Identification of Immune Checkpoint Inhibitor-Induced Diabetes.

Importance: Immune checkpoint inhibitors (ICIs) have revolutionized cancer care; however, accompanying immune-related adverse events (irAEs) confer substantial morbidity and occasional mortality. Life-threatening irAEs may require permanent cessation of ICI, even in patients with positive tumor response. Therefore, it is imperative to comprehensively define the spectrum of irAEs to aid individualized decision-making around the initiation of ICI therapy.

Proteomic evidence of depression-associated astrocytic dysfunction in the human male olfactory bulb.

The olfactory bulb (OB), a major structure of the limbic system, has been understudied in human investigations of psychopathologies such as depression. To explore more directly the molecular features of the OB in depression, a global comparative proteome analysis was carried out with human post-mortem OB samples from 11 males having suffered from depression and 12 healthy controls. We identified 188 differentially abundant proteins (with adjusted p < 0.

Immune-related adverse events requiring hospitalization in patients with lung cancer: implications and insights.

Background: Immune checkpoint inhibitors (ICI) are associated with a distinct spectrum of toxicities. Data on irAE hospitalization rates and clinical course of patients with thoracic malignancies are lacking.

Methods: Patients with advanced thoracic malignancy treated with ICI (2/2016 to 6/2021) were retrospectively identified.

Development of a differential treatment selection model for depression on consolidated and transformed clinical trial datasets.

Major depressive disorder (MDD) is the leading cause of disability worldwide, yet treatment selection still proceeds via "trial and error". Given the varied presentation of MDD and heterogeneity of treatment response, the use of machine learning to understand complex, non-linear relationships in data may be key for treatment personalization. Well-organized, structured data from clinical trials with standardized outcome measures is useful for training machine learning models; however, combining data across trials poses numerous challenges.

Correction: Evaluating the Clinical Feasibility of an Artificial Intelligence-Powered, Web-Based Clinical Decision Support System for the Treatment of Depression in Adults: Longitudinal Feasibility Study.

[This corrects the article DOI: 10.2196/31862.].

Towards Outcome-Driven Patient Subgroups: A Machine Learning Analysis Across Six Depression Treatment Studies.

Background: Major depressive disorder (MDD) is a heterogeneous condition; multiple underlying neurobiological and behavioral substrates are associated with treatment response variability. Understanding the sources of this variability and predicting outcomes has been elusive. Machine learning (ML) shows promise in predicting treatment response in MDD, but its application is limited by challenges to the clinical interpretability of ML models, and clinicians often lack confidence in model results.

Cell type specific transcriptomic differences in depression show similar patterns between males and females but implicate distinct cell types and genes.

Major depressive disorder (MDD) is a common, heterogenous, and potentially serious psychiatric illness. Diverse brain cell types have been implicated in MDD etiology. Significant sexual differences exist in MDD clinical presentation and outcome, and recent evidence suggests different molecular bases for male and female MDD.

Microbial and host factors contribute to bloodstream infection in a pediatric acute lymphocytic leukemia mouse model.

Background: Hematological malignancies are the most common cancers in the pediatric population, and T-cell acute lymphocytic leukemia (T-ALL) is the most common hematological malignancy in children. Bloodstream infection (BSI) is a commonly occurring complication in leukemia due to underlying conditions and therapy-induced neutropenia. Several studies identified the gut microbiome as a major source of BSI due to bacterial translocation.

Quantitative and carbon isotope ratio analysis of fatty acids isolated from human brain hemispheres.

Our knowledge surrounding the overall fatty acid profile of the adult human brain has been largely limited to extrapolations from brain regions in which the distribution of fatty acids varies. This is especially problematic when modeling brain fatty acid metabolism, therefore, an updated estimate of whole-brain fatty acid concentration is necessitated. Here, we sought to conduct a comprehensive quantitative analysis of fatty acids from entire well-characterized human brain hemispheres (n = 6) provided by the Douglas-Bell Canada Brain Bank.

A systematic meta-review of patient-level predictors of psychological therapy outcome in major depressive disorder.

Background: Psychological therapies are effective for treating major depressive disorder, but current clinical guidelines do not provide guidance on the personalization of treatment choice. Established predictors of psychotherapy treatment response could help inform machine learning models aimed at predicting individual patient responses to different therapy options. Here we sought to comprehensively identify known predictors.

Targeting microglia-oligodendrocyte crosstalk in neurodegenerative and psychiatric disorders.

To understand how various brain cell types communicate with each other to orchestrate functional processes, it is crucial to comprehend the signals used to relay such information. Therefore, an important challenge to studying complex brain diseases is to interrogate relevant interactions between cell types. The microglia-oligodendroglia interaction is an important example that has fundamental roles in physiological state and brain pathologies.

Evaluating the perceived utility of an artificial intelligence-powered clinical decision support system for depression treatment using a simulation center.

Aifred is a clinical decision support system (CDSS) that uses artificial intelligence to assist physicians in selecting treatments for major depressive disorder (MDD) by providing probabilities of remission for different treatment options based on patient characteristics. We evaluated the utility of the CDSS as perceived by physicians participating in simulated clinical interactions. Twenty physicians who were either staff or residents in psychiatry or family medicine completed a study in which they had three 10-minute clinical interactions with standardized patients portraying mild, moderate, and severe episodes of MDD.

Evaluating the Clinical Feasibility of an Artificial Intelligence-Powered, Web-Based Clinical Decision Support System for the Treatment of Depression in Adults: Longitudinal Feasibility Study.

Background: Approximately two-thirds of patients with major depressive disorder do not achieve remission during their first treatment. There has been increasing interest in the use of digital, artificial intelligence-powered clinical decision support systems (CDSSs) to assist physicians in their treatment selection and management, improving the personalization and use of best practices such as measurement-based care. Previous literature shows that for digital mental health tools to be successful, the tool must be easy for patients and physicians to use and feasible within existing clinical workflows.

Syringocystadenoma papilliferum of the scalp.

Syringocystadenoma papilliferum is a rare, benign adnexal tumor of eccrine or apocrine origin that typically presents at birth or before puberty. Syringocystadenoma papilliferum is associated with a nevus sebaceus in about 40% of cases. We present a 50-year old woman with a pink-orange plaque and nodule on the scalp, consistent with syringocystadenoma papilliferum that arose within a nevus sebaceus.