TDO2-overexpressed Dendritic Cells Possess Tolerogenicity and Ameliorate Collagen-induced Arthritis by Modulating the Th17/Regulatory T Cell Balance.

Tolerogenic dendritic cells are promising for restoring immune homeostasis and may be an alternative therapy for autoimmune diseases such as rheumatoid arthritis. The kynurenine pathway is a vital mechanism that induces tolerance in dendritic cells (DCs). Tryptophan 2,3-dioxygenase (TDO2) is an important rate-limiting enzyme in the kynurenine pathway and participates in immune regulation. However, the role of TDO2 in shaping the tolerogenic phenotypes of DCs remains unclear. In this study, we investigated the effects and mechanisms of TDO2-overexpressed DCs in regulating the T cell balance both in vivo and in vitro. TDO2-overexpressed DC2.4 and TDO2-/- mouse bone marrow-derived DCs (BMDCs) were generated to verify the role of TDO2 in DC maturation and functionality. TDO2 overexpression in BMDCs via PGE2 treatment exhibited an immature phenotype and tolerogenic state, whereas TDO2-/- BMDCs exhibited a mature phenotype and a proinflammatory state. Furthermore, transplant of TDO2-overexpressed BMDCs alleviated collagen-induced arthritis severity in mice, which was correlated with a reduction in Th17 populations and an increase in regulatory T cells. Collectively, these results indicate that TDO2 plays an important role in the tolerogenic phenotype and may be a promising target for the generation tolerogenic DCs for rheumatoid arthritis treatment.

Impact of Gold-Standard Label Errors on Evaluating Performance of Deep Learning Models in Diabetic Retinopathy Screening: Nationwide Real-World Validation Study.

BACKGROUND: For medical artificial intelligence (AI) training and validation, human expert labels are considered the gold standard that represents the correct answers or desired outputs for a given data set. These labels serve as a reference or benchmark against which the model's predictions are compared. OBJECTIVE: This study aimed to assess the accuracy of a custom deep learning (DL) algorithm on classifying diabetic retinopathy (DR) and further demonstrate how label errors may contribute to this assessment in a nationwide DR-screening program. METHODS: Fundus photographs from the Lifeline Express, a nationwide DR-screening program, were analyzed to identify the presence of referable DR using both (1) manual grading by National Health Service England-certificated graders and (2) a DL-based DR-screening algorithm with validated good lab performance. To assess the accuracy of labels, a random sample of images with disagreement between the DL algorithm and the labels was adjudicated by ophthalmologists who were masked to the previous grading results. The error rates of labels in this sample were then used to correct the number of negative and positive cases in the entire data set, serving as postcorrection labels. The DL algorithm's performance was evaluated against both pre- and postcorrection labels. RESULTS: The analysis included 736,083 images from 237,824 participants. The DL algorithm exhibited a gap between the real-world performance and the lab-reported performance in this nationwide data set, with a sensitivity increase of 12.5% (from 79.6% to 92.5%, P<.001) and a specificity increase of 6.9% (from 91.6% to 98.5%, P<.001). In the random sample, 63.6% (560/880) of negative images and 5.2% (140/2710) of positive images were misclassified in the precorrection human labels. High myopia was the primary reason for misclassifying non-DR images as referable DR images, while laser spots were predominantly responsible for misclassified referable cases. The estimated label error rate for the entire data set was 1.2%. The label correction was estimated to bring about a 12.5% enhancement in the estimated sensitivity of the DL algorithm (P<.001). CONCLUSIONS: Label errors based on human image grading, although in a small percentage, can significantly affect the performance evaluation of DL algorithms in real-world DR screening.

Association of Retinal Age Gap With Arterial Stiffness and Incident Cardiovascular Disease.

