Objective To collect oral exhaled odor spectrum of patients of chronic atrophic gastritis(CAG)with yin deficiency syndrome and detect their gut microbiota;To elucidate the mechanism of odor changes from the perspective of gut microbiota changes;To provide a basic research for the objectification of TCM olfactory diagnosis in CAG.Methods Totally 110 patients with CAG,including 55 patients with CAG yin deficiency syndrome,55 patients with CAG non-yin deficiency syndrome,and 30 healthy individuals were collected.The electronic nose technology was used to collect the oral exhaled odor spectrum of all subjects,and an improved Transformer model was used to identify the breath odor spectrum of CAG yin deficiency syndrome patients and healthy individuals,CAG non-yin deficiency syndrome patients and healthy individuals,CAG yin deficiency syndrome patients and CAG non-yin deficiency syndrome patients.At the same time,16S rRNA high-throughput sequencing method was used to detect the gut microbiota of the subjects'fecal samples,and the correlation analysis between the odor spectrum characteristics of CAG yin deficiency syndrome and gut microbiota was conducted.Results ① Analysis and recognition of odor spectrum characteristic.Amplitude characteristics:The response curves A,C,D,G,H,I and J of the odor spectrum in the CAG yin deficiency syndrome group and the CAG non-yin deficiency syndrome group were all lower in amplitude than those in the healthy group(P<0.01,P<0.05).Slope characteristics:The slopes of response curves A,B,C,D,E,G,H,I and J in the odor spectrum of the CAG yin deficiency syndrome group and the CAG non-yin deficiency syndrome group were lower than those of the healthy group(P<0.01,P<0.05).Pattern recognition:The accuracy of pattern recognition between the CAG yin deficiency syndrome group and the healthy group reached 0.904,with an area under ROC curve(AUC)of 0.91;the accuracy of pattern recognition between the CAG non-yin deficiency syndrome group and the healthy group reached 0.885,AUC=0.89;the accuracy of pattern recognition between the CAG yin deficiency syndrome group and the CAG non-yin deficiency syndrome group reached 0.747,AUC=0.75.② Species composition:At the genus level,compared with the healthy group,the abundance of Actinomyces,Escherichia-Shigella and Tyzzerella in the CAG yin deficiency syndrome group increased(P<0.05),while the abundance of Prevotella,Sutterella and Subdoligranulum decreased(P<0.05);the abundance of[Ruminococcus]_gnavus_group and Escherichia-Shigella in the CAG non-yin deficiency syndrome group increased significantly(P<0.01),while the abundance of Prevotella and Subdoligranulum decreased(P<0.05).Compared with the CAG yin deficiency syndrome group,the non-yin deficiency syndrome group showed significant enrichment of the Dialister(P<0.05).③ Correlation analysis between odor spectrum characteristics and gut microbiota in CAG yin deficiency syndrome:This study identified 17 bacterial genera that showed positive and negative correlations with the amplitude and slope characteristics of the odor spectrum in CAG yin deficiency syndrome,namely Lachnospiraceae_NK4A136_group,Lachnospiraceae_ND3007_group,Faecalibacterium,UCG-002,UCG-005,Coprococcus,CAG-352,Parabacteroides,Actinomyces,Streptococcus,Anaerostipes,Blautia,Dorea,[Eubacterium]_hallii_group,Phascolarctobacterium,Clostridium_sensu_stricto_1,Enterobacter.The above-mentioned bacterial genera could be classified into the following bacterial families:Trichomonas,Clostridia,Porphyromonas,Actinobacteria,Ruminococcus,Streptococcus,Bacteroidetes,Clostridium,Veillonellaceae and Enterobacteriaceae.Conclusion The use of electronic nose technology can accurately identify the oral exhaled odor of patients with CAG yin deficiency syndrome,CAG non-yin deficiency syndrome,and healthy individuals;the odor spectrum characteristics of patients with CAG yin deficiency syndrome are correlated with multiple bacterial genera,and the changes in related metabolites and gases produced by the disruption of their gut microbiota may be one of the biological bases for the changes in oral exhaled odor in CAG yin deficiency syndrome.

