Identification of pathogenic variants for the development of ultra-long axial length in myopic children

by YanYing Zhu, XueYan Li, YueXin Chen, HaiYan Xie, YuKun Liu, XiaoChen Xu, Jing Wang

Axial elongation is a key factor in myopia progression, yet its genetic basis remains incompletely understood. This study aims to identify pathogenic genetic variants associated with excessively elongated axial length in children.

This study included 56 children with axial lengths exceeding the normal range for their age group, and whole-exome sequencing (WES) was performed on their oral mucosal samples. Clinical evaluations included axial length measurement, refraction testing, and fundus photography to assess the degree of myopia and retinal changes. Co-segregation analysis was conducted in selected families (F#1, F#2, F#5) to validate the familial inheritance patterns of the variants.

Fifteen children carried variants in genes including BBS2, OPN1LW, P4HA2, FBN1, LOXL3, FZD4, USH2A, COL2A1, and BFSP2, with five novel variants identified: BBS2 (c.700C > T), P4HA2 (c.1382C > G), FBN1 (c.7130T > C), LOXL3 (c.1580delC), and FZD4 (c.1315G > A). Notably, a rare compound heterozygous BBS2 variant (c.700C > T/c.534 + 1G > T) was found in a non-syndromic child, and the P4HA2 (c.419A > G) variant in family F#5 exhibited a phenotype distinct from previous studies.

This study identified five novel variants sites and discovered two cases with phenotypes distinct from previous studies, thereby expanding the genetic variant spectrum associated with myopia and providing new targets for genetic screening and intervention.

Development of a Deep Learning‐Based Model for Pressure Injury Surface Assessment

ABSTRACT

Aim

To develop a deep learning-based smart assessment model for pressure injury surface.

Design

Exploratory analysis study.

Methods

Pressure injury images from four Guangzhou hospitals were labelled and used to train a neural network model. Evaluation metrics included mean intersection over union (MIoU), pixel accuracy (PA), and accuracy. Model performance was tested by comparing wound number, maximum dimensions and area extent.

Results

From 1063 images, the model achieved 74% IoU, 88% PA and 83% accuracy for wound bed segmentation. Cohen's kappa coefficient for wound number was 0.810. Correlation coefficients were 0.900 for maximum length (mean difference 0.068 cm), 0.814 for maximum width (mean difference 0.108 cm) and 0.930 for regional extent (mean difference 0.527 cm²).

Conclusion

The model demonstrated exceptional automated estimation capabilities, potentially serving as a crucial tool for informed decision-making in wound assessment.

Implications and Impact

This study promotes precision nursing and equitable resource use. The AI-based assessment model serves clinical work by assisting healthcare professionals in decision-making and facilitating wound assessment resource sharing.

Reporting Method

The STROBE checklist guided study reporting.

Patient or Public Contribution

Patients provided image resources for model training.