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Objective: To analyse the histopathological features of eosinophilic sialodochitis by using terminal duct biopsy. Methods: Sixty‐five patients with suspected eosinophilic sialodochitis and four with chronic obstructive sialadenitis were prospectively enrolled. Clinical features, laboratory tests and sialograms were comparatively analysed. Terminal duct biopsy of the parotid or submandibular glands was performed concomitantly with endoscopy‐assisted duct dilatation to determine the histopathological features of eosinophilic sialodochitis. Results: Based on eosinophil quantification, the samples of suspected patients were scored as 'definite', 'highly suspected' and 'negative' in 26 (40%), 15 (23.1%) and 24 (36.9%) cases, respectively. Gland types and peripheral blood eosinophil counts were significantly different among these three groups. The proportions of itching glands, mucus plug exudations and elevated immunoglobulin E levels were higher in the 'definite' group than in the other two groups; however, the intergroup differences were insignificant. The primary pathological features of eosinophilic sialodochitis were abundant eosinophils and lymphocytes infiltrated around the duct, degranulation of eosinophils, extensive fibrosis and scattered mastocytes. Periductal eosinophils were not found in cases of chronic obstructive sialadenitis. Conclusion: Our findings suggest that terminal duct biopsy is safe and valuable for the pathological confirmation of eosinophilic sialodochitis, and can be used simultaneously with endoscopy‐assisted duct dilatation. [ABSTRACT FROM AUTHOR]

BackgroundAbnormal brain development is common in children with cerebral palsy (CP), but there are no recent reports on the actual brain age of children with CP.ObjectiveOur objective is to use the brain age prediction model to explore the law of brain development in children with CP.MethodsA two-dimensional convolutional neural networks brain age prediction model was designed without segmenting the white and gray matter. Training and testing brain age prediction model using magnetic resonance images of healthy people in a public database. The brain age of children with CP aged 5–27 years old was predicted.ResultsThe training dataset mean absolute error (MAE) = 1.85, r = 0.99; test dataset MAE = 3.98, r = 0.95. The brain age gap estimation (BrainAGE) of the 5- to 27-year-old patients with CP was generally higher than that of healthy peers (p < 0.0001). The BrainAGE of male patients with CP was higher than that of female patients (p < 0.05). The BrainAGE of patients with bilateral spastic CP was higher than those with unilateral spastic CP (p < 0.05).ConclusionA two-dimensional convolutional neural networks brain age prediction model allows for brain age prediction using routine hospital T1-weighted head MRI without segmenting the white and gray matter of the brain. At the same time, these findings suggest that brain aging occurs in patients with CP after brain damage. Female patients with CP are more likely to return to their original brain development trajectory than male patients after brain injury. In patients with spastic CP, brain aging is more serious in those with bilateral cerebral hemisphere injury than in those with unilateral cerebral hemisphere injury.