Assessment of OCT-Based Macular Curvature and Its Relationship with Macular Microvasculature in Children with Anisomyopia.

INTRODUCTION: To evaluate the intraocular differences in optical coherence tomography (OCT)-based macular curvature index (MCI) among children with anisomyopia and to investigate the relationship between MCI and the macular microvasculature. METHODS: Fifty-two schoolchildren with anisometropia > 2.00 D were enrolled and underwent comprehensive examinations including cycloplegic refraction, axial length (AL), and swept source OCT/OCT angiography. OCT-based MCIs were determined from horizontal and vertical B-scans by a customized curve fitting model in MATLAB R2022 at 1-mm-, 3-mm-, and 6-mm-diameter circles at fovea. Characteristics and topographic variation of MCI was analyzed, and the relationships with microvascularity and its associated factors were investigated. RESULTS: MCI achieved high reliability and repeatability. There were overall larger MCIs in the more myopic eyes than the less myopic eyes in 1-mm-, 3-mm-, and 6-mm-diameter circles at fovea (all p < 0.001). For the topographic variation, horizontal MCI was significantly greater than vertical MCI (all p < 0.001), and was the largest in 6-mm circle, followed by 3-mm and 1-mm circles. Stronger correlation of horizontal MCI with myopic severity than vertical MCI was found. Partial Pearson's correlation found MCI was negatively associated with deep capillary plexus (DCP) vessel density (p = 0.016). Eyes with a higher MCI in a 6-mm circle were more likely to have longer AL (p < 0.001), lower DCP vessel density (p = 0.037), and thinner choroidal thickness (ChT) (p = 0.045). CONCLUSION: Larger MCI was found in the more myopic eyes of children with anisomyopia and was significantly associated with smaller DCP density, suggesting that MCI was an important indicator of myopia-related retinal microvascularity change, and it could be a valuable metric for myopia assessment in children.

Inhalation bioaccessibility of imidacloprid in particulate matter: Implications for risk assessment during spraying.

Adverse health outcomes due to the inhalation of pesticide residues in atmospheric particulate matter (PM) are gaining global attention. Quantitative health risk assessments of pesticide inhalation exposure highlight the need to understand the bioaccessibility of pesticide residues. Herein, the inhalation bioaccessibility of imidacloprid in PM was determined using three commonly used in vitro lung modeling methods (Artificial Lysosomal Fluid, Gamble Solution, and Simulated Lung Fluid). To validate its feasibility and effectiveness, we evaluated the bioavailability of imidacloprid using a mouse nasal instillation assay. The in vitro inhalation bioaccessibility of imidacloprid was extracted using Gamble Solution with a solid-liquid ratio of 1/1000, an oscillation rate of 150 r/min, and an extraction time of 24 h, showed a strong linear correlation with its in vivo liver-based bioavailability (R2 =0.8928). Moreover, the margin of exposure was incorporated into the inhalation exposure risk assessment, considering both formulations and nozzles. The inhalation unit exposure of imidacloprid for residents was 0.95-4.09 ng/m3. The margin of exposure for imidacloprid was determined to be acceptable when considering inhalation bioaccessibility. Taken together, these results indicate that the inhalation bioaccessibility of pesticides should be incorporated into assessments of human health risks posed by PM particles.

In vivo-in vitro correlations (IVIVC) for the assessment of pyrethroid bioavailability in honey.

Bioaccessibility/bioavailability is an important factor in assessing the potential human health risk via oral exposure. However, methods for accurately predicting the bioaccessibility/bioavailability of pesticide residues are still limited, preventing accurate measurements of actual exposure to pesticide residues. In this study, pyrethroid bioavailability in honey were analysed using a mouse bioassay and bioaccessibility via in vitro methods with Tenax extraction. The results demonstrated that the combined liver plus kidney data served as an appropriate biomarker to estimate the relative bioavailability. Notably, significant in vivo-in vitro correlations (IVIVC) were observed between bioavailability and bioaccessibility (R2 = 0.7898-0.9793). Estimation of the bioavailability of honey from different nectar plants using derived IVIVC confirmed that different contents and physicochemical properties might affect its bioavailability. The findings provide insight into assessing human exposure to pesticides based on bioavailability and can decrease the uncertainty about the assessment of the risk of dietary exposure to pesticides.

