A) in COL1A1 gene resulting in OI in a Chinese family. The detailed molecular and clinical features will be useful for extending the evidence for genetic and phenotypic heterogeneity in OI and exploring the phenotype-genotype correlations in OI.

Objective To report a familial dentinogenesis imperfecta type Ⅱ (DGI type Ⅱ) with a novel splicing mutation in DSPP (dentin sialophosphoprotein) gene.Methods Based on the result of linkage analysis performed previously to map the candidate gene DSPP in the family, the promoter,the first four exons and exon-intron boundaries of DSPP were directly sequenced for the members of the DGI type Ⅱ family. Denaturing high performance liquid chromatography (DHPLC) analysis was performed to confirm the results of sequencing.Results A novel splicing mutation of 23 bp deletion in intron 2 of DSPP gene was identified by DNA sequence analysis. The mutation changed acceptor site sequence from CAG to AAG, and might result in functional abolition of possible branch point site in intron 2. DHPLC result was consistent with that of sequencing. The mutation may be identified in all affected individuals, but not found in normal members of the family and 50 controls.Conclusion These results suggest the deleted mutation of DSPP gene causes DGI type Ⅱ in the family. The mutation has not been reported before.

Objective:To deeply explore the pain experience of patients with rheumatoid arthritis, so as to provide a basis for the practical interventions in the next step.Methods:Using the phenomenological research method, 18 patients with rheumatoid arthritis who experienced pain in the First Hospital of Soochow University from September 2020 to January 2021 were selected for semi-structured interviews, and the Colaizzi 7-step analysis method was used for data analysis.Results:The pain experience of patients with rheumatoid arthritis were summarized into six themes. Pain was complex and erratic, pain relief strategies were ineffective, pain negatively affected daily life, expected more pain relief, seeking help selectively when pain occurs, and experienced pain brings positive change.Conclusions:Medical staff must pay attention to the real experiences and demands of pain in rheumatoid arthritis patients, use information technology and multidisciplinary collaboration to provide patients with effective pain management strategies and encourage patients to make more positive changes to relieve pain symptoms.

Objective:To explore the performance of the attention-multiple instance learning (MIL) framework, an attention fusion network-based MIL, in the automated diagnosis of chronic gastritis with multiple indicators.Methods:A total of 1 015 biopsy cases of gastritis diagnosed in Fudan University Cancer Hospital, Shanghai, China and 115 biopsy cases of gastritis diagnosed in Shanghai Pudong Hospital, Shanghai, China were collected from January 1st to December 31st in 2018. All pathological sections were digitally converted into whole slide imaging (WSI). The WSI label was based on the corresponding pathological report, including "activity" "atrophy" and "intestinal metaplasia". The WSI were divided into a training set, a single test set, a mixed test set and an independent test set. The accuracy of automated diagnosis for the Attention-MIL model was validated in three test sets.Results:The area under receive-operator curve (AUC) values of Attention-MIL model in single test sets of 240 WSI were: activity 0.98, atrophy 0.89, and intestinal metaplasia 0.98; the average accuracy of the three indicators was 94.2%. The AUC values in mixed test sets of 117 WSI were: activity 0.95, atrophy 0.86, and intestinal metaplasia 0.94; the average accuracy of the three indicators was 88.3%. The AUC values in independent test sets of 115 WSI were: activity 0.93, atrophy 0.84, and intestinal metaplasia 0.90; the average accuracy of the three indicators was 85.5%.Conclusions:To assist in pathological diagnosis of chronic gastritis, the diagnostic accuracy of Attention-MIL model is very close to that of pathologists. Thus, it is suitable for practical application of artificial intelligence technology.

The accurate annotation of transcription start sites(TSSs)and their usage are critical for the mechanistic understanding of gene regulation in different biological contexts.To fulfill this,specific high-throughput experimental technologies have been developed to capture TSSs in a genome-wide manner,and various computational tools have also been developed for in silico pre-diction of TSSs solely based on genomic sequences.Most of these computational tools cast the problem as a binary classification task on a balanced dataset,thus resulting in drastic false positive predictions when applied on the genome scale.Here,we present DeeReCT-TSS,a deep learning-based method that is capable of identifying TSSs across the whole genome based on both DNA sequence and conventional RNA sequencing data.We show that by effectively incorporating these two sources of information,DeeReCT-TSS significantly outperforms other solely sequence-based methods on the precise annotation of TSSs used in different cell types.Furthermore,we develop a meta-learning-based extension for simultaneous TSS annotations on 10 cell types,which enables the identification of cell type-specific TSSs.Finally,we demonstrate the high precision of DeeReCT-TSS on two independent datasets by correlating our predicted TSSs with experimentally defined TSS chromatin states.The source code for DeeReCT-TSS is available at https://github.-com/JoshuaChou2018/DeeReCT-TSS_release and https://ngdc.cncb.ac.cn/biocode/tools/BT007316.