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Autoantibodies to IL-1Ra and PGRN in severe COVID-19 are associated with inflammation-induced hyperphosphorylated antigen isoforms
Abstract SARS-CoV-2 infection affects multiple immune mechanisms and leads to severe COVID-19 and death, in part related to infection-induced or pre-existing autoantibodies. Here, we describe severe COVID-19 to associate with autoantibodies against interleukin-1 receptor antagonist (IL-1Ra) and progranulin (PGRN), endogenous antagonists of IL-1 and TNF signaling, respectively. These autoantibodies coincide with hyperphosphorylation of IL-1Ra (Thr111) or PGRN (Ser81), form immune complexes independent of phosphorylation, reduce antigen plasma levels, and permit enhanced IL-1 and TNF signaling. Using phage-display selected Fabs specific for hyperphosphorylated isoforms, we track phospho-antigens and autoantibodies in a German national pandemic network cohort. Most seropositive patients show both autoantibodies. Levels peak at baseline and decline over 12 months, with phospho-antigens decreasing before autoantibodies. Seropositivity associates with hyperinflammation and cytokine profiles. Importantly, signaling by key inflammatory cytokines induce IL-1Ra and PGRN hyperphosphorylation in healthy monocytes, but require up to 1000-fold higher doses than in monocytes from previously seropositive severe COVID-19 survivors.
From prediction to action: developing a risk-stratified management tool for children with new-onset tic disorders
Structural basis of chondroitin sulfate backbone polymer synthesis
A federated learning–enabled energy-aware anomaly detection algorithm for secure big data analytics in IoT-based smart healthcare systems
Abstract The rapid expansion of Internet of Things (IoT) devices in smart healthcare systems has led to the generation of large volumes of diverse medical data. This creates challenges in ensuring secure, scalable, and energy-efficient anomaly detection. Traditional centralized deep learning methods rely on continuously sending sensitive patient data to cloud servers, which increases communication overhead, consumes more energy, and raises privacy concerns. To overcome these limitations, this paper presents a Federated Learning–enabled Energy-Aware Anomaly Detection framework (FL-EAD) designed for IoT-based smart healthcare environments. The proposed approach allows distributed IoT devices to collaboratively train models while keeping patient data stored locally, sharing only model updates instead of raw data. An energy-aware client selection strategy is incorporated to determine device participation in each federated learning round based on factors such as residual energy and communication cost. This helps reduce unnecessary energy consumption. Furthermore, a hybrid deep learning model combining an autoencoder with an attention-based Long Short-Term Memory (LSTM) network is used to effectively capture both spatial and temporal patterns in healthcare data streams. The proposed framework is evaluated using a publicly available healthcare IoT dataset. Experimental results show that FL-EAD improves anomaly detection performance and overall system efficiency compared to traditional centralized methods and standard federated learning approaches. Notable improvements are observed in accuracy, F1-score, energy usage, communication overhead, and detection latency. Overall, the results suggest that the proposed framework offers a practical and privacy-preserving solution for scalable anomaly detection in next-generation IoT-enabled smart healthcare systems.
Lifespan normative modeling of brain microstructure
Three-dimensional geological body numerical model–control information model mapping: a broken-chain correction method
Functional modules for enhanced amorphous composite halide solid electrolytes for low-temperature all-solid-state lithium batteries
Abstract Solid-state electrolytes (SSEs) are the essential component of all-solid-state batteries (ASSBs). Designing better SSEs holds the key to the success of the ASSBs. Here, this study effectively realises the design of SSEs through functional modules. Various functional designs have been achieved by incorporating different functional models. Here, we initially introduce LaCl 3 , which possesses a UCl 3 structure, as a functional module to demonstrate the feasibility of our approach. The Li 2 O-1.8TaCl 5 -0.2LaCl 3 (LTLOC) SSE enable the ASSB with LiNi 0.88 Co 0.09 Mn 0.03 O 2 (NCM88) to exhibit stable cycling and stable operation at low temperature (−30 °C). Additionally, various types of functional modules, including chloride, oxide, and fluoride, have been successfully introduced, further supporting the universality of amorphous functional module design. Furthermore, the incorporation of low-cost and low-density AlF 3 highlights the benefits of this design approach, as it allows for a high proportion of fluoride to be introduced without compromising ionic conductivity. Li 2 O-1.8TaCl 5 -5AlF 3 (LTOC-5AlF 3 ) exhibits stability in humid conditions, resistance to high voltage, and compatibility with lithium metal simultaneously. The key strength of this design approach is its ability to maintain advantages and make up for the shortcomings.