Incorporating Bioaccessibility into Inhalation Exposure Assessment of Emamectin Benzoate from Field Spraying.

The inhalation exposure of pesticide applicators and residents who live close to pesticide-treated fields is a worldwide concern in public health. Quantitative assessment of exposure to pesticide inhalation health risk highlights the need to accurately assess the bioaccessibility rather than the total content in ambient air. Herein, we developed an in vitro method to estimate the inhalation bioaccessibility of emamectin benzoate and validated its applicability using a rat plasma pharmacokinetic bioassay. Emamectin benzoate was extracted using the Gamble solution, with an optimized solid-to-liquid ratio (1/250), extraction time (24 h), and agitation (200 rpm), which obtained in vitro inhalation bioaccessibility consistent with its inhalation bioavailability in vivo (32.33%). The margin of exposure (MOE) was used to assess inhalation exposure risk. The inhalation unit exposures to emamectin benzoate of applicators and residents were 11.05-28.04 and 0.02-0.04 ng/m3, respectively, varying markedly according to the methods of application, e.g., formulations and nozzles. The inhalation risk assessment using present application methods appeared to be acceptable; however, the MOE of emamectin benzoate might be overestimated by 32% without considering inhalation bioaccessibility. Collectively, our findings contribute insights into the assessment of pesticide inhalation exposure based on bioaccessibility and provide guidance for the safe application of pesticides.

Cerebral haemodynamics in symptomatic intracranial atherosclerotic disease: a narrative review of the assessment methods and clinical implications.

Intracranial atherosclerotic disease (ICAD) is a common cause of ischaemic stroke and transient ischaemic attack (TIA) with a high recurrence rate. It is often referred to as intracranial atherosclerotic stenosis (ICAS), when the plaque has caused significant narrowing of the vessel lumen. The lesion is usually considered 'symptomatic ICAD/ICAS' (sICAD/sICAS) when it has caused an ischaemic stroke or TIA. The severity of luminal stenosis has long been established as a prognostic factor for stroke relapse in sICAS. Yet, accumulating studies have also reported the important roles of plaque vulnerability, cerebral haemodynamics, collateral circulation, cerebral autoregulation and other factors in altering the stroke risks across patients with sICAS. In this review article, we focus on cerebral haemodynamics in sICAS. We reviewed imaging modalities/methods in assessing cerebral haemodynamics, the haemodynamic metrics provided by these methods and application of these methods in research and clinical practice. More importantly, we reviewed the significance of these haemodynamic features in governing the risk of stroke recurrence in sICAS. We also discussed other clinical implications of these haemodynamic features in sICAS, such as the associations with collateral recruitment and evolution of the lesion under medical treatment, and indications for more individualised blood pressure management for secondary stroke prevention. We then put forward some knowledge gaps and future directions on these topics.

Analysis on the Development of Digital Economy in Guangdong Province Based on Improved Entropy Method and Multivariate Statistical Analysis.

The lack of adequate indicators in the research of digital economy may lead to the shortage of data support on decision making for governments. To solve this problem, first we establish a digital economy indicator evaluation system by dividing the digital economy into four types: "basic type", "technology type", "integration type" and "service type" and select 5 indicators for each type. On this basis, the weight of each indicator is calculated to find the deficiencies in the development of some digital economic fields by the improved entropy method. By drawing on the empowerment idea of Analytic Hierarchy Process, the improved entropy method firstly compares the difference coefficient of indicators in pairs and maps the comparison results to the scales 1-9. Then, the judgment matrix is constructed based on the information entropy, which can solve as much as possible the problem that the difference among the weight of each indicator is too large in traditional entropy method. The results indicate that: the development of digital economy in Guangdong Province was relatively balanced from 2015 to 2018 and will be better in the future while the development of rural e-commerce in Guangdong Province is relatively backward, and there is an obvious digital gap between urban and rural areas. Next we extract two new variables respectively to replace the 20 indicators we select through principal component analysis and factor analysis methods in multivariate statistical analysis, which can retain the original information to the greatest extent and provide convenience for further research in the future. Finally, we and provide constructive comments of digital economy in Guangdong Province from 2015 to 2018.

