Citations

A self-management intervention is a personalized approach to individuals aiming to engage individuals in a behavior change to develop skills to live better with their condition. Self-management involves an iterative process between participants and providers in which goals are formulated and feedback is given. All respiratory societies advocate self-management as part of chronic care because it may improve quality of life and health-care utilization. Self-management is an integral part of pulmonary rehabilitation. Self-management interventions usually involve education and exercise prescription, and that is an asset of current programs; however, recent reports indicate that effective strategies for motivation and a behavior change focus are often missed. A recent systematic review on self-management urges the need for a specific aspect and characteristic of self-management interventions: iterative interactions between participants and health-care professionals competent in using behavior change practices to elicit participants’ motivation, confidence, and competence to develop skills to better manage their disease. A recent review of self-care intervention in chronic disease states that the major deficits found in self-care interventions included a lack of attention and/or innovation to the psychological consequences of chronic illness, technology, and behavior change techniques to help patients manage symptoms. There is a need for exploration of mechanisms to explain the relationships between both anxiety and depression, and adherence to treatment in COPD. The latter is particularly appropriate for pulmonary rehabilitation, for which greater adherence is needed. This report aims to introduce basic aspects of behavior change and a proposed roadmap to introduce behavior change into pulmonary rehabilitation and chronic care programs.

Cardiac sarcoidosis (CS) is characterized by various arrhythmic manifestations ranging from catastrophic sudden cardiac death secondary to ventricular arrhythmia, severe conduction disease, sinus node dysfunction, and atrial fibrillation. The management of CS is complex and includes not only addressing the arrhythmia but also controlling the myocardial inflammation resultant from the autoimmune reaction. Arrhythmic manifestations of CS carry significant prognostic implications and invariably affect long-term survival in these patients. In this review, we focus on management of arrhythmic manifestation of cardiac sarcoidosis as well as risk stratification for sudden cardiac death in these patients.

Deep learning offers significant advancements in the accuracy of prostate identification and classification, underscoring its potential for clinical integration. However, the opacity of deep learning models presents interpretability challenges, critical for their acceptance and utility in medical diagnosis and detection. While explanation methods have been proposed to demystify these models, enhancing their clinical viability, the efficacy and acceptance of these methods in medical tasks are not well documented. This pilot study investigates the effectiveness of deep learning explanation methods in clinical settings and identifies the attributes that radiologists consider crucial for explainability, aiming to direct future enhancements. This study reveals that while explanation methods can improve clinical task performance by up to 20%, their perceived usefulness varies, with some methods being rated poorly. Radiologists prefer explanation methods that are robust against noise, precise, and consistent. These preferences underscore the need for refining explanation methods to align with clinical expectations, emphasizing clarity, accuracy, and reliability. The findings highlight the importance of developing explanation methods that not only improve performance but also are tailored to meet the stringent requirements of clinical practice, thereby facilitating deeper trust and a broader acceptance of deep learning in medical diagnostics.

Tumor evolution is driven by genetic variation; however, it is the tumor microenvironment (TME) that provides the selective pressure contributing to evolution in cancer. Despite high histopathological heterogeneity within glioblastoma (GBM), the most aggressive brain tumor, the interactions between the genetically distinct GBM cells and the surrounding TME are not fully understood. To address this, we analyzed matched primary and recurrent GBM archival tumor tissues with imaging-based techniques aimed to simultaneously evaluate tumor tissues for the presence of hypoxic, angiogenic, and inflammatory niches, extracellular matrix (ECM) organization, TERT promoter mutational status, and several oncogenic amplifications on the same slide and location. We found that the relationships between genetic and TME diversity are different in primary and matched recurrent tumors. Interestingly, the texture of the ECM, identified by label-free reflectance imaging, was predictive of single-cell genetic traits present in the tissue. Moreover, reflectance of ECM revealed structured organization of the perivascular niche in recurrent GBM, enriched in immunosuppressive macrophages. Single-cell spatial transcriptomics further confirmed the presence of the niche-specific macrophage populations and identified interactions between endothelial cells, perivascular fibroblasts, and immunosuppressive macrophages. Our results underscore the importance of GBM tissue organization in tumor evolution and highlight genetic and spatial dependencies.

AbstractSuccinate dehydrogenase (SDH) is a key mitochondrial enzyme involved in the tricarboxylic acid cycle, where it facilitates the oxidation of succinate to fumarate, and is coupled to the reduction of ubiquinone in the electron transport chain as Complex II. Previously, we developed a confocal‐based quantitative histochemical technique to determine the maximum velocity of the SDH reaction (SDHmax) in single cells and observed that SDHmax corresponds with mitochondrial volume density. In addition, mitochondrial volume and motility varied within different compartments of human airway smooth muscle (hASM) cells. Therefore, we hypothesize that the SDH activity varies relative to the intracellular mitochondrial volume within hASM cells. Using 3D confocal imaging of labeled mitochondria and a concentric shell method for analysis, we quantified mitochondrial volume density, mitochondrial complexity index, and SDHmax relative to the distance from the nuclear membrane. The mitochondria within individual hASM cells were more filamentous in the immediate perinuclear region and were more fragmented in the distal parts of the cell. Within each shell, SDHmax also corresponded to mitochondrial volume density, where both peaked in the perinuclear region and decreased in more distal parts of the cell. Additionally, when normalized to mitochondrial volume, SDHmax was lower in the perinuclear region when compared to the distal parts of the cell. In summary, our results demonstrate that SDHmax measures differences in SDH activity within different cellular compartments. Importantly, our data indicate that mitochondria within individual cells are morphologically heterogeneous, and their distribution varies substantially within different cellular compartments, with distinct functional properties.

AbstractBackgroundPrecursor plasma cell disorders such as monoclonal gammopathy of undetermined significance (MGUS) always precede the development of active malignancies such as multiple myeloma (MM). There is a need for novel biomarkers to identify those patients with such precursor plasma cell disorders who rapidly progress to MM. Plasma‐derived extracellular vesicles (EVs) may serve as a reservoir of potential biomarkers that can shed light on the pathogenesis and disease biology of MM.MethodsThis study isolated small EVs (SEVs) and large EVs (LEVs) from the platelet‐poor peripheral blood plasma of MGUS (n = 9) and MM (n = 12) patients using the size exclusion chromatography‐based method and evaluated their proteome using a label‐free proteomics workflow.ResultsIn total, 2055 proteins were identified in SEVs, while 2794 proteins were identified in LEVs. The transferrin receptor (or CD71) protein was upregulated in both populations of EVs derived from MM patients compared to MGUS patients and was of prognostic significance. Similarly, three isoforms of serum amyloid A (SAA) protein, SAA1, SAA2, and SAA4, were also highly upregulated in SEVs within MM patients relative to MGUS patients. Finally, CD40 expression was also higher in the LEVs derived from MM patients than in MGUS patients.ConclusionsThis study demonstrates the feasibility of successfully isolating both SEVs and LEVs from the peripheral blood of patients with plasma cell disorders and quantifying protein biomarkers within these EVs that could be of prognostic and diagnostic interest.