A hybrid transformer–zero-shot learning framework with Muon optimization for intelligent channel estimation in MIMO wireless systems
Gaps and drivers of global marine animal biodiversity from the surface to abyss
Abstract With advances in global biodiversity data sharing, particularly following the Census of Marine Life, understanding of marine biodiversity has improved but remains incomplete. The Ocean Biodiversity Information System and Global Biodiversity Information Facility host over 150 million marine occurrence records, enabling reassessment of global biodiversity and data gaps. Here, we compile a quality-controlled dataset of ca. 48 million records covering 184,141 marine animal species, representing ~87% of accepted World Register of Marine Species and 91% of Ocean Biodiversity Information System taxa. Generalised Linear and Additive Models assess how geoecological drivers and human impact influence species richness while accounting for sampling effort and spatial autocorrelation across depth and taxa. Approximately 50% of the global ocean remains insufficiently sampled, with more than 160 million km² below 200 m lacking data. Sampling is biased toward developed regions, especially the North Atlantic, with major gaps in equatorial and Global South regions. Central tropical areas ( − 5° to 5°) contribute only <2.5% of global records, helping explain non-significant bimodal latitudinal patterns. Shallow-water richness is mainly associated with temperature, while deep-sea patterns relate to human impact (sampling intensity) and nitrate-driven remineralisation. These results highlight major global data gaps and the need for depth-explicit, bias-aware biodiversity assessment and monitoring to support conservation and the UN Ocean Decade.
Blockchain-enabled supply chain finance risk intelligent assessment and trust mechanism construction
Critical misalignments in climate pledges reveal imbalanced sustainable development pathways
Abstract We explore the integration of climate action and Sustainable Development Goals (SDGs) in the first two submissions of nationally determined contributions (NDCs) using an AI-based, human-validated framework. Our goal is to provide ex-ante evidence relevant to assessing policy adequacy. We find disparities in topics of interest with high-income countries emphasizing systemic challenges (health, SDG3) and low-income nations prioritizing the water-energy-food nexus (SDGs 6-7-12) and natural resource management (SDG15). We discuss what these diverging development trajectories imply for the Paris Agreement and the 2030 Agenda for sustainable development in terms of global inequality, sustainable finance flows and multilateral governance.
Discovery of a Potent Antisarcopenia Drug Molecular Scaffold Derived from <i>Ligusticum chuanxiong</i> and Its Photochemical Synthesis
Long-term cognitive outcomes after mild COVID-19, critical COVID-19, and non-COVID critical illness: a prospective cohort comparison
Abstract Long-term cognitive impairment is a recognized sequela of the post-COVID-19 condition (PCC). While it is unclear how acute COVID-19 severity contributes to these lasting deficits, some evidence suggests that critical illness may lead to cognitive deficits similar to post-intensive care syndrome. This study examined long-term cognitive, mental, and physical health in patients with varying acute COVID-19 severity (mild to critical), alongside patients critically ill from non-COVID-19 causes. We conducted a bicentric prospective observational study comparing patients with PCC after mild COVID-19 ( n = 30), those requiring ICU care for acute COVID-19 ( n = 14), and patients with prolonged non-COVID-19 ICU stays ( n = 7), all assessed ≥ 12 weeks post-onset. Comprehensive neuropsychological assessments were conducted alongside evaluations of physical impairments, psychiatric symptoms, fatigue and health-related quality of life. Overall cognition was comparable between the mild COVID-19 and ICU groups. However, the mild COVID-19 group experienced higher cognitive fatigue and lower memory satisfaction than both ICU groups, along with higher rates of anxiety (59% vs. 15%) and depression (38% vs. 15%), and reduced mental health-related quality of life compared to COV-ICU patients. Long-term cognitive impairment occurred in PCC patients and ICU survivors, irrespective of acute disease severity. Patients with mild COVID-19 reported greater long-term psychological distress. Trial registration : This study was retrospectively registered at the German Clinical Trials Register, DRKS00025523 on 21 June 2021 ( https://drks.de/search/de/trial/DRKS00025523 ).
African swine fever virus B66L drives extracellular mitochondrial release to promote systemic inflammation in mice
Association of latent class trajectories of frailty with incident symptomatic knee osteoarthritis in Chinese postmenopausal women: a nationwide cohort study from China
Dual human milk oligosaccharide-fibre utilisation is a selection cue for the weaning gut microbiome
Efficient clarification of apple juice using immobilized pectinase on glass surface in a novel support-free plate bioreactor
Tianwen-2 mission target asteroid (469219) Kamoʻoalewa probably develops an Itokawa-compositional but more space-weathered surface
Designing Globally Inclusive and Ethically Deliberate Neurofutures
Unlike any other organ, the brain's role in identity, agency, and experience makes it biologically and culturally unique. The transformative potential of global neuroscience demands robust, integrated ethical engagement across the research life cycle. Neuroscientists and neuroengineers need to be equipped with a neuroethics familiarity that transcends the compliance training of older generations. Neuroethics must be an integral part of their work. This paper advocates for a proactive “neuroethics-by-design” (NxbD) approach. NxbD offers a reflective lens as well as an operational methodology that can iteratively shape hypotheses, experiments, technological architectures, and translation of the work to wider society. While NxbD is conceived as a toolkit for researchers, the responsibility of considering and addressing these issues is not theirs alone. NxbD is most effectively conceptualized as a shared responsibility that is evaluated and enacted by multiple communities. Such an approach is an investment in our collective future in which we can all contribute.