BACKGROUND: Retinal parameters could reflect systemic vascular changes. With the advances of deep learning technology, we have recently developed an algorithm to predict retinal age based on fundus images, which could be a novel biomarker for aging and mortality. Therefore, we aim to investigate associations of retinal age gap with arterial stiffness index and incident cardiovascular disease (CVD). METHODS: A deep learning model was trained based on 19 200 fundus images of 11 052 participants without any medical history at baseline to predict the retinal age. Retinal age gap (retinal age predicted minus chronological age) was generated for the remaining 35 917 participants. Regression models were used to assess the association between retinal age gap and arterial stiffness index. Cox proportional hazards regression models and restricted cubic splines were used to explore the association between retinal age gap and incident CVD. RESULTS: We found each 1-year increase in retinal age gap was associated with increased arterial stiffness index (ß=0.002 [95% CI, 0.001-0.003]; P<0.001). After a median follow-up of 5.83 years (interquartile range: 5.73-5.97), 675 (2.00%) developed CVD. In the fully adjusted model, each 1-year increase in retinal age gap was associated with a 3% increase in the risk of incident CVD (hazard ratio=1.03 [95% CI, 1.01-1.06]; P=0.014). In the restricted cubic splines analysis, the risk of incident CVD increased significantly when retinal age gap reached 1.21 (hazard ratio=1.05 [95% CI, 1.00-1.10]; P-overall <0.0001; P-nonlinear=0.0681). CONCLUSIONS: We found that retinal age gap was significantly associated with arterial stiffness index and incident CVD events, supporting the potential of this novel biomarker in identifying individuals at high risk of future CVD events.

Plasma metabolomic profiles of dementia: a prospective study of 110,655 participants in the UK Biobank.

BACKGROUND: Plasma metabolomic profile is disturbed in dementia patients, but previous studies have discordant conclusions. METHODS: Circulating metabolomic data of 110,655 people in the UK Biobank study were measured with nuclear magnetic resonance technique, and incident dementia records were obtained from national health registers. The associations between plasma metabolites and dementia were estimated using Cox proportional hazard models. The 10-fold cross-validation elastic net regression models selected metabolites that predicted incident dementia, and a 10-year prediction model for dementia was constructed by multivariable logistic regression. The predictive values of the conventional risk model, the metabolites model, and the combined model were discriminated by comparison of area under the receiver operating characteristic curves (AUCs). Net reclassification improvement (NRI) was used to estimate the change of reclassification ability when adding metabolites into the conventional prediction model. RESULTS: Amongst 110,655 participants, the mean (standard deviation) age was 56.5 (8.1) years, and 51 186 (46.3%) were male. A total of 1439 (13.0%) developed dementia during a median follow-up of 12.2 years (interquartile range: 11.5-12.9 years). A total of 38 metabolites, including lipids and lipoproteins, ketone bodies, glycolysis-related metabolites, and amino acids, were found to be significantly associated with incident dementia. Adding selected metabolites (n=24) to the conventional dementia risk prediction model significantly improved the prediction for incident dementia (AUC: 0.824 versus 0.817, p =0.042) and reclassification ability (NRI = 4.97%, P = 0.009) for identifying high risk groups. CONCLUSIONS: Our analysis identified various metabolomic biomarkers which were significantly associated with incident dementia. Metabolomic profiles also provided opportunities for dementia risk reclassification. These findings may help explain the biological mechanisms underlying dementia and improve dementia prediction.

Retinal age gap as a predictive biomarker of future risk of Parkinson's disease.

