Evaluation of the Current State of Chatbots for Digital Health: Scoping Review.

BACKGROUND: Chatbots have become ubiquitous in our daily lives, enabling natural language conversations with users through various modes of communication. Chatbots have the potential to play a significant role in promoting health and well-being. As the number of studies and available products related to chatbots continues to rise, there is a critical need to assess product features to enhance the design of chatbots that effectively promote health and behavioral change. OBJECTIVE: This scoping review aims to provide a comprehensive assessment of the current state of health-related chatbots, including the chatbots' characteristics and features, user backgrounds, communication models, relational building capacity, personalization, interaction, responses to suicidal thoughts, and users' in-app experiences during chatbot use. Through this analysis, we seek to identify gaps in the current research, guide future directions, and enhance the design of health-focused chatbots. METHODS: Following the scoping review methodology by Arksey and O'Malley and guided by the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist, this study used a two-pronged approach to identify relevant chatbots: (1) searching the iOS and Android App Stores and (2) reviewing scientific literature through a search strategy designed by a librarian. Overall, 36 chatbots were selected based on predefined criteria from both sources. These chatbots were systematically evaluated using a comprehensive framework developed for this study, including chatbot characteristics, user backgrounds, building relational capacity, personalization, interaction models, responses to critical situations, and user experiences. Ten coauthors were responsible for downloading and testing the chatbots, coding their features, and evaluating their performance in simulated conversations. The testing of all chatbot apps was limited to their free-to-use features. RESULTS: This review provides an overview of the diversity of health-related chatbots, encompassing categories such as mental health support, physical activity promotion, and behavior change interventions. Chatbots use text, animations, speech, images, and emojis for communication. The findings highlight variations in conversational capabilities, including empathy, humor, and personalization. Notably, concerns regarding safety, particularly in addressing suicidal thoughts, were evident. Approximately 44% (16/36) of the chatbots effectively addressed suicidal thoughts. User experiences and behavioral outcomes demonstrated the potential of chatbots in health interventions, but evidence remains limited. CONCLUSIONS: This scoping review underscores the significance of chatbots in health-related applications and offers insights into their features, functionalities, and user experiences. This study contributes to advancing the understanding of chatbots' role in digital health interventions, thus paving the way for more effective and user-centric health promotion strategies. This study informs future research directions, emphasizing the need for rigorous randomized control trials, standardized evaluation metrics, and user-centered design to unlock the full potential of chatbots in enhancing health and well-being. Future research should focus on addressing limitations, exploring real-world user experiences, and implementing robust data security and privacy measures.

Using Twitter-Based Data for Sexual Violence Research: Scoping Review.

BACKGROUND: Scholars have used data from in-person interviews, administrative systems, and surveys for sexual violence research. Using Twitter as a data source for examining the nature of sexual violence is a relatively new and underexplored area of study. OBJECTIVE: We aimed to perform a scoping review of the current literature on using Twitter data for researching sexual violence, elaborate on the validity of the methods, and discuss the implications and limitations of existing studies. METHODS: We performed a literature search in the following 6 databases: APA PsycInfo (Ovid), Scopus, PubMed, International Bibliography of Social Sciences (ProQuest), Criminal Justice Abstracts (EBSCO), and Communications Abstracts (EBSCO), in April 2022. The initial search identified 3759 articles that were imported into Covidence. Seven independent reviewers screened these articles following 2 steps: (1) title and abstract screening, and (2) full-text screening. The inclusion criteria were as follows: (1) empirical research, (2) focus on sexual violence, (3) analysis of Twitter data (ie, tweets or Twitter metadata), and (4) text in English. Finally, we selected 121 articles that met the inclusion criteria and coded these articles. RESULTS: We coded and presented the 121 articles using Twitter-based data for sexual violence research. About 70% (89/121, 73.6%) of the articles were published in peer-reviewed journals after 2018. The reviewed articles collectively analyzed about 79.6 million tweets. The primary approaches to using Twitter as a data source were content text analysis (112/121, 92.5%) and sentiment analysis (31/121, 25.6%). Hashtags (103/121, 85.1%) were the most prominent metadata feature, followed by tweet time and date, retweets, replies, URLs, and geotags. More than a third of the articles (51/121, 42.1%) used the application programming interface to collect Twitter data. Data analyses included qualitative thematic analysis, machine learning (eg, sentiment analysis, supervised machine learning, unsupervised machine learning, and social network analysis), and quantitative analysis. Only 10.7% (13/121) of the studies discussed ethical considerations. CONCLUSIONS: We described the current state of using Twitter data for sexual violence research, developed a new taxonomy describing Twitter as a data source, and evaluated the methodologies. Research recommendations include the following: development of methods for data collection and analysis, in-depth discussions about ethical norms, exploration of specific aspects of sexual violence on Twitter, examination of tweets in multiple languages, and decontextualization of Twitter data. This review demonstrates the potential of using Twitter data in sexual violence research.

