Ocular surface parameters repeatability and agreement -A comparison between Keratograph 5M and IDRA.

PURPOSE: To evaluate the repeatability and agreement in dry eye measurements using Oculus Keratograph 5M (K5M) and SBM Sistemi IDRA (IDRA). METHODS: A total of 108 participants were enrolled and 108 eyes were evaluated. Tear meniscus height (TMH) and first and average non-invasive break-up time (NIBUT) were measured using the K5M and IDRA (order randomly assigned). TMH was measured using the built-in caliper tool while NIBUT was computed by the automatic algorithm of the instruments. RESULTS: The Bland Altman plots analysis showed a good agreement between the two instruments for TMH (95 % Limits of Agreement (LoA), -0.17 to 0.16), but not the first NIBUT (95 % LoA, -8.13 to 14.79) and average NIBUT (95 % LoA, -7.89 to 10.32). The values of the first and average NIBUT measured using IDRA were significantly shorter than in K5M (difference = median (IQR) -2.75 (-6.48- -0.28)s, p < 0.001 and difference = median (IQR) -1.65 (-3.97-1.89)s, p = 0.008 respectively). The TMH (p = 0.037) and NIBUT average (p = 0.033) measured by K5M, as well as the TMH (p = 0.040) measured by IDRA, exhibited unstable measurements across the three measurement times. The remaining parameters exhibited stability with three repeated measurements. CONCLUSION: The NIBUT measurements are not interchangeable between IDRA and K5M, while the TMH was little difference between the two instruments. It is important to exercise caution when using different ocular surface analyzers to minimize errors in comparing multiple measurements.

Graph theoretical analysis and independent component analysis of diabetic optic neuropathy: A resting-state functional magnetic resonance imaging study.

AIMS: This study aimed to investigate the resting-state functional connectivity and topologic characteristics of brain networks in patients with diabetic optic neuropathy (DON). METHODS: Resting-state functional magnetic resonance imaging scans were performed on 23 patients and 41 healthy control (HC) subjects. We used independent component analysis and graph theoretical analysis to determine the topologic characteristics of the brain and as well as functional network connectivity (FNC) and topologic properties of brain networks. RESULTS: Compared with HCs, patients with DON showed altered global characteristics. At the nodal level, the DON group had fewer nodal degrees in the thalamus and insula, and a greater number in the right rolandic operculum, right postcentral gyrus, and right superior temporal gyrus. In the internetwork comparison, DON patients showed significantly increased FNC between the left frontoparietal network (FPN-L) and ventral attention network (VAN). Additionally, in the intranetwork comparison, connectivity between the left medial superior frontal gyrus (MSFG) of the default network (DMN) and left putamen of auditory network was decreased in the DON group. CONCLUSION: DON patients altered node properties and connectivity in the DMN, auditory network, FPN-L, and VAN. These results provide evidence of the involvement of specific brain networks in the pathophysiology of DON.

Ocular surface and meibomian gland evaluation in euthyroid Graves' ophthalmopathy.

PURPOSE: Euthyroid Graves' ophthalmology (EGO) refers to the subgroup of thyroid eye disease patients with distinct clinical presentations. This study evaluated the ocular surface and meibomian gland changes in EGO patients. METHODS: A cross-sectional study was conducted at The Chinese University of Hong Kong including 34 EGO patients and 34 age-and sex- matched healthy controls. Outcome measures include anterior segment examination, keratographic and meibographic imaging. RESULTS: Between 34 EGO patients and 34 age and sex-matched healthy controls, EGO was associated with a higher ocular surface disease index (P < 0.01), higher severity of meibomian gland dropout (upper: P < 0.001, lower: P < 0.00001) and higher percentage of partial blinking (P = 0.0036). The worse affected eyes of the EGO patients were associated with corneal staining (P = 0.0019), eyelid telangiectasia (P = 0.0009), eyelid thickening (P = 0.0013), eyelid irregularity (P = 0.0054), meibomian gland plugging (P < 0.00001), expressibility (P < 0.00001), and meibum quality (P < 0.00001). When the two eyes of the same EGO patient were compared, the degree of meibomian gland dropout was higher among the worse affected eyes (upper: P < 0.00001, and lower: P < 0.00001). Tear meniscus height, lipid layer thickness, and noninvasive break-up time were comparable between the two eyes of EGO patients and also between EGO patients and healthy controls. TMH was positively correlated with the degree of exophthalmos (r = 0.383, P < 0.05). CONCLUSION: EGO patients have more ocular surface complications and meibomian gland dropouts than healthy controls. Almost 60% of them had dry eye symptoms, but aqueous deficiency was not apparent. Further studies are warranted to clarify the mechanism of dry eye in EGO. (249 words).

