Krüppel-like factor 9 alleviates Alzheimer’s disease via IDE-mediated Aβ degradation
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Common and disease-specific patterns of functional connectivity and topology alterations across unipolar and bipolar disorder during depressive episodes: a transdiagnostic study
Bipolar disorder (BD) and unipolar depression (UD) are defined as distinct diagnostic categories. However, due to some common clinical and pathophysiological features, it is a clinical challenge to distinguish them, especially in the early stages of BD. This study aimed to explore the common and disease-specific connectivity patterns in BD and UD. This study was constructed over 181 BD, 265 UD and 204 healthy controls. In addition, an independent group of 90 patients initially diagnosed with major depressive disorder at the baseline and then transferred to BD with the episodes of mania/hypomania during follow-up, was identified as initial depressive episode BD (IDE-BD). All participants completed resting-state functional magnetic resonance imaging (R-fMRI) at recruitment. Both network-based analysis and graph theory analysis were applied. Both BD and UD showed decreased functional connectivity (FC) in the whole brain network. The shared aberrant network across groups of patients with depressive episode (BD, IDE-BD and UD) mainly involves the visual network (VN), somatomotor networks (SMN) and default mode network (DMN). Analysis of the topological properties over the three networks showed that decreased clustering coefficient was found in BD, IDE-BD and UD, however, decreased shortest path length and increased global efficiency were only found in BD and IDE-BD but not in UD. The study indicate that VN, SMN, and DMN, which involve stimuli reception and abstraction, emotion processing, and guiding external movements, are common abnormalities in affective disorders. The network separation dysfunction in these networks is shared by BD and UD, however, the network integration dysfunction is specific to BD. The aberrant network integration functions in BD and IDE-BD might be valuable diagnostic biomarkers.
Preventive effect of Tyr-Pro, a blood-brain barrier transportable dipeptide, on memory impairment in SAMP8 mice
In a series of studies on blood-brain barrier transportable peptides, a soybean dipeptide, Tyr-Pro, penetrated the mouse brain parenchyma after oral intake and improved short and long memory impairment in acute Alzheimer’s model mice. Here, we aimed to clarify the anti-dementia effects of this peptide administered to SAMP8 mice prior to dementia onset. At the end of the 25-week protocol in 16-week-old SAMP8 mice, Tyr-Pro (10 mg/kg/day) significantly improved the reduced spatial learning ability compared with that in the control and amino acid (Tyr + Pro) groups as indicated by the results of Morris water maze tests conducted for five consecutive days. The hippocampus and cortex regions of SAMP8 harvested after the test showed lower amyloid ß (Aß) accumulation in the Tyr-Pro group than those in the control and amino acid groups. Consistent with the lower level of Aß, decreased expression of ß-secretase (BACE1) and markedly increased expression (4-times higher) of insulin degrading enzyme (IDE) were obtained compared to those in the control group. Collectively, we demonstrated that long-term daily intake of the dipeptide Tyr-Pro in SAMP8 mice may be sufficient for maintaining cognitive ability by preventing excess Aß accumulation through downregulated BACE1 and particularly upregulated IDE.
Tissue macrophages: origin, heterogenity, biological functions, diseases and therapeutic targets
Macrophages are immune cells belonging to the mononuclear phagocyte system. They play crucial roles in immune defense, surveillance, and homeostasis. This review systematically discusses the types of hematopoietic progenitors that give rise to macrophages, including primitive hematopoietic progenitors, erythro-myeloid progenitors, and hematopoietic stem cells. These progenitors have distinct genetic backgrounds and developmental processes. Accordingly, macrophages exhibit complex and diverse functions in the body, including phagocytosis and clearance of cellular debris, antigen presentation, and immune response, regulation of inflammation and cytokine production, tissue remodeling and repair, and multi-level regulatory signaling pathways/crosstalk involved in homeostasis and physiology. Besides, tumor-associated macrophages are a key component of the TME, exhibiting both anti-tumor and pro-tumor properties. Furthermore, the functional status of macrophages is closely linked to the development of various diseases, including cancer, autoimmune disorders, cardiovascular disease, neurodegenerative diseases, metabolic conditions, and trauma. Targeting macrophages has emerged as a promising therapeutic strategy in these contexts. Clinical trials of macrophage-based targeted drugs, macrophage-based immunotherapies, and nanoparticle-based therapy were comprehensively summarized. Potential challenges and future directions in targeting macrophages have also been discussed. Overall, our review highlights the significance of this versatile immune cell in human health and disease, which is expected to inform future research and clinical practice.
Implications of lymphocyte kinetics after chimeric antigen receptor T cell therapy for multiple myeloma
To the Editor Therapy for relapsed and refractory multiple myeloma (RRMM) has been revolutionized with the availability of chimeric antigen receptor T cell therapy (CAR-T)…
Probabilistic machine learning for battery health diagnostics and prognostics—review and perspectives
Diagnosing lithium-ion battery health and predicting future degradation is essential for driving design improvements in the laboratory and ensuring safe and reliable operation over a product’s expected lifetime. However, accurate battery health diagnostics and prognostics is challenging due to the unavoidable influence of cell-to-cell manufacturing variability and time-varying operating circumstances experienced in the field. Machine learning approaches informed by simulation, experiment, and field data show enormous promise to predict the evolution of battery health with use; however, until recently, the research community has focused on deterministic modeling methods, largely ignoring the cell-to-cell performance and aging variability inherent to all batteries. To truly make informed decisions regarding battery design in the lab or control strategies for the field, it is critical to characterize the uncertainty in a model’s predictions. After providing an overview of lithium-ion battery degradation, this paper reviews the current state-of-the-art probabilistic machine learning models for health diagnostics and prognostics. Details of the various methods, their advantages, and limitations are discussed in detail with a primary focus on probabilistic machine learning and uncertainty quantification. Last, future trends and opportunities for research and development are discussed.
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