In order to study the compounds from Glechoma longituba (Nakai) Kupr. and explore the substance bases of its dissipating blood stasis, MCI, silica gel, Sephadex LH-20, ODS column chromatography and preparative thin layer chromatography were used to isolate and purify the compounds. The structures were identified by MS, 1D NMR, and 2D NMR spectra analysis, etc. Eight triterpenoids were isolated from dichloromethane fraction of ethanol extract from G. longituba and identified as 2α,3α,16β-trihydroxy-13α,27-cyclours-11-en-28-oic acid (1), 28-norurs-12-ene-3β,17β-diol (2), 28-norolean-12-ene-3β,17β-diol (3), oleanonic acid (4), 3β,13β-dihydroxyurs-11-en-28-oic acid (5), uvaol (6), oleanolic acid (7), ursolic acid (8). Compound 1 is a new hexacyclic triterpene with 13α,27-cyclopropane structure, named euscaphic acid H; compounds 2 and 3 were isolated from Lamiaceae for the first time while compounds 4 and 5 were isolated from this genus. The in vitro anticoagulant activity of triterpenoids was evaluated by four coagulation indexes (activated partial thromboplastin time, APTT; thrombin time, TT; prothrombin time, PT; fibrinogen, FIB). Among them, compounds 1 and 6 showed obvious anticoagulant effects.

Methods: CHB patients and healthy volunteers were enrolled from Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine between August 21, 2018 and December 31, 2020. They were divided into three groups: healthy group, LGDHS group, and latent syndrome (LP) group. Proteomic analysis using isobaric tags for relative and absolute quantitation (iTRAQ) was performed to identify differentially expressed proteins (DEPs). Metabolomic profiling via ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) was applied to serum samples to detect differentially regulated metabolites (DMs). Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment were employed to explore dysregulated pathways. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were utilized to visualize group separation and identify key metabolites and proteins contributing to LGDHS differentiation. Receiver operating characteristic (ROC) curve analysis evaluated the diagnostic performance of key biomarkers, while logistic regression models assessed their predictive accuracy. P values were corrected for multiple tests using the Benjamini-Hochberg method to control the false discovery rate (FDR). Validation of potential biomarkers was conducted using independent microarray data and real-time quantitative polymerase chain reaction (RT-qPCR). Results: A total of 150 participants were enrolled, including healthy group (n = 45), LGDHS group (n = 60), and LP group (n = 45). 254 DEPs from proteomics data and 72 DMs from metabolomic profiling were identified by PCA and OPLS-DA. DEPs were mainly enriched in immune and complement pathways, while DMs involved in amino acid and energy metabolism. The integrated analysis identified seven key biomarkers: α1-acid glycoprotein (ORM1), asparagine synthetase (ASNS), solute carrier family 27 member 5 (SLC27A5), glucosidase II alpha subunit (GANAB), hexokinase 2 (HK2), 5-methyltetrahydrofolate-homocysteine methyltransferase (MTR), and maltase-glucoamylase (MGAM). Microarray validation confirmed the diagnostic potential of these genes, with area under the curve (AUC) values for ROC analysis ranging from 0.536 to 0.759. Among these, ORM1, ASNS, and SLC27A5 showed significant differential ability in differentiating LGDHS patients (P = 0.016, P = 0.035, and P < 0.001, respectively), with corresponding AUC of 0.749, 0.743, and 0.759, respectively. A logistic regression model incorporating these three genes demonstrated an AUC of 0.939, indicating a high discriminatory power for LGDHS. RT-qPCR further validated the differential expression of ORM1 and SLC27A5 between LGDHS and LP groups (P = 0.011 and P = 0.034, respectively), with ASNS showing a consistent trend in expression (P = 0.928). Conclusion This study integrates multi-omics approaches to uncover the molecular mechanisms underlying LGDHS in CHB. The identification of biomarkers ORM1, ASNS, and SLC27A5 offers a solid basis for the objective diagnosis of LGDHS, contributing to the standardization and modernization of TCM diagnostic practices.