Objective To collect oral exhaled odor spectrum of patients of chronic atrophic gastritis(CAG)with yin deficiency syndrome and detect their gut microbiota;To elucidate the mechanism of odor changes from the perspective of gut microbiota changes;To provide a basic research for the objectification of TCM olfactory diagnosis in CAG.Methods Totally 110 patients with CAG,including 55 patients with CAG yin deficiency syndrome,55 patients with CAG non-yin deficiency syndrome,and 30 healthy individuals were collected.The electronic nose technology was used to collect the oral exhaled odor spectrum of all subjects,and an improved Transformer model was used to identify the breath odor spectrum of CAG yin deficiency syndrome patients and healthy individuals,CAG non-yin deficiency syndrome patients and healthy individuals,CAG yin deficiency syndrome patients and CAG non-yin deficiency syndrome patients.At the same time,16S rRNA high-throughput sequencing method was used to detect the gut microbiota of the subjects'fecal samples,and the correlation analysis between the odor spectrum characteristics of CAG yin deficiency syndrome and gut microbiota was conducted.Results ① Analysis and recognition of odor spectrum characteristic.Amplitude characteristics:The response curves A,C,D,G,H,I and J of the odor spectrum in the CAG yin deficiency syndrome group and the CAG non-yin deficiency syndrome group were all lower in amplitude than those in the healthy group(P<0.01,P<0.05).Slope characteristics:The slopes of response curves A,B,C,D,E,G,H,I and J in the odor spectrum of the CAG yin deficiency syndrome group and the CAG non-yin deficiency syndrome group were lower than those of the healthy group(P<0.01,P<0.05).Pattern recognition:The accuracy of pattern recognition between the CAG yin deficiency syndrome group and the healthy group reached 0.904,with an area under ROC curve(AUC)of 0.91;the accuracy of pattern recognition between the CAG non-yin deficiency syndrome group and the healthy group reached 0.885,AUC=0.89;the accuracy of pattern recognition between the CAG yin deficiency syndrome group and the CAG non-yin deficiency syndrome group reached 0.747,AUC=0.75.② Species composition:At the genus level,compared with the healthy group,the abundance of Actinomyces,Escherichia-Shigella and Tyzzerella in the CAG yin deficiency syndrome group increased(P<0.05),while the abundance of Prevotella,Sutterella and Subdoligranulum decreased(P<0.05);the abundance of[Ruminococcus]_gnavus_group and Escherichia-Shigella in the CAG non-yin deficiency syndrome group increased significantly(P<0.01),while the abundance of Prevotella and Subdoligranulum decreased(P<0.05).Compared with the CAG yin deficiency syndrome group,the non-yin deficiency syndrome group showed significant enrichment of the Dialister(P<0.05).③ Correlation analysis between odor spectrum characteristics and gut microbiota in CAG yin deficiency syndrome:This study identified 17 bacterial genera that showed positive and negative correlations with the amplitude and slope characteristics of the odor spectrum in CAG yin deficiency syndrome,namely Lachnospiraceae_NK4A136_group,Lachnospiraceae_ND3007_group,Faecalibacterium,UCG-002,UCG-005,Coprococcus,CAG-352,Parabacteroides,Actinomyces,Streptococcus,Anaerostipes,Blautia,Dorea,[Eubacterium]_hallii_group,Phascolarctobacterium,Clostridium_sensu_stricto_1,Enterobacter.The above-mentioned bacterial genera could be classified into the following bacterial families:Trichomonas,Clostridia,Porphyromonas,Actinobacteria,Ruminococcus,Streptococcus,Bacteroidetes,Clostridium,Veillonellaceae and Enterobacteriaceae.Conclusion The use of electronic nose technology can accurately identify the oral exhaled odor of patients with CAG yin deficiency syndrome,CAG non-yin deficiency syndrome,and healthy individuals;the odor spectrum characteristics of patients with CAG yin deficiency syndrome are correlated with multiple bacterial genera,and the changes in related metabolites and gases produced by the disruption of their gut microbiota may be one of the biological bases for the changes in oral exhaled odor in CAG yin deficiency syndrome.

