A Unique Manifestation of Bardet-Biedl Syndrome with Otolaryngologic Symptoms and Bronchopneumonia in a One-year-old Girl

Chest Computed Tomography Images in Neonatal Bronchial Pneumonia under the Adaptive Statistical Iterative Reconstruction Algorithm

This study was to explore the application value of chest computed tomography (CT) images processed by artificial intelligence (AI) algorithms in the diagnosis of neonatal bronchial pneumonia (NBP). The AI adaptive statistical iterative reconstruction (ASiR) algorithm was adopted to reconstruct the chest CT image to compare and analyze the effect of the reconstruction of CT image under the ASiR algorithm under different preweight and postweight values based on the objective measurement and subjective evaluation. 85 neonates with pneumonia treated in hospital from September 1, 2015, to July 1, 2020, were selected as the research objects to analyze their CT imaging characteristics. Subsequently, the peripheral blood of healthy neonates during the same period was collected, and the levels of C-reactive protein (CRP) and erythrocyte sedimentation rate (ESR) were detected. The efficiency of CT examination, CRP, ESR, and combined examination in the diagnosis of NBP was analyzed. The results showed that the subjective quality score, lung window subjective score, and mediastinal window subjective score were the highest after CT image reconstruction when the preweight value of the ASiR algorithm was 50%. After treatment, 79 NBP cases (92.9%) showed ground-glass features in CT images. Compared with the healthy neonates, the levels of CRP and ESR in the peripheral blood of neonates with bronchial pneumonia were much lower (P < 0.05). The accuracy rates of CT examination, CRP examination, ESR examination, CRP + ESR examination, and CRP + ESR + CT examination for the diagnosis of NBP were 80.7%, 75.3%, 75.1%, 80.3%, and 98.6%, respectively. CT technology based on AI algorithm showed high clinical application value in the feature analysis of NBP.

Laurence-Moon-Bardet-Biedl Syndrome: A Rare Case With a Literature Review

Laurence-Moon-Bardet-Biedl syndrome (LMBBS), a rare autosomal recessive genetic disorder, results from consanguineous marriage. It is a congenital ciliopathy manifesting with primary and secondary characteristics. Primary clinical features include rod and cone dystrophy, polydactyly, central obesity, genital abnormalities, and mental retardation, often presenting as poor schooling skills. Secondary clinical features include developmental delay, speech deficit, brachydactyly/syndactyly, dental defects, ataxia, olfactory deficit, diabetes mellitus (DM), and congenital heart disease. Herein, we report a case of a 15-year-old male with clinical manifestations of LMBBS, namely learning disabilities, night blindness, hypogonadism, polydactyly, polysyndactyly, and obesity. Physicians must be familiar with this syndrome, for which an early diagnosis, multidisciplinary approach, and regular follow-ups can profoundly diminish morbidity and mortality in LMBBS patients.