Citations

ABSTRACTMonoclonal gammopathy of undetermined significance (MGUS) is the term used for a minimal clonal expansion of plasma cells that is most typically detected by the presence of a small serum monoclonal protein. For most—but not all—patients, the presence of a small monoclonal protein will have no clinical significance at the time of recognition or even over time. It is imperative, therefore, that a careful—but sensible—evaluation be performed both at the time of recognition and subsequently because these monoclonal may represent significant pathology including diseases such as multiple myeloma, low grade lymphoma or any of the diseases captured under the moniker of monoclonal gammopathy of clinical significance (MGCS), which includes—but is not limited to—immunoglobulin light chain amyloidosis and POEMS syndrome. Herein is a discussion of the diagnosis and follow‐up MGUS and MGCS.

ABSTRACTCastleman disease is a term that represents a spectrum of diseases whose driving factors are poorly understood. Increased interest prompted by the Castleman's Disease Collaborative Network has led to a better understanding of classification, which will lead to a better understanding of underlying pathology and best practices. It is essential that this entity be recognized and correctly classified by anatomic classification (unicentric, oligocentric, and multicentric), by histopathology (the gradient of hypervascular to plasmacytic), and by clinical phenotype (NOS, TAFRO, IPL, HHV8‐associated, or POEMS‐associated). Most of what we know about therapy is from retrospective studies rather than prospective therapeutic trials, but data are emerging through collaboration. The mainstay of therapy for unicentric disease is surgery. For multicentric disease interleukin‐6 blockade is essential for the non‐POEMS, non‐HHV8 multicentric cases, rituximab‐based (plus or minus antiviral) therapy for HHV8 associated cases, and IL‐6 blockade versus plasma cell directed therapy for the POEMS subtype.

Abstract Predicting host phenotypes from shotgun metagenomic data is essential for translating microbiome research into clinical practice)Despite the development of numerous computational tools for this task, researchers often default to traditional machine learning methods such as Random Forest)This hesitancy to adopt newer methods stems from their complexity as well as the lack of standardized evaluations, as most tools are assessed on different datasets and compared against a limited set of methods)Here, we introduce LAMPP, a standardized benchmark for evaluating methods for predicting host phenotypes from gut metagenomic data)LAMPP features a diverse range of prediction tasks and enables consistent, comparative assessments across prediction tools)Our systematic evaluation of existing tools shows that classic machine learning methods (e.g., Random Forest) perform competitively, offering both ease of use and state-of-the-art results)At the same time, it demonstrates that microbiome-based phenotype prediction remains a challenging problem)By providing a consistent platform for ongoing evaluation and access to raw sequencing data, LAMPP motivates the development of novel prediction pipelines from raw sequencing data to phenotype prediction, including novel sample representation and data augmentation strategies)LAMPP is publicly available for ongoing benchmarking at https://lampp.yassourlab.com/ .

Abstract Cues such as fast-food advertisements associated with food can provoke food cravings which may lead to unhealthy overeating. To effectively control such cravings, we need to better understand the factors that reduce food cue reactivity and reveal corresponding ‘anti-craving’ brain mechanisms. We previously reported that access to environmental enrichment (EE) that provides cognitive and physical stimulation in mice reduced cue-evoked sucrose seeking and prelimbic cortex (PL) neuronal reactivity. To date, the phenotype of PL neurons that undergo EE-induced adaptations has not been fully elucidated. Therefore, we used brain slice electrophysiology to investigate how EE modulated intrinsic excitability in the general population of PL interneurons and pyramidal cells. Additionally, we used retrograde tracing and the neuronal activity marker ‘Fos’ to investigate how EE modulated cue-evoked recruitment of pyramidal cells projecting to the paraventricular nucleus of the thalamus (PVT) and nucleus accumbens core (NAc). Before the cue-evoked sucrose seeking test, EE enhanced the general, baseline excitability of inhibitory interneurons, but not pyramidal cells, thereby promoting inhibitory ‘overdrive’. During cue-evoked sucrose seeking, EE suppressed recruitment of PVT-, but not NAc-projecting, neurons thereby selectively promoting corticothalamic, but not corticoaccumbens, excitatory ‘underdrive’. Collectively, we further illuminate EE’s ‘anti-food seeking’ actions whereby EE promotes both cell type-specific (inhibitory interneuron ‘overdrive’) and circuit-specific (excitatory corticothalamic ‘underdrive’) neuroadaptations in the PL.