Balancing risks and benefits of cannabis use: umbrella review of meta-analyses of randomised controlled trials and observational studies.

OBJECTIVE: To systematically assess credibility and certainty of associations between cannabis, cannabinoids, and cannabis based medicines and human health, from observational studies and randomised controlled trials (RCTs). DESIGN: Umbrella review. DATA SOURCES: PubMed, PsychInfo, Embase, up to 9 February 2022. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: Systematic reviews with meta-analyses of observational studies and RCTs that have reported on the efficacy and safety of cannabis, cannabinoids, or cannabis based medicines were included. Credibility was graded according to convincing, highly suggestive, suggestive, weak, or not significant (observational evidence), and by GRADE (Grading of Recommendations, Assessment, Development and Evaluations) (RCTs). Quality was assessed with AMSTAR 2 (A Measurement Tool to Assess Systematic Reviews 2). Sensitivity analyses were conducted. RESULTS: 101 meta-analyses were included (observational=50, RCTs=51) (AMSTAR 2 high 33, moderate 31, low 32, or critically low 5). From RCTs supported by high to moderate certainty, cannabis based medicines increased adverse events related to the central nervous system (equivalent odds ratio 2.84 (95% confidence interval 2.16 to 3.73)), psychological effects (3.07 (1.79 to 5.26)), and vision (3.00 (1.79 to 5.03)) in people with mixed conditions (GRADE=high), improved nausea/vomit, pain, spasticity, but increased psychiatric, gastrointestinal adverse events, and somnolence among others (GRADE=moderate). Cannabidiol improved 50% reduction of seizures (0.59 (0.38 to 0.92)) and seizure events (0.59 (0.36 to 0.96)) (GRADE=high), but increased pneumonia, gastrointestinal adverse events, and somnolence (GRADE=moderate). For chronic pain, cannabis based medicines or cannabinoids reduced pain by 30% (0.59 (0.37 to 0.93), GRADE=high), across different conditions (n=7), but increased psychological distress. For epilepsy, cannabidiol increased risk of diarrhoea (2.25 (1.33 to 3.81)), had no effect on sleep disruption (GRADE=high), reduced seizures across different populations and measures (n=7), improved global impression (n=2), quality of life, and increased risk of somnolence (GRADE=moderate). In the general population, cannabis worsened positive psychotic symptoms (5.21 (3.36 to 8.01)) and total psychiatric symptoms (7.49 (5.31 to 10.42)) (GRADE=high), negative psychotic symptoms, and cognition (n=11) (GRADE=moderate). In healthy people, cannabinoids improved pain threshold (0.74 (0.59 to 0.91)), unpleasantness (0.60 (0.41 to 0.88)) (GRADE=high). For inflammatory bowel disease, cannabinoids improved quality of life (0.34 (0.22 to 0.53) (GRADE=high). For multiple sclerosis, cannabinoids improved spasticity, pain, but increased risk of dizziness, dry mouth, nausea, somnolence (GRADE=moderate). For cancer, cannabinoids improved sleep disruption, but had gastrointestinal adverse events (n=2) (GRADE=moderate). Cannabis based medicines, cannabis, and cannabinoids resulted in poor tolerability across various conditions (GRADE=moderate). Evidence was convincing from observational studies (main and sensitivity analyses) in pregnant women, small for gestational age (1.61 (1.41 to 1.83)), low birth weight (1.43 (1.27 to 1.62)); in drivers, car crash (1.27 (1.21 to 1.34)); and in the general population, psychosis (1.71 (1.47 to 2.00)). Harmful effects were noted for additional neonatal outcomes, outcomes related to car crash, outcomes in the general population including psychotic symptoms, suicide attempt, depression, and mania, and impaired cognition in healthy cannabis users (all suggestive to highly suggestive). CONCLUSIONS: Convincing or converging evidence supports avoidance of cannabis during adolescence and early adulthood, in people prone to or with mental health disorders, in pregnancy and before and while driving. Cannabidiol is effective in people with epilepsy. Cannabis based medicines are effective in people with multiple sclerosis, chronic pain, inflammatory bowel disease, and in palliative medicine but not without adverse events. STUDY REGISTRATION: PROSPERO CRD42018093045. FUNDING: None.

