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BackgroundDiabetic peripheral neuropathy (DPN) is common and can go unnoticed until it is firmly developed. This study aims to establish a transformer-based deep learning algorithm (DLA) to classify corneal confocal microscopy (CCM) images, identifying DPN in diabetic patients.MethodsOur classification model differs from traditional convolutional neural networks (CNNs) using a Swin transformer network with a hierarchical architecture backbone. Participants included those with (DPN+, n = 57) or without (DPN−, n = 37) DPN as determined by the updated Toronto consensus criteria. The CCM image dataset (consisting of 570 DPN+ and 370 DPN− images, with five images selected from each participant’s left and right eyes) was randomly divided into training, validation, and test subsets at a 7:1:2 ratio, considering individual participants. The effectiveness of the algorithm was assessed using diagnostic accuracy measures, such as sensitivity, specificity, and accuracy, in conjunction with Grad-CAM visualization techniques to interpret the model’s decisions.ResultsIn the DPN + group (n = 12), the transformer model successfully predicted all participants, while in the DPN− group (n = 7), one participant was misclassified as DPN+, with an area under the curve (AUC) of 0.9405 (95% CI 0.8166, 1.0000). Among the DPN + images (n = 120), 117 were correctly classified, and among the DPN− images (n = 70), 49 were correctly classified, with an AUC of 0.8996 (95% CI 0.8502, 0.9491). For single-image predictions, the transformer model achieved a superior AUC relative to the ResNet50 model (0.8761, 95% CI 0.8155, 0.9366), the Inception_v3 model (0.8802, 95% CI 0.8231, 0.9374), and the DenseNet121 model (0.8965, 95% CI 0.8438, 0.9491).ConclusionTransformer-based networks outperform CNN-based networks in rapid binary DPN classification. Transformer-based DLAs have clinical DPN screening potential.

Abstract Background Autophagy has recently been shown to be critical for protecting peripheral nerve regeneration. This study explored the impact of miR-542-3p on diabetic corneal nerve regeneration and epithelial healing through the regulation of autophagy. Methods A type 1 diabetes model was established in male mice through streptozotocin administration. Immunofluorescence staining of β-Tubulin III and sodium fluorescein staining were performed to observe corneal nerve fiber density and corneal epithelial healing, respectively. Western blotting, immunofluorescence and transmission electron microscopy were used to determine autophagy levels. Subconjunctival injection of RAPA and 3-MA altered autophagy levels; with them, we evaluated the role of autophagy in diabetic keratopathy. miRNA sequencing and bioinformatics analysis were performed to identify miRNA-mRNA networks with potential autophagy-regulating roles, and miR-542-3p was measured by quantitative real-time polymerase chain reaction (qRT-PCR). miR-542-3p antagomir was injected subconjunctivally to assess the role in diabetic corneal neuropathy. Results Our data suggest that autophagy is suppressed in the diabetic corneal nerve and that activation of autophagy promotes diabetic corneal wound healing. We identified a potential autophagy-regulating miRNA-mRNA network in the diabetic trigeminal ganglion, in which miR-542-3p expression was significantly upregulated. Inhibition of miR-542-3p significantly enhanced the level of autophagy in trigeminal ganglion by upregulating ATG4D expression, thereby accelerating diabetic corneal nerve regeneration and epithelial healing. Conclusions Dysregulated autophagy is an important contributor to delayed diabetic corneal injury healing. Inhibiting miR-542-3p promotes diabetic corneal nerve regeneration and epithelial healing through autophagy activation by ATG4D.

Maritime surveillance is extensively concerned by worldwide authorities, in which ship recognition in synthetic aperture radar (SAR) images is a significant and fundamental component. Though some development has been achieved in the SAR ship recognition task, two areas remain inadequately explored, which are the comprehensive utilization of multiscale features and the deployment of the prior knowledge of the ship shape. In this article, a novel ship augmented attention network (SA$^{2}$Net) for ship recognition is proposed, which comprehensively utilizes the multiscale features and integrates the ship shape prior to the end-to-end network. On one hand, due to the unequal effects of different scales, a scale attention module is proposed to adaptively select and assign weights to desired feature scales while disregarding irrelevant scales. Moreover, a feature weaving module (FWM) is constructed to merge semantic and detailed features produced by the high-to-low backbone, enriching representations across all scales of ship targets. On the other hand, in order to incorporate the priory knowledge of the ship shape into the network, we develop a feature augmentation module (FAM) to further boost the ship recognition accuracy. This module can provide rectangular receptive fields that align with the shape of ships, wherein a limitation encountered with traditional square convolutions. Comprehensive experiments on representative three- and six-category OpenSARShip tasks and seven-category FUSAR-Ship tasks show that our SA$^{2}$Net demonstrates superior performance when compared to the current state-of-the-art methods.

Primary neuroendocrine carcinoma (NEC) is a rare malignancy in the otolaryngeal region. Computed tomography and magnetic resonance imaging findings in 16 patients with primary otolaryngeal NECs were summarized. Most of tumors occurred in the sinonasal cavity (n = 9 ; 56.3%), then supraglottis (n = 3 ; 18.8%). All sinonasal and one hard palate tumors were ill defined, and the other tumors were well defined. All lesions showed moderate to marked enhancement, but homogeneously in well-differentiated tumors while heterogeneously in most of poorly differentiated tumors. Most sinonasal tumors were poorly differentiated and invaded adjacent bones. Primary otolaryngeal NECs might have some distinct features related to their origin and differentiation. [ABSTRACT FROM AUTHOR]