Case Report: A Novel COL1A1 Missense Mutation Associated With Dentineogenesis Imperfecta Type I

Background: Osteogenesis imperfecta (OI) is a clinical and genetic disorder that results in bone fragility, blue sclerae and dentineogenesis imperfecta (DGI), which is mainly caused by a mutation in the COL1A1 or COL1A2 genes, which encode type I procollagen.Case Report: A missense mutation (c.1463G > C) in exon 22 of the COL1A1 gene was found using whole-exome sequencing. However, the cases reported herein only exhibited a clinical DGI-I phenotype. There were no cases of bone disease or any other common abnormal symptom caused by a COL1A1 mutation. In addition, the ultrastructural analysis of the tooth affected with non-syndromic DGI-I showed that the abnormal dentine was accompanied by the disruption of odontoblast polarization, a reduced number of odontoblasts, a reduction in hardness and elasticity, and the loss of dentinal tubules, suggesting a severe developmental disorder. We also investigated the odontoblast differentiation ability using dental pulp stem cells (DPSCs) that were isolated from a patient with DGI-I and cultured. Stem cells isolated from patients with DGI-I are important to elucidate their pathogenesis and underlying mechanisms to develop regenerative therapies.Conclusion: This study can provide new insights into the phenotype-genotype association in collagen-associated diseases and improve the clinical diagnosis of OI/DGI-I

Modeling location-based user rating profiles for personalized recommendation

This article proposes LA-LDA, a location-aware probabilistic generative model that exploits location-based ratings to model user profiles and produce recommendations. Most of the existing recommendation models do not consider the spatial information of users or items; however, LA-LDA supports three classes of locationbased ratings, namely spatial user ratings for nonspatial items, nonspatial user ratings for spatial items, and spatial user ratings for spatial items. LA-LDA consists of two components, ULA-LDA and ILA-LDA, which are designed to take into account user and item location information, respectively. The component ULA-LDA explicitly incorporates and quantifies the influence from local public preferences to produce recommendations by considering user home locations, whereas the component ILA-LDA recommends items that are closer in both taste and travel distance to the querying users by capturing item co-occurrence patterns, as well as item location co-occurrence patterns. The two components of LA-LDA can be applied either separately or collectively, depending on the available types of location-based ratings. To demonstrate the applicability and flexibility of the LA-LDA model, we deploy it to both top-k recommendation and cold start recommendation scenarios. Experimental evidence on large-scale real-world data, including the data from Gowalla (a location-based social network), DoubanEvent (an event-based social network), and MovieLens (a movie recommendation system), reveal that LA-LDA models user profiles more accurately by outperforming existing recommendation models for top-k recommendation and the cold start problem

Lcars: A location-content-aware recommender system

ABSTRACT Newly emerging location-based and event-based social network services provide us with a new platform to understand users' preferences based on their activity history. A user can only visit a limited number of venues/events and most of them are within a limited distance range, so the user-item matrix is very sparse, which creates a big challenge for traditional collaborative filtering-based recommender systems. The problem becomes more challenging when people travel to a new city where they have no activity history. In this paper, we propose LCARS, a location-content-aware recommender system that offers a particular user a set of venues (e.g., restaurants) or events (e.g., concerts and exhibitions) by giving consideration to both personal interest and local preference. This recommender system can facilitate people's travel not only near the area in which they live, but also in a city that is new to them. Specifically, LCARS consists of two components: offline modeling and online recommendation. The offline modeling part, called LCA-LDA, is designed to learn the interest of each individual user and the local preference of each individual city by capturing item cooccurrence patterns and exploiting item contents. The online recommendation part automatically combines the learnt interest of the querying user and the local preference of the querying city to produce the top-k recommendations. To speed up this online process, a scalable query processing technique is developed by extending the classic Threshold Algorithm (TA). We evaluate the performance of our recommender system on two large-scale real data sets, DoubanEvent and Foursquare. The results show the superiority of LCARS in recommending spatial items for users, especially when traveling to new cities, in terms of both effectiveness and efficiency

