Prostate Cancer Risk Prediction Model Using Clinical and Magnetic Resonance Imaging-Related Findings: Impact of Combining Lesions' Locations and Apparent Diffusion Coefficient Values.

Objectives: The aims of the study are to develop a prostate cancer risk prediction model that combines clinical and magnetic resonance imaging (MRI)-related findings and to assess the impact of adding Prostate Imaging-Reporting and Data System (PI-RADS) ≥3 lesions-level findings on its diagnostic performance.

Methods: This 3-center retrospective study included prostate MRI examinations performed with clinical suspicion of clinically significant prostate cancer (csPCa) between 2018 and 2022. Pathological diagnosis within 1 year after the MRI was used to diagnose csPCa.

Transition-zone PSA-density calculated from MRI deep learning prostate zonal segmentation model for prediction of clinically significant prostate cancer.

Purpose: To develop a deep learning (DL) zonal segmentation model of prostate MR from T2-weighted images and evaluate TZ-PSAD for prediction of the presence of csPCa (Gleason score of 7 or higher) compared to PSAD.

Methods: 1020 patients with a prostate MRI were randomly selected to develop a DL zonal segmentation model. Test dataset included 20 cases in which 2 radiologists manually segmented both the peripheral zone (PZ) and TZ.

Asymmetric cortical projections to striatal direct and indirect pathways distinctly control actions.

The striatal direct and indirect pathways constitute the core for basal ganglia function in action control. Although both striatal D1- and D2-spiny projection neurons (SPNs) receive excitatory inputs from the cerebral cortex, whether or not they share inputs from the same cortical neurons, and how pathway-specific corticostriatal projections control behavior remain largely unknown. Here using a new G-deleted rabies system in mice, we found that more than two-thirds of excitatory inputs to D2-SPNs also target D1-SPNs, while only one-third do so .

Clinical Implementation of an Artificial Intelligence Algorithm for Magnetic Resonance-Derived Measurement of Total Kidney Volume.

Objective: To evaluate the performance of an internally developed and previously validated artificial intelligence (AI) algorithm for magnetic resonance (MR)-derived total kidney volume (TKV) in autosomal dominant polycystic kidney disease (ADPKD) when implemented in clinical practice.

Patients And Methods: The study included adult patients with ADPKD seen by a nephrologist at our institution between November 2019 and January 2021 and undergoing an MR imaging examination as part of standard clinical care. Thirty-three nephrologists ordered MR imaging, requesting AI-based TKV calculation for 170 cases in these 161 unique patients.

Sarcopenia identified by computed tomography imaging using a deep learning-based segmentation approach impacts survival in patients with newly diagnosed multiple myeloma.

Background: Sarcopenia increases with age and is associated with poor survival outcomes in patients with cancer. By using a deep learning-based segmentation approach, clinical computed tomography (CT) images of the abdomen of patients with newly diagnosed multiple myeloma (NDMM) were reviewed to determine whether the presence of sarcopenia had any prognostic value.

Methods: Sarcopenia was detected by accurate segmentation and measurement of the skeletal muscle components present at the level of the L3 vertebrae.

Volumetric Pancreas Segmentation on Computed Tomography: Accuracy and Efficiency of a Convolutional Neural Network Versus Manual Segmentation in 3D Slicer in the Context of Interreader Variability of Expert Radiologists.

Purpose: This study aimed to compare accuracy and efficiency of a convolutional neural network (CNN)-enhanced workflow for pancreas segmentation versus radiologists in the context of interreader reliability.

Methods: Volumetric pancreas segmentations on a data set of 294 portal venous computed tomographies were performed by 3 radiologists (R1, R2, and R3) and by a CNN. Convolutional neural network segmentations were reviewed and, if needed, corrected ("corrected CNN [c-CNN]" segmentations) by radiologists.

Efficient Generation of CA3 Neurons from Human Pluripotent Stem Cells Enables Modeling of Hippocampal Connectivity In Vitro.

Despite widespread interest in using human induced pluripotent stem cells (hiPSCs) in neurological disease modeling, a suitable model system to study human neuronal connectivity is lacking. Here, we report a comprehensive and efficient differentiation paradigm for hiPSCs that generate multiple CA3 pyramidal neuron subtypes as detected by single-cell RNA sequencing (RNA-seq). This differentiation paradigm exhibits characteristics of neuronal network maturation, and rabies virus tracing revealed synaptic connections between stem cell-derived dentate gyrus (DG) and CA3 neurons in vitro recapitulating the neuronal connectivity within the hippocampus.

Differential inputs to striatal cholinergic and parvalbumin interneurons imply functional distinctions.

