Layperson knowledge on naloxone and medications for opioid use disorder in an urban population: a cross sectional survey study.

BACKGROUND AND OBJECTIVES: There has been little research in an urban population regarding knowledge of harm reduction measures and treatment options. The objective of our study was to evaluate knowledge and perceptions of harm reduction measures and types of treatment available for opioid use disorder among patients and family in an urban emergency department (ED) waiting room. METHODS: We conducted a single center, cross-sectional survey study that occurred between September 2021 and August 2022. A convenience sample of patients and family members that were above 18 and English speaking were recruited by research assistants. Participants were assessed on knowledge and preferences around drug treatment options and harm reduction. Data were summarized using descriptive statistics and compared using the Freeman-Halton/Kruskall-Wallis/Mann-Whitney U tests. p-Values were reported at the 0.05 significance level. RESULTS: We collected 200 responses. Of these, 104 people had a connection to someone with a substance use disorder (SUD) and 50 had an SUD. Of those who had a connection to someone with SUD, 63 had heard of naloxone (60.6%, CI: [50.5, 69.9]). Fewer than 60% of respondents in each group had heard of Medications for Opioid Use Disorder (MOUD) (p = 0.46) and fewer than 50% thought that among people who use drugs that they knew would be interested in receiving treatment (p = 0.10). DISCUSSION AND CONCLUSIONS: Our study found that among people who came to an urban emergency department, there was a lack of awareness of harm reduction and MOUD. Interventions should be put into place to educate on the importance of MOUD and harm reduction.

Artificial Intelligence-Enabled Software Prototype to Inform Opioid Pharmacovigilance From Electronic Health Records: Development and Usability Study.

Background: The use of patient health and treatment information captured in structured and unstructured formats in computerized electronic health record (EHR) repositories could potentially augment the detection of safety signals for drug products regulated by the US Food and Drug Administration (FDA). Natural language processing and other artificial intelligence (AI) techniques provide novel methodologies that could be leveraged to extract clinically useful information from EHR resources. Objective: Our aim is to develop a novel AI-enabled software prototype to identify adverse drug event (ADE) safety signals from free-text discharge summaries in EHRs to enhance opioid drug safety and research activities at the FDA. Methods: We developed a prototype for web-based software that leverages keyword and trigger-phrase searching with rule-based algorithms and deep learning to extract candidate ADEs for specific opioid drugs from discharge summaries in the Medical Information Mart for Intensive Care III (MIMIC III) database. The prototype uses MedSpacy components to identify relevant sections of discharge summaries and a pretrained natural language processing (NLP) model, Spark NLP for Healthcare, for named entity recognition. Fifteen FDA staff members provided feedback on the prototype's features and functionalities. Results: Using the prototype, we were able to identify known, labeled, opioid-related adverse drug reactions from text in EHRs. The AI-enabled model achieved accuracy, recall, precision, and F1-scores of 0.66, 0.69, 0.64, and 0.67, respectively. FDA participants assessed the prototype as highly desirable in user satisfaction, visualizations, and in the potential to support drug safety signal detection for opioid drugs from EHR data while saving time and manual effort. Actionable design recommendations included (1) enlarging the tabs and visualizations; (2) enabling more flexibility and customizations to fit end users' individual needs; (3) providing additional instructional resources; (4) adding multiple graph export functionality; and (5) adding project summaries. Conclusions: The novel prototype uses innovative AI-based techniques to automate searching for, extracting, and analyzing clinically useful information captured in unstructured text in EHRs. It increases efficiency in harnessing real-world data for opioid drug safety and increases the usability of the data to support regulatory review while decreasing the manual research burden.

Development of Tbet- and CD11c-expressing B cells in a viral infection requires T follicular helper cells outside of germinal centers.

