Adoptive T-cell therapy with gene-modified T cells expressing a tumor-reactive T-cell receptor or chimeric antigen receptor (CAR) is a rapidly growing field of translational medicine and has shown success in the treatment of B-cell malignancies and solid tumors. In all reported trials, patients have received T-cell products comprising random compositions of CD4(+) and CD8(+) naive and memory T cells, meaning that each patient received a different therapeutic agent. This variation may have influenced the efficacy of T-cell therapy, and complicates comparison of outcomes between different patients and across trials. We analyzed CD19 CAR-expressing effector T cells derived from different subsets (CD4(+)/CD8(+) naive, central memory, effector memory). T cells derived from each of the subsets were efficiently transduced and expanded, but showed clear differences in effector function and proliferation in vitro and in vivo. Combining the most potent CD4(+) and CD8(+) CAR-expressing subsets, resulted in synergistic antitumor effects in vivo. We show that CAR-T-cell products generated from defined T-cell subsets can provide uniform potency compared with products derived from unselected T cells that vary in phenotypic composition. These findings have important implications for the formulation of T-cell products for adoptive therapies.

Chimeric antigen receptor-modified T cells derived from defined CD8 + and CD4 + subsets confer superior antitumor reactivity in vivo

Intrinsic Coupling Modes: Multiscale Interactions in Ongoing Brain Activity

An automated detection system for Parkinson’s disease (PD) employing the convolutional neural network (CNN) is proposed in this study. PD is characterized by the gradual degradation of motor function in the brain. Since it is related to the brain abnormality, electroencephalogram (EEG) signals are usually considered for the early diagnosis. In this work, we have used the EEG signals of twenty PD and twenty normal subjects in this study. A thirteen-layer CNN architecture which can overcome the need for the conventional feature representation stages is implemented. The developed model has achieved a promising performance of 88.25% accuracy, 84.71% sensitivity, and 91.77% specificity. The developed classification model is ready to be used on large population before installation of clinical usage.

A deep learning approach for Parkinson’s disease diagnosis from EEG signals

Identifying the molecular alterations that underlie the pathophysiology of critical clinical features of schizophrenia is an essential step in the rational development of new therapeutic interventions for this devastating illness. Cognitive deficits, such as the impairments in working memory that arise from dysfunction of the dorsolateral prefrontal cortex, are a major determinant of functional outcome in schizophrenia. Here we consider the contributions of disturbances in glutamate, dopamine and GABA neurotransmission to the pathophysiology of working memory impairments in schizophrenia, suggest a cascade of molecular events that might link these disturbances, and argue that the molecular alterations most proximal to the pathophysiology of prefrontal dysfunction offer the most promise as targets for new drug development.

Pathophysiologically based treatment interventions in schizophrenia

Context: Postmortem studies have found evidence of -aminobutyric acid (GABA) deficits in fast-spiking, parvalbumin-positive interneurons in the prefrontal cortex in schizophrenia. Magnetic resonance spectroscopy studies in unmedicated patients have reported glutamine or glutamate-glutamine (Glx) elevations in this region. Abnormalities in these transmitters are thought to play a role in cognitive impairments in the illness. Objective: To measure GABA and Glx levels in vivo in 2 prefrontal brain regions in unmedicated and medicated patients with schizophrenia and healthy controls. Design: Case-control study. Setting: Inpatient psychiatric research unit and associated outpatient clinic. Participants: Sixteen unmedicated patients with schizophrenia, 16 medicated patients, and 22 healthy controls matched for age, sex, ethnicity, parental socioeconomic status, and cigarette smoking. Methods: Proton magnetic resonance spectroscopy with a 3-T system and the J-edited spin-echo difference method. The GABA and Glx levels were measured in the dorsolateral and medial prefrontal cortex and normalized to the simultaneously acquired water signal. Working memory performance was assessed in all subjects. Main Outcome Measures: The GABA and Glx concentrations determined by proton magnetic resonance spectroscopy.

https://jamanetwork.com/journals/jamapsychiatry/articlepdf/1151488/yoa110040_449_459.pdf

Elevated prefrontal cortex γ-aminobutyric acid and glutamate-glutamine levels in schizophrenia measured in vivo with proton magnetic resonance spectroscopy.

Kerstin Klook

Papers

Gamma synchrony: towards a translational biomarker for the treatment-resistant symptoms of schizophrenia.