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The aim of this study was to explore the method for obtaining the thin sectional anatomy data of the adult temporal bone and study the fine structures using this method. Three fresh adult cadaveric heads were scanned with multi-slice computer tomography (MSCT) centered on petrous bones. The CT images of 0.6 mm were obtained by multi-planar reformation (MPR). The slices of 0.1 mm were shaved off the specimen in the axial direction with the numerical control milling machine after being embedded and frozen, pictures of which were taken by the digital camera and saved in the computer. The thin axial sectional anatomic structures of the intra-temporal were investigated and correlated with MPR images. Via the comparison, fifty micro-anatomic structures of the temporal bone that can't be delineated clearly or missed in the thick sections were evaluated. The anatomical details of the temporal bone can be clearly delineated in MSCT in sub-millimeter and were identical to those in sectional anatomy images. This method can supply anatomical details that had been missed or overlooked for imaging diagnosis and surgical anatomy.

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The size of SCCs remains constant from children to the elderly people, unlike the other human organs. The reference values provided by multidetector CT can serve as an aid for the interpretation of CT images.

The purpose of this study was to determine the performance of low-dose computed tomography (CT) scanning with integrated circuit (IC) detector in defining fine structures of temporal bone in children by comparing with the conventional detector.The study was performed with the approval of our institutional review board and the patients’ anonymity was maintained. A total of 86 children <3 years of age underwent imaging of temporal bone with low-dose CT (80 kV/150 mAs) equipped with either IC detector or conventional discrete circuit (DC) detector. The image noise was measured for quantitative analysis. Thirty-five structures of temporal bone were further assessed and rated by 2 radiologists for qualitative analysis. κ Statistics were performed to determine the agreement reached between the 2 radiologists on each image. Mann–Whitney U test was used to determine the difference in image quality between the 2 detector systems.Objective analysis showed that the image noise was significantly lower (P < 0.001) with the IC detector than with the DC detector. The κ values for qualitative assessment of the 35 fine anatomical structures revealed high interobserver agreement. The delineation for 30 of the 35 landmarks (86%) with the IC detector was superior to that with the conventional DC detector (P < 0.05) although there were no differences in the delineation of the remaining 5 structures (P > 0.05).The low-dose CT images acquired with the IC detector provide better depiction of fine osseous structures of temporal bone than that with the conventional DC detector.

Retinal illnesses such as age-related macular degeneration and diabetic macular edema will lead to irreversible blindness. With optical coherence tomography (OCT), doctors are able to see cross-sections of the retinal layers and provide patients with a diagnosis. Manual reading of OCT images is time-consuming, labor-intensive and even error-prone. Computer-aided diagnosis algorithms improve efficiency by automatically analyzing and diagnosing retinal OCT images. However, the accuracy and interpretability of these algorithms can be further improved through effective feature extraction, loss optimization and visualization analysis. In this paper, we propose an interpretable Swin-Poly Transformer network for performing automatically retinal OCT image classification. By shifting the window partition, the Swin-Poly Transformer constructs connections between neighboring non-overlapping windows in the previous layer and thus has the flexibility to model multi-scale features. Besides, the Swin-Poly Transformer modifies the importance of polynomial bases to refine cross entropy for better retinal OCT image classification. In addition, the proposed method also provides confidence score maps, assisting medical practitioners to understand the models’ decision-making process. Experiments in OCT2017 and OCT-C8 reveal that the proposed method outperforms both the convolutional neural network approach and ViT, with an accuracy of 99.80% and an AUC of 99.99%.

Drug addiction is a common problem worldwide. Research has shown adverse childhood experiences (ACEs) to be an important factor related to drug addiction. However, there are few studies on how ACEs lead to drug addiction and the role of resilience and depression in this process. Thus, the main purposes of the study were to determine the proportion of those with adverse childhood experiences who take drugs in adulthood and how resilience and depression affect this relationship. The results showed that (1) greater severity of ACEs made individuals more likely to take drugs; (2) ACEs were positively correlated with depression, and resilience was negatively correlated with ACEs and depression; and (3) ACEs not only affected drug addiction through resilience or depression alone but also through the combined action of resilience and depression, indicating that depression led to drug addiction while resilience weakened the effect of ACEs on depression and drug addiction. Furthermore, in the serial mediation model, abuse, neglect, and family dysfunction were significant predictors of drug addiction. Our results are encouraging in that they provide guidance in understanding the complex relationships among ACEs, resilience, depression, and drug addiction.

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