Novel Quantitative Contrast Sensitivity Function Enhances the Prediction of Treatment Outcome and Recurrence in Amblyopia.

Purpose: Although effective amblyopia treatments are available, treatment outcome is unpredictable, and the condition recurs in up to 25% of the patients. We aimed to evaluate whether a large-scale quantitative contrast sensitivity function (CSF) data source, coupled with machine learning (ML) algorithms, can predict amblyopia treatment response and recurrence in individuals. Methods: Visual function measures from traditional chart vision acuity (VA) and novel CSF assessments were used as the main predictive variables in the models. Information from 58 potential predictors was extracted to predict treatment response and recurrence. Six ML methods were applied to construct models. The SHapley Additive exPlanations was used to explain the predictions. Results: A total of 2559 consecutive records of 643 patients with amblyopia were eligible for modeling. Combining variables from VA and CSF assessments gave the highest accuracy for treatment response prediction, with the area under the receiver operating characteristic curve (AUC) of 0.863 and 0.815 for outcome predictions after 3 and 6 months, respectively. Variables from the VA assessment alone predicted the treatment response, with AUC values of 0.723 and 0.675 after 3 and 6 months, respectively. Variables from the CSF assessment gave rise to an AUC of 0.909 for recurrence prediction compared to 0.539 for VA assessment alone, and adding VA variables did not improve predictive performance. The interocular differences in CSF features are significant contributors to recurrence risk. Conclusions: Our models showed CSF data could enhance treatment response prediction and accurately predict amblyopia recurrence, which has the potential to guide amblyopia management by enabling patient-tailored decision making.

iHerd: an integrative hierarchical graph representation learning framework to quantify network changes and prioritize risk genes in disease.

Different genes form complex networks within cells to carry out critical cellular functions, while network alterations in this process can potentially introduce downstream transcriptome perturbations and phenotypic variations. Therefore, developing efficient and interpretable methods to quantify network changes and pinpoint driver genes across conditions is crucial. We propose a hierarchical graph representation learning method, called iHerd. Given a set of networks, iHerd first hierarchically generates a series of coarsened sub-graphs in a data-driven manner, representing network modules at different resolutions (e.g., the level of signaling pathways). Then, it sequentially learns low-dimensional node representations at all hierarchical levels via efficient graph embedding. Lastly, iHerd projects separate gene embeddings onto the same latent space in its graph alignment module to calculate a rewiring index for driver gene prioritization. To demonstrate its effectiveness, we applied iHerd on a tumor-to-normal GRN rewiring analysis and cell-type-specific GCN analysis using single-cell multiome data of the brain. We showed that iHerd can effectively pinpoint novel and well-known risk genes in different diseases. Distinct from existing models, iHerd's graph coarsening for hierarchical learning allows us to successfully classify network driver genes into early and late divergent genes (EDGs and LDGs), emphasizing genes with extensive network changes across and within signaling pathway levels. This unique approach for driver gene classification can provide us with deeper molecular insights. The code is freely available at https://github.com/aicb-ZhangLabs/iHerd. All other relevant data are within the manuscript and supporting information files.

Impact of COVID-19 pandemic control measures on amblyopia treatment: a retrospective study of records from a tertiary eye hospital in China.

OBJECTIVES: Amblyopia is the most common cause of unilateral visual impairment in children and requires long-term treatment. This study aimed to quantify the impact of pandemic control measures on amblyopia management. DESIGN AND SETTING: This was a retrospective cohort study of data from a large amblyopia management database at a major tertiary eye care centre in China. PARTICIPANTS: Outpatients with amblyopia who visited the hospital from 1 June 2019, through 28 February 2022. PRIMARY AND SECONDARY OUTCOME MEASURES: The primary outcome was the number of first and follow-up in-person visits to the hospital for amblyopia treatment. Secondary outcomes included the time interval between consecutive visits and improvement of vision (visual acuity, contrast sensitivity and stereopsis). Patient records were grouped into prepandemic and during pandemic periods. RESULTS: A total of 10 060 face-to-face visits for 5361 patients (median age 6.7 years, IQR 5.4, 8.9) that spanned two lockdown periods were included in the analysis, of which 28% were follow-up visits. Pandemic control measures caused a sharp decline in the number of outpatient visits (3% and 30% of prepandemic levels in the months directly after the start of the first (2020) and second (2021) periods of pandemic control measures, respectively). However, these drops were followed by pronounced rebounds in visits that exceeded prepandemic levels by 51.1% and 108.5%, respectively. The interval between consecutive visits increased significantly during the pandemic from a median (IQR) of 120 (112, 127) days in 2019 to 197 (179, 224) in 2020 (p<0.001) and 189 (182, 221) in 2021 (p<0.001). There were no significant differences in the improvement of visual function or treatment compliance between the prepandemic and postpandemic groups. CONCLUSIONS: The number of amblyopia patient hospital visits spiked well above prepandemic levels following lockdown periods. This pattern of patient behaviour can inform planning for amblyopia treatment services during and after public health-related disruptions.