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IoMT–Blockchain framework for secure and real-time heart disease monitoring using hybrid black-winged optimized spherical structural graph convolutional neural networks
Abstract Cardiovascular diseases are a primary global health concern, requiring continuous monitoring and accurate diagnostic mechanisms for early detection and prevention. The integration of the Internet of Medical Things (IoMT), deep learning, and blockchain technologies has enabled intelligent healthcare systems capable of real-time cardiac assessment and enhanced security in the management of medical information. However, existing heart disease monitoring systems are affected by noisy physiological signals, inefficient feature extraction, limited classification accuracy, and insufficient security in distributed environments, motivating the development of a robust and reliable diagnostic framework with improved security. To address these challenges, this research proposes an IoMT and Blockchain-Based Heart Disease Monitoring System Using Hybrid Black-Winged Spherical Structural Graph Convolution Neural Network (HBW-SSG-CNN). The proposed workflow begins with IoMT-based acquisition of ECG and PCG signals, followed by preprocessing using quasi-cross bilateral filtering (QCBF) to suppress noise while preserving critical cardiac structures. Signal decomposition is performed using Spectral Envelope-Based Adaptive Empirical Fourier Decomposition (SE-AEFD), followed by feature extraction using the Short-Time Quaternion Quadratic Phase Fourier Transform (ST-QQPFT). Feature dimensionality is optimized using the Success-Based Optimization Algorithm (SBOA), and heart disease classification is performed using a Hybrid Structural Graph Attention Network with Spherical Convolutional Neural Network (HS-GAT-SCNN), further enhanced by the Black-Winged Kite Algorithm (BWKA). To enhance data integrity and improve security, an Adaptive Hash Algorithm with Weighted Probability Model (AHA-WPM) is integrated with a Fair Consensus Blockchain for Heterogeneous Miners (FCB-HM). The proposed model is evaluated using publicly available benchmark datasets, including PhysioNet cardiac signal datasets and the Cleveland heart disease dataset. Experimental evaluation demonstrates superior performance with an accuracy of 99.21%, sensitivity of 98.94%, specificity of 99.08%, and F1-score of 99.02%. The results confirm that the proposed framework provides a highly accurate, scalable, and security-enhanced solution for near real-time heart disease monitoring in IoMT-enabled healthcare systems.
PAWR augments anti-RNA viral innate immunity by promoting the PIM2-XBP1s-RIG-I signaling axis
The anti-inflammatory efficacy of melanocortin drugs is influenced by genetic variation at MC1R
Abstract The melanocortin 1 receptor ( MC1R ) is a pro-resolving anti-inflammatory target under clinical development for scleroderma, arthritis, light intolerance or melanoma prevention. Genetic diversity at MC1R is high in certain populations, with some variants associated with loss-of function (LoF) resulting in red hair and poor tanning response. However, how these variants influence the anti-inflammatory efficacy of drug candidates targeting MC1R is unknown. We analysed the impact of 30 variants on receptor signalling (cAMP and phospho-ERK) and the anti-inflammatory response to nine agonists on the whole-blood assay on healthy volunteers. LoF presents as a continuum rather than as a binary characteristic (benign/pathogenic) and differentially affects each signalling pathway, undermining the usefulness of in silico tools to predict variants pathogenicity. Moreover, variants affected compounds differently, even causing LoF, no effect or gain-of-function depending on the compound tested. We identified responders and non-responders to melanocortin compounds, and the efficacy of most compounds (determined as reduction of cytokine release) was diminished by the presence of variants at MC1R . Carrying red-hair variants (RHC) also associated with reduced efficacy but not having light skin phototype. These data encourage the incorporation of pharmacogenetics strategies during melanocortin drug development programs to ensure targeted interventions for maximal efficacy and safety.
Author Correction: The ubiquitin ligase RNF5 determines acute myeloid leukemia growth and susceptibility to histone deacetylase inhibitors
The Development and validation of an RP-HPLC-FD method for the multi-residue analysis of antiparasitic macrocycliclactones in cattle edible tissues
Co-ordinated shifts in deep-water formation and Gulf Stream migration during abrupt climate changes
Abstract Theory and models suggest the Gulf Stream may shift northwards under projected Atlantic Meridional Overturning Circulation weakening. Yet Gulf Stream behaviour during past abrupt cold events remains poorly constrained. Here we present high-resolution paleoceanographic records from the Northwest Atlantic during the last deglaciation. During the Younger Dryas cold period, we document a northward Gulf Stream shift evidenced from coherent surface and subsurface warming. Our sortable silt data suggest a strengthening of upper North Atlantic Deep Water that opposes weakening lower North Atlantic Deep Water, consistent with a seesaw feedback between the Nordic overflows and subpolar gyre. Our results constrain a co-ordinated sequence at the Younger Dryas onset: initial lower North Atlantic Deep Water weakening and subpolar sea‑ice expansion, lagged (58 ± 38 yr) by an increase in upper North Atlantic Deep Water and an eventual atmospheric reorganization (84 ± 51 yr after onset). These findings provide empirical support for model projections of future Gulf Stream shifts.