INTRODUCTION: retinal age derived from fundus images using deep learning has been verified as a novel biomarker of ageing. We aim to investigate the association between retinal age gap (retinal age-chronological age) and incident Parkinson's disease (PD). METHODS: a deep learning (DL) model trained on 19,200 fundus images of 11,052 chronic disease-free participants was used to predict retinal age. Retinal age gap was generated by the trained DL model for the remaining 35,834 participants free of PD at the baseline assessment. Cox proportional hazards regression models were utilised to investigate the association between retinal age gap and incident PD. Multivariable logistic model was applied for prediction of 5-year PD risk and area under the receiver operator characteristic curves (AUC) was used to estimate the predictive value. RESULTS: a total of 35,834 participants (56.7 ± 8.04 years, 55.7% female) free of PD at baseline were included in the present analysis. After adjustment of confounding factors, 1-year increase in retinal age gap was associated with a 10% increase in risk of PD (hazard ratio [HR] = 1.10, 95% confidence interval [CI]: 1.01-1.20, P = 0.023). Compared with the lowest quartile of the retinal age gap, the risk of PD was significantly increased in the third and fourth quartiles (HR = 2.66, 95% CI: 1.13-6.22, P = 0.024; HR = 4.86, 95% CI: 1.59-14.8, P = 0.005, respectively). The predictive value of retinal age and established risk factors for 5-year PD risk were comparable (AUC = 0.708 and 0.717, P = 0.821). CONCLUSION: retinal age gap demonstrated a potential for identifying individuals at a high risk of developing future PD.

Association between dual sensory impairment and risk of mortality: a cohort study from the UK Biobank.

BACKGROUND: Dual sensory impairment is affecting over 10% of older adults worldwide. However, the long-term effect of dual sensory impairment (DSI) on the risk of mortality remains controversial. We aim to investigate the impact of single or/and dual sensory impairment on the risk of mortality in a large population-based sample of the adult in the UK with 14-years of follow-up. METHODS: This population-based prospective cohort study included participants aged 40 and over with complete records of visual and hearing functions from the UK Biobank study. Measurements of visual and hearing functions were performed at baseline examinations between 2006 and 2010, and data on mortality was obtained by 2021. Dual sensory impairment was defined as concurrent visual and hearing impairments. Cox proportional hazards regression models were employed to evaluate the impact of sensory impairment (dual sensory impairment, single visual or hearing impairment) on the hazard of mortality. RESULTS: Of the 113,563 participants included in this study, the mean age (standard deviation) was 56.8 (8.09) years, and 61,849 (54.5%) were female. At baseline measurements, there were 733 (0.65%) participants with dual sensory impairment, 2,973 (2.62%) participants with single visual impairment, and 13,560 (11.94%) with single hearing impairment. After a follow-up period of 14 years (mean duration of 11 years), 5,992 (5.28%) participants died from all causes. Compared with no sensory impairment, dual sensory impairment was significantly associated with an estimated 44% higher hazard of mortality (hazard ratio: 1.44 [95% confidence interval, 1.11-1.88], p = 0.007) after multiple adjustments. CONCLUSIONS: Individuals with dual sensory impairment were found to have an independently 44% higher hazard of mortality than those with neither sensory impairment. Timely intervention of sensory impairment and early prevention of its underlying causes should help to reduce the associated risk of mortality.

A comprehensive analysis of TDO2 expression in immune cells and characterization of immune cell phenotype in TDO2 knockout mice.

Tryptophan 2,3-dioxygenase (TDO2) was an initial rate-limiting enzyme of the kynurenine (Kyn) pathway in tryptophan (Trp) metabolism. We undertook this study to determine a comprehensive analysis of TDO2 expression in immune cells and assess the characterization of immune cell phenotype in TDO2 knockout mice. The expression of TDO2 in various tissues of DBA/1 mice was detected by quantitative real-time PCR (qPCR) and immunohistochemistry. Both flow cytometry and immunofluorescence were used to analyze the expression of TDO2 in immune cells. Furthermore, TDO2 knockout (KO) mice were generated by CRISPR/Cas9 technology to detect immune cell phenotype. TDO2 protein level in liver was tested by western blot. High-performance liquid chromatography was used to detect the level of Trp and Kyn. Flow cytometry was used to test the proportions of splenic lymphocyte subsets in wild-type (WT) and TDO2 KO mice. We found that TDO2 was expressed in various tissues and immune cells, and TDO2 staining was mainly observed in the cytoplasm of cells. There was no difference in the development of immune cells between TDO2 KO mice and WT mice, including T cells, B cells, memory B cells, plasma cells, dendritic cells, and natural killer cells. Interestingly, the reduced M1/M2 ratio was observed in the peritoneal macrophages of TDO2 KO mice. Taken together, these findings enriched the known expression profile of TDO2, especially its expression in immune cells. Our study suggested that TDO2-mediated Trp-Kyn metabolism pathway might be involved in the immune response.

