Objective To evaluate an ultrasound-based deep learning radiomics nomogram(DLR_Nomo-gram)for non-invasively differentiating between type Ⅰ and type Ⅱ epithelial ovarian cancer(EOC)before surgery.Methods In this study,a cohort of 195 patients diagnosed with EOC was analyzed.Participants were randomly divided into a training set and a testing set at an 8∶2 ratio.Following data preprocessing,region of interest(ROI)delineation,feature extraction and selection,as well as the clipping and extraction of the maximum section sonogram for each sample,three initial models were developed:the radiomics signature(Rad_Sig),the deep transfer learning signature(DTL_Sig),and the clinical signature(Clinic_Sig).Subsequently,an integrated model—referred to as the DLR_Nomogram—was constructed by combining Rad_Sig,DTL_Sig,and Clinic_Sig,and was presented in the form of a nomogram.The performance of the model was evaluated using the receiver operating characteristic(ROC)curve and the corresponding area under the curve(AUC).Results In the testing set,the DLR_Nomogram demonstrated superior predictive performance(AUC:0.951,95%CI:0.876～1.000)compared to Rad_Sig(AUC:0.709,95%CI:0.539～0.880),DTL_Sig(AUC:0.842,95%CI:0.712～0.972),and Clinic_Sig(AUC:0.916,95%CI:0.827～1.000).The Hosmer-Lemeshow goodness-of-fit test for the DLR_Nomogram resulted in a p-value exceeding 0.05,indicating adequate model calibration.Moreover,decision curve analysis revealed that the DLR_No-mogram offers a higher net clinical benefit across a defined range of threshold probabilities.Conclusions The ultrasound-based DLR_Nomogram exhibits a robust ability to differentiate between Type Ⅰ and Type Ⅱ EOC,and may serve as a valuable clinical tool for guiding individualized preoperative diagnostic and therapeutic decision-making.

Objective To evaluate an ultrasound-based deep learning radiomics nomogram(DLR_Nomo-gram)for non-invasively differentiating between type Ⅰ and type Ⅱ epithelial ovarian cancer(EOC)before surgery.Methods In this study,a cohort of 195 patients diagnosed with EOC was analyzed.Participants were randomly divided into a training set and a testing set at an 8∶2 ratio.Following data preprocessing,region of interest(ROI)delineation,feature extraction and selection,as well as the clipping and extraction of the maximum section sonogram for each sample,three initial models were developed:the radiomics signature(Rad_Sig),the deep transfer learning signature(DTL_Sig),and the clinical signature(Clinic_Sig).Subsequently,an integrated model—referred to as the DLR_Nomogram—was constructed by combining Rad_Sig,DTL_Sig,and Clinic_Sig,and was presented in the form of a nomogram.The performance of the model was evaluated using the receiver operating characteristic(ROC)curve and the corresponding area under the curve(AUC).Results In the testing set,the DLR_Nomogram demonstrated superior predictive performance(AUC:0.951,95%CI:0.876～1.000)compared to Rad_Sig(AUC:0.709,95%CI:0.539～0.880),DTL_Sig(AUC:0.842,95%CI:0.712～0.972),and Clinic_Sig(AUC:0.916,95%CI:0.827～1.000).The Hosmer-Lemeshow goodness-of-fit test for the DLR_Nomogram resulted in a p-value exceeding 0.05,indicating adequate model calibration.Moreover,decision curve analysis revealed that the DLR_No-mogram offers a higher net clinical benefit across a defined range of threshold probabilities.Conclusions The ultrasound-based DLR_Nomogram exhibits a robust ability to differentiate between Type Ⅰ and Type Ⅱ EOC,and may serve as a valuable clinical tool for guiding individualized preoperative diagnostic and therapeutic decision-making.