Association between autism spectrum disorder and inflammatory bowel disease: A systematic review and meta-analysis.

Children with autism spectrum disorder (ASD) are frequently diagnosed with co-occurring medical conditions including inflammatory bowel disease (IBD). To investigate the association, we conducted a systematic review registered in PROSPERO (ID:CRD42021236263) with a random-effects meta-analysis. We searched PubMed, Embase, and PsycInfo (last search on January 25, 2021), and manually searched relevant publications. We included observational studies measuring the association between ASD and IBD. The primary outcome was the association (odds ratio, OR) between ASD and later development of IBD. Sensitivity analyses were conducted by quality, confounding adjustment, and study design. We performed meta-regression analyses and assessed heterogeneity, publication bias, and quality of studies with the Newcastle-Ottawa Scale. Overall, we included six studies consisting of eight datasets, including over 11 million participants. We found that ASD was significantly associated with subsequent incident IBD (any IBD, OR = 1.66, 95% confidence interval[CI] = 1.25-2.21, p < 0.001; ulcerative colitis, OR = 1.91, 95%CI = 1.41-2.6, p < 0.001; Crohn's disease, OR = 1.47, 95%CI = 1.15-1.88, p = 0.002). ASD and IBD were also associated regardless of temporal sequence of diagnosis (any IBD, OR = 1.57, 95%CI = 1.28-1.93, p < 0.001; ulcerative colitis, OR = 1.7, 95%CI = 1.36-2.12, p < 0.001; Crohn's disease, OR = 1.37, 95%CI = 1.12-1.69, p = 0.003). Sensitivity analyses confirmed the findings of the main analysis. Meta-regression did not identify any significant moderators. Publication bias was not detected. Quality was high in four datasets and medium in four. In conclusion, our findings highlight the need to screen for IBD in individuals with ASD, and future research should identify who, among those with ASD, has the highest risk of IBD, and elucidate the shared biological mechanisms between ASD and IBD. LAY SUMMARY: This systematic review and meta-analysis of eight observational datasets found that individuals with autism spectrum disorder (ASD) are more likely to develop any inflammatory bowel disease, ulcerative colitis, or Crohn's disease. Our findings highlight the need to screen for inflammatory bowel disease in patients with ASD and elucidate the shared biological mechanisms between the two disorders.

Influence of Oxidative Etching Solution Temperatures on the Surface Roughness and Wettability of a Titanium Alloy.

Recently, a simple surface modification treatment of titanium (Ti) was developed to produce nano-and micro-scale features on the surfaces via simple immersion in an oxidative aqueous solution (30% hydrogen peroxide/5% sodium bicarbonate). However, this treatment method of Ti surfaces requires a relatively long immersion time (4 h) in the oxidative solution. In this study, we investigated whether an increase in the temperature of the oxidative etching solution can shorten the immersion time of Ti effectively. Polished grade 5 dental Ti (Ti-6Al-4V) discs were immersed in the oxidative aqueous solution either for 30 or 60 min. The temperature of the etching solution was maintained at 25 (similar to room temperature), 35, or 45 °C during etching. The etched surfaces were studied in terms of micro- and nano-structures, surface roughness, and wettability (surface energy). The increase in the temperature of the solution accelerated the etching effect of Ti and created both micro- and nano-structures on the surfaces more effectively. In particular, immersion for 60 min at the solution temperature of 35 °C significantly increased the surface roughness and wettability, although the etching effect was enhanced further at the solution temperature of 45 °C.

RetainVis: Visual Analytics with Interpretable and Interactive Recurrent Neural Networks on Electronic Medical Records.

We have recently seen many successful applications of recurrent neural networks (RNNs) on electronic medical records (EMRs), which contain histories of patients' diagnoses, medications, and other various events, in order to predict the current and future states of patients. Despite the strong performance of RNNs, it is often challenging for users to understand why the model makes a particular prediction. Such black-box nature of RNNs can impede its wide adoption in clinical practice. Furthermore, we have no established methods to interactively leverage users' domain expertise and prior knowledge as inputs for steering the model. Therefore, our design study aims to provide a visual analytics solution to increase interpretability and interactivity of RNNs via a joint effort of medical experts, artificial intelligence scientists, and visual analytics researchers. Following the iterative design process between the experts, we design, implement, and evaluate a visual analytics tool called RetainVis, which couples a newly improved, interpretable, and interactive RNN-based model called RetainEX and visualizations for users' exploration of EMR data in the context of prediction tasks. Our study shows the effective use of RetainVis for gaining insights into how individual medical codes contribute to making risk predictions, using EMRs of patients with heart failure and cataract symptoms. Our study also demonstrates how we made substantial changes to the state-of-the-art RNN model called RETAIN in order to make use of temporal information and increase interactivity. This study will provide a useful guideline for researchers that aim to design an interpretable and interactive visual analytics tool for RNNs.