Macular hole retinal detachment after intravitreal Conbercept injection for the treatment of choroidal neovascularization secondary to degenerative myopia: a case report.

BACKGROUND: We report a case of macular hole (MH) formation and retinal detachment after intravitreal conbercept injection for the treatment of choroidal neovascularization (CNV) secondary to degenerative myopia. CASE PRESENTATION: A 60-year-old woman presented with blurred vision in her left eye was diagnosed as CNV secondary to degenerative myopia. Intravitreal injection of conbercept, an anti -vascular endothelial growth factor (VEGF) agent, was uneventfully performed in the left eye. Unfortunately, a full thickness MH and retinal detachment was found three weeks postoperatively by ophthalmoscopy and spectral-domain optical coherence tomography. Vitrectomy, internal limiting membrane peeling and silicone oil tamponade were then performed, and macular retina was reattached soon after surgery. However, MH still kept open during three months' follow-up. CONCLUSION: MH is a quite rare complication of intravitreal anti- VEGF agent injection, tangential contraction secondary to CNV shrinkage and regression caused by anti-VEGF agent is proposed to be the major pathogenesis of MH formation.

[Research Progress of Feasibility of Dendritic Cell Antifungal Vaccines].

Fungal infection is an important clinical problem for patients with immune deficiency or immunosuppression. With deadly fungus infection case increasing, the development of antifungal vaccine attracts the attention of researchers. Dendritic cell (DC) is the unique antigen presenting cell (APC) to trigger the antifungal immune reaction, and recent studies indicate that the targeted vaccination strategy based on DC have prospective antifungal potentials. In this paper, we review the antifungal immunity mechanism and recent development of the targeted DC antifungal strategy.

Towards regulatory generative AI in ophthalmology healthcare: a security and privacy perspective.

As the healthcare community increasingly harnesses the power of generative artificial intelligence (AI), critical issues of security, privacy and regulation take centre stage. In this paper, we explore the security and privacy risks of generative AI from model-level and data-level perspectives. Moreover, we elucidate the potential consequences and case studies within the domain of ophthalmology. Model-level risks include knowledge leakage from the model and model safety under AI-specific attacks, while data-level risks involve unauthorised data collection and data accuracy concerns. Within the healthcare context, these risks can bear severe consequences, encompassing potential breaches of sensitive information, violating privacy rights and threats to patient safety. This paper not only highlights these challenges but also elucidates governance-driven solutions that adhere to AI and healthcare regulations. We advocate for preparedness against potential threats, call for transparency enhancements and underscore the necessity of clinical validation before real-world implementation. The objective of security and privacy improvement in generative AI warrants emphasising the role of ophthalmologists and other healthcare providers, and the timely introduction of comprehensive regulations.

Forecasting Myopic Maculopathy Risk Over a Decade: Development and Validation of an Interpretable Machine Learning Algorithm.

