flow cytometry and confocal microscopy. The immunotherapeutic efficacy of mono or combination therapy using statin was assessed in KRASmut tumor models, including syngeneic colorectal cancer and genetically engineered lung and pancreatic tumors. Using NanoString analysis, we analyzed how statin influenced the gene signatures associated with the antigen presentation of dendritic cells in vivo and evaluated whether statin could induce CD8+ T-cell immunity. Multiplex immunohistochemistry was performed to better understand the complicated tumor-immune microenvironment. RESULTS: Statin-mediated inhibition of KRAS prenylation provoked severe endoplasmic reticulum (ER) stress by attenuating the anti-ER stress effect of KRAS mutation, thereby resulting in the immunogenic cell death (ICD) of KRASmut cancer cells. Moreover, statin-mediated ICD enhanced the cross-priming ability of dendritic cells, thereby provoking CD8+ T-cell immune responses against KRASmut tumors. Combination therapy using statin and oxaliplatin, an ICD inducer, significantly enhanced the immunogenicity of KRASmut tumors and promoted tumor-specific immunity in syngeneic and genetically engineered KRASmut tumor models. Along with immune-checkpoint inhibitors, the abovementioned combination therapy overcame resistance to PD-1 blockade therapies, improving the survival rate of KRASmut tumor models. CONCLUSIONS: Our findings suggest that KRAS mutation could be a molecular target for statins to elicit potent tumor-specific immunity.