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Social isolation is a pervasive and debilitating condition that has adverse prognostic impacts. This condition often co-occurs with other geriatric syndromes, further exacerbating negative health outcomes. Given these considerations, the present study aims to elucidate the roles of social isolation in older adults with anorexia of aging and/or sarcopenia with respect to long-term mortality using a nationally representative cohort study. Data were obtained from the Taiwan Longitudinal Study on Aging (TLSA), with a sample size of 3,762 study participants aged 50 years and older. Data from 1999 (wave 4) to 2015 (wave 9) were analyzed. The TLSA questionnaire was used to define social isolation, anorexia, and sarcopenia. Logistic regressions were employed to explore the associations between social isolation, anorexia, and sarcopenia. The Cox proportional hazard model was utilized to examine the synergistic effects of social isolation and anorexia or sarcopenia on 16-year all-cause mortality. After controlling for demographic information and comorbidities, older adults with social isolation were significantly associated with anorexia (adjusted odds ratio [aOR] 1.46 [95% confidence interval: 1.00–2.12, p=0.0475]) and sarcopenia (aOR 1.35 [95% CI: 1.12–1.64, p=0.0021]). Furthermore, the synergistic effects of social isolation with anorexia (aOR 1.65 [95% CI: 1.25–2.18, p=0.0004]) or sarcopenia (aOR 1.65 [95% CI: 1.42–1.92, p<0.0001]) were both significantly associated with higher risks of all-cause mortality, while social isolation alone revealed borderline statistical significance. Our findings indicate that social isolation is closely linked to anorexia and sarcopenia among middle-aged and older adults. Additionally, social isolation significantly exacerbates the long-term mortality risk associated with anorexia of aging and sarcopenia. However, social isolation alone appears to have borderline long-term mortality risk in this cohort. These findings underscore the importance of addressing social isolation in older adults with anorexia and/or sarcopenia to optimize their health outcomes and mitigate long-term mortality risk.

A chain of Josephson junctions represents one of the simplest many-body models undergoing a superconductor–insulator quantum phase transition1,2. Apart from zero resistance, the superconducting state is necessarily accompanied by a sound-like mode due to collective oscillations of the phase of the complex-valued order parameter3,4. Little is known about the fate of this mode on entering the insulating state, where the order parameter’s amplitude remains non-zero, but the phase ordering is ‘melted’ by quantum fluctuations5. Here, we show that the phase mode survives far into the insulating regime, such that megaohm-resistance chains can carry gigahertz-frequency alternating currents as nearly ideal superconductors. The insulator reveals itself through interaction-induced broadening and random frequency shifts of collective mode resonances. Our spectroscopic experiment puts forward the problem of quantum electrodynamics of a Bose glass for both theory and experiment6–8. By pushing the chain parameters deeper into the insulating state, we achieved a wave impedance of the phase mode exceeding the predicted critical value by an order of magnitude9–14. The effective fine structure constant of such a one-dimensional electromagnetic vacuum exceeds unity, promising transformative applications to quantum science and technology.

Infection by hepatitis B virus (HBV) accounts for 50–80% of hepatocellular carcinoma (HCC) development worldwide, in which the HBV-encoded X protein (HBx) has critical role in the induction of carcinogenesis. Several studies have shown that thyroid hormone (TH) suppresses HCC development and protects hepatocytes from HBx-induced damage, thus it is of interest to examine whether TH can protect hepatocytes from HBx-induced carcinogenesis. By treating HBx- transgenic mice with or without TH, we confirmed the protective effects of TH on HBx-induced hepatocarcinogenesis, which was achieved via reduction of reactive oxygen species (ROS) inflicted DNA damage. We further found that TH induced biogenesis of mitochondria (MITO) and autophagy of HBx-targeted MITO simultaneously, consequently leading to suppression of HBx-promoted ROS and carcinogenesis. Using microarray data analysis, this protective effect of TH was found to be mediated via activation of PTEN-induced kinase 1 (PINK1) in hepatocytes. PINK1, in turn, activated and recruited Parkin, an E3 ligase, to ubiquitinate MITO-associated HBx protein and trigger selective mitophagy. The pathological significance of the TH/PINK1 pathway in liver protection was confirmed by the concomitant decrease in expression of both TR and PINK1 in matched HCC tumor tissues and negatively correlated with aggressive progression of cancer and poor prognosis. Our data indicate that TH/PINK1/Parkin pathway has a critical role in protecting hepatocytes from HBx-induced carcinogenesis. Notably, several liver-targeting therapeutic derivatives of TH facilitating prevention or therapy of steatosis have been identified. Furthermore, our proof-of-concept experiments suggest that application of T 3 constitutes an effective novel therapeutic or preventive option for HCC. Thus, the utilization of the agonists of TRs could be the meaningful strategy in liver relative diseases, ranging from simple hepatic steatosis to HCC.