Investigation of lacrimal gland and extraocular muscle in thyroid eye disease patients with severe subjective dry eye disease: a multiparametric magnetic resonance imaging study.

PURPOSE: To analyze the radiological features of the lacrimal gland (LG) and extraocular muscle (EOM) in thyroid eye disease (TED) patients with severe subjective dry eye disease (DED) using magnetic resonance imaging (MRI) measurements. METHODS: In this cross-sectional study, mechanical ocular exposure, dry eye assessment and MRI data were collected. Patients were classified into non-severe subjective DED group with ocular surface disease index (OSDI) < 33 and severe subjective DED group with OSDI ≥ 33. Linear regression model was applied for comparing the OSDI < 33 and OSDI ≥ 33 group in TED patients. The predictive performance of MRI parameters and models was assessed by receiver operating characteristic curve (ROC) analysis. RESULTS: Consecutive 88 TED patients (176 eyes) were included in this study. In the OSDI < 33 group, 52 TED patients (104 eyes) with a mean clinical activity score (CAS) of 0.63 ± 0.75. In the OSDI ≥ 33 group, there are 36 TED patients (72 eyes), with a mean CAS of 1.50 ± 1.54. The age and sex of the patients were matched between the two groups. The OSDI ≥ 33 group had shorter tear break-up time, larger levator palpebrae superioris / superior rectus (LPS/SR), inferior rectus and lateral rectus, smaller LG, more inflammatory LPS/SR and inferior rectus than OSDI < 33 DED group (P < 0.05). In the linear regression analysis, compare to the OSDI < 33 DED group, the OSDI ≥ 33 group had larger medial rectus cross-sectional area (ß = 0.06, 95%CI: (0.02, 0.10), P = 0.008), larger inferior rectus cross-sectional area (ß = 0.06, 95%CI: (0.00, 0.12), P = 0.048), smaller LG cross-sectional area (ß = -0.14, 95%CI: (-0.25, -0.04), P = 0.008). In the ROC analysis, the area under curve of medial rectus, inferior rectus, LG, and combined model are 0.625, 0.640, 0.661 and 0.716, respectively. CONCLUSION: Multiparametric MRI parameters of the LG and EOM in TED patients with severe subjective DED were significantly altered. Novel models combining the cross-sectional area of LG, medial rectus and inferior rectus showed good predictive performance in TED patients with severe subjective DED.

Prediction Model of Ocular Metastases in Gastric Adenocarcinoma: Machine Learning-Based Development and Interpretation Study.

Background: Although gastric adenocarcinoma (GA) related ocular metastasis (OM) is rare, its occurrence indicates a more severe disease. We aimed to utilize machine learning (ML) to analyze the risk factors of GA-related OM and predict its risks. Methods: This is a retrospective cohort study. The clinical data of 3532 GA patients were collected and randomly classified into training and validation sets in a ratio of 7:3. Those with or without OM were classified into OM and non-OM (NOM) groups. Univariate and multivariate logistic regression analyses and least absolute shrinkage and selection operator were conducted. We integrated the variables identified through feature importance ranking and further refined the selection process using forward sequential feature selection based on random forest (RF) algorithm before incorporating them into the ML model. We applied six ML algorithms to construct the predictive GA model. The area under the receiver operating characteristic (ROC) curve indicated the model's predictive ability. Also, we established a network risk calculator based on the best performance model. We used Shapley additive interpretation (SHAP) to identify risk factors and to confirm the interpretability of the black box model. We have de-identified all patient details. Results: The ML model, consisting of 13 variables, achieved an optimal predictive performance using the gradient boosting machine (GBM) model, with an impressive area under the curve (AUC) of 0.997 in the test set. Utilizing the SHAP method, we identified crucial factors for OM in GA patients, including LDL, CA724, CEA, AFP, CA125, Hb, CA153, and Ca2+. Additionally, we validated the model's reliability through an analysis of two patient cases and developed a functional online web prediction calculator based on the GBM model. Conclusion: We used the ML method to establish a risk prediction model for GA-related OM and showed that GBM performed best among the six ML models. The model may identify patients with GA-related OM to provide early and timely treatment.