Search

Objective To explore the therapeutic effects of biofeedback in the treatment of faecal incontinence (FI) after surgery for anorectal malformation (ARM).Methods Clinical data were collected from paediatric patients for postoperative biofeedback due to FI caused by ARM between May 2017 and November 2021. The data included the duration of symptoms, the integrity of the anal sphincter, anorectal manometry parameters, and FI scores. These patients were divided into the low ARM group (group A) and the high ARM group (group B).Results A total of 45 paediatric patients were enrolled in the study. There were 28 cases in group A and 17 cases in group B. The differences in age, gender, and body weight were not statistically significant between the two groups (p > 0.05). The differences in the clinical indicators were also not statistically significant between the two groups at the time of the initial evaluation. The duration of symptoms was 2.21 ± 0.71 years and 4.14 ± 1.89 years in groups A and B. There were 16 cases with an intact anal sphincter in group A and only two cases with an intact anal sphincter in group B. This difference was statistically significant between the two groups (p

Recommendation systems and computing advertisements are of great value for commercial applications. Click-through rate (CTR) prediction is a critical issue because the prediction accuracy affects the user experience and the revenue of merchants and platforms. Feature engineering is usually used to improve the click-through rate prediction; however, it heavily relies on user experience. It is difficult to construct a feature combination that can describe the complex patterns implied in the data. This paper combines the traditional feature combination methods and the deep neural networks to automate the feature combinations to improve the accuracy of the click-through rate prediction. We propose a mechanism named Field-aware Neural Factorization Machine (FNFM). This paper can have strong second-order feature interactive learning ability, such as Field-aware Factorization Machine; on this basis, a deep neural network is used for higher order feature combination learning. This experiment shows that the model has stronger expression ability than previous deep learning feature combination models, such as the DeepFM, DCN, and NFM.

Copyright of Transformations in Business & Economics is the property of Vilnius University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: Premenstrual dysphoric disorder (PMDD) represents a significant psychiatric condition affecting women of reproductive age. It is characterized by cyclical emotional and physical symptoms that considerably impair psychosocial functioning. This study aims to investigate the prevalence of PMDD among adult working women and identify associated etiological factors. Methods: From January 2022 to March 2022, a crosssectional study utilizing cluster sampling methodology was conducted to investigate the prevalence of PMDD among adult working women in six communities in Hangzhou (China). A total of 1710 participants were selected for research through a questionnaire-based survey. To elucidate the factors associated with PMDD, logistic regression analysis was employed. Results: Among 1710 working women, 52 had PMDD, representing an incidence of 3.04%. Age (odds ratio (OR) = 3.196, p = 0.002), age at menarche (OR = 0.362, p = 0.011), income (OR = 0.449, p = 0.043), menstrual cycle (OR = 2.352, p = 0.022), menstrual symptoms (OR = 0.317, p = 0.003), physical activity (OR = 2.234, p = 0.028), and caffeine intake (OR = 2.318, p = 0.022) were independent influencing factors of PMDD in adult working women. Conclusions: The study highlights that PMDD, which has a prevalence of 3.04% in the surveyed population, is a notable concern among adult working women. Key factors, such as age, age at menarche, income level, menstrual cycle characteristics, menstrual symptom presence, physical activity, and caffeine intake, significantly contribute to the likelihood of developing PMDD. These findings underscore the importance of recognizing and monitoring the above risk factors by clinicians to identify and manage PMDD in working women. [ABSTRACT FROM AUTHOR]

Copyright of Transformations in Business & Economics is the property of Vilnius University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Gliomas are the most common malignant brain tumors. High-grade gliomas, represented by glioblastoma multiforme (GBM), have a poor prognosis and are prone to recurrence. The standard treatment strategy is tumor removal combined with radiotherapy and chemotherapy, such as temozolomide (TMZ). However, even after conventional treatment, they still have a high recurrence rate, resulting in an increasing demand for effective anti-glioma drugs. Drug repurposing is a method of reusing drugs that have already been widely approved for new indication. It has the advantages of reduced research cost, safety, and increased efficiency. Disulfiram (DSF), originally approved for alcohol dependence, has been repurposed for adjuvant chemotherapy in glioma. This article reviews the drug repurposing method and the progress of research on disulfiram reuse for glioma treatment.