Objective To compare the application of ambulatory urodynamic(AUM)and conventional urodynamic(CUD)in detecting stress urinary incontinence(SUI)and detrusor overactivity(DO)in females.Methods Incontinence questionnaire short form(ICI-Q-SF),CUD and AUM were administrated on 30 female patients with the mean age of 49.4(32-63)years.The duration of symptom was 4.7 (1-9)years.The patients were divided into 3 groups of mild(n =9),moderate(n =15)and severe (n =6)according to ICI-Q-SF.Three micturition cycles were recorded during AUM.Results SUI and DO detected by AUM were 90％ and 37％,significantly more than those by CUD of 70％ and 10％(P ＜0.05).Twenty-one moderate and severe SUI patients diagnosed by ICI-Q-SF,detected by AUM and CUD simultaneously showed that abdominal leak point pressure(ALPP)and voided volume were lower,and detrusor pressure was higher recorded by AUM than those by CUD significantly(P ＜ 0.05).Conclusions SUI and DO are easier detected by AUM than by CUD.AUM is a useful additional tool in clinical practice for those patients CUD failed to explain their symptoms.

Objective To investigate the expression of SLC35A2 and PFDN2 in breast cancer and their relationship with clinical indicators and prognosis.Methods TCGA database and TIMER 2.0 database were used to analyze the differences of SLC35A2 and PFDN2 expression in breast cancer tissues and paracancerous tissues;K-M database was used to create the survival curves of patients in the high and low expression groups of the two.qRT-PCR and immunohistochemistry were used to detect the expression of SLC35A2 and PFDN2 in the cancerous and paracancerous tissues,and the expression differences,the relationship between their expression levels and the clinical observation indexes were statistically analyzed,and the independent prognostic factors of breast cancer were screened out;K-M survival analysis was used to compare the prognostic differences between the groups and create the survival curves.Results The expression levels of SLC35A2 and PFDN2 in breast cancer tissues were significantly higher than those in paracancerous tissues according to the results of biopsy,qRT-PCR and immuno-histochemistry,and the expression levels of SLC35A2 were significantly correlated with lymph node metastasis,while the expression of PFDN2 was significantly correlated with the diameter of the tumor and the metastasis of lymph nodes,and the expression of SLC35A2 and PFDN2 was an independent prognostic factor for breast cancer.patients had the worst prognosis.Conclusion The expression of SLC35A2 and PFDN2 in breast cancer tissues was closely related to clinical indicators and prognosis of breast cancer,and could be used as a potential target for breast cancer treatment.

The disease risk prediction model is the basis of precision prevention and an essential reference for clinical treatment decisions. The development of risk prediction models requires the support of a large amount of high-quality data. A large population cohort study is an important basis for this study. The United Kingdom Biobank (UKB), as a mega-population cohort and biobank, has played an essential role in the exploration of disease etiology and research related to disease prevention and control, with its rich baseline and follow-up data and concepts and mechanisms shared globally. This study followed PRISMA guidelines and included 210 articles with corresponding authors from 18 countries, of which 58 (27.62%) were from the UKB. A total of 491 disease risk prediction models were extracted for cancer, cardiovascular and cerebrovascular diseases, endocrine and metabolic diseases, respiratory diseases, and other diseases and their subgroups, of which 132 were developed by UKB without validation, 183 were developed by UKB with internal validation, 17 were developed by UKB with external validation, and 159 were developed by external development with UKB validation. A total of 188 models used only macro variables (38.29%), and 303 models combined macro and micro variables (61.71%). Model construction methods included survival outcome models, logistic regression, and machine learning. Survival outcome models were dominated by Cox proportional risk regression models and a few models considering competitive risk, accelerated failure models, or different baseline risk functions. Machine learning models included random forest, XGBoost, CatBoost, support vector machine, convolutional neural network, and other methods. The UKB is an essential resource for multiple disease risk prediction modeling studies.

Nowadays potential preclinical drugs for the treatment of nonalcoholic steatohepatitis (NASH) have failed to achieve expected therapeutic efficacy because the pathogenic mechanisms are underestimated. Inactive rhomboid protein 2 (IRHOM2), a promising target for treatment of inflammation-related diseases, contributes to deregulated hepatocyte metabolism-associated nonalcoholic steatohepatitis (NASH) progression. However, the molecular mechanism underlying Irhom2 regulation is still not completely understood. In this work, we identify the ubiquitin-specific protease 13 (USP13) as a critical and novel endogenous blocker of IRHOM2, and we also indicate that USP13 is an IRHOM2-interacting protein that catalyzes deubiquitination of Irhom2 in hepatocytes. Hepatocyte-specific loss of the Usp13 disrupts liver metabolic homeostasis, followed by glycometabolic disorder, lipid deposition, increased inflammation, and markedly promotes NASH development. Conversely, transgenic mice with Usp13 overexpression, lentivirus (LV)- or adeno-associated virus (AAV)-driven Usp13 gene therapeutics mitigates NASH in 3 models of rodent. Mechanistically, in response to metabolic stresses, USP13 directly interacts with IRHOM2 and removes its K63-linked ubiquitination induced by ubiquitin-conjugating enzyme E2N (UBC13), a ubiquitin E2 conjugating enzyme, and thus prevents its activation of downstream cascade pathway. USP13 is a potential treatment target for NASH therapy by targeting the Irhom2 signaling pathway.