Pu-erh Tea Restored Circadian Rhythm Disruption by Regulating Tryptophan Metabolism.

Pu-erh tea is a healthy beverage rich in phytochemicals, and its effect on the risk of inducing circadian rhythm disorders (CRD) is unclear. In this study, healthy mice were given water or 0.25% (w/v) Pu-erh tea for 7 weeks, followed by a 40 day disruption of the light/dark cycle. CRD caused dysregulation of neurotransmitter secretion and clock gene oscillations, intestinal inflammation, and disruption of intestinal microbes and metabolites. Pu-erh tea boosted the indole and 5-hydroxytryptamine pathways of tryptophan metabolism via the gut-liver-brain axis. Furthermore, its metabolites (e.g., IAA, Indole, 5-HT) enhanced hepatic glycolipid metabolism and down-regulated intestinal oxidative stress by improving the brain hormone release. Tryptophan metabolites and bile acids also promoted liver lipid metabolism and inhibited intestinal inflammation (MyD88/NF-κB) via the enterohepatic circulation. Collectively, 0.25% (w/v) Pu-erh tea has the potential to prevent CRD by promoting indole and 5-HT pathways of tryptophan metabolism and signaling interactions in the gut-liver-brain axis.

Discriminant Analysis of Pu-Erh Tea of Different Raw Materials Based on Phytochemicals Using Chemometrics.

Pu-erh tea processed from the sun-dried green tea leaves can be divided into ancient tea (AT) and terrace tea (TT) according to the source of raw material. However, their similar appearance makes AT present low market identification, resulting in a disruption in the tea market rules of fair trade. Therefore, this study analyzed the classification by principal component analysis/hierarchical clustering analysis and conducted the discriminant model through stepwise Fisher discriminant analysis and decision tree analysis based on the contents of water extract, phenolic components, alkaloid, and amino acids, aiming to investigate whether phytochemicals coupled with chemometric analyses distinguish AT and TT. Results showed that there were good separations between AT and TT, which was caused by 16 components with significant (p < 0.05) differences. The discriminant model of AT and TT was established based on six discriminant variables including water extract, (+)-catechin, (−)-epicatechin, (−)-epigallocatechin, theacrine, and theanine. Among them, water extract comprised multiple soluble solids, representing the thickness of tea infusion. The model had good generalization capability with 100% of performance indexes according to scores of the training set and model set. In conclusion, phytochemicals coupled with chemometrics analyses are a good approach for the identification of different raw materials.

MicroRNA-518-3p suppresses cell proliferation, invasiveness, and migration in colorectal cancer via targeting TRIP4.

MicroRNA (miR)-518-3p has been shown to function as a tumor suppressor. This study was conducted to investigate the effects of miR-518-3p in colorectal cancer (CRC). The miR-518-3p mimic, mimic negative control (NC), miR-518-3p inhibitor, inhibitor-NC, ShRNA-TRIP4, and ShRNA-NC vectors were transfected into SW480 cells using Lipofectamine 2000. Cell viability was detected using CCK-8. Colony formation, cell invasiveness, and cell migration were assessed by plate colony formation, Transwell assays, and wound healing assays, respectively. Relative mRNA and protein levels were detected using RT-qPCR and Western blot, respectively. The target gene thyroid hormone receptor interactor 4 (TRIP4) of miR-518-3p was identified and further verified using dual-luciferase reporter assay. Compared with normal tissues, levels of miR-518-3p were decreased and TRIP4 was significantly increased in the tissues from patients with CRC. Following transfection with a miR-518-3p mimic or ShRNA-TRIP4, cell viability decreased in a time-dependent manner, and colony formation rate, wound closure rate, and the number of invasive cells were much lower for the transfected cells than in the corresponding NC and control groups. miR-518-3p overexpression or silencing of TRIP4 significantly down-regulated the expression of MMP-2 and MMP-9. Knockdown of miR-518-3p had the opposite effects, and TRIP4 was identified as a target of miR-518-3p. The inhibitory effects of miR-518-3p on the progressions of CRC are associated with TRIP4.