Purpose: The purpose of this study was to develop and validate prediction model for myopic macular degeneration (MMD) progression in patients with high myopia. Methods: The Zhongshan High Myopia Cohort for model development included 660 patients aged 7 to 70 years with a bilateral sphere of ≤-6.00 diopters (D). Two hundred twelve participants with an axial length (AL) ≥25.5 mm from the Chinese Ocular Imaging Project were used for external validation. Thirty-four clinical variables, including demographics, lifestyle, myopia history, and swept source optical coherence tomography data, were analyzed. Sequential forward selection was used for predictor selection, and binary classification models were created using five machine learning algorithms to forecast the risk of MMD progression over 10 years. Results: Over a median follow-up of 10.9 years, 133 patients (20.2%) showed MMD progression in the development cohort. Among them, 69 (51.9%) developed newly-onset MMD, 11 (8.3%) developed patchy atrophy from diffuse atrophy, 54 (40.6%) showed an enlargement of lesions, and 9 (6.8%) developed plus signs. Top six predictors for MMD progression included thinner subfoveal choroidal thickness, longer AL, worse best-corrected visual acuity, older age, female gender, and shallower anterior chamber depth. The eXtreme Gradient Boosting algorithm yielded the best discriminative performance (area under the receiver operating characteristic curve [AUROC] = 0.87 ± 0.02) with good calibration in the training cohort. In a less myopic external validation group (median -5.38 D), 48 patients (22.6%) developed MMD progression over 4 years, with the model's AUROC validated at 0.80 ± 0.008. Conclusions: Machine learning model effectively predicts MMD progression a decade ahead using clinical and imaging indicators. This tool shows promise for identifying "at-risk" high myopes for timely intervention and vision protection.

Economic evaluation for medical artificial intelligence: accuracy vs. cost-effectiveness in a diabetic retinopathy screening case.

Artificial intelligence (AI) models have shown great accuracy in health screening. However, for real-world implementation, high accuracy may not guarantee cost-effectiveness. Improving AI's sensitivity finds more high-risk patients but may raise medical costs while increasing specificity reduces unnecessary referrals but may weaken detection capability. To evaluate the trade-off between AI model performance and the long-running cost-effectiveness, we conducted a cost-effectiveness analysis in a nationwide diabetic retinopathy (DR) screening program in China, comprising 251,535 participants with diabetes over 30 years. We tested a validated AI model in 1100 different diagnostic performances (presented as sensitivity/specificity pairs) and modeled annual screening scenarios. The status quo was defined as the scenario with the most accurate AI performance. The incremental cost-effectiveness ratio (ICER) was calculated for other scenarios against the status quo as cost-effectiveness metrics. Compared to the status quo (sensitivity/specificity: 93.3%/87.7%), six scenarios were cost-saving and seven were cost-effective. To achieve cost-saving or cost-effective, the AI model should reach a minimum sensitivity of 88.2% and specificity of 80.4%. The most cost-effective AI model exhibited higher sensitivity (96.3%) and lower specificity (80.4%) than the status quo. In settings with higher DR prevalence and willingness-to-pay levels, the AI needed higher sensitivity for optimal cost-effectiveness. Urban regions and younger patient groups also required higher sensitivity in AI-based screening. In real-world DR screening, the most accurate AI model may not be the most cost-effective. Cost-effectiveness should be independently evaluated, which is most likely to be affected by the AI's sensitivity.

Association of retinal microvascular density and complexity with incident coronary heart disease.

BACKGROUND AND AIMS: The high mortality rate and huge disease burden of coronary heart disease (CHD) highlight the importance of its early detection and timely intervention. Given the non-invasive nature of fundus photography and recent development in the quantification of retinal microvascular parameters with deep learning techniques, our study aims to investigate the association between incident CHD and retinal microvascular parameters. METHODS: UK Biobanks participants with gradable fundus images and without a history of diagnosed CHD at recruitment were included for analysis. A fully automated artificial intelligence system was used to extract quantitative measurements that represent the density and complexity of the retinal microvasculature, including fractal dimension (Df), number of vascular segments (NS), vascular skeleton density (VSD) and vascular area density (VAD). RESULTS: A total of 57,947 participants (mean age 55.6 ± 8.1 years; 56% female) without a history of diagnosed CHD were included. During a median follow-up of 11.0 (interquartile range, 10.88 to 11.19) years, 3211 incident CHD events occurred. In multivariable Cox proportional hazards models, we found decreasing Df (adjusted HR = 0.80, 95% CI, 0.65-0.98, p = 0.033), lower NS of arteries (adjusted HR = 0.69, 95% CI, 0.54-0.88, p = 0.002) and venules (adjusted HR = 0.77, 95% CI, 0.61-0.97, p = 0.024), and reduced arterial VSD (adjusted HR = 0.72, 95% CI, 0.57-0.91, p = 0.007) and venous VSD (adjusted HR = 0.78, 95% CI, 0.62-0.98, p = 0.034) were related to an increased risk of incident CHD. CONCLUSIONS: Our study revealed a significant association between retinal microvascular parameters and incident CHD. As the lower complexity and density of the retinal vascular network may indicate an increased risk of incident CHD, this may empower its prediction with the quantitative measurements of retinal structure.

