Addressing brain metabolic connectivity in treatment-resistant schizophrenia: a novel graph theory-driven application of F-FDG-PET with antipsychotic dose correction.

Few studies using Positron Emission Tomography with F-fluorodeoxyglucose (F-FDG-PET) have examined the neurobiological basis of antipsychotic resistance in schizophrenia, primarily focusing on metabolic activity, with none investigating connectivity patterns. Here, we aimed to explore differential patterns of glucose metabolism between patients and controls (CTRL) through a graph theory-based approach and network comparison tests. PET scans with F-FDG were obtained by 70 subjects, 26 with treatment-resistant schizophrenia (TRS), 28 patients responsive to antipsychotics (nTRS), and 16 CTRL.

De novo variants in LRRC8C resulting in constitutive channel activation cause a human multisystem disorder.

Volume-regulated anion channels (VRACs) are multimeric proteins composed of different paralogs of the LRRC8 family. They are activated in response to hypotonic swelling, but little is known about their specific functions. We studied two human individuals with the same congenital syndrome affecting blood vessels, brain, eyes, and bones.

The Added Value of Prostate-specific Membrane Antigen Positron Emission Tomography/Computed Tomography to Magnetic Resonance Imaging for Local Staging of Prostate Cancer in Patients Undergoing Radical Prostatectomy.

Background And Objective: The role of prostate-specific membrane antigen (PSMA)-based positron emission tomography (PET)/computed tomography (CT) in addition to magnetic resonance imaging (MRI) for local staging of prostate cancer (PC) has been poorly addressed so far. Our aim was to assess the diagnostic accuracy of PSMA PET/CT and MRI, alone and combined, for detection of extraprostatic extension (EPE) and seminal vesicle invasion (SVI) in PC.

Methods: We conducted a multicenter retrospective study evaluating patients undergoing PSMA PET/CT and MRI before radical prostatectomy.

Human intelligence can safeguard against artificial intelligence: individual differences in the discernment of human from AI texts.

Artificial intelligence (AI) models can produce output that closely mimics human-generated content. We examined individual differences in the human ability to differentiate human- from AI-generated texts, exploring relationships with fluid intelligence, executive functioning, empathy, and digital habits. Overall, participants exhibited better than chance text discrimination, with substantial variation across individuals.