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.

Perceptual Learning Based on the Lateral Masking Paradigm in Anisometropic Amblyopia With or Without a Patching History.

Purpose: Perceptual learning (PL) has shown promising performance in restoring visual function in adolescent amblyopes. We retrospectively compared the effect of a well-accepted PL paradigm on patients with anisometropic amblyopia with or without a patching therapy history (patching therapy [PT] group versus no patching therapy [NPT] group). Methods: Eighteen PT and 13 NPT patients with anisometropic amblyopia underwent monocular PL for 3 months. During training, patients practiced a Gabor detection task following the lateral masking paradigm by applying a temporal two-alternative forced choice procedure with the amblyopic eye. Monocular contrast sensitivity functions (CSF), visual acuity, interocular differences in visual function metrics, and stereoacuity were compared before and after training. Results: PL improved the visual acuity of the amblyopia eyes by 0.5 lines on average in the PT group and 1.5 lines in the NPT group. A significant reduction in the interocular difference in visual acuity was observed in the NPT group (P < 0.01) but not in the PT group (P = 0.05). Regarding CSF metrics, the area under the log CSF and cutoff in the amblyopic eyes of the NPT groups increased after training (P < 0.05). In addition, the interocular differences of the CSF metrics (P < 0.05) in the NPT group were significantly reduced. However, in the PT group, all the CSF metrics were unchanged after training. A total of 27 of 31 patients in both groups had no measurable stereopsis pretraining, and recovery after training was not significant. Conclusions: PL based on a lateral masking training paradigm improved visual function in anisometropic amblyopia. Patients without a patching history achieved greater benefits. Translational Relevance: PL based on a lateral masking training paradigm could be a new treatment for amblyopia.

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.

Reconstruction of a Genome-Scale Metabolic Network for Shewanella oneidensis MR-1 and Analysis of its Metabolic Potential for Bioelectrochemical Systems.

Bioelectrochemical systems (BESs) based on Shewanella oneidensis MR-1 offer great promise for sustainable energy/chemical production, but the low rate of electron generation remains a crucial bottleneck preventing their industrial application. Here, we reconstructed a genome-scale metabolic model of MR-1 to provide a strong theoretical basis for novel BES applications. The model iLJ1162, comprising 1,162 genes, 1,818 metabolites and 2,084 reactions, accurately predicted cellular growth using a variety of substrates with 86.9% agreement with experimental results, which is significantly higher than the previously published models iMR1_799 and iSO783. The simulation of microbial fuel cells indicated that expanding the substrate spectrum of MR-1 to highly reduced feedstocks, such as glucose and glycerol, would be beneficial for electron generation. In addition, 31 metabolic engineering targets were predicted to improve electricity production, three of which have been experimentally demonstrated, while the remainder are potential targets for modification. Two potential electron transfer pathways were identified, which could be new engineering targets for increasing the electricity production capacity of MR-1. Finally, the iLJ1162 model was used to simulate the optimal biosynthetic pathways for six platform chemicals based on the MR-1 chassis in microbial electrosynthesis systems. These results offer guidance for rational design of novel BESs.

[Graph-based and constraint-based heterologous metabolic pathway design methods and application].

It is among the goals in metabolic engineering to construct microbial cell factories producing high-yield and high value-added target products, and an important solution is to design efficient synthetic pathway for the target products. However, due to the difference in metabolic capacity among microbial chassises, the available substrate and the yielded products are limited. Therefore, it is urgent to design related metabolic pathways to improve the production capacity. Existing metabolic engineering approaches to designing heterologous pathways are mainly based on biological experience, which are inefficient. Moreover, the yielded results are in no way comprehensive. However, systems biology provides new methods for heterologous pathway design, particularly the graph-based and constraint-based methods. Based on the databases containing rich metabolism information, they search for and uncover possible metabolic pathways with designated strategy (graph-based method) or algorithm (constraint-based method) and then screen out the optimal pathway to guide the modification of strains. In this paper, we reviewed the databases and algorithms for pathway design, and the applications in metabolic engineering and discussed the strengths and weaknesses of existing algorithms in practical application, hoping to provide a reference for the selection of optimal methods for the design of product synthesis pathway.

[A graph-theory-based method for processing of currency metabolites in metabolic networks].

Graph-theory-based pathway analysis is a commonly used method for pathway searching in genome-scale metabolic networks. However, such searching often results in many pathways biologically infeasible due to the presence of currency metabolites (e.g. H+, H2O, CO2, ATP etc.). Several methods have been proposed to address the problem but up to now there is no well-recognized methods for processing the currency metabolites. In this study, we proposed a new method based on the function of currency metabolites for transferring of functional groups such as phosphate. We processed most currency metabolites as pairs rather than individual metabolites, and ranked the pairs based on their importance in transferring functional groups, in order to make sure at least one main metabolite link exists for any reaction. The whole process can be done automatically by programming. Comparison with existing approaches indicates that more biologically infeasible pathways were removed by our method and the calculated pathways were more reliable, which may facilitate the graph-theory-based pathway design and visualization.