Objective: To investigate the incidence of adverse event following immunization (AEFI) with 13-valent pneumococcal polysaccharide conjugate vaccine (PCV13) in Jiaxing City, Zhejiang Province, so as to provide insights into safety monitoring and evaluation of PCV13. Methods: Surveillance data of AEFI with PCV13 in Jiaxing City from 2020 to 2022 were collected from the AEFI Monitoring Information Management System of the Immunization Planning System of Chinese Disease Prevention and Control Information System, including demographic information, vaccination time, time of AEFI occurrence and clinical symptoms, and the reported incidence, population and district distribution, and clinical symptoms of AEFI with PCV13 were descriptively analyzed. Results: Totally 455 cases of AEFI with PCV13 were reported in Jiaxing City from 2020 to 2022, with a reported incidence rate of 232.33/105 doses. There were 431, 21 and 3 cases of general, abnormal, coincidence and psychogenic reactions, with reported incidence rates of 220.07/105 doses, 10.72/105 doses and 1.53/105 doses, respectively, and no reports of causal reaction, vaccine quality accident and vaccination accident. The AEFI cases included 258 boys and 197 girls, with a boy/girl ratio of 1.31∶1, and 288 children at ages of less than a year (63.30%). The largest number of AEFI was reported in Haining City (87 cases, 19.12%), and there were 349 AEFI cases (76.70%) within 24 hours following vaccination. The clinical symptoms mainly included redness and swelling, fever and induration, with reported incidence rates of 132.76/105 doses (260 cases), 109.27/105 doses (214 cases), and 55.66/105 doses (109 cases), respectively. There were 450 cases cured and 5 cases improved in 455 cases of AEFI. Conclusions General reaction is the predominant AEFI in Jiaxing City from 2020 to 2022, with mild symptoms. Most AEFI occurs within 24 hours following vaccination, and has a good prognosis.

In order to investigate the enzyme production mechanism of yak rumen-derived anaerobic fungus Orpinomyces sp. YF3 under the induction of different carbon sources, anaerobic culture tubes were used for in vitro fermentation. 8 g/L of glucose (Glu), filter paper (Flp) and avicel (Avi) were respectively added to 10 mL of basic culture medium as the sole carbon source. The activity of fiber-degrading enzyme and the concentration of volatile fatty acid in the fermentation liquid were detected, and the enzyme producing mechanism of Orpinomyces sp. YF3 was explored by transcriptomics. It was found that, in glucose-induced fermentation solution, the activities of carboxymethyl cellulase, microcrystalline cellulase, filter paper enzyme, xylanase and the proportion of acetate were significantly increased (P < 0.05), the proportion of propionate, butyrate, isobutyrate were significantly decreased (P < 0.05). The results of transcriptome analysis showed that there were 5 949 differentially expressed genes (DEGs) between the Glu group and the Flp group, 10 970 DEGs between the Glu group and the Avi group, and 6 057 DEGs between the Flp group and the Avi group. It was found that the DEGs associated with fiber degrading enzymes were significantly up-regulated in the Glu group. Gene ontology (GO) function enrichment analysis identified that DEGs were mainly associated with the xylan catabolic process, hemicellulose metabolic process, β-glucan metabolic process, cellulase activity, endo-1,4-β-xylanase activity, cell wall polysaccharide metabolic process, carbohydrate catabolic process, glucan catabolic process and carbohydrate metabolic process. Moreover, the differentially expressed pathways associated with fiber degrading enzymes enriched by Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were mainly starch and sucrose metabolic pathways and other glycan degradation pathways. In conclusion, Orpinomyces sp. YF3 with glucose as carbon source substrate significantly increased the activity of cellulose degrading enzyme and the proportion of acetate, decreased the proportion of propionate, butyrate and isobutyrate. Furthermore, the degradation ability and energy utilization efficiency of fungus in the presence of glucose were improved by means of regulating the expression of cellulose degrading enzyme gene and participating in starch and sucrose metabolism pathway, and other glycan degradation pathways, which provides a theoretical basis for the application of Orpinomyces sp. YF3 in practical production and facilitates the application of Orpinomyces sp. YF3 in the future.
Animals ; Cattle ; Neocallimastigales/metabolism* ; Anaerobiosis ; Rumen/microbiology* ; Propionates/metabolism* ; Isobutyrates/metabolism* ; Cellulose/metabolism* ; Fungi ; Starch/metabolism* ; Glucose/metabolism* ; Acetates ; Sucrose/metabolism* ; Cellulases ; Cellulase