Hype or hope? Ketamine for the treatment of depression: results from the application of deep learning to Twitter posts from 2010 to 2023.

Objective: To investigate societal perceptions of ketamine's use in depression therapy by analysing Twitter posts from January 1, 2010 to April 1, 2023. Methods: Using Twitter as the social media platform of choice, and employing search terms based on (depression OR depressed OR depressive) AND (ketamine OR esketamine OR Spravato), we collected English-language tweets from January 1, 2010, to April 1, 2023. Using unsupervised machine learning and natural language processing (NLP) techniques, including Bidirectional Encoder Representations from Transformers (BERT) and BERTopic, the study identified prevalent topics surrounding public chatter around the use of ketamine in depression treatment. Manual thematic analyses further refined these topics into themes. Results: Out of an initial dataset of 99,405 tweets, after removing duplicate tweets, re-tweets and tweets posted by organizations over Twitter, 18,899 unique tweets from presumably individual users were analysed. Analysis of temporal trends revealed a shift in public attitudes, particularly after the United States Food and Drug Administration (FDA)'s 2019 approval of ketamine for depression. Three major themes emerged: a changing regulatory landscape, cautious optimism, and personal experiences with the drug. There was an initial spike in discussions post-FDA approval in 2019. Thereafter, cautious optimism (Theme 2) decreased among the general public, with more personal accounts (Theme 3) highlighting the potential benefits for some treatment-resistant patients. Limitations of the study include Twitter's inherent biases towards younger, English-speaking demographics. Conclusion: In summary, the public's multifaceted perception leans towards a hopeful stance on ketamine's therapeutic potential for depression.

Extracellular Vesicles and Their Mimetics: A Comparative Study of Their Pharmacological Activities and Immunogenicity Profiles.

Extracellular vesicles (EVs), which are miniaturised carriers loaded with functional proteins, lipids, and nucleic acid material, are naturally secreted by cells and show intrinsic pharmacological effects in several conditions. As such, they have the potential to be used for the treatment of various human diseases. However, the low isolation yield and laborious purification process are obstacles to their translation for clinical use. To overcome this problem, our lab developed cell-derived nanovesicles (CDNs), which are EV mimetics produced by shearing cells through membrane-fitted spin cups. To evaluate the similarities between EVs and CDNs, we compare the physical properties and biochemical composition of monocytic U937 EVs and U937 CDNs. Besides having similar hydrodynamic diameters, the produced CDNs had proteomic, lipidomic, and miRNA profiles with key communalities compared to those of natural EVs. Further characterisation was conducted to examine if CDNs could exhibit similar pharmacological activities and immunogenicity when administered in vivo. Consistently, CDNs and EVs modulated inflammation and displayed antioxidant activities. EVs and CDNs both did not exert immunogenicity when administered in vivo. Overall, CDNs could serve as a scalable and efficient alternative to EVs for further translation into clinical use.