Review of anti-stigma social media interventions for mental illness

As a global health concern, mental illness mental illness is a leading cause of mortality and morbidity worldwide. The stigma attached to mental illness leads the delay of treatment as well as decrease the quality of life. Therefore, reducing stigma for mental illness is extremely urgent. Mass media shows the potential of many interventions for decreasing mental illness stigma. As a new form of mass media, social media can be more promising in stigma reduction. The benefits of social media include cost-efficiency, privacy-protection, high-accessibility, broad-coverage, and no limitations for time and geography. The most important benefit is the enrichment of interactions. 10 research studies were selected by screening titles, abstracts, and full texts from a database search that yielded 145 results. The research data were collected from two databases on 26 July 2018. The publication date ranges from 2011-2018. By comparing and analyzing these 10 research studies, three questions are answered: (1) What kinds of social media were used? (2) How was social media used in the interventions? (3) What was the effect of social media? As expected, social media interventions are effective at reducing stigma. The effects are more significant in females than in males. Future research and interventions should explore new ways to use the interactive functions of social media and exploit more types of social media platforms. Additionally, the endeavor should also be made to deconstruct social media to find its inner logic and mechanism, in order to develop precise models and techniques to assess its effects on reducing mental illness stigma.Advertisin

Does chlorhexidine improve outcomes in non-surgical management of peri-implant mucositis or peri-implantitis? : a systematic review and meta-analysis

With greater number of implants being placed in clinical practice, incidence of peri-implant diseases are on the rise. It is not known whether chlorhexidine (CHX) improves outcomes in the management of peri-implant diseases. The aim of this systematic review and meta-analysis was to evaluate the role of CHX in improving outcomes with non-surgical management of peri-implant mucositis and peri-implantitis. An electronic search of PubMed, Scopus, Embase, and CENTRAL (Cochrane Central Register of Controlled Trials) databases up to 1st August 2019 was carried out to search for studies evaluating the efficacy of CHX for non-surgical management of peri-implant diseases. Seven studies were included. Four studies evaluated the role of CHX in peri-implant mucositis and three in peri-implantitis. Oral prophylaxis with mechanical cleansing of implant surface prior to CHX use was carried out in all seven studies. Meta-analysis indicated that use of CHX did not improve probing depths in peri-implant mucositis (SMD= 0.11; 95% CI: -0.16 to 0.38; p=0.42, I2= 0%). Similarly, CHX did not significantly reduce probing depths in patients with peri-implantitis (MD= 1.57; 95% CI: -0.88 to 4.0; p=0.21, I2= 98%). Results on the efficacy of CHX in reducing BOP in peri-implantitis are conflicting. Results of our study indicate that adjunctive therapy with CHX may not improve outcomes with non-surgical management of peri-implant mucositis. Conclusions with regards to its role in non-surgical management of peri-implantitis cannot be drawn. There is a need for more homogenous RCTs with large sample size to define the role of CHX in non-surgical management of peri-implant mucositis and peri-implantitis

The First Zagreb Index, Vertex-Connectivity, Minimum Degree And Independent Number in Graphs

Let G be a simple, undirected and connected graph. Defined by M1(G) and RMTI(G) the first Zagreb index and the reciprocal Schultz molecular topological index of G, respectively. In this paper, we determined the graphs with maximal M1 among all graphs having prescribed vertex-connectivity and minimum degree, vertex-connectivity and bipartition, vertex-connectivity and vertex-independent number, respectively. As applications, all maximal elements with respect to RMTI are also determined among the above mentioned graph families, respectively

Down-regulation of GRP78 Enhances Chemotherapy Sensitivity to VP-16 in Lung Adenocarcinoma

