Cannabis-Induced Gastrointestinal Tract Symptoms in the Adult Population: A Systematic Review.

OBJECTIVE: Cannabinoid usage is widespread in the self-management of various medical ailments. However, adverse effects have been reported with use, especially pertaining to the gastrointestinal system in adults and aged patients. These range from nausea, vomiting, bloating, or abdominal pain. This systematic review of previously reported cannabis-induced gastrointestinal symptoms in the adult population from the literature provides an analysis of relevant data to enhance knowledge and awareness of this topic. METHODS: PubMed, Ovid MEDLINE, Cochrane Central, EMBASE, and Google Scholar databases were searched for relevant studies published from inception to March 2023. RESULTS: The search yielded 598 results, of which 13 were deemed relevant and underwent further review. These included two systematic reviews, one retrospective cohort study, one retrospective chart review, two cross-sectional studies, one survey, and six case reports. The Cochrane Risk Tool for bias analysis was applied where relevant. The total number of people in the studies selected for analysis was 79, 779. Twelve out of the thirteen included studies reported some type of gastrointestinal tract symptoms experienced in medical and/or recreational cannabis users ranging from nausea, vomiting, diarrhoea, abdominal pain to adult intussusception. CONCLUSION: Potential limitations include small sample sizes, variation in research methodologies, varied studied designs, and limited availability of data on specific populations such as geriatric users. Further research is warranted to add to current evidence pertaining to this emerging topic of significance, fill the broad knowledge gaps and contribute to evidence-based guidelines for healthcare professionals, ensuring safe prescribing practices and provision of quality care.

The therapygenetics of anxiety disorders.

Treatment of anxiety disorders primarily includes pharmacological treatment and psychotherapy, yet a substantial portion of patients do not experience sufficient clinical response. Given the significant impact of anxiety disorders on well-being and quality of life, it is pertinent to strive to ensure available treatments are of paramount efficacy. This review aimed to identify genetic variants and genes that may moderate the outcome of psychotherapy in patients with anxiety disorders, termed 'therapygenetics.' A comprehensive search of the current literature following relevant guidelines was conducted. Eighteen records were included in the review. Seven studies reported significant associations between genetic variants and response to psychotherapy. The most investigated polymorphisms were the serotonin transporter-linked polymorphic region (5-HTTLPR), nerve growth factor rs6330, catechol-O-methyltransferase Val158Met, and brain-derived neurotrophic factor Val166Met. However, current findings are inconsistent and thus do not support the use of genetic variants for the prediction of psychotherapy response in anxiety disorders.

Neuro-fuzzy network incorporating multiple lexicons for social sentiment analysis.

We have proposed MultiLexANFIS which is an adaptive neuro-fuzzy inference system (ANFIS) that incorporates inputs from multiple lexicons to perform sentiment analysis of social media posts. We classify tweets into two classes: neutral and non-neutral; the latter class includes both positive and negative polarity. This type of classification will be considered for applications that aim to test the neutrality of content posted by the users in social media platforms. In our proposed model, features are extracted by integrating natural language processing with fuzzy logic; hence, it is able to deal with the fuzziness of natural language in a very efficient and automatic manner. We have proposed a novel set of 64 rules for the proposed neuro-fuzzy network that can classify tweets correctly by working on fuzzy features fetched from VADER, AFINN and SentiWordNet lexicons. The proposed novel rules are domain independent, i.e., we can extend these rules for any textual data that employs lexicons. The antecedent and consequent parameters of the ANFIS are optimized by gradient descent and least squares estimate algorithms, respectively, in an iterative manner. The key contributions of this paper are: (1) a novel neuro-fuzzy system: MultiLexANFIS that takes as its input the positive and negative sentiment scores of tweets computed from multiple lexicons-VADER, AFINN and SentiWordNet, in order to classify the tweets into neutral and non-neutral content, (2) a novel set of 64 rules for the Sugeno-type fuzzy inference system-MultiLexANFIS, (3) single-lexicon-based ANFIS variants to classify tweets when multiple lexicons are not available and (4) comparison of MultiLexANFIS with different fuzzy, non-fuzzy and deep learning state of the art on various benchmark datasets revealing the superiority of our proposed neuro-fuzzy system for social sentiment analysis.