Using Linguistic Features to Estimate Suicide Probability of Chinese Microblog Users

If people with high risk of suicide can be identified through social media like microblog, it is possible to implement an active intervention system to save their lives. Based on this motivation, the current study administered the Suicide Probability Scale(SPS) to 1041 weibo users at Sina Weibo, which is a leading microblog service provider in China. Two NLP (Natural Language Processing) methods, the Chinese edition of Linguistic Inquiry and Word Count (LIWC) lexicon and Latent Dirichlet Allocation (LDA), are used to extract linguistic features from the Sina Weibo data. We trained predicting models by machine learning algorithm based on these two types of features, to estimate suicide probability based on linguistic features. The experiment results indicate that LDA can find topics that relate to suicide probability, and improve the performance of prediction. Our study adds value in prediction of suicidal probability of social network users with their behaviors

A Model of Trust, Moods, and Emotions in Multiagent Systems and its Empirical Evaluation

Abstract We study the interplay of moods, emotions, and trust in decisionmaking contexts characterized by commitments among agents. We develop a general approach representing the relationships among these concepts via a Bayesian network model. Our approach incorporates insights from the literature and provides a computational methodology for identifying improved Bayesian models. Based on observations from an empirical study, we motivate a refined Bayesian model involving the above-mentioned concepts that goes beyond the relationships known in the literature. Our findings include (1) the violation of a commitment affects trust more than its satisfaction; (2) goal satisfaction affects mood and emotion more than commitment satisfaction, but the outcome of a commitment affects trust more than the outcome of a goal; and (3) an agent's prior mood and trust affect whether it satisfies its commitments

Differential effects of diabetes on CYP2E1 and CYP2B4 proteins and associated drug metabolizing enzyme activities in rabbit liver

The effects of diabetes on cytochrome P450 (CYP)-dependent drug metabolizing enzymes are yet to be clarified. The most widely used animals in these studies have been rats, and information on the effects of diabetes on rabbit liver drug metabolizing enzymes have been unavailable until now. In this study, for the first time, a significant induction of liver CYP2E1 is demonstrated via immunoblot analysis in alloxan-induced rabbits. The CYP2E1 content of diabetic microsomes was highly correlated with the activities of liver aniline 4-hydroxylase (r=0.82, p < 0.05), and p-nitrophenol hydroxylase (r=0.86, p < 0.01), and diabetes increased the activities of the enzymes associated with CYP2E1. The activities of aniline 4-hydroxylase and p-nitrophenol hydroxylase were significantly increased by 1.7 and 1.8-fold, respectively compared to those of control rabbits. In marked contrast, diabetes had no effect on the protein levels of CYP2B4 as determined by immunoblotting and on benzphetamine N-demethylase activity, which is known to be specifically metabolized by CYP2B4 in rabbit liver. The present study demonstrates that diabetes increases the activities of CYP2E1 and associated enzymes but does not change the activity levels of CYP2B4 and associated enzymes in diabetic rabbits. These findings are in contrast to those of mice, hamsters and rats, and that suggest the presence of species-dependent responses of CYP-dependent drug metabolizing enzymes to diabetes

Does Concentrated Growth Factor Used With Allografts in Maxillary Sinus Lifting Have Adjunctive Benefits?

PubMed: 32866488Purpose: Recent studies have shown that the use of platelet concentration products alone, or in combination with variable graft materials, accelerates the new bone formation associated with sinus augmentation. The aim of this study was to measure the adjunctive effects of concentrated growth factor (CGF) used with allograft on new bone formation and augmentation stability in sinus lifting. Materials and Methods: This randomized controlled study included patients who presented for dental implant placement in atrophic posterior maxilla at the Department of Oral and Maxillofacial Surgery, Faculty of Dentistry, Ege University, and who needed maxillary sinus augmentation. All patients were treated with a 2-stage surgical technique using sinus lifting bilaterally and implant placement 6 months later. During sinus lifting surgery, one side was grafted with allograft (group 1) and the other side with allograft mixed CGF (group 2). Cone-beam computed tomography was carried out immediately postoperatively and also 6 months after the augmentation to evaluate vertical bone height and percentage of resorption. Bone specimens were obtained at the time of implant placement and evaluated histomorphometrically to analyze the percentage of new bone formation and residual graft particle. Statistical comparisons were conducted between groups for all these measurements. Results: A total of 10 patients (2 females and 8 males) with a mean age of 57 years (range, 39 to 72) were enrolled in the study. Cone-beam computed tomography analysis revealed a significantly higher percentage of bone height resorption at the sixth month in group 1 (median, 9.32%) compared with group 2 (median, 6.37%) (P <.05). According to the histomorphometric examination, the percentage of new bone formation in group 2 (median, 36.41%) was higher than group 1 (median, 35.49%), but this difference did not reach statistical significance. Conclusions: Using CGF with allografts supports the stabilization of gained vertical bone height after sinus augmentation, but further research is needed to determine the accelerating effects of CGF on new bone formation. © 2020 American Association of Oral and Maxillofacial SurgeonsConflict of Interest Disclosures: None of the authors have any relevant financial relationship(s) with a commercial interest