Nonparametric Bayesian Correlated Group Regression With Applications to Image Classification.

Sparse Bayesian learning has emerged as a powerful tool to tackle various image classification tasks. The existing sparse Bayesian models usually use independent Gaussian distribution as the prior knowledge for the noise. However, this assumption often contradicts to the practical observations in which the noise is long tail and pixels containing noise are spatially correlated. To handle the practical noise, this paper proposes to partition the noise image into several 2-D groups and adopt the long-tail distribution, i.e., the scale mixture of the matrix Gaussian distribution, to model each group to capture the intragroup correlation of the noise. Under the nonparametric Bayesian estimation, the low-rank-induced prior and the matrix Gamma distribution prior are imposed on the covariance matrix of each group, respectively, to induce two Bayesian correlated group regression (BCGR) methods. Moreover, the proposed methods are extended to the case with unknown group structure. Our BCGR method provides an effective way to automatically fit the noise distribution and integrates the long-tail attribute and structure information of the practical noise into model. Therefore, the estimated coefficients are better for reconstructing the desired data. We apply BCGR to address image classification task and utilize the learned covariance matrices to construct a grouped Mahalanobis distance to measure the reconstruction residual of each class in the design of a classifier. Experimental results demonstrate the effectiveness of our new BCGR model.

Mediastinal lymph node dissection versus mediastinal lymph node sampling for early stage non-small cell lung cancer: a systematic review and meta-analysis.

OBJECTIVE: This systematic review and meta-analysis aimed to evaluate the overall survival, local recurrence, distant metastasis, and complications of mediastinal lymph node dissection (MLND) versus mediastinal lymph node sampling (MLNS) in stage I-IIIA non-small cell lung cancer (NSCLC) patients. METHODS: A systematic search of published literature was conducted using the main databases (MEDLINE, PubMed, EMBASE, and Cochrane databases) to identify relevant randomized controlled trials that compared MLND vs. MLNS in NSCLC patients. Methodological quality of included randomized controlled trials was assessed according to the criteria from the Cochrane Handbook for Systematic Review of Interventions (Version 5.1.0). Meta-analysis was performed using The Cochrane Collaboration's Review Manager 5.3. The results of the meta-analysis were expressed as hazard ratio (HR) or risk ratio (RR), with their corresponding 95% confidence interval (CI). RESULTS: We included results reported from six randomized controlled trials, with a total of 1,791 patients included in the primary meta-analysis. Compared to MLNS in NSCLC patients, there was no statistically significant difference in MLND on overall survival (HR = 0.77, 95% CI 0.55 to 1.08; P = 0.13). In addition, the results indicated that local recurrence rate (RR = 0.93, 95% CI 0.68 to 1.28; P = 0.67), distant metastasis rate (RR = 0.88, 95% CI 0.74 to 1.04; P = 0.15), and total complications rate (RR = 1.10, 95% CI 0.67 to 1.79; P = 0.72) were similar, no significant difference found between the two groups. CONCLUSIONS: Results for overall survival, local recurrence rate, and distant metastasis rate were similar between MLND and MLNS in early stage NSCLC patients. There was no evidence that MLND increased complications compared with MLNS. Whether or not MLND is superior to MLNS for stage II-IIIA remains to be determined.

Mixed noise removal by weighted encoding with sparse nonlocal regularization.

Mixed noise removal from natural images is a challenging task since the noise distribution usually does not have a parametric model and has a heavy tail. One typical kind of mixed noise is additive white Gaussian noise (AWGN) coupled with impulse noise (IN). Many mixed noise removal methods are detection based methods. They first detect the locations of IN pixels and then remove the mixed noise. However, such methods tend to generate many artifacts when the mixed noise is strong. In this paper, we propose a simple yet effective method, namely weighted encoding with sparse nonlocal regularization (WESNR), for mixed noise removal. In WESNR, there is not an explicit step of impulse pixel detection; instead, soft impulse pixel detection via weighted encoding is used to deal with IN and AWGN simultaneously. Meanwhile, the image sparsity prior and nonlocal self-similarity prior are integrated into a regularization term and introduced into the variational encoding framework. Experimental results show that the proposed WESNR method achieves leading mixed noise removal performance in terms of both quantitative measures and visual quality.