Data assimilation (DA) is integrated with machine learning in order to perform entirely data‐driven online state estimation. To achieve this, recurrent neural networks (RNNs) are implemented as pretrained surrogate models to replace key components of the DA cycle in numerical weather prediction (NWP), including the conventional numerical forecast model, the forecast error covariance matrix, and the tangent linear and adjoint models. It is shown how these RNNs can be initialized using DA methods to directly update the hidden/reservoir state with observations of the target system. The results indicate that these techniques can be applied to estimate the state of a system for the repeated initialization of short‐term forecasts, even in the absence of a traditional numerical forecast model. Further, it is demonstrated how these integrated RNN‐DA methods can scale to higher dimensions by applying domain localization and parallelization, providing a path for practical applications in NWP. Plain Language Summary Weather forecast models derived from fundamental equations of physics continue to increase in detail and complexity. While this evolution leads to consistently improving daily weather forecasts, it also leads to associated increases in computational costs. In order to make a forecast at any given moment, these models must be initialized with our best guess of the current state of the atmosphere, which typically includes information from a limited set of observations as well as forecasts from the recent past. Modern methods for initializing these computer forecasts typically require running many copies of the model, either simultaneously or in sequence, to compare with observations over the recent past and ensure that our best guess estimate of the current state of the atmosphere agrees closely with those observations before making a new forecast. This repeated execution of the computer forecast model is often a time‐consuming and costly bottleneck in the initialization process. Here, it is shown that techniques from the fields of artificial intelligence and machine learning (AI/ML) can be used to produce simple surrogate models that provide sufficiently accurate approximations to replace the original costly model in the initialization phase. The resulting process is self‐contained, and does not require any further utilization of the original computer model when making daily forecasts. Key Points Recurrent neural networks (RNNs) can replace conventional forecast models, producing accurate ensemble forecast statistics and linearized dynamics Data assimilation (DA) is compatible with RNNs by applying state estimation in the hidden state space using a modified observation operator The integrated RNN‐DA methods can be scaled to higher dimensions by applying domain localization techniques

Clonal hematopoiesis (CH) is defined as a single hematopoietic stem/progenitor cell (HSPC) gaining selective advantage over a broader range of HSPCs. When linked to somatic mutations in myeloid malignancy-associated genes, such as TET2-mediated clonal hematopoiesis of indeterminate potential or CHIP, it represents increased risk for hematological malignancies and cardiovascular disease. IL1β is elevated in patients with CHIP, however, its effect is not well understood. Here we show that IL1β promotes expansion of pro-inflammatory monocytes/macrophages, coinciding with a failure in the demethylation of lymphoid and erythroid lineage associated enhancers and transcription factor binding sites, in a mouse model of CHIP with hematopoietic-cell-specific deletion of Tet2 . DNA-methylation is significantly lost in wild type HSPCs upon IL1β administration, which is resisted by Tet2 -deficient HSPCs, and thus IL1β enhances the self-renewing ability of Tet2 -deficient HSPCs by upregulating genes associated with self-renewal and by resisting demethylation of transcription factor binding sites related to terminal differentiation. Using aged mouse models and human progenitors, we demonstrate that targeting IL1 signaling could represent an early intervention strategy in preleukemic disorders. In summary, our results show that Tet2 is an important mediator of an IL1β-promoted epigenetic program to maintain the fine balance between self-renewal and lineage differentiation during hematopoiesis. The expansion of cells with TET2 mutations within the blood is associated with increased risk for all-cause mortality, development of leukemia and cardiovascular disease. Here authors show IL1 promotes the clonal expansion TET2 knockout cells, enhancing their self-renewal, promoting their myeloid bias and impairing an IL1 driven loss of methylation at lymphoid and erythroid regulatory elements.

End-stage kidney disease (ESKD) presents a significant public health challenge, with hemodialysis (HD) remaining one of the most prevalent kidney replacement therapies. Ensuring the longevity and functionality of arteriovenous accesses is challenging for HD patients. Blood flow sound, which contains valuable information, has often been neglected in the past. However, machine learning offers a new approach, leveraging data non-invasively and learning autonomously to match the experience of healthcare professionas. This study aimed to devise a model for detecting arteriovenous grafts (AVGs) stenosis. A smartphone stethoscope was used to record the sound of AVG blood flow at the arterial and venous sides, with each recording lasting one minute. The sound recordings were transformed into mel spectrograms, and a 14-layer convolutional neural network (CNN) was employed to detect stenosis. The CNN comprised six convolution blocks with 3x3 kernel mapping, batch normalization, and rectified linear unit activation function. We applied contrastive learning to train the pre-training audio neural networks model with unlabeled data through self-supervised learning, followed by fine-tuning. In total, 27,406 dialysis session blood flow sounds were documented, including 180 stenosis blood flow sounds. Our proposed framework demonstrated a significant improvement (p<0.05) over training from scratch and a popular pre-trained audio neural networks (PANNs) model, achieving an accuracy of 0.9279, precision of 0.8462, and recall of 0.8077, compared to previous values of 0.8649, 0.7391, and 0.6538. This study illustrates how contrastive learning with unlabeled blood flow sound data can enhance convolutional neural networks for detecting AVG stenosis in HD patients.

