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IL-18 Binding Protein, a biomarker of strength maintenance after surgery but reduced physical performance in age-related sarcopenia
Inflammation is thought to contribute to muscle loss in acute and chronic sarcopenia. Which inflammatory proteins contribute to sarcopenia in any condition is not clear. In a well-characterised cohort of patients experiencing acute sarcopenia following surgery, we used a proteomic screen of plasma to identify proteins associated with the change in strength. We compared change in handgrip strength over 7 days in surgery patients with plasma protein levels quantified by SOMAscan before and 24h after surgery. Surgery increased circulating concentrations of 295 proteins and decreased 301. Analysis of the day 1 protein levels showed that IL-18BP associated with maintenance of strength. To further investigate relationships between IL18BP and strength, IL-18BP as well as its ligands IL-18 and IL-37, were quantified by ELISA and in surgery patients and in 129 individuals (68 women) with age-related sarcopenia recruited to the Leucine and/or ACE inhibitor (LACE) trial. In LACE participants, the proteins were compared to grip strength, quadriceps maximal voluntary contraction (QMVC) and 6-minute walk distance (6MWD) and baseline SARC-F score. In the LACE cohort, IL-18BP was negatively associated with grip strength in men but not women, at baseline (r = −0.314, p = 0.014) and 12 months (r = −0.446, p = 0.001). QMVC and 6MWD showed similar associations. IL-18BP was associated with SARC-F in men (r = 0.389, p = 0.003) but not women. Investigation of SOMAscan data from surgery patients at baseline showed similar inverse associations of IL-18BP with strength. Comparison of circulating IL-18BP with the muscle transcriptome in these patients showed negative enrichment for mitochondrial genes. Analysis of the ligands showed that free IL-18 was proportional to 6MWD. After surgery high IL-18BP levels associate with maintenance of strength but circulating IL-18BP concentrations are associated with reduced muscle strength in men with sarcopenia. These data are consistent with known effects of IL-18BP ligands on the maintenance of mitochondrial function.
Integrated chemical and biological characterization of Hypericum perforatum extract using LC-MS/MS and in vitro functional assays
mGluR1 signaling is necessary for strengthening winner climbing fiber inputs in the developing mouse cerebellum
Functional neural circuits are sculpted by strengthening frequently used synapses and removing unnecessary connections. At birth, cerebellar Purkinje cells receive inputs with similar synaptic strengths from multiple climbing fibers (CFs). During postnatal development, a single “winner” CF is selectively strengthened and expands its dendritic innervation territory, while somatic “loser” synapses are eliminated. Here, we report that deleting metabotropic glutamate receptor 1 (mGluR1) or protein kinase Cγ (PKCγ) in mice disrupts this selective strengthening and territory expansion of “winner” CFs during early development. This impairment leads to weaker synaptic transmission and diminished dendritic innervation territory of “winner” CFs at later stages. Notably, “winner” CF synapses in these mutants exhibit impaired long-term potentiation, reduced AMPA receptor expression, and simpler postsynaptic organizations. These findings reveal a previously unappreciated role for mGluR1–PKCγ signaling, besides its established role in eliminating “loser” CFs, in promoting the functional and structural maturation of “winner” CF synapses.
FM-DLM: A new method for image classification based on the fusion of multi-level deep learning models
Currently, deep learning models are widely used in many classification applications, but their utilization is limited by some factors. The large models can ensure classification of wide range, but they cannot be deployed to some small devices. The small models can be deployed to the small devices, but the number of labels is limited. To solve these problems, this paper proposes a classification method based on the Fusion of Multi-level Deep Learning Models (FM-DLM). We apply the Baidu-AI platform as a Level 0 model for classification of wide range samples. Then, we use the difference between Level 1 models to perform dataset prediction. Then, we can use the Level 2 models that were trained on the predicted dataset, which is to perform label classification. Finally, we use label distribution to achieve higher accuracy. The experimental results show that our method can achieve higher accuracy than the existing methods while ensuring a wide range of classification.