Objective To propose a method for classifying small bowel capsule endoscopy images by combining the Swin Transformer network with an improved Adapt-RandAugment data augmentation approach,aiming to enhance the accuracy and efficiency of small bowel lesion classification and recognition.Methods An Adapt-RandAugment data augmentation approach was formulated based on the RandAugment data enhancement sub-strategy and the principles of no feature loss and no distortion when enhancing small bowel capsule endoscopy images.In the publicly available Kvasir-Capsule dataset of small bowel capsule endoscopic images,the Adapt-RandAugment data augmentation approach was trained based on the Swin Transformer network,and the convolutional neural networks ResNet152 and DenseNet161 were used as the benchmarks to validate the combined Swin Transformer network and Adapt-RandAugment data augmentation approach for small bowel capsule endoscopy image classification.Results The proposed algorithm gained advantages over ResNet152 and DenseNet161 networks in the indicators,which had the macro average precision(MAC-PRE),macro average recall(MAC-REC),macro average F1 score(MAC-Fi-S)being 0.383 2,0.314 8 and 0.290 5 respectively,the micro average precision(MIC-PRE),micro average recall(MIC-REC)and micro average F1 score(MIC-Fi-S)all being 0.755 3,and the Matthews correlation coe-fficient(MCC)being 0.452 3.Conclusion The proposed small bowel capsule endoscopy image classification method based on Swin Transformer network and Adapt-RandAugment data augmentation approach behaves well in classified recognition efficiency and accuracy.[Chinese Medical Equipment Journal,2024,45(6):9-16]

AIM: To establish an intelligent diagnostic model of keratoconus for small-diameter corneas by data mining and analysis of patients' clinical data.METHODS: Diagnostic study. A total of 830 patients(830 eyes)were collected, including 338 male(338 eyes)and 492 female(492 eyes), with an average age of 14-36(23.19±5.71)years. Among them, 731 patients(731 eyes)had undergone corneal refractive surgery at Chongqing Nanping Aier Eye Hospital from January 2020 to March 2022, and 99 patients had a diagnosed keratoconus from January 2015 to March 2022. Corneal diameter ≤11.1 mm was measured by Pentacam in all patients. Two cornea specialists classified patients' data into normal corneas, suspect keratoconus, and keratoconus groups based on the Belin/Ambrósio enhanced ectasia display(BAD)system in Pentacam. The data of 665 patients were randomly selected as the training set and the other 165 patients as the validation set by computer random sampling method. Seven parametric corneal features were extracted by convolutional neural networks(CNN), and the models were built by Residual Network(ResNet), Vision Transformer(ViT), and CNN+Transformer, respectively. The diagnostic accuracy of models was verified by cross-entropy loss and cross-validation method. In addition, sensitivity and specificity were evaluated using receiver operating characteristic curve.RESULTS: The accuracy of ResNet, ViT, and CNN+Transfermer for the diagnosis of normal cornea and suspect keratoconus was 85.57%, 86.11%, and 86.54% respectively, and the area under the receiver operating characteristic curve(AUC)was 0.823, 0.830 and 0.842 respectively. The accuracy of models for the diagnosis of suspect keratoconus and keratoconus was 97.22%, 95.83%, and 98.61%, respectively, and the AUC was 0.951, 0.939, and 0.988 respectively.CONCLUSION: For corneas ≤11.1 mm in diameter, the data model established by CNN+Transformer has a high accuracy rate for classifying keratoconus, which provides real and effective guidance for early screening.

Objective: To understand the vaccination of varicella attenuated live vaccine (VarV) among students in collective institutions, to provide a basis for analying the protective effect of vaccination. Methods: All collective institutions with chickenpox epidemic and post exposure vaccination in Jing an District from 2017 to 2019 were investigated. All students( n =6 473) in the affected class were included. Vaccination status and the incidence information of disease were collected to analyze vaccine effectiveness (VE). Results: The proportion of study subjects without an immunization history decreased year by year, and 7.5% in 2017, 7.2 % in 2018, and 4.9% in 2019. The proportion with a history of one dose prior to exposure in cases was 90.0%, it was lower than 93.5% in the non cases ( χ 2=6.53, P <0.05). The proportion with one dose as post exposure prophylaxis in cases was 8.3%, it was much lower than 44.1% in the non cases ( χ 2=179.06, P <0.01). The proportion with one dose as post exposure prophylaxis in secondary cases was 28.6%, much lower than 44.1% in the non cases ( χ 2=9.44, P <0.01).Unvaccinated ones and the second dose as post exposure prophylaxis ones in cases had the highest rate of varicella development (11.0%), a history of one dose prior to exposure and one dose as post exposure prophylaxis in cases had the lowest varicella rate (1.0%).There was a clear protective effect within two years after one dose of VarV inoculation, VE was 63.1%(95% CI =11.0%-84.7%). Conclusion The vaccine effectiveness of one dose VarV was limited. Post exposure prophylaxis as early as possible was highly effective in decreasing secondary attack rate.