Clinical Prediction Performance of Glaucoma Progression Using a 2-Dimensional Continuous-Time Hidden Markov Model with Structural and Functional Measurements.

PURPOSE: Previously, we introduced a state-based 2-dimensional continuous-time hidden Markov model (2D CT HMM) to model the pattern of detected glaucoma changes using structural and functional information simultaneously. The purpose of this study was to evaluate the detected glaucoma change prediction performance of the model in a real clinical setting using a retrospective longitudinal dataset. DESIGN: Longitudinal, retrospective study. PARTICIPANTS: One hundred thirty-four eyes from 134 participants diagnosed with glaucoma or as glaucoma suspects (average follow-up, 4.4±1.2 years; average number of visits, 7.1±1.8). METHODS: A 2D CT HMM model was trained using OCT (Cirrus HD-OCT; Zeiss, Dublin, CA) average circumpapillary retinal nerve fiber layer (cRNFL) thickness and visual field index (VFI) or mean deviation (MD; Humphrey Field Analyzer; Zeiss). The model was trained using a subset of the data (107 of 134 eyes [80%]) including all visits except for the last visit, which was used to test the prediction performance (training set). Additionally, the remaining 27 eyes were used for secondary performance testing as an independent group (validation set). The 2D CT HMM predicts 1 of 4 possible detected state changes based on 1 input state. MAIN OUTCOME MEASURES: Prediction accuracy was assessed as the percentage of correct prediction against the patient's actual recorded state. In addition, deviations of the predicted long-term detected change paths from the actual detected change paths were measured. RESULTS: Baseline mean ± standard deviation age was 61.9±11.4 years, VFI was 90.7±17.4, MD was -3.50±6.04 dB, and cRNFL thickness was 74.9±12.2 µm. The accuracy of detected glaucoma change prediction using the training set was comparable with the validation set (57.0% and 68.0%, respectively). Prediction deviation from the actual detected change path showed stability throughout patient follow-up. CONCLUSIONS: The 2D CT HMM demonstrated promising prediction performance in detecting glaucoma change performance in a simulated clinical setting using an independent cohort. The 2D CT HMM allows information from just 1 visit to predict at least 5 subsequent visits with similar performance.

Efficient Learning of Continuous-Time Hidden Markov Models for Disease Progression.

The Continuous-Time Hidden Markov Model (CT-HMM) is an attractive approach to modeling disease progression due to its ability to describe noisy observations arriving irregularly in time. However, the lack of an efficient parameter learning algorithm for CT-HMM restricts its use to very small models or requires unrealistic constraints on the state transitions. In this paper, we present the first complete characterization of efficient EM-based learning methods for CT-HMM models. We demonstrate that the learning problem consists of two challenges: the estimation of posterior state probabilities and the computation of end-state conditioned statistics. We solve the first challenge by reformulating the estimation problem in terms of an equivalent discrete time-inhomogeneous hidden Markov model. The second challenge is addressed by adapting three approaches from the continuous time Markov chain literature to the CT-HMM domain. We demonstrate the use of CT-HMMs with more than 100 states to visualize and predict disease progression using a glaucoma dataset and an Alzheimer's disease dataset.

Longitudinal modeling of glaucoma progression using 2-dimensional continuous-time hidden Markov model.

We propose a 2D continuous-time Hidden Markov Model (2D CT-HMM) for glaucoma progression modeling given longitudinal structural and functional measurements. CT-HMM is suitable for modeling longitudinal medical data consisting of visits at arbitrary times, and 2D state structure is more appropriate for glaucoma since the time courses of functional and structural degeneration are usually different. The learned model not only corroborates the clinical findings that structural degeneration is more evident than functional degeneration in early glaucoma and the opposite is observed in more advanced stages, but also reveals the exact stages where the trend reverses. A method to detect time segments of fast progression is also proposed. Our results show that this detector can effectively identify patients with rapid degeneration. The model and the derived detector can be of clinical value for glaucoma monitoring.