Contrast-enhanced T1-weighted MRI, 11C-DPA-713 PET and 11C-CPPC PET as predictive imaging biomarkers of neuroinflammation in radiotherapy-induced brain injury
Clever algorithms for glasses work by time reparameterization
The ultraslow dynamics of glass-formers has been explained by two views often considered as mutually exclusive: One invokes locally hindered mobility, and the other rests on the complexity of the configuration space. Here, we show that time evolution responds strongly to details of the dynamics by changing the speed of time flow: It has time-reparameterization softness. This finding reconciles both views: While local constraints reparameterize the flow of time, the global landscape determines relationships between different correlations at the same times. We show that modern algorithms developed to accelerate the relaxation to equilibrium act by changing the time reparameterization. Their success thus relies on their ability to exploit reparameterization softness. We conjecture that these results extend beyond the realm of glasses to the optimization of more general constraint satisfaction problems and to broader classes of algorithms.
S100-alarmins, antenatal corticosteroids and the risk of late-onset sepsis in preterm infants: A prospective cohort study
Objectives Antenatal corticosteroids (aCS) are an important measure improving the outcome of preterm infants. Their influence on late-onset sepsis (LOS) risk remains inconclusive. The alarmin S100A8/A9 protects from LOS by regulating innate immune responses. We examined whether aCS impact on postnatal S100A8/A9 serum-levels and consequently on LOS risk. Study design In a prospective birth-cohort study of 162 preterm infants born before 32 gestational weeks, we determined postnatal S100A8/A9 serum-levels in relation to the timing of aCS and LOS incidence. Results aCS administration within 7 days before birth decreased LOS incidence in infants born via primary C-section compared to infants not exposed to aCS (5/69 (7.2%) vs. 4/27 (14.8%)). This effect was linked to increased S100A8/A9 levels, with nocturnal aCS administration being most effective. Opposite, S100A8/A9 levels were lower and the LOS incidence higher compared to unexposed infants (7/23 (30.4%) vs. 4/27 (14.8%)) when aCS were administered more than 14 days before delivery. Conclusion Our data suggest that aCS administration affects the risk of LOS in preterm infants in dependence of the timing of administration by influencing the infant’s S100A8/A9 levels. This underlines the importance of optimal timing of aCS facing imminent preterm birth.
Factors determining survival in oligometastatic breast cancer in a retrospective cohort study from a low and middle income country
Coral species from another ocean may be the only way to save Caribbean reefs
“My safe haven turned into a terror zone”: A qualitative study of family members’ experiences of violence by brain tumor patients
Background Knowledge is lacking regarding how the experience of being exposed to violence is affected when the perpetrator suffers from behavioral and personality changes (BPC) due to a brain tumor. This study is part of the Swedish national research project BRAVE - B rain Tumor R elated A ggression and V iolence E xposure . The aim was to explore experiences of family members exposed to violence by a person suffering from BPC associated with a brain tumor. Methods Individual interviews were conducted with 25 family members who have been exposed to violence by patients with primary brain tumor. The interviews were analyzed using qualitative content analysis. Results The participants reported various forms of violence and expressed intense suffering, loneliness and social isolation. The homes sometimes shifted from being a safe place to being a place marked by fear and unpredictability. In adapting to violence, what initially seemed unreasonable, gradually became “the new normal”. Different strategies to minimize risks and damage were described. Self-blame and shame were often associated with an inability to love the patient “in sickness and in health”, despite the violent actions by the patient. When the death of the perpetrator was viewed as the only means of escape, participants also expressed feelings of guilt and shame. Conclusions Our study highlights extensive suffering, vulnerability, loneliness and isolation among family members exposed to violence by brain tumor patients. An intervention that provides appropriate support for brain tumor patients, family members and staff who encounter them is urgently needed.
Spiritual well-being and quality of life among newly diagnosed Palestinian women with breast cancer: a prospective study
Perceiving AI as labor-replacing reduces democratic legitimacy and political engagement
AI is expected to reshape society and labor markets, yet experts remain divided on whether AI will primarily displace human labor or generate new employment opportunities. Despite the importance of this debate, little is known about how the public perceives AI’s labor market impact—and how these perceptions affect democratic attitudes and behaviors. Large-scale survey data ( N = 37,079; 38 European countries) indicate that the public tends to view AI as labor-replacing rather than labor-creating. Controlling for technology-related, political, and sociodemographic factors, these data further show that perceiving AI as labor-replacing (vs. labor-creating) is associated with lower satisfaction with democracy and political engagement with technology. Two preregistered, nationally representative experiments ( N = 1,202, United Kingdom; replication study N = 1,200, United States) provide causal evidence for this relationship. Participants exposed to a labor-replacing (vs. labor-creating) AI frame report greater erosion of trust in democracy and lower willingness to politically engage with future AI developments. Together, our findings suggest that perceptions about AI’s labor market consequences—regardless of actual outcomes—may decrease democratic legitimacy and public engagement in shaping the future of AI.