Tbet+CD11c+ B cells arise during type 1 pathogen challenge, aging, and autoimmunity in mice and humans. Here, we examined the developmental requirements of this B cell subset. In acute infection, T follicular helper (Tfh) cells, but not Th1 cells, drove Tbet+CD11c+ B cell generation through proximal delivery of help. Tbet+CD11c+ B cells developed prior to germinal center (GC) formation, exhibiting phenotypic and transcriptional profiles distinct from GC B cells. Fate tracking revealed that most Tbet+CD11c+ B cells developed independently of GC entry and cell-intrinsic Bcl6 expression. Tbet+CD11c+ and GC B cells exhibited minimal repertoire overlap, indicating distinct developmental pathways. As the infection resolved, Tbet+CD11c+ B cells localized to the marginal zone where splenic retention depended on integrins LFA-1 and VLA-4, forming a competitive memory subset that contributed to antibody production and secondary GC seeding upon rechallenge. Therefore, Tbet+CD11c+ B cells comprise a GC-independent memory subset capable of rapid and robust recall responses.

Type I Interferon-Activated STAT4 Regulation of Follicular Helper T Cell-Dependent Cytokine and Immunoglobulin Production in Lupus.

OBJECTIVE: To assess the role of STAT4 activation in driving pathogenic follicular helper T (Tfh) cell secretion of the cytokines interleukin-21 (IL-21) and interferon-γ (IFNγ) in murine and human lupus. METHODS: The effect of STAT4-dependent Tfh cell signaling on cytokine production and autoreactive B cell maturation was assessed temporally during the course of lupus in a murine model, with further assessment of Tfh cell gene transcription performed using RNA-Seq technology. STAT4-dependent signaling and cytokine production were also determined in circulating Tfh-like cells in patients with systemic lupus erythematosus (SLE), as compared to cells from healthy control subjects, and correlations with disease activity were assessed in the Tfh-like cells from SLE patients. RESULTS: IL-21- and IFNγ-coproducing Tfh cells expanded prior to the detection of potentially pathogenic IgG2c autoantibodies in lupus-prone mice. Tfh cells transcriptionally evolved during the course of disease with acquisition of a STAT4-dependent gene signature. Maintenance of Tfh cell cytokine synthesis was dependent upon STAT4 signaling, driven by type I IFNs. Circulating Tfh-like cells from patients with SLE also secreted IL-21 and IFNγ, with STAT4 phosphorylation enhanced by IFNß, in association with the extent of clinical disease activity. CONCLUSION: We identified a role for type I IFN signaling in driving STAT4 activation and production of IL-21 and IFNγ by Tfh cells in murine and human lupus. Enhanced STAT4 activation in Tfh cells may underlie pathogenic B cell responses in both murine and human lupus. These data indicate that STAT4 guides pathogenic cytokine and immunoglobulin production in SLE, demonstrating a potential therapeutic target to modulate autoimmunity.

Exosomes released by metabotropic glutamate receptor 1 (GRM1) expressing melanoma cells increase cell migration and invasiveness.

Exosomes are naturally occurring membrane-bound nanovesicles generated constitutively and released by various cell types, and often in higher quantities by tumor cells. Exosomes may facilitate communication between the primary tumor and its local microenvironment, supporting cell invasion and other early events in metastasis. A neuronal receptor, metabotropic glutamate receptor 1 (GRM1), when ectopically expressed in melanocytes, induces in vitro melanocytic transformation and spontaneous malignant melanoma development in vivo in a transgenic mouse model. Our earlier studies showed that genetic modulation in GRM1 expression by siRNA or disruption of GRM1-mediated glutamate signaling interfere with downstream effectors resulting in a decrease in both cell proliferation in vitro and tumor progression in vivo. In this study, we sought to determine whether exosome formation might play a role in GRM1 mediated melanoma development and progression. To test this, we utilized in vitro cultured cells in which GRM1 expression and function could be modulated by pharmacological and genetic means and determined effects on exosome production. We also tested the effects of exosomes from GRM1 expressing melanoma cells on growth, migration and invasion of GRM1 negative cells. Our results show that although GRM1 expression has no influence on exosome quantity, exosomes produced by GRM1-positive cells modulate the ability of the recipient cell to migrate, invade and exhibit anchorage-independent cell growth.