Submicrometre sampling of living cells by macrophages
Abstract An effective immune system must sample and develop healthy self-identity to prevent autoimmunity and to discern pathogenic insults 1–3 . Self-proteins are presented to T cells in the thymus during immune cell development 2,3 and must be presented throughout the body to maintain regulatory T cell populations 4–6 and to provide tonic signals to sustain conventional T cells over time 7–9 . Observations of continuous apoptosis in some organs together with the ingestion of that material by myeloid populations has led to a conventional understanding of ongoing cell death as a major source of self-antigens 10 . Here we used a series of companion imaging and vesicular labelling technologies to reveal an alternative process undertaken by macrophages that results in non-destructive, direct sampling of living cells. This process requires cell–cell contact, does not require caspase activation and occurs via trogocytosis-like stretching of the target cell into the macrophage, which leads to the generation of submicrometre-sized vesicles that contain cytoplasm. Using a high-dimensional flow-based method for labelling vesicles, we demonstrate that live-sampled material is distinctly processed and is poorly subjected to fusion with lysosomes. The material also produces differential effects on the presentation of antigen to CD4 T cells compared with CD8 T cells. Disruption of this trafficking by redirecting antigen to the lysosome significantly reduced the associated macrophage-mediated priming of CD8 T cells. These results demonstrate an important and substantial sampling of living cells by the immune system, with clear consequences for maintaining the border of immunity.
A physics-regularized machine learning approach for predicting time-temperature-transformation curves in alloys: application to uranium-based alloys
Hypothalamic POMC neurons regulate intestinal glucose absorption via a gut–brain circuit
A treasure trove of Neolithic necklace beads
Four-dimensional left ventricular motion clustering reveals cardiovascular phenotypes at population scale
Abstract Characterisation of the motion dynamics of the left ventricle is key to understanding pathophysiological mechanisms and transitions from health to disease. Conventional volumetric assessments of the heart using imaging represent mainly aggregate global features of function that are poorly discriminating. Here we present a novel approach to quantify and visualise how the left ventricle is affected by cardiovascular risk factors through efficient representations of motion trajectories. We use computer vision to survey four-dimensional cardiac motion traits using densely sampled point clouds of the left ventricle in over 20,000 participants of UK Biobank. We developed a computational framework for dimensionality reduction of spatiotemporal information to derive a human-interpretable signature summarising variation in complex patterns of motion. We found six phenogroups representing a novel classification of heterogeneous motion phenotypes with differential enrichment of cardiovascular outcomes and genetic risk. Low dimensional representations of motion are visualised as a simple spatial signature capturing deviation from an average state. Discovering compact cardiac motion signatures of health and disease from dynamic point clouds enables efficient classification of patient risk and predisposing polygenic factors.
TOFU-MAaPO: fast, scalable and reproducible analysis of large metagenome sequence data from the Sequence Read Archive
Abstract Metagenomic shotgun sequencing data from over 600,000 metagenomes are publicly available in repositories such as NCBI’s Sequence Read Archive (SRA). Technically advanced and easy-to-use best-practice metagenome software workflows for raw data pre-processing, assembly of metagenome-assembled genomes, and taxonomic and functional annotation of metagenome-assembled genomes are needed for reproducible analysis and harmonization of large-scale metagenomic datasets. We introduce TOFU-MAaPO (Taxonomic Or FUnctional Metagenomic Assembly and PrOfiling), a portable, automated single-command Nextflow pipeline for large-scale analysis of metagenomic short-read sequencing data. It analyzes metagenome files locally or directly from the SRA using accession or study IDs. In a benchmark against three established metagenome software pipelines, the TOFU-MAaPO workflow yielded 12%, 42% to 77% more high-quality metagenome-assembled genomes, likely reflecting the integration of multiple complementary binning tools with a unified refinement strategy. Using its assembly-free taxonomic abundance profiling module, we also automatically downloaded 16,462 uniquely identifiable and accessible human gut metagenome samples from the SRA and taxonomically annotated them against the Genome Taxonomy Database on a high-performance cluster in less than 55 hours, including download time. TOFU-MAaPO makes large metagenome projects more accessible to individual research groups and is freely available at https://github.com/ikmb/TOFU-MAaPO .
