Inflammatory proteins associated with Alzheimer's disease reduced by a GLP1 receptor agonist: a post hoc analysis of the EXSCEL randomized placebo controlled trial.

Background: Glucagon-like peptide-1 receptor agonists are a viable option for the prevention of Alzheimer's disease (AD) but the mechanisms of this potential disease modifying action are unclear. We investigated the effects of once-weekly exenatide (EQW) on AD associated proteomic clusters.

Methods: The Exenatide Study of Cardiovascular Event Lowering study compared the cardiovascular effects of EQW 2 mg with placebo in 13,752 people with type 2 diabetes mellitus.

Optimising the use of electronic medical records for large scale research in psychiatry.

The explosion and abundance of digital data could facilitate large-scale research for psychiatry and mental health. Research using so-called "real world data"-such as electronic medical/health records-can be resource-efficient, facilitate rapid hypothesis generation and testing, complement existing evidence (e.g.

Mayo Clinic Validation of the AUA Risk Groups for Localized Renal Cell Carcinoma.

Purpose: The AUA guidelines introduced a new risk group stratification system based primarily on tumor stage and grade to guide surveillance for patients treated surgically for localized renal cell carcinoma (RCC). We sought to evaluate the predictive ability of these risk groups using progression-free survival (PFS) and cancer-specific survival (CSS), and to compare their performance to that of our published institutional risk models.

Materials And Methods: We queried our Nephrectomy Registry to identify adults treated with radical or partial nephrectomy for unilateral, M0, clear cell RCC, or papillary RCC from 1980 to 2012.

Defining acceptable data collection and reuse standards for queer artificial intelligence research in mental health: protocol for the online PARQAIR-MH Delphi study.

Introduction: For artificial intelligence (AI) to help improve mental healthcare, the design of data-driven technologies needs to be fair, safe, and inclusive. Participatory design can play a critical role in empowering marginalised communities to take an active role in constructing research agendas and outputs. Given the unmet needs of the LGBTQI+ (Lesbian, Gay, Bisexual, Transgender, Queer and Intersex) community in mental healthcare, there is a pressing need for participatory research to include a range of diverse queer perspectives on issues of data collection and use (in routine clinical care as well as for research) as well as AI design.

Management of antipsychotics in primary care: Insights from healthcare professionals and policy makers in the United Kingdom.

Introduction: Antipsychotic medication is increasingly prescribed to patients with serious mental illness. Patients with serious mental illness often have cardiovascular and metabolic comorbidities, and antipsychotics independently increase the risk of cardiometabolic disease. Despite this, many patients prescribed antipsychotics are discharged to primary care without planned psychiatric review.

Clinical Prompt Learning With Frozen Language Models.

When the first transformer-based language models were published in the late 2010s, pretraining with general text and then fine-tuning the model on a task-specific dataset often achieved the state-of-the-art performance. However, more recent work suggests that for some tasks, directly prompting the pretrained model matches or surpasses fine-tuning in performance with few or no model parameter updates required. The use of prompts with language models for natural language processing (NLP) tasks is known as prompt learning.

Automatic Detection of Cognitive Impairment with Virtual Reality.

Cognitive impairment features in neuropsychiatric conditions and when undiagnosed can have a severe impact on the affected individual's safety and ability to perform daily tasks. Virtual Reality (VR) systems are increasingly being explored for the recognition, diagnosis and treatment of cognitive impairment. In this paper, we describe novel VR-derived measures of cognitive performance and show their correspondence with clinically-validated cognitive performance measures.

Explainable artificial intelligence for mental health through transparency and interpretability for understandability.

The literature on artificial intelligence (AI) or machine learning (ML) in mental health and psychiatry lacks consensus on what "explainability" means. In the more general XAI (eXplainable AI) literature, there has been some convergence on explainability meaning model-agnostic techniques that augment a complex model (with internal mechanics intractable for human understanding) with a simpler model argued to deliver results that humans can comprehend. Given the differing usage and intended meaning of the term "explainability" in AI and ML, we propose instead to approximate model/algorithm explainability by understandability defined as a function of transparency and interpretability.

Psychosocial markers of age at onset in bipolar disorder: a machine learning approach.

Background: Bipolar disorder is a chronic and severe mental health disorder. Early stratification of individuals into subgroups based on age at onset (AAO) has the potential to inform diagnosis and early intervention. Yet, the psychosocial predictors associated with AAO are unknown.

Outcomes in treatment-resistant schizophrenia: symptoms, function and clozapine plasma concentrations.

Background: Clozapine is the only medication licenced for treating patients with treatment-refractory schizophrenia. However, there are no evidence-based guidelines as to the optimal plasma level of clozapine to aim for, and their association with clinical and functional outcome.

Objective: We assessed the relationship between clinical and functional outcome measures and blood concentrations of clozapine among patients with treatment-refractory psychosis.

Changes in daily mental health service use and mortality at the commencement and lifting of COVID-19 'lockdown' policy in 10 UK sites: a regression discontinuity in time design.

Objectives: To investigate changes in daily mental health (MH) service use and mortality in response to the introduction and the lifting of the COVID-19 'lockdown' policy in Spring 2020.

Design: A regression discontinuity in time (RDiT) analysis of daily service-level activity.

Setting And Participants: Mental healthcare data were extracted from 10 UK providers.