ObjectiveTo analyze the epidemiological characteristics and the vaccination status of the cases in Jing’an District from 2017 to 2019， so as to provide reference basis for the strategy of prevention and control of varicella epidemic. MethodsDescriptive epidemiological methods were used to analyze the epidemiological characteristics of varicella in Jing’an District. The differences between the vaccinated group and the unvaccinated group were compared by statistical methods. ResultsA total of 2 508 cases of varicella were reported with an average annual incidence of 78.7/10⁵ from 2017 to 2019. There was no significant difference in the incidence among the three years（χ²=5.535，P=0.063）. There were 1 308 males and 1 200 females， and sex ratio was 1.1∶1. The highest incidence occurred in the age group from 5 to 9 years old （562 cases， 479.3/10⁵）. Two annual peaks occured in May and November. The incidence in the aged 18 and below decreased year by year. There was significant difference in the proportion among the three years（χ²=78.129， P<0.001）. The median interval from vaccination to onset was 5 years among the vaccinated cases. There was significant difference in the cases who received two doses of vaccine in three years（χ²=90.902， P<0.001）. ConclusionWe should strengthen the monitoring system and pay more attention to the epidemiological characteristics of varicella. The protective efficacy of two-doses vaccine needs to be systematically evaluated.

OBJECTIVE: To explore the genotyping characteristics of human fecal Escherichia coli( E. coli) and the relationships between antibiotic resistance genes (ARGs) and multidrug resistance (MDR) of E. coli in Miyun District, Beijing, an area with high incidence of infectious diarrheal cases but no related data. METHODS: Over a period of 3 years, 94 E. coli strains were isolated from fecal samples collected from Miyun District Hospital, a surveillance hospital of the National Pathogen Identification Network. The antibiotic susceptibility of the isolates was determined by the broth microdilution method. ARGs, multilocus sequence typing (MLST), and polymorphism trees were analyzed using whole-genome sequencing data (WGS). RESULTS: This study revealed that 68.09% of the isolates had MDR, prevalent and distributed in different clades, with a relatively high rate and low pathogenicity. There was no difference in MDR between the diarrheal (49/70) and healthy groups (15/24). CONCLUSION We developed a random forest (RF) prediction model of TEM.1 + baeR + mphA + mphB + QnrS1 + AAC.3-IId to identify MDR status, highlighting its potential for early resistance identification. The causes of MDR are likely mobile units transmitting the ARGs. In the future, we will continue to strengthen the monitoring of ARGs and MDR, and increase the number of strains to further verify the accuracy of the MDR markers.
Humans ; Escherichia coli/genetics* ; Escherichia coli Infections/epidemiology* ; Multilocus Sequence Typing ; Genotype ; Beijing ; Drug Resistance, Multiple, Bacterial/genetics* ; Anti-Bacterial Agents/pharmacology* ; Diarrhea ; Microbial Sensitivity Tests

BACKGROUND: Clinical outcomes are poor if patients with acute heart failure (AHF) are discharged with residual congestion in the presence of renal dysfunction. However, there is no single indication to reflect the combined effects of the two related pathophysiological processes. We, therefore, proposed an indicator, congestion and renal index (CRI), and examined the associations between the CRI and one-year outcomes and the incremental prognostic value of CRI compared with the established scoring systems in a multicenter prospective cohort of AHF. METHODS: We enrolled AHF patients and calculated the ratio of thoracic fluid content index divided by estimated glomerular filtration rate before discharge, as CRI. Then we examined the associations between CRI and one-year outcomes. RESULTS: A total of 944 patients were included in the analysis (mean age 63.3 ± 13.8 years, 39.3% women). Compared with patients with CRI ≤ 0.59 mL/min per kΩ, those with CRI > 0.59 mL/min per kΩ had higher risks of cardiovascular death or HF hospitalization (HR = 1.56 [1.13-2.15]) and all-cause death or all-cause hospitalization (HR = 1.33 [1.01-1.74]). CRI had an incremental prognostic value compared with the established scoring system. CONCLUSIONS In patients with AHF, CRI is independently associated with the risk of death or hospitalization within one year, and improves the risk stratification of the established risk models.