Objective:To analyze the correlation factors between peripapillary duodenal diverticulum (PDD) and choledochectasia by CT scan.Methods:The clinical data of 220 patients with duodenal diverticulum detected by multi-slice spiral CT scan and confirmed by gastrointestinal angiography or endoscopic retrograde cholangiopancreatography (ERCP) in Dahua Hospital, Xuhui District of Shanghai City were retrospectively analyzed. The correlation of the PDD, the contact of common bile duct (CBD), length of contact and exudation with choledochectasia in patients with PDD were analyzed.Results:A total of 236 duodenal diverticulum were found in 220 patients. Among them, there were 152 PDD, 41 diverticulum located superior to the duodenal papilla, 28 diverticulum located inferior to the duodenal papilla, 3 diverticulum located lateral to the duodenal papilla, and 12 diverticulumlocated in the horizontal portion. The incidence of choledochectasia in patients with PDD contacted with CBD was significantly higher than that in patients with PDD not contacted with CBD: 59.35% (73/123) vs. 37.93% (11/29), and there was statistical difference ( P<0.05); the incidence of choledochectasia in patients with contact length of PDD and CBD ≥1.5 cm was significantly higher than that in patients without contact of PDD and CBD and patients with contact length of PDD and CBD <1.5 cm: 82.43% (61/74) vs. 24.49% (12/49) and 37.93% (11/29), and there was statistical difference ( P<0.05); the incidence of choledochectasia in PDD patients with exudation was significantly higher than that in PDD patients without exudation: 10/11 vs. 52.48% (74/141), and there was statistical difference ( P<0.05). Conclusions:The patients with contact length of PDD and CBD ≥1.5 cm and patients with PDD combined with exudation could be prone to choledochectasia.

Objective:To compare and analyze the calcification characteristics and diagnostic efficiency of different breast lesion types using digtal breast tomosynthesis (DBT) and full-field digital mammography (FFDM).Methods:Totally 1 263 patients who underwent both DBT and FFDM at the same time from January 2015 to December 2018 in Dahua Hospital, Xuhui district, Shanghai were analyzed retrospectively. Benign and malignant calcification should be confirmed by pathology or a follow-up of 24 months or more using mammography, and the results of DBT and FFDM were taken as a gold standard. The detection rate and diagnostic efficiency of different types of benign and malignant breast calcification with the two methods were compared and analyzed. The detection rate of morphology and distribution of malignant calcification were compared among groups.Results:There were 240 cases with non-dense breast including 56 cases with benign calcification and 13 cases with malignant calcification. Meanwhile, there were 1 023 cases with dense breast, including 356 cases with benign calcification and 63 cases with malignant calcification. In the cases of non-dense breast, the detection rates of benign calcification by DBT and FFDM were 22.9% (55/240) and 21.7% (52/240), whereas the rates of malignant calcification were 5.0% (12/240) and 4.6% (11/240), all without statistically significances（χ2=0.108, 0.046, P>0.05). No significant differences were observed in the morphology and distribution of malignant calcification detection rates ( P>0.05). In the cases of dense breast, the benign calcification detection rates by DBT and FFDM were 34.2% (350/1 023) and 31.9% (326/1 023), whereas the detection rates of malignant calcification were 6.0% (61/1 023) and 4.9% (50/1 023), all without statistically significances (χ2=1.273 and 1.153, P>0.05). DBT detected more cases of amorphous and cluster distribution of malignant calcification than FFDM, with statistically significant differences (χ2=12.921 and 11.667, P<0.05). The area under ROC curve of DBT and FFDM in diagnosis of non-dense breast were 0.993 and 0.992, and 0.987 and 0.964 in dense breast, respectively, with no significant differences ( Z= 0.136 and 1.391, P>0.05). Conclusions:Compared with FFDM, DBT shows no statistical difference in the diagnostic efficiency of breast calcification. However, it has certain advantages in detecting malignant, amorphous, and clustered calcification in dense breast. DBT has a potential to improve the accuracy of BI-RADS classification of breast calcification.

0.05).Conclusion HV-CVVH is effective in the treatment of severe acute pancreatitis.For SAP patients who are suit for the hemofiltration treatment,the treatment of HV-CVVH for 8h and for more than 8h has nearly the same effectiveness.