Lcars: A location-content-aware recommender system

ABSTRACT Newly emerging location-based and event-based social network services provide us with a new platform to understand users' preferences based on their activity history. A user can only visit a limited number of venues/events and most of them are within a limited distance range, so the user-item matrix is very sparse, which creates a big challenge for traditional collaborative filtering-based recommender systems. The problem becomes more challenging when people travel to a new city where they have no activity history. In this paper, we propose LCARS, a location-content-aware recommender system that offers a particular user a set of venues (e.g., restaurants) or events (e.g., concerts and exhibitions) by giving consideration to both personal interest and local preference. This recommender system can facilitate people's travel not only near the area in which they live, but also in a city that is new to them. Specifically, LCARS consists of two components: offline modeling and online recommendation. The offline modeling part, called LCA-LDA, is designed to learn the interest of each individual user and the local preference of each individual city by capturing item cooccurrence patterns and exploiting item contents. The online recommendation part automatically combines the learnt interest of the querying user and the local preference of the querying city to produce the top-k recommendations. To speed up this online process, a scalable query processing technique is developed by extending the classic Threshold Algorithm (TA). We evaluate the performance of our recommender system on two large-scale real data sets, DoubanEvent and Foursquare. The results show the superiority of LCARS in recommending spatial items for users, especially when traveling to new cities, in terms of both effectiveness and efficiency

LCARS: a location-content-aware recommender system

Newly emerging location-based and event-based social network services provide us with a new platform to understand users' preferences based on their activity history. A user can only visit a limited number of venues/events and most of them are within a limited distance range, so the user-item matrix is very sparse, which creates a big challenge for traditional collaborative filtering-based recommender systems. The problem becomes more challenging when people travel to a new city where they have no activity history. In this paper, we propose LCARS, a location-content-aware recommender system that offers a particular user a set of venues (e.g., restaurants) or events (e.g., concerts and exhibitions) by giving consideration to both personal interest and local preference. This recommender system can facilitate people's travel not only near the area in which they live, but also in a city that is new to them. Specifically, LCARS consists of two components: offline modeling and online recommendation. The offline modeling part, called LCA-LDA, is designed to learn the interest of each individual user and the local preference of each individual city by capturing item co-occurrence patterns and exploiting item contents. The online recommendation part automatically combines the learnt interest of the querying user and the local preference of the querying city to produce the top-k recommendations. To speed up this online process, a scalable query processing technique is developed by extending the classic Threshold Algorithm (TA). We evaluate the performance of our recommender system on two large-scale real data sets, DoubanEvent and Foursquare. The results show the superiority of LCARS in recommending spatial items for users, especially when traveling to new cities, in terms of both effectiveness and efficiency

Efficacy of an Oral Solution Prepared from the Ultrasonic Extract of Radix dichroae roots against Eimeria tenella in Broiler Chickens

This study was conducted to determine the optimal dose of the oral solution of the ultrasonic extract of Radix dichroae (UERD) and to provide experimental support for a safe clinical dose for anticoccidial treatment of broiler chickens. Radix dichroae root extracts were prepared using the ultrasonic extraction method. The anticoccidial activity of the oral solution prepared from the ultrasonic extract of Radix dichroae roots was tested in broiler chickens following oral infection with a field isolate of E. tenella. Ninety Lingnan yellow broiler chickens (14 days old) were randomly divided into nine groups (n = 10), including six UERD oral solution treatments (0.25, 0.50, 1.50, 2.50, 3.50, and 5.00%), a toltrazuril group (0.10%), an E. tenella-infected control group, and a healthy control group. All groups were inoculated orally with 7 × 104 sporulated E. tenella oocysts (Guangdong strain) except for the healthy control group. The chickens in the seven drug-treated groups were administered a UERD oral solution or toltrazuril in drinking water for 7 days. The anticoccidial efficacy of the UERD oral solution was evaluated by the bloody diarrhoea severity level, relative body weight gain (rBWG), lesion score, oocyst per gram (OPG), and anticoccidial index (ACI). Compared with the infected control group, there were no significant differences in the groups treated with UERD oral solution or toltrazuril with regard to the lesion changes in the caecal regions (P>0.05); however, the blood contents, OPG, and oocyst score in three UERD oral solution treatment groups (0.50, 1.50, and 2.50%) were significantly reduced, and the bloody diarrhoea was also alleviated. The ACI in three UERD oral solution treatment groups (0.50%, ACI = 143.7; 1.50%, ACI = 151.0; and 2.50%, ACI = 144.3) was higher than that in the toltrazuril group (ACI = 127.0), and the rBWG in the 1.50% UERD oral solution treatment group (95.0%) was similar to that in the healthy control group (100%), which was also 12.5% higher than that in the toltrazuril group (82.5%). The findings of this study demonstrated that the UERD oral solution (0.50% ～ 2.50% dose range) showed better prevention, anticoccidial efficacy, and growth promotion effects than toltrazuril (0.10%), and the 1.50% dose level of UERD oral solution in water is the clinically recommended dose according to the present study conditions

Identifying the insomnia-related psychological issues associated with hyperarousal:A network perspective

