Therapeutic benefit of aripiprazole-olanzapine combination in the treatment of senile Alzheimer’s disease complicated by mental disorders

Purpose: To determine the clinical efficacy of aripiprazole-olanzapine combination treatment in elderly Alzheimer’s disease complicated with mental disorders. Methods: Ninety-two elderly patients with Alzheimer’s disease and mental disorders who were admitted to Binzhou People's Hospital, were enrolled in the study. They were randomized into control and study groups. Control group was treated with olanzapine, while the study group was treated with aripiprazole as an adjuvant therapy in addition to olanzapine. The clinical efficacy, scores on different scales (MMSE, ADAS-cog, CDR, ADL, NPI and CMAI), and incidence of adverse reactions were determined. Results: The overall degree of response was significantly higher in the study group than in the control group (p < 0.05). There were no significant differences in MMSE, ADAS-cog, CDR, ADL, NPI and CMAI scores between the two groups before treatment (p > 0.05). The MMSE score of the study group was significantly higher than that of the control group, and the scores in the other scales in the study group were significantly lower after treatment (p < 0.05). The study group had significantly lower incidence of adverse reactions than control group (p < 0.05). Conclusion: Aripiprazole-olanzapine combination has significant therapeutic benefit in the treatment of elderly Alzheimer’s disease patients complicated with mental disorders. It promotes recovery of neurological function, as well as produces a lower incidence of adverse reactions. Keywords: Aripiprazole, Olanzapine, Alzheimer’s disease, Mental disorder

Multi-Granularity Attention Model for Group Recommendation

Group recommendation provides personalized recommendations to a group of users based on their shared interests, preferences, and characteristics. Current studies have explored different methods for integrating individual preferences and making collective decisions that benefit the group as a whole. However, most of them heavily rely on users with rich behavior and ignore latent preferences of users with relatively sparse behavior, leading to insufficient learning of individual interests. To address this challenge, we present the Multi-Granularity Attention Model (MGAM), a novel approach that utilizes multiple levels of granularity (i.e., subsets, groups, and supersets) to uncover group members' latent preferences and mitigate recommendation noise. Specially, we propose a Subset Preference Extraction module that enhances the representation of users' latent subset-level preferences by incorporating their previous interactions with items and utilizing a hierarchical mechanism. Additionally, our method introduces a Group Preference Extraction module and a Superset Preference Extraction module, which explore users' latent preferences on two levels: the group-level, which maintains users' original preferences, and the superset-level, which includes group-group exterior information. By incorporating the subset-level embedding, group-level embedding, and superset-level embedding, our proposed method effectively reduces group recommendation noise across multiple granularities and comprehensively learns individual interests. Extensive offline and online experiments have demonstrated the superiority of our method in terms of performance

Real-world pharmacological treatment of pregnant patients with rheumatic diseases from China: a retrospective analysis from 2016 to 2021

Introduction: We investigated trends in the use of therapeutic drugs for pregnant patients with rheumatic diseases in nine Chinese cities (Beijing, Chengdu, Guangzhou, Harbin, Hangzhou, Shanghai, Shenyang, Tianjin, and Zhengzhou) to provide a reference for drug use in clinic.Methods: Outpatient prescription data for pregnant patients diagnosed with rheumatic diseases in nine cities across China in 2016–2021 were extracted from the Hospital Prescription Cooperation Project of the Hospital Pharmacy Professional Committee of the Chinese Pharmaceutical Association. A retrospective analysis was then performed, incorporating data on patient age, defined daily doses (DDDs), defined daily cost (DDC), and other metrics.Results: In 2016–2020, more than 70% of the pregnant patients diagnosed with rheumatic diseases in these nine cities were 25 to < 35 years of age. The most common rheumatic diseases during pregnancy were antiphospholipid antibody syndrome (APS) and systemic lupus erythematosus (SLE). In terms of the routine use of daily therapeutic drugs, the DDDs of low molecular weight heparins (LMWHs), glucocorticoids, and immunosuppressive agents dominated the top three. Intravenous immunoglobulin (IVIG) and tumor necrosis factor inhibitors (TNFi) have been used since 2019 and had been in the forefront of the DDC.Conclusion: The number and total cost of prescriptions for therapeutic drugs of pregnancy complicated by rheumatic diseases, have increased significantly over the study interval. Conventional therapeutic drugs, especially glucocorticoids, LMWHs, and hydroxychloroquine were the most widely used drugs in pregnant patients with rheumatic diseases. However, IVIG and TNFi, relatively high cost, have shown gradual increases in clinical use since 2019

BASM: A Bottom-up Adaptive Spatiotemporal Model for Online Food Ordering Service

Online Food Ordering Service (OFOS) is a popular location-based service that helps people to order what you want. Compared with traditional e-commerce recommendation systems, users' interests may be diverse under different spatiotemporal contexts, leading to various spatiotemporal data distribution, which limits the fitting capacity of the model. However, numerous current works simply mix all samples to train a set of model parameters, which makes it difficult to capture the diversity in different spatiotemporal contexts. Therefore, we address this challenge by proposing a Bottom-up Adaptive Spatiotemporal Model(BASM) to adaptively fit the spatiotemporal data distribution, which further improve the fitting capability of the model. Specifically, a spatiotemporal-aware embedding layer performs weight adaptation on field granularity in feature embedding, to achieve the purpose of dynamically perceiving spatiotemporal contexts. Meanwhile, we propose a spatiotemporal semantic transformation layer to explicitly convert the concatenated input of the raw semantic to spatiotemporal semantic, which can further enhance the semantic representation under different spatiotemporal contexts. Furthermore, we introduce a novel spatiotemporal adaptive bias tower to capture diverse spatiotemporal bias, reducing the difficulty to model spatiotemporal distinction. To further verify the effectiveness of BASM, we also novelly propose two new metrics, Time-period-wise AUC (TAUC) and City-wise AUC (CAUC). Extensive offline evaluations on public and industrial datasets are conducted to demonstrate the effectiveness of our proposed modle. The online A/B experiment also further illustrates the practicability of the model online service. This proposed method has now been implemented on the Ele.me, a major online food ordering platform in China, serving more than 100 million online users