Activation of the kynurenine-aryl hydrocarbon receptor axis impairs the chondrogenic and chondroprotective effects of human umbilical cord-derived mesenchymal stromal cells in osteoarthritis rats.

It has been proven that intra-articular injection of mesenchymal stromal cells (MSCs) can alleviate cartilage damage in osteoarthritis (OA) by differentiating into chondrocytes and protecting inherent cartilage. However, the mechanism by which the OA articular microenvironment affects MSCs' therapeutic efficiency is yet to be fully elucidated. The aryl hydrocarbon receptor (AHR) is a ligand-activated transcription factor involved in various cellular processes, such as osteogenesis and immune regulation. Tryptophan (Trp) metabolites, most of which are endogenous ligand for AHR, are abnormally increased in synovial fluid (SF) of OA and rheumatoid arthritis (RA) patients. In this study, the effects of kynurenine (KYN), one of the most important metabolites of Trp, were evaluated on the chondrogenic and chondroprotective effects of human umbilical cord-derived mesenchymal stromal cells (hUC-MSCs). hUC-MSCs were cultured in conditioned medium containing different proportions of OA/RA SF, or stimulated with KYN directly, and then, AHR activation, proliferation, and chondrogenesis of hUC-MSCs were measured. Moreover, the chondroprotective efficiency of short hairpin-AHR-UC-MSC (shAHR-UC-MSC) was determined in a rat surgical OA model (right hind joint). OA SF could activate AHR signaling in hUC-MSCs in a concentration-dependent manner and inhibit the chondrogenic differentiation and proliferation ability of hUC-MSCs. Similar results were observed in hUC-MSCs stimulated with KYN in vitro. Notably, shAHR-UC-MSC exhibited superior therapeutic efficiency in OA rat upon intra-articular injection. Taken together, this study indicates that OA articular microenvironment is not conducive to the therapeutic effect of hUC-MSCs, which is related to the activation of the AHR pathway by tryptophan metabolites, and thus impairs the chondrogenic and chondroprotective effects of hUC-MSCs. AHR might be a promising modification target for further improving the therapeutic efficacy of hUC-MSCs on treatment of cartilage-related diseases such as OA.

Bridging the Camera Domain Gap With Image-to-Image Translation Improves Glaucoma Diagnosis.

Purpose: The purpose of this study was to improve the automated diagnosis of glaucomatous optic neuropathy (GON), we propose a generative adversarial network (GAN) model that translates Optain images to Topcon images. Methods: We trained the GAN model on 725 paired images from Topcon and Optain cameras and externally validated it using an additional 843 paired images collected from the Aravind Eye Hospital in India. An optic disc segmentation model was used to assess the disparities in disc parameters across cameras. The performance of the translated images was evaluated using root mean square error (RMSE), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), 95% limits of agreement (LOA), Pearson's correlations, and Cohen's Kappa coefficient. The evaluation compared the performance of the GON model on Topcon photographs as a reference to that of Optain photographs and GAN-translated photographs. Results: The GAN model significantly reduced Optain false positive results for GON diagnosis, with RMSE, PSNR, and SSIM of GAN images being 0.067, 14.31, and 0.64, respectively, the mean difference of VCDR and cup-to-disc area ratio between Topcon and GAN images being 0.03, 95% LOA ranging from -0.09 to 0.15 and -0.05 to 0.10. Pearson correlation coefficients increased from 0.61 to 0.85 in VCDR and 0.70 to 0.89 in cup-to-disc area ratio, whereas Cohen's Kappa improved from 0.32 to 0.60 after GAN translation. Conclusions: Image-to-image translation across cameras can be achieved by using GAN to solve the problem of disc overexposure in Optain cameras. Translational Relevance: Our approach enhances the generalizability of deep learning diagnostic models, ensuring their performance on cameras that are outside of the original training data set.