Tumor-associated macrophages (TAMs) are heterogeneous and can adopt a spectrum of activation states between pro-inflammatory and pro-tumorigenic in response to the microenvironment. We have previously shown that TTI-621, a soluble SIRPαFc fusion protein that blocks the CD47 \"do-not-eat\" signal, promotes tumor cell phagocytosis by IFN-γ-primed macrophages. To assess the impact of CD47 blockade on diverse types of macrophages that are found within the tumor microenvironment, six different polarized human macrophage subsets (M(-), M(IFN-γ), M(IFN-γ+LPS), M(IL-4), M(HAGG+IL-1β), M(IL-10 + TGFβ)) with distinct cell surface markers and cytokine profiles were generated. Blockade of CD47 using TTI-621 significantly increased phagocytosis of lymphoma cells by all macrophage subsets, with M(IFN-γ), M(IFN-γ+LPS) and M(IL-10 + TGFβ) macrophages having the highest phagocytic response. TTI-621-mediated phagocytosis involves macrophage expression of both the low- and high-affinity Fcγ receptors II (CD32) and I (CD64), respectively. Moreover, macrophages with lower phagocytic capabilities (M(-), M(IL-4), M(HAGG+IL-1β)) could readily be re-polarized into highly phagocytic macrophages using various cytokines or TLR agonists. In line with the in vitro study, we further demonstrate that TTI-621 can trigger phagocytosis of tumor cells by diverse subsets of isolated mouse TAMs ex vivo. These data suggest that TTI-621 may be efficacious in triggering the destruction of cancer cells by a diverse population of TAMs found in vivo and support possible combination approaches to augment the activity of CD47 blockade.

Isomeric epoxydiols from isoprene photooxidation (IEPOX) have been shown to produce substantial amounts of secondary organic aerosol (SOA) mass and are therefore considered a major isoprene-derived SOA precursor. Heterogeneous reactions of IEPOX on atmospheric aerosols form various aerosol-phase components or \"tracers\" that contribute to the SOA mass burden. A limited number of the reaction rate constants for these acid-catalyzed aqueous-phase tracer formation reactions have been constrained through bulk laboratory measurements. We have designed a chemical box model with multiple experimental constraints to explicitly simulate gas- and aqueous-phase reactions during chamber experiments of SOA growth from IEPOX uptake onto acidic sulfate aerosol. The model is constrained by measurements of the IEPOX reactive uptake coefficient, IEPOX and aerosol chamber wall losses, chamber-measured aerosol mass and surface area concentrations, aerosol thermodynamic model calculations, and offline filter-based measurements of SOA tracers. By requiring the model output to match the SOA growth and offline filter measurements collected during the chamber experiments, we derive estimates of the tracer formation reaction rate constants that have not yet been measured or estimated for bulk solutions.

MicroRNAs (miRNAs) are thought to control tumor metastasis through direct interactions with target genes. Thyroid hormone (T 3 ) and its receptor (TR) are involved in cell growth and cancer progression. However, the issue of whether miRNAs participate in T 3 /TR-mediated tumor migration is yet to be established. In the current study, we demonstrated that T 3 /TR negatively regulates mature miR-17 transcript expression, both in vitro and in vivo . Luciferase reporter and chromatin immunoprecipitation (ChIP) assays localized the regions responding to TR-mediated repression to positions −2234/−2000 of the miR-17 promoter sequence. Overexpression of miR-17 markedly inhibited cell migration and invasion in vitro and in vivo , mediated via suppression of matrix metalloproteinases (MMP)-3. Moreover, p-AKT expression was increased in miR-17-knockdown cells that led to enhanced cell invasion, which was blocked by LY294002. Notably, low miR-17 expression was evident in highly metastatic cells. The cell migration ability was increased by T 3 , but partially reduced upon miR-17 overexpression. Notably, TRα1 was frequently upregulated in hepatocellular carcinoma (HCC) samples and associated with low overall survival ( P =0.023). miR-17 expression was significantly negatively associated with TRα1 ( P =0.033) and MMP3 ( P =0.043) in HCC specimens. Data from our study suggest that T 3 /TR, miR-17, p-AKT and MMP3 activities are interlinked in the regulation of cancer cell metastasis.