Toward Predicting Social Support Needs in Online Health Social Networks.

BACKGROUND: While online health social networks (OHSNs) serve as an effective platform for patients to fulfill their various social support needs, predicting the needs of users and providing tailored information remains a challenge. OBJECTIVE: The objective of this study was to discriminate important features for identifying users' social support needs based on knowledge gathered from survey data. This study also provides guidelines for a technical framework, which can be used to predict users' social support needs based on raw data collected from OHSNs. METHODS: We initially conducted a Web-based survey with 184 OHSN users. From this survey data, we extracted 34 features based on 5 categories: (1) demographics, (2) reading behavior, (3) posting behavior, (4) perceived roles in OHSNs, and (5) values sought in OHSNs. Features from the first 4 categories were used as variables for binary classification. For the prediction outcomes, we used features from the last category: the needs for emotional support, experience-based information, unconventional information, and medical facts. We compared 5 binary classifier algorithms: gradient boosting tree, random forest, decision tree, support vector machines, and logistic regression. We then calculated the scores of the area under the receiver operating characteristic (ROC) curve (AUC) to understand the comparative effectiveness of the used features. RESULTS: The best performance was AUC scores of 0.89 for predicting users seeking emotional support, 0.86 for experience-based information, 0.80 for unconventional information, and 0.83 for medical facts. With the gradient boosting tree as our best performing model, we analyzed the strength of individual features in predicting one's social support need. Among other discoveries, we found that users seeking emotional support tend to post more in OHSNs compared with others. CONCLUSIONS: We developed an initial framework for automatically predicting social support needs in OHSNs using survey data. Future work should involve nonsurvey data to evaluate the feasibility of the framework. Our study contributes to providing personalized social support in OHSNs.

Personas in online health communities.

Many researchers and practitioners use online health communities (OHCs) to influence health behavior and provide patients with social support. One of the biggest challenges in this approach, however, is the rate of attrition. OHCs face similar problems as other social media platforms where user migration happens unless tailored content and appropriate socialization is supported. To provide tailored support for each OHC user, we developed personas in OHCs illustrating users' needs and requirements in OHC use. To develop OHC personas, we first interviewed 16 OHC users and administrators to qualitatively understand varying user needs in OHC. Based on their responses, we developed an online survey to systematically investigate OHC personas. We received 184 survey responses from OHC users, which informed their values and their OHC use patterns. We performed open coding analysis with the interview data and cluster analysis with the survey data and consolidated the analyses of the two datasets. Four personas emerged-Caretakers, Opportunists, Scientists, and Adventurers. The results inform users' interaction behavior and attitude patterns with OHCs. We discuss implications for how these personas inform OHCs in delivering personalized informational and emotional support.

Paroxysmal atrial fibrillation developed during incomplete epidural anesthesia -A case report-.

Atrial fibrillation (AF) is the most common sustained tachyarrhythmia, and occurs in organic heart disease such as rheumatic, atherosclerotic and hypertensive heart disease. In recent studies, the sympathetic and parasympathetic nervous systems have been shown to have important roles in initiating paroxysmal AF. We report here a patient who developed paroxysmal AF that might be a result of an imbalance of the sympathetic-parasympathetic systems due to epidural anesthesia, and that was potentiated by pain with inadequate analgesia. A 69-year-old woman was scheduled for operation of a right-sided ankle fracture. Twenty minutes after epidural drug injection, paroxysmal AF occurred. Even after intravenous administration of esmolol and digoxin, AF continued. After transfer to the intensive care unit, her heart rate gradually decreased and AF disappeared. During perioperative anesthetic management, the proper preoperative prevention and intraoperative treatment are needed in AF high-risk patients.