Tryptophan 2,3-dioxygenase 2 plays a key role in regulating the activation of fibroblast-like synoviocytes in autoimmune arthritis.

BACKGROUND AND PURPOSE: Abnormal kynurenine (Kyn) metabolism has been closely linked to the pathogenesis of rheumatoid arthritis (RA). The aims of this study were to investigate the role of tryptophan 2,3-dioxygenase 2 (TDO2), a rate-limiting enzyme that converts tryptophan (Trp) to Kyn, in regulating fibroblast-like synoviocyte (FLS)-mediated synovial inflammation in autoimmune arthritis. EXPERIMENTAL APPROACH: The expression of TDO2 was determined by immunohistochemistry, confocal laser scanning fluorescence microscopy, imaging flow cytometry and Western blot. TDO2 activity was tested by HPLC and colorimetric assay. TDO2 siRNA and TDO2 inhibitor 680C91 were used to inhibit TDO2 in AA-FLS function in vitro. A rat model of adjuvant-induced arthritis (AA) was used to evaluate the in vivo effect of allopurinol (Allo), a TDO2 inhibitor. KEY RESULTS: TDO2 expression was strongly increased in synovial tissue and FLS of RA and AA. Immune cells were found to express high amount of TDO2 proteins at the peak stage of AA. Pharmacological inhibition or knockdown of TDO2 in AA-FLS resulted in a reduced proliferation, secretion, migration and invasion. Kyn restored the inhibitory effect of TDO2 inhibition on activation of AA-FLS. Allo treatment ameliorated the arthritis severity and decreased the activity of TDO2. CONCLUSION AND IMPLICATIONS: Our results suggest that elevated TDO2 expression may contribute to synovial inflammation and joint destruction during arthritis. Therefore, targeting TDO2 activity and the Kyn pathway of Trp degradation may represent a potential therapeutic strategy in RA.

Association of Visual, Hearing, and Dual Sensory Impairment With Incident Dementia.

Introduction: The relationship between sensory impairments and the risk of dementia is inconclusive. We aim to investigate the association of visual impairment (VI), hearing impairment (HI), and dual sensory impairment (DSI) with incident dementia. Methods: The UK Biobank study recruited more than 500,000 participants aged 40-69 years across the United Kingdom. Participants with available visual acuity (VA) measurements and speech-reception-threshold (SRT) information and free of dementia at the baseline assessment were included in the analysis. VI was defined as VA worse than 0.3 LogMAR units and HI were defined as an SRT of -5.5 dB or over. DSI was defined as the presence of both VI and HI. Incident dementia was identified through linked data to primary care or hospital admission records and death registries. Multivariable Cox proportional hazard regression models were used to examine the association of VI, HI, and DSI with incident dementia. Results: Among 113,511 participants (mean age: 56.8 ± 8.09 years, female: 54.4%), a total number of 1,135 (1.00%) cases of incident dementia were identified during a median follow up period of 11.1 years [interquartile range (IQR): 10.9-11.4 years]. The incidence of dementia showed significant differences among the non-sensory impairment (NSI) group, VI-only group, HI-only group, and DSI group (p < 0.001). After adjusting for demographic, lifestyle, health, and genetic factors, isolated VI (HR = 1.50, 95% CI: 1.06-2.12, p = 0.023), isolated HI (HR = 1.42, 95% CI:1.20-1.69, p < 0.001), and DSI (HR = 1.82, 95% CI: 1.10-3.00, p = 0.020) were independently associated with higher risks of incident dementia. Conclusions: Visual, hearing, and dual sensory impairments were associated with an increased risk of developing dementia, suggesting that visual and hearing impairments are modifiable risk factors that can be targeted to prevent dementia.