Benznidazole therapy improves pressure overload and cardiac electrical profile in an experimental model of Angiotensin II infusion-induced hypertension: Mechanistic insights
High blood pressure is one of the leading global causes of cardiovascular diseases. The chronic action of high concentrations of angiotensin II (Ang II) promotes arterial hypertension. Ang II acts via AT 1 and AT 2 receptors. Acting via AT 1 R, Ang II can induce the production of inflammatory cytokines and reactive oxygen species, promoting oxidative stress, which may influence cardiac electrical traits. In hypertensive patients, a dispersed QTc interval may predict cardiovascular events and mortality. Benznidazole (Bz), an antiprotozoal prodrug, also has immunomodulatory properties. Here, we tested the idea that in a model of Ang II-induced BP overload, cardiomyopathy will be associated with a prolonged QTc interval. Then, we investigated the effects of Bz therapy on BP overload, electrical changes, and oxidant/antioxidant imbalance. C57BL/6 mice were implanted with an osmotic minipump containing Ang II or saline as a control. At 7 days post-surgery (dps), Ang II infusion increased mean BP, which was sustained until 28 dps. Further, the Ang II-infused group had prolonged QTc interval and QRS complex. Bz or the AT 1 R antagonist losartan (Los) were administered from 7 to 28 dps. Compared with the vehicle-treated group, Los therapy restored mean BP to normal but did not affect long-QTc. At 14 and 28 dps, Bz therapy improved BP, and restored QTc dispersion to normal, while improving RR interval and QRS complex changes. Ang II infusion increased IL-6 concentrations and oxidant/antioxidant imbalance in cardiac tissue. Bz therapy showed a beneficial effect, tending to restore the IL-6 concentrations and oxidant/antioxidant balance to physiological levels, which was correlated with reversal of the dispersed QTc interval. Altogether, our data support that Bz therapy deserves further evaluation as an anti-inflammatory and antioxidant adjuvant tool to improve BP overload and long-QTc syndrome underlying cardiovascular diseases.
Adverse effects of 6PPD-quinone bioaccumulation at environmentally relevant concentrations on Cyprinus carpio growth and development
Nuclear MBL-1 modulates mitochondrial morphology through carnitine palmitoyltransferase in <i>Caenorhabditis elegans</i> with toxic trinucleotide repeats
Expansion of nucleotide repeat sequences is linked to a growing number of neuromuscular degenerative disorders. Metabolic changes, including disruptions in mitochondrial function and dynamics, characterize these disorders and are believed to contribute to organismal toxicity. To investigate how toxic RNA repeats affect mitochondria, we used a Caenorhabditis elegans model that expresses expanded CUG repeat RNAs in muscle cells and recapitulates muscle dysfunction. We found that the RNA-binding protein Muscleblind-like 1 (MBL-1) is essential for normal mitochondrial function and regulates organelle morphology. In animals expressing expanded CUG repeats, where MBL-1 function is impaired, we identified two distinct mechanisms of mitochondrial disruption: altered mitochondrial morphology regulated by MBL-1, and oxidative phosphorylation (OxPhos) dysfunction occurring independently of MBL-1. Our data further show that changes in mitochondrial morphology are specifically linked to nuclear MBL-1 dysfunction, which affects cpt-3 expression, a gene encoding carnitine palmitoyltransferase—an enzyme required for fatty acid transport into mitochondria. This mechanism is conserved, with similar disruptions observed in patients with Myotonic Dystrophy type 1. Importantly, our findings indicate that increased organelle fragmentation is not central to cellular pathogenesis. Instead, OxPhos dysfunction appears to be a primary contributor to organismal toxicity.