Background and objective GRP78, a member of GRPs, plays a critical role in chemotherapy resistance in some cancers. To investigate the relationship between the expression of GRP78 and resistance to anti-cancer drug VP-16 in vitro in lung adenocarcinoma SPCA-1 cell line. Methods SPCA-1 cells were divided into three groups: BAPTA-AM-treated group, A23187-treated group and the control group. RT-PCR and immunofluorescence were used to analyze the expression of GRP78 at both mRNA and protein levels, respectively. Cell apoptosis was analyzed by flow cytometry in order to evaluate the therapeutic sensitivity to VP-16. Results The expression of GRP78 at both protein and mRNA levels in the BAPTA-AM-treated cells dramatically decreased as compared to that of both A23187-treated and control groups. After treatment by VP-16, the percentages of apoptotic cells were 10.84±0.86, 6.85±0.20, 4.95±0.19 in BAPTA-M-treated group, the control group and A23187-treated group, respectively. Conclusion BAPTA-AM is highly effective in the inhibition of GRP78, down-regulation of GRP78 can significantly increase the sensitivity of adenocacinoma lung cancer to VP-16. All these suggest that inhibition of the expression of GRP78 by chemicals such as BAPTA-AM or anti-sense RNA may be a new therapeutic strategies to lung cancer

Growing 3D Artefacts and Functional Machines with Neural Cellular Automata

Neural Cellular Automata (NCAs) have been proven effective in simulating morphogenetic processes, the continuous construction of complex structures from very few starting cells. Recent developments in NCAs lie in the 2D domain, namely reconstructing target images from a single pixel or infinitely growing 2D textures. In this work, we propose an extension of NCAs to 3D, utilizing 3D convolutions in the proposed neural network architecture. Minecraft is selected as the environment for our automaton since it allows the generation of both static structures and moving machines. We show that despite their simplicity, NCAs are capable of growing complex entities such as castles, apartment blocks, and trees, some of which are composed of over 3,000 blocks. Additionally, when trained for regeneration, the system is able to regrow parts of simple functional machines, significantly expanding the capabilities of simulated morphogenetic systems. The code for the experiment in this paper can be found at: https://github.com/real-itu/3d-artefacts-nca

Current Status of the Chinese National Twin Registry

The Chinese National Twin Registry is the first and largest population-based twin registry in China. It was established in 2001. The primary goal of this program is the establishment of a population-based twin registry of 45,000 twin pairs from several regions representing north, south, urban, and rural areas in China. A secondary goal is to study genetic contributions to complex diseases, and to test associations of candidate genes with related phenotypes. Seven thousand, four hundred and twenty-three twin pairs have been enrolled in the registry in which 1613 pairs have undergone detailed questionnaire assessments and physical examination. Based on the baseline registry, a twin cohort was established. Continued research includes studies on intermediate phenotypes of cardiovascular and cerebrovascular diseases and psychological studies in adult twins, studies on growth and development in adolescent twins, and so forth. The current state and future plans for the Chinese National Twin Registry will be discussed in this article.http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000243216600009&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701Genetics & HeredityObstetrics & GynecologySCI(E)PubMed17ARTICLE6747-752

Investigating Personalized Driving Behaviors in Dilemma Zones: Analysis and Prediction of Stop-or-Go Decisions

Dilemma zones at signalized intersections present a commonly occurring but unsolved challenge for both drivers and traffic operators. Onsets of the yellow lights prompt varied responses from different drivers: some may brake abruptly, compromising the ride comfort, while others may accelerate, increasing the risk of red-light violations and potential safety hazards. Such diversity in drivers' stop-or-go decisions may result from not only surrounding traffic conditions, but also personalized driving behaviors. To this end, identifying personalized driving behaviors and integrating them into advanced driver assistance systems (ADAS) to mitigate the dilemma zone problem presents an intriguing scientific question. In this study, we employ a game engine-based (i.e., CARLA-enabled) driving simulator to collect high-resolution vehicle trajectories, incoming traffic signal phase and timing information, and stop-or-go decisions from four subject drivers in various scenarios. This approach allows us to analyze personalized driving behaviors in dilemma zones and develop a Personalized Transformer Encoder to predict individual drivers' stop-or-go decisions. The results show that the Personalized Transformer Encoder improves the accuracy of predicting driver decision-making in the dilemma zone by 3.7% to 12.6% compared to the Generic Transformer Encoder, and by 16.8% to 21.6% over the binary logistic regression model