Different Phenotypes Represent Advancing Stages of ABCA4-Associated Retinopathy: A Longitudinal Study of 212 Chinese Families From a Tertiary Center.

Purpose: To evaluate the nature and association of different phenotypes associated with ABCA4 mutations in Chinese. Methods: All patients were recruited from our pediatric and genetic eye clinic. Detailed ocular phenotypes were characterized. The disease course was evaluated by long-term follow-up observation, with a focus on fundus changes. Cox regression was used to identify the factors associated with disease progression. Results: A systematic review of genetic and clinical data for 228 patients and follow-up data for 42 patients indicated specific features in patients with two ABCA4 variants. Of 185 patients with available fundus images, 107 (57.8%) showed focal lesions restricted to the central macula without flecks. Among these 107 patients, 30 patients (28.0%) initially presented with relatively preserved visual acuity and inconspicuous performance on routine fundus screening. A pigmentary change in the posterior pole was observed in 22 of 185 patients (11.9%), and this change mimicked retinitis pigmentosa in 10 cases (45.5%). Follow-up visits and sibling comparisons demonstrated disease progression from cone-rod dystrophy, Stargardt disease, to retinitis pigmentosa. An earlier age of onset was associated with a more rapid decrease in visual acuity (P = 0.03). Patients with two truncation variants had an earlier age of onset. Conclusion: Phenotypic variation in ABCA4-associated retinopathy may represent sequential changes in a single disease: early-stage Stargardt disease may resemble cone-rod dystrophy, whereas the presence of diffuse pigmentation in the late stage may mimic retinitis pigmentosa. Recognizing the natural progression of fundus changes, especially those visualized by wide-field fundus autofluorescence, is valuable for diagnostics and therapeutic decision-making.

Paeoniflorin-6'-o-benzene sulfonate ameliorates the progression of adjuvant-induced arthritis by inhibiting the interaction between Ahr and GRK2 of fibroblast-like synoviocytes.

Aryl hydrocarbon receptor (Ahr) is thought to be a crucial factor that regulates immune responses, which may be involved in the pathogenesis of autoimmune inflammation including rheumatoid arthritis (RA). The results of our group in recent years have shown that Paeoniflorin-6'-O-benzene sulfonate (code: CP-25), a novel ester derivative of paeoniflorin, has a good effect on improving RA animal models. However, whether the anti-arthritis effect of CP-25 is related to Ahr remains unclear. Here, we showed that CP-25 treatment ameliorated adjuvant-induced arthritis (AA), a rat model of RA, by inhibiting Ahr-related activities in fibroblasts like synoviocytes (FLS). AA rats were treated with CP-25 or paroxetine from days 17 to 33 after immunization. We showed that CP-25 alleviated arthritis symptoms and the pathological changes. Treatment with CP-25 decreased the expression of Ahr in the synovium of AA rats. CP-25 inhibited the expression of Ahr and the G protein-coupled receptor kinase 2 (GRK2) as well as the co-expression of GRK2 with Ahr in FLS of AA rats. Furthermore, CP-25 down-regulated the production of Kyn in FLS of AA rats. These results suggested that CP-25 may inhibit the expression and activation of Ahr. Besides, treatment with CP-25 reduced the proliferation and migration of MH7A caused by Ahr activation. In addition, we also demonstrated that CP-25 down-regulated the total and nuclear expression of Ahr and the expression of GRK2 in Kyn-treated MH7A. Moreover, the co-expression and co-localization of Ahr and GRK2in Kyn-treated MH7A were also repressed by CP-25. The data presented here demonstrated that CP-25 suppressed FLS dysfunction in rats with AA, which were associated with reduced Ahr activation and the interaction between Ahr and GRK2.