Hyperarousal, recognized as a fundamental characteristic of insomnia for decades, has yielded limited evidence concerning its direct psychological associations. This study aimed to explore the psychological factors linked to hyperarousal within the framework of interrelated variables. Two independent samples, comprising n = 917 and n = 652 young adults, were included in the study. Employing the first dataset as a discovery sample and the second dataset as a replication sample, network analyses were conducted using 26 variables derived from 17 scales. The objective was to estimate the direct and indirect associations between psychological issues, including hyperarousal and insomnia. Additionally, linear regression analysis was employed to assess the convergence of findings obtained from the network analysis. Network analyses in both samples converged to reveal direct associations between insomnia severity and several psychological factors, including negative sleep beliefs, physical fatigue, insomnia response to stress, hyperarousal, self-reported depression, and mental fatigue. Notably, the nodes with relative importance within the network include trait anxiety, depressive rumination, hyperarousal, perfectionism sub-dimension of concern over mistakes, and private self-consciousness. Hyperarousal is one of the key factors linking insomnia with a variety of psychological issues, including emotion-related factors (rumination, perveived stress), sleep-related factors (dysfunctional sleep beliefs and attitudes, insomnia response to stress, fatigue, chronotype), and self-related factors (self-consciousness, perfectionism). The results suggest that forthcoming strategies for enhancing the treatment efficacy of insomnia could consider supplementary interventions that specifically address hyperarousal, other factors directly linked to insomnia, or the hub nodes within the network.</p

Enhanced therapeutic efficacy of Listeria-based cancer vaccine with codon-optimized HPV16 E7

Cervical cancer is a leading cause of high mortality in women in developing countries and has a serious impact on women’s health. Human papilloma virus (HPV) prophylactic vaccines have been produced and may hold promise for reducing the incidence of cervical cancer. However, the limitations of current HPV vaccine strategies make the development of HPV therapeutic vaccines particularly important for the treatment of HPV related lesions. Our previous work has demonstrated that LM4Δhly::E7 was safe and effective in inducing antitumor effect by antigen-specific cellular immune responses and direct killing of tumor cell on a cervical cancer model. In this study, the codon usage effect of a novel Listeria-based cervical cancer vaccine LM4Δhly::E7-1, was evaluated for effects of codon-optimized E7 expression, cellular immune response and therapeutic efficacy in a tumor-bearing murine model. Our data demonstrated that up-regulated expression of E7 was strikingly elevated by codon usage optimization, and thus induced significantly higher Th1-biased immunity, lymphocyte proliferation, and strong specific CTL activity ex-vivo compared with LM4Δhly::E7-treated mice. Furthermore, LM4Δhly::E7-1 enhanced a remarkable therapeutic effect in establishing tumors. Taken together, our results suggest that codon usage optimization is an important consideration in constructing live bacterial-vectored vaccines and is required for promoting effective T cell responses

Vitamin D, Folic Acid and Vitamin B₁₂ Can Reverse Vitamin D Deficiency-Induced Learning and Memory Impairment by Altering 27-Hydroxycholesterol and S-Adenosylmethionine

The cholesterol-oxidized metabolite 27-hydroxycholesterol (27-OHC) is synthesized by CYP27A1, which is a key factor in vitamin D and oxysterol metabolism. Both vitamin D and 27-OHC are considered to play important roles in Alzheimer’s disease (AD). The study aims to research the effects of co-supplementation of vitamin D, folic acid, and vitamin B12 on learning and memory ability in vitamin D-deficient mice, and to explore the underlying mechanism. In this study, C57BL/6J mice were fed a vitamin D-deficient diet for 13 weeks to establish a vitamin D-deficient mice model. The vitamin D-deficient mice were then orally gavaged with vitamin D (VD), folic acid (FA), and vitamin B12 (VB12) alone or together for eight weeks. Following the gavage, the learning and memory ability of the mice were evaluated by Morris Water Maze and Novel object recognition test. The CYP27A1-related gene and protein expressions in the liver and brain were determined by qRT-PCR. The serum level of 27-OHC was detected by HPLC-MS. Serum levels of 25(OH)D, homocysteine (Hcy), and S-Adenosylmethionine (SAM) were measured by ELISA. After feeding with the vitamin D-deficient diet, the mice performed longer latency to a platform (p p = 0.026) in the Morris Water Maze, a lower time discrimination index (p = 0.009) in Novel object recognition, and performances were reversed after vitamin D, folic acid and vitamin B12 supplementation alone or together (p p = 0.015), while it was downregulated in VDD + VD and VDD + VD-FA/VB12 groups compared with the VDD group (p 12 group (p p = 0.008), and increased in the vitamin D-supplemented group (p p 12 significantly reverses this effect by affecting the expression of CYP27A1, which in turn regulates the metabolism of 27-OHC, 25(OH)D, and SAM