Asthenoteratozoospermia is one of the most severe types of qualitative sperm defects. Most cases are due to mutations in genes encoding the components of sperm flagella, which have an ultrastructure similar to that of motile cilia. Coiled-coil domain containing 103 (CCDC103) is an outer dynein arm assembly factor, and pathogenic variants of CCDC103 cause primary ciliary dyskinesia (PCD). However, whether CCDC103 pathogenic variants cause severe asthenoteratozoospermia has yet to be determined. Whole-exome sequencing (WES) was performed for two individuals with nonsyndromic asthenoteratozoospermia in a consanguineous family. A homozygous CCDC103 variant segregating recessively with an infertility phenotype was identified (ENST00000035776.2, c.461A>C, p.His154Pro). CCDC103 p.His154Pro was previously reported as a high prevalence mutation causing PCD, though the reproductive phenotype of these PCD individuals is unknown. Transmission electron microscopy (TEM) of affected individuals' spermatozoa showed that the mid-piece was severely damaged with disorganized dynein arms, similar to the abnormal ultrastructure of respiratory ciliary of PCD individuals with the same mutation. Thus, our findings expand the phenotype spectrum of CCDC103 p.His154Pro as a novel pathogenic gene for nonsyndromic asthenospermia.
Asthenozoospermia/pathology* ; Dyneins/genetics* ; Homozygote ; Humans ; Male ; Microtubule-Associated Proteins ; Mutation ; Mutation, Missense ; Sperm Tail/metabolism*

Objective To investigate the diversity and composition of microflora in laboratory-reared Aedes albopictus at different developmental stages and larval habitat waters. Methods The larval habitat waters and different developmental stages of laboratory-reared A. albopictus were collected, and the V3 and V4 regions of the bacterial 16S rRNA gene were sequenced using Illumina Miseq next-generation sequencing. The abundance and diversity of the microflora were examined using alpha diversity index in A. albopictus at different developmental stages, and the difference in the microflora compositions was compared in A. albopictus at different developmental stages using principal component analysis (PCA). In addition, the species composition and relative abundance of microflora in A. albopictus at different developmental stages were described using histograms and Venn diagrams. Results A total of 16 phyla, 30 classes, 72 orders, 129 families and 224 genera of bacteria were detected in larval habitat waters and different developmental stages of A. albopictus. The highest bacterial diversity was seen in larval A. albopictus, with Chao index of 125.20 ± 30.48 and Shannon diversity index of 2.04 ± 0.39, which were comparable to those (Chao index of 118.52 ± 15.07 and Shannon diversity index of 2.15 ± 0.30) in larval habitat waters (t = 0.35 and -0.41, both P values > 0.05). The bacterial abundance and evenness were significantly greater in female adults than in male adults (Chao index: 42.50 ± 3.54 vs. 18.50 ± 2.13, t = 8.23, P < 0.05; Shannon diversity index: 1.25 ± 1.67 vs. 0.50 ± 0.05, t = 6.00, P < 0.05). Proteobacteria, Bacteroidota, Actinobacteriota and Finnicutes were four common phyla of bacteria at each developmental stage of A. albopictus, with Proteobacteria dominated at the pupal stage (90.36%), Bacteroidota dominated at the adult stage (46.01% in female adults and 86.11% in male adults), and Actinobacteriota dominated at the larval stage (32.10%). Elizabethkingia and Rahnella 1 were common dominant genera of bacteria at each developmental stage of A. albopictus, with Rahnellal as the major component at the pupal stage (87.56%), Elizabethkingia as the main component at the adult stage (46.01% in female adults and 86.11% in male adults, respectively), and Microbacteria as the dominant bacterial genus at the larval stage (12.11%). In addition, Delftia, Elizabethkingia, Romboutsia, Serratia, Rahnella 1, Enterococcus and Microbacterium were common genera of bacteria at each developmental stage of A. albopictus, with Edaphobaculum dominated at the larval stage (17.54%) and Sphingobacterium dominated in larval habitat waters (13.93%). Conclusions There are differences in the composition of symbiotic bacteria at different developmental stages of A. albopictus; however, similar microflora diversity is maintained at the phylum level. The microflora diversity is comparable in larvae and larval habitat waters of A. albopictus.