Striatal cholinergic (ChAT) and parvalbumin (PV) interneurons exert powerful influences on striatal function in health and disease, yet little is known about the organization of their inputs. Here using rabies tracing, electrophysiology and genetic tools, we compare the whole-brain inputs to these two types of striatal interneurons and dissect their functional connectivity in mice. ChAT interneurons receive a substantial cortical input from associative regions of cortex, such as the orbitofrontal cortex.

Genetic-Based Dissection Unveils the Inputs and Outputs of Striatal Patch and Matrix Compartments.

The striatum contains neurochemically defined compartments termed patches and matrix. Previous studies suggest patches preferentially receive limbic inputs and project to dopamine neurons in substantia nigra pars compacta (SNc), whereas matrix neurons receive sensorimotor inputs and do not innervate SNc. Using BAC-Cre transgenic mice with viral tracing techniques, we mapped brain-wide differences in the input-output organization of the patch/matrix.

Loss of motoneuron-specific microRNA-218 causes systemic neuromuscular failure.

Dysfunction of microRNA (miRNA) metabolism is thought to underlie diseases affecting motoneurons. One miRNA, miR-218, is abundantly and selectively expressed by developing and mature motoneurons. Here we show that mutant mice lacking miR-218 die neonatally and exhibit neuromuscular junction defects, motoneuron hyperexcitability, and progressive motoneuron cell loss, all of which are hallmarks of motoneuron diseases such as amyotrophic lateral sclerosis and spinal muscular atrophy.

Genetic inhibition of CaMKII in dorsal striatal medium spiny neurons reduces functional excitatory synapses and enhances intrinsic excitability.

Ca(2+)/calmodulin-dependent protein kinase II (CaMKII) is abundant in striatal medium spiny neurons (MSNs). CaMKII is dynamically regulated by changes in dopamine signaling, as occurs in Parkinson's disease as well as addiction. Although CaMKII has been extensively studied in the hippocampus where it regulates excitatory synaptic transmission, relatively little is known about how it modulates neuronal function in the striatum.

AMPAR-independent effect of striatal αCaMKII promotes the sensitization of cocaine reward.

Changes in CaMKII-regulated synaptic excitability are a means through which experience may modify neuronal function and shape behavior. While behavior in rodent addiction models is linked with CaMKII activity in the nucleus accumbens (NAc) shell, the key cellular adaptations that forge this link are unclear. Using a mouse strain with striatal-specific expression of autonomously active CaMKII (T286D), we demonstrate that while persistent CaMKII activity induces behaviors comparable to those in mice repeatedly exposed to psychostimulants, it is insufficient to increase AMPAR-mediated synaptic strength in NAc shell.

GluN2B subunit deletion reveals key role in acute and chronic ethanol sensitivity of glutamate synapses in bed nucleus of the stria terminalis.

The bed nucleus of the stria terminalis (BNST) is a critical region for alcohol/drug-induced negative affect and stress-induced reinstatement. NMDA receptor (NMDAR)-dependent plasticity, such as long-term potentiation (LTP), has been postulated to play key roles in alcohol and drug addiction; yet, to date, little is understood regarding the mechanisms underlying LTP of the BNST, or its regulation by ethanol. Acute and chronic exposure to ethanol modulates glutamate transmission via actions on NMDARs.

Distinct forms of Gq-receptor-dependent plasticity of excitatory transmission in the BNST are differentially affected by stress.

Long-term depression (LTD) is an important synaptic mechanism for limiting excitatory influence over circuits subserving cognitive and emotional behavior. A major means of LTD induction is through the recruitment of signaling via G(q)-linked receptors activated by norepinephrine (NE), acetylcholine, and glutamate. Receptors from these transmitter families have been proposed to converge on a common postsynaptic LTD maintenance mechanism, such that hetero- and homosynaptic induction produce similar alterations in glutamate synapse efficacy.

D1 dopamine receptor activation of NFAT-mediated striatal gene expression.

Exposure to drugs of abuse activates gene expression and protein synthesis that result in long-lasting adaptations in striatal signaling. Therefore, identification of the transcription factors that couple drug exposure to gene expression is of particular importance. Members of the nuclear factor of activated T-cells (NFATc) family of transcription factors have recently been implicated in shaping neuronal function throughout the rodent nervous system.

Cocaine experience controls bidirectional synaptic plasticity in the nucleus accumbens.

Plasticity of glutamatergic synapses is a fundamental mechanism through which experience changes neural function to impact future behavior. In animal models of addiction, glutamatergic signaling in the nucleus accumbens (NAc) exerts powerful control over drug-seeking behavior. However, little is known about whether, how or when experience with drugs may trigger synaptic plasticity in this key nucleus.