Existing high-throughput methods to identify RNA-binding proteins (RBPs) involving capture of polyadenylated RNAs can not recover proteins that interact with non-adenylated RNAs, including lncRNA, pre-mRNA and bacterial RNAs. We present orthogonal organic phase separation (OOPS) which does not require molecular tagging or capture of polyadenylated RNA. We verify OOPS in HEK293, U2OS and MCF10A human cell lines, finding 96% of proteins recovered are bound to RNA. We demonstrate that all long RNAs can be crosslinked to proteins and recover 1838 RBPs, including 926 putative novel RBPs. Importantly, OOPS is approximately 100-fold more efficient than current techniques, enabling analysis of dynamic RNA-protein interactions. We identified 749 proteins with altered RNA binding following release from nocodazole arrest. Finally, OOPS allowed the characterisation of the first RNA-interactome for a bacterium, Escherichia coli. OOPS is an easy to use and flexible technique, compatible with downstream proteomics and RNA sequencing and applicable to any organism.

The social and behavioral sciences have been increasingly using automated text analysis to measure psychological constructs in text. We explore whether GPT, the large-language model underlying the artificial intelligence chatbot ChatGPT, can be used as a tool for automated psychological text analysis in various languages. Across 15 datasets (n = 31,789 manually annotated tweets and news headlines), we tested whether GPT-3.5 and GPT-4 can accurately detect psychological constructs (sentiment, discrete emotions, and offensiveness) across 12 languages (English, Arabic, Indonesian, and Turkish, as well as eight African languages including Swahili, Amharic, Yoruba and Kinyarwanda). We found that GPT performs much better than English-language dictionary-based text analysis (r = 0.66-0.75 for correlations between manual annotations and GPT-4, as opposed to r = 0.20-0.30 for correlations between manual annotations and dictionary methods). Further, GPT performs nearly as well as or better than several fine-tuned machine learning models, though GPT had poorer performance in African languages and in comparison to more recent fine-tuned models. Overall, GPT may be superior to many existing methods of automated text analysis, since it achieves relatively high accuracy across many languages, requires no training data, and is easy to use with simple prompts (e.g., “is this text negative?”) and little coding experience. We provide sample code for analyzing text with the GPT application programming interface. GPT and other large-language models may be the future of psychological text analysis, and may help facilitate more cross-linguistic research with understudied languages.

The vast personal and economic burden of mood disorders is largely caused by their under- and misdiagnosis, which is associated with ineffective treatment and worsening of outcomes. Here, we aimed to develop a diagnostic algorithm, based on an online questionnaire and blood biomarker data, to reduce the misdiagnosis of bipolar disorder (BD) as major depressive disorder (MDD). Individuals with depressive symptoms (Patient Health Questionnaire-9 score ≥5) aged 18–45 years were recruited online. After completing a purpose-built online mental health questionnaire, eligible participants provided dried blood spot samples for biomarker analysis and underwent the World Health Organization World Mental Health Composite International Diagnostic Interview via telephone, to establish their mental health diagnosis. Extreme Gradient Boosting and nested cross-validation were used to train and validate diagnostic models differentiating BD from MDD in participants who self-reported a current MDD diagnosis. Mean test area under the receiver operating characteristic curve (AUROC) for separating participants with BD diagnosed as MDD (N = 126) from those with correct MDD diagnosis (N = 187) was 0.92 (95% CI: 0.86–0.97). Core predictors included elevated mood, grandiosity, talkativeness, recklessness and risky behaviour. Additional validation in participants with no previous mood disorder diagnosis showed AUROCs of 0.89 (0.86–0.91) and 0.90 (0.87–0.91) for separating newly diagnosed BD (N = 98) from MDD (N = 112) and subclinical low mood (N = 120), respectively. Validation in participants with a previous diagnosis of BD (N = 45) demonstrated sensitivity of 0.86 (0.57–0.96). The diagnostic algorithm accurately identified patients with BD in various clinical scenarios, and could help expedite accurate clinical diagnosis and treatment of BD.

Background Web-based assessments of mental health concerns hold great potential for earlier, more cost-effective, and more accurate diagnoses of psychiatric conditions than that achieved with traditional interview-based methods. Objective The aim of this study was to assess the impact of a comprehensive web-based mental health assessment on the mental health and well-being of over 2000 individuals presenting with symptoms of depression. Methods Individuals presenting with depressive symptoms completed a web-based assessment that screened for mood and other psychiatric conditions. After completing the assessment, the study participants received a report containing their assessment results along with personalized psychoeducation. After 6 and 12 months, participants were asked to rate the usefulness of the web-based assessment on different mental health–related outcomes and to self-report on their recent help-seeking behavior, diagnoses, medication, and lifestyle changes. In addition, general mental well-being was assessed at baseline and both follow-ups using the Warwick-Edinburgh Mental Well-being Scale (WEMWBS). Results Data from all participants who completed either the 6-month or the 12-month follow-up (N=2064) were analyzed. The majority of study participants rated the study as useful for their subjective mental well-being. This included talking more openly (1314/1939, 67.77%) and understanding one’s mental health problems better (1083/1939, 55.85%). Although most participants (1477/1939, 76.17%) found their assessment results useful, only a small proportion (302/2064, 14.63%) subsequently discussed them with a mental health professional, leading to only a small number of study participants receiving a new diagnosis (110/2064, 5.33%). Among those who were reviewed, new mood disorder diagnoses were predicted by the digital algorithm with high sensitivity (above 70%), and nearly half of the participants with new diagnoses also had a corresponding change in medication. Furthermore, participants’ subjective well-being significantly improved over 12 months (baseline WEMWBS score: mean 35.24, SD 8.11; 12-month WEMWBS score: mean 41.19, SD 10.59). Significant positive predictors of follow-up subjective well-being included talking more openly, exercising more, and having been reviewed by a psychiatrist. Conclusions Our results suggest that completing a web-based mental health assessment and receiving personalized psychoeducation are associated with subjective mental health improvements, facilitated by increased self-awareness and subsequent use of self-help interventions. Integrating web-based mental health assessments within primary and/or secondary care services could benefit patients further and expedite earlier diagnosis and effective treatment. International Registered Report Identifier (IRRID) RR2-10.2196/18453