1.A pilot study of lung ultrasound B-lines in diagnosis of rheumatoid arthritis associated interstitial lung diseases
Yukai WANG ; Guangzhou DU ; Zhangzhang LIN ; Shaoqi CHEN ; Qisheng LIN ; Yaobin WU ; Chanjun LIN ; Chuling WU
Chinese Journal of Rheumatology 2017;21(11):738-742
Objective To assess the clinical value of lung ultrasound (LUS) B-lines in diagnosis of rheumatoid arthritis (RA) associated interstitial lung diseases (RA-ILD).Methods Forty-five consecutive patients with RA who underwent a high resolution computed tomography (HRCT) scan of the chest,were also examined by LUS for detection of B-lines(within 1 month independently in all patients).The B-lines score was obtained by summing the number of total 50 inter-costal spaces (ICSs) of chest wall.Pulmonary fibrosis was quantified by HRCT as previously described by the 30-point Warrick score.Results B-lines score significantly correlated with the Warrick score [(r=0.778,95%CI(0.627,0.872),P<0.05].Receiver operating characteristic (ROC) curve confirmed that B-lines cut-off point 77[sensitivity of 100%,specificity of 64.3% respectively,area under curve [AUC] =0.86,95%CI(0.724,0.945)] and 108[sensitivity of 90%,specificity of 88.6% respectively,AUC=0.879,95%CI(0.747,0.957)] had an optimal power to discriminate mild (Warrick score<8) and severe fibrosis (Warrick score>15):Conclusion The data confirm that LUS is a useful technique to identify ILD in RA.In RA-ILD,B-lines correlate significantly with HRCT and are able to identify mild and severe degree of fibrosis.LUS is a promising non-invasive and non-ionizing strategy for screening RA-ILD.
2.Analysis of neurofibromatosis 1 gene mutation in a family with neurofibromatosis and its clinical significance
Yaobin ZHU ; Jiewei LUO ; Xinfu LIN ; Jie XU ; Wu ZHENG ; Yunlong YU ; Xiufen ZHENG ; Xingyu ZHENG
Chinese Journal of Neurology 2018;51(8):618-622
To screen the pathogenic mutation location in a genetic family with the neurofibromatosis (NF1) by the next generation sequencing and analyze the clinical phenotype,Illumina Miseq sequencing was applied to capture and analyze the target regions of NF1 family's probands,and furtherly find out the suspicious mutations,as well as to verify the family members by Sanger sequencing.Two rare variants were identified in proband,including the heterozygous missense mutation c.C3649T (p.P1217S) in KIF1B gene and the missense mutation c.T6311C (p.L2104P) on exon 41 of NF1 gene (NM_000267.3).The amino acid at position 2104 was found to be changed from leucine to proline in NF1.The protein prediction SIFT and Polyphen-2 values were 0,0.997,which predicted a conformational change in the encoded protein and eventually affected its function.The mutation c.T6311C in NF1 gene was detected in all patients in this family,which showed genetic co-segregation.The clinical phenotype was neurofibroma in the spinal canal.There were no café au lait spots,iris Lisch nodules,scoliosis,tinnitus,heating loss,or elevated intracranial pressure.The missense mutation c.T6311C (p.L2104P) in NF1 gene might be the genetic cause of this hereditary disease of neurofibromatosis.
3.Interpretation of Chinese experts consensus on artificial intelligence assisted management for pulmonary nodule (2022 version)
Yaobin LIN ; Yongbin LIN ; Zerui ZHAO ; Zhichao LIN ; Long JIANG ; Bin ZHENG ; Hu LIAO ; Wanpu YAN ; Bin LI ; Luming WANG ; Hao LONG
Chinese Journal of Clinical Thoracic and Cardiovascular Surgery 2023;30(05):665-671
The increasing number of pulmonary nodules being detected by computed tomography scans significantly increase the workload of the radiologists for scan interpretation. Limitations of traditional methods for differential diagnosis of pulmonary nodules have been increasingly prominent. Artificial intelligence (AI) has the potential to increase the efficiency of discrimination and invasiveness classification for pulmonary nodules and lead to effective nodule management. Chinese Experts Consensus on Artificial Intelligence Assisted Management for Pulmonary Nodule (2022 Version) has been officially released recently. This article closely follows the context, significance, core implications, and the impact of future AI-assisted management on the diagnosis and treatment of pulmonary nodules. It is hoped that through our joint efforts, we can promote the standardization of management for pulmonary nodules and strive to improve the long-term survival and postoperative life quality of patients with lung cancer.