Spatiotemporal-Enhanced Network for Click-Through Rate Prediction in Location-based Services

In Location-Based Services(LBS), user behavior naturally has a strong dependence on the spatiotemporal information, i.e., in different geographical locations and at different times, user click behavior will change significantly. Appropriate spatiotemporal enhancement modeling of user click behavior and large-scale sparse attributes is key to building an LBS model. Although most of existing methods have been proved to be effective, they are difficult to apply to takeaway scenarios due to insufficient modeling of spatiotemporal information. In this paper, we address this challenge by seeking to explicitly model the timing and locations of interactions and proposing a Spatiotemporal-Enhanced Network, namely StEN. In particular, StEN applies a Spatiotemporal Profile Activation module to capture common spatiotemporal preference through attribute features. A Spatiotemporal Preference Activation is further applied to model the personalized spatiotemporal preference embodied by behaviors in detail. Moreover, a Spatiotemporal-aware Target Attention mechanism is adopted to generate different parameters for target attention at different locations and times, thereby improving the personalized spatiotemporal awareness of the model.Comprehensive experiments are conducted on three large-scale industrial datasets, and the results demonstrate the state-of-the-art performance of our methods. In addition, we have also released an industrial dataset for takeaway industry to make up for the lack of public datasets in this community.Comment: accepted by CIKM workshop 202

Image restoration with point-spread function regularization and active learning

Large-scale astronomical surveys can capture numerous images of celestial objects, including galaxies and nebulae. Analysing and processing these images can reveal the intricate internal structures of these objects, allowing researchers to conduct comprehensive studies on their morphology, evolution, and physical properties. However, varying noise levels and point-spread functions can hamper the accuracy and efficiency of information extraction from these images. To mitigate these effects, we propose a novel image restoration algorithm that connects a deep-learning-based restoration algorithm with a high-fidelity telescope simulator. During the training stage, the simulator generates images with different levels of blur and noise to train the neural network based on the quality of restored images. After training, the neural network can restore images obtained by the telescope directly, as represented by the simulator. We have tested the algorithm using real and simulated observation data and have found that it effectively enhances fine structures in blurry images and increases the quality of observation images. This algorithm can be applied to large-scale sky survey data, such as data obtained by the Large Synoptic Survey Telescope (LSST), Euclid, and the Chinese Space Station Telescope (CSST), to further improve the accuracy and efficiency of information extraction, promoting advances in the field of astronomical research

Mediastinal Lymph Node Metastases in Thyroid Cancer: Characteristics, Predictive Factors, and Prognosis

Background. Mediastinal lymph node metastases (MLNM) have not been extensively studied. The aim of this study is to investigate the characteristics, predictive factors, and prognosis of MLNM in thyroid cancer. Methods. This is a retrospective study based on the thyroid cancer patients with MLNM at our institution from 2008 to 2015. Results. In total, 73 thyroid cancer patients with positive MLNM were included in this study. It contained sixty patients (82.2%) with papillary thyroid carcinoma (PTC), twelve (16.4%) with medullary thyroid carcinoma, and one (1.4%) with anaplastic thyroid carcinoma. Forty-eight patients had the surgery as initial treatment. Fifty-three (72.6%) patients remained disease-free, and fifteen (20.5%) developed a regional recurrence. Distant metastases occurred in four (5.5%) patients and five (6.8%) patients died. Five-year overall survival rate and disease-free survival (DFS) rate of the PTC patients for initial treatment are 95.4% and 77.2%, respectively. Extrathyroidal extension and multiple lymph nodes involved were associated with DFS in PTC patients. Conclusions. Initial therapeutic control is very important for the thyroid cancer patients. Extrathyroidal extension and multiple mediastinal lymph nodes involved were the influence factors of prognosis in the thyroid cancer patients with MLNM

Endoscopic Ultrasonography in the Diagnosis and Treatment Strategy Choice of Esophageal Leiomyoma

OBJECTIVES: Esophageal leiomyoma is the most common benign tumor of the esophagus, and it originates from mesenchymal tissue. This study analyzed the clinicopathological characteristics of esophageal leiomyoma and aimed to evaluate the role of endoscopic ultrasonography in the diagnosis and treatment selection for these lesions. METHODS: Two hundred and twenty-five patients who had suspected esophageal leiomyomas in endoscopic ultrasonography were enrolled at the Endoscopy Center of The First Affiliated Hospital, Zhejiang University from January 1st, 2009 to May 31th, 2015. The main outcomes included the demographic and morphological characteristics, symptoms, comparisons of diagnosis and treatment methods, adverse events, and prognosis. RESULTS: One hundred and sixty-seven patients were diagnosed as having an esophageal leiomyoma by pathological examination. The mean patient age was 50.57±9.983 years. In total, 62.9% of the lesions originated from the muscularis mucosa, and the others originated from the muscularis propria. The median distance to the incisors was 30±12 cm. The median diameter was 0.72±0.99 cm. As determined by endoscopic ultrasonography, most existing leiomyomas were homogeneous, endophytic, and spherical. The leiomyomas from the muscularis mucosa were smaller than those from the muscularis propria and much closer to the incisors (