Deep learning for the screening of primary ciliary dyskinesia based on cranial computed tomography.

Objective: To analyze the cranial computed tomography (CT) imaging features of patients with primary ciliary dyskinesia (PCD) who have exudative otitis media (OME) and sinusitis using a deep learning model for early intervention in PCD. Methods: Thirty-two children with PCD diagnosed at the Children's Hospital of Fudan University, Shanghai, China, between January 2010 and January 2021 who had undergone cranial CT were retrospectively analyzed. Thirty-two children with OME and sinusitis diagnosed using cranial CT formed the control group. Multiple deep learning neural network training models based on PyTorch were built, and the optimal model was trained and selected to observe the differences between the cranial CT images of patients with PCD and those of general patients and to screen patients with PCD. Results: The Swin-Transformer, ConvNeXt, and GoogLeNet training models had optimal results, with an accuracy of approximately 0.94; VGG11, VGG16, VGG19, ResNet 34, and ResNet 50, which are neural network models with fewer layers, achieved relatively strong results; and Transformer and other neural networks with more layers or neural network models with larger receptive fields exhibited a relatively weak performance. A heat map revealed the differences in the sinus, middle ear mastoid, and fourth ventricle between the patients with PCD and the control group. Transfer learning can improve the modeling effect of neural networks. Conclusion: Deep learning-based CT imaging models can accurately screen for PCD and identify differences between the cranial CT images.

Increasing prevalence of hypervirulent ST5 methicillin susceptible Staphylococcus aureus subtype poses a serious clinical threat.

Staphylococcus aureus (S. aureus) is a clinical pathogen of great significance causing metastatic or complicated infections. ST5 clonotype isolates have dominated S. aureus infections for more than 10 years in Shanghai, China, and the proportion of methicillin-susceptible S. aureus (MSSA) has remarkably increased in the past decades. By whole-genome sequencing (WGS) 121 ST5 clonotype S. aureus isolates using next-generation sequencing (NGS) platforms and characterizing the evolutionary dynamics of ST5 linages, we found that MSSA evolved independently, making it a subtype differed from other MRSA clones. Drug resistance gene analysis by using the NGS data demonstrated that ST5 clonotype MRSA might be more tolerant under the threat of antimicrobials, which was confirmed in further in vitro susceptibility tests. However, MSSA subtype isolates exhibited relatively high virulence upon the analysis of virulence factors. Furthermore, MSSA subtype isolates displayed higher hemolysis capacity and higher ability to adhere to epithelial cells including A549 human alveolar epithelial cells and HaCaT human skin keratinocytes, caused more severe infections in murine abscess model. With its high virulence and enhanced magnitude in the past decades, the ST5 MSSA subtype poses a serious clinical threat hence more attention should be paid to the prevention and control.