CoSMIC: A hybrid approach for large-scale, high-resolution microbial profiling of novel niches
Standard microbial profiling based on 16S rRNA (16S) sequencing suffers from a lack of primer universality, primer biases, and often yields low resolution. We introduce ‘Comprehensive Small Ribosomal Subunit Mapping and Identification of Communities’ (CoSMIC), addressing these challenges, especially in unexplored niches. CoSMIC begins with long-read sequencing of the full-length 16S gene, amplified by generic Locked Nucleic Acid primers over pooled samples, thus augmenting reference databases with novel niche-specific gene sequences. Subsequently, CoSMIC amplifies multiple non-consecutive variable regions along the gene, followed by short-read sequencing of each sample. Data from the different regions are integrated using the SMURF framework, alleviating primer biases and providing de facto full gene resolution. Using a mock community, CoSMIC identified full-length 16S genes with significantly higher specificity and sensitivity while dramatically increasing resolution compared to standard methods. Evaluating CoSMIC across environmental samples yielded higher accuracy and unparalleled resolution at a fraction of the cost of standard long-read sequencing per sample while allowing the detection of thousands of novel full-length 16S sequences.
A multidimensional framework for mapping social need to electronic health records in people with multimorbidity
Abstract Social needs are sociocultural and economic factors influencing health and quality of life, including, for example, mobility limitations or financial difficulties. Multimorbidity - the presence of two or more long-term conditions (LTCs) - is an increasing public health challenge, with social needs often compounding the negative health outcomes associated with multimorbidity. In this study, we present a novel multidimensional framework for identifying and characterising social needs within a population-based cohort of adults with multimorbidity in England, utilising data from the Clinical Practice Research Datalink. The framework identifies eight critical domains of social needs: activities of daily living, mobility, financial constraints, disability, community care, housing status, social support, and bereavement. More than 100 related variables were captured in the dataset. Among 7,290,716 individuals with multimorbidity, 36.96% reported at least one social need, with the majority of affected individuals being older, female, and experiencing a higher burden of LTCs. The most prevalent social needs were related to community and social care services. Our findings underscore the significant association between social needs and multimorbidity, revealing a disproportionate burden of social needs in this population. This framework offers a systematic approach to quantifying and measuring social needs, providing a foundation for incorporating these factors into clinical care and interventions.
Metabolic rewiring and biomass redistribution enable optimized mixotrophic growth in Chlamydomonas
Aquatic photosynthetic systems account for approximately one-half of all global carbon assimilation and could be a significant source of renewable fuels and feedstocks. However, rapid growth and biomass production in algae have not always translated into high product yields, partly because central metabolism is context specific, with metabolic fluxes being influenced by nutrient conditions and other environmental factors. In the green microalga Chlamydomonas reinhardtii (Chlamydomonas), mixotrophic cultures (acetate + light) grow far faster than phototrophic (light only) or heterotrophic (acetate + dark) cultures, even though acetate partially suppresses photosynthesis. Here, an isotopic dilution strategy with unlabeled acetate was combined with 13 CO 2 transient labeling to perform isotopically nonstationary metabolic flux analysis (INST-MFA) and to directly compare autotrophic and mixotrophic metabolism in Chlamydomonas supported by data from transcriptomics, proteomics, and metabolomics. INST-MFA indicated that acetate induces a synergistic rewiring of metabolism, conserving carbon by using the glyoxylate cycle and suppressing gluconeogenesis, the latter of which was discordant with omics results and prior models. Additionally, our data provide a plausible rationale for the well-known suppression of photosynthesis by acetate. We propose that reduced total protein content in mixotrophic versus phototrophic cells, much of which is attributed to reduced levels of photosynthetic proteins, decreases the costly metabolic burden of protein synthesis and represents a growth rate optimization strategy.
Causal interventions in bond multi-dealer-to-client platforms
The digitalization of financial markets has shifted trading from voice to electronic channels, with Multi-Dealer-to-Client (MD2C) platforms now enabling clients to request quotes (RfQs) for financial instruments like bonds from multiple dealers simultaneously. In this competitive landscape, dealers cannot see each other’s prices, making a rigorous analysis of the negotiation process crucial to ensure their profitability. This article introduces a novel general framework for analyzing the RfQ process using probabilistic graphical models and causal inference. Within this framework, we explore different inferential questions that are relevant for dealers participating in MD2C platforms, such as the computation of optimal prices, estimating potential revenues and the identification of clients that might be interested in trading the dealer’s axes. We then move into analyzing two different approaches for model specification: a generative model built on the work of (Fermanian, Guéant, & Pu, 2017); and discriminative models utilizing machine learning techniques. Our results show that generative models can match the predictive accuracy of leading discriminative algorithms such as LightGBM (ROC-AUC: 0.742 vs. 0.743) while simultaneously enforcing critical business requirements, notably spread monotonicity.