A tri-target in silico analysis of Testolift: a nutraceutical formulation targeting aromatase, myostatin, and prolyl hydroxylase-2 in testosterone regulation and muscle performance
Abstract Declining testosterone levels and associated impairments in muscle function, endurance, and metabolic health represent a growing global concern, while limitations associated with long-term testosterone replacement therapy have intensified interest in safer, mechanism-based nutraceutical approaches. In the present study, a systems-oriented multi-target computational framework integrating physicochemical profiling, ADMET prediction, molecular docking, and molecular dynamics (MD) simulations was employed to investigate the mechanistic basis of the Testolift formulation composed of protodioscin, diosgenin, 5,7-dimethoxyflavone (DMF), and 5,7,4′-trimethoxyflavone (TMF). The phytochemicals were evaluated against three mechanistically interconnected targets namely aromatase (CYP19A1), myostatin, and prolyl hydroxylase-2 (PHD2), which are associated with testosterone regulation, muscle physiology, and oxygen-dependent metabolic adaptation. Protodioscin demonstrated extensive polar interactions and persistent hydrogen bonding within the CYP19A1 binding region, whereas diosgenin exhibited favorable lipophilicity and interaction characteristics consistent with steroidogenic signaling. DMF and TMF displayed the most favorable drug-like physicochemical profiles (QED = 0.74), high predicted intestinal absorption, and stable interactions with myostatin and PHD2. Molecular dynamics simulations performed over 150 ns confirmed stable ligand–protein complexes without major conformational destabilization. ADMET analysis further indicated complementary pharmacokinetic behavior together with an overall favorable predicted safety profile. Collectively, these findings support a coordinated multi-target nutraceutical framework in which structurally distinct phytochemicals modulate complementary peripheral pathways associated with testosterone balance, muscle anabolic regulation, and endurance-related metabolic adaptation, thereby supporting further experimental validation of the Testolift formulation.
Digitally encoded dual-narrowband photodetectors for secure optical wireless communication
Arson attacks at Ebola hospitals are a cry for regional development
Label-free classification of breast cancer subtypes in ex vivo human tissues using Raman spectroscopy and machine learning
Abstract Breast conserving surgery (BCS) aims to excise breast tumors whilst preserving breast-related quality of life, but is complicated by the challenge of accurately identifying the margin between healthy and cancerous tissue. Raman spectroscopy (RS) has been shown to distinguish between normal breast tissue and breast cancer. Thus, this study aimed to further evaluate the diagnostic performance of RS in ex vivo breast tissue subtype classification via investigation of signals from healthy tissues and three breast cancer subtypes (invasive ductal carcinoma, IDC; invasive lobular carcinoma, ILC; and ductal carcinoma in situ, DCIS). A total of 80 tissue samples (46 normal and 34 cancerous) from 71 individuals were measured using a confocal Raman microscope. Spectral signatures were investigated, and supervised classification was performed for both two-class (healthy vs. cancer) and four-class (healthy vs. IDC vs. ILC vs. DCIS) classification tasks. RS successfully differentiated cancerous from normal breast tissue (97.84% sensitivity, 97.18% specificity). For four-class classification, RS achieved in-class sensitivity ranging from 83 to 96% and specificity from 93 to 99%. These findings demonstrate that RS can accurately distinguish normal from cancerous tissue and capture clinically relevant differences among histological subtypes, including invasive and pre-invasive disease, supporting its promise for intraoperative tissue characterization during BCS.
Detected impacts of atmospheric rivers on marine heatwaves
Abstract Marine heatwaves (MHWs) are periods of unusually high sea surface temperature that can persist for weeks to months and extend across thousands of kilometers. Their increasing frequency and intensity under climate change threaten marine ecosystems and fisheries, yet the physical processes that govern their occurrence and evolution remain poorly understood. Here we analyze satellite and reanalysis data to show that atmospheric rivers (ARs)—long, narrow corridors of concentrated atmospheric moisture, often described as “rivers in the sky”—play a previously overlooked role in the development of MHWs in the North Pacific and North Atlantic. Under an AR, increased cloud cover cools the ocean through reduced solar radiation, while anomalously warm, humid air warms the ocean through the reduction of turbulent heat fluxes from the ocean. These two opposing mechanisms, dominant among others, vary with background climate state, causing seasonally and regionally varying ARs’ impacts on MHWs. These findings stress the importance of understanding ocean-atmosphere compound extremes and their changes under a warming climate.
Removal of toxic hexavalent chromium ions from water using magnetic protic poly (ionic liquids) nanocomposites
Abstract Novel magnetic nanocomposites (MNCs) based on Fe 3 O 4 and NiFe 2 O 4 nanoparticles were synthesized at low temperature via an in-situ approach using linear and crosslinked quaternized protic poly (ionic liquid) (PIL) matrices. Linear PILs were prepared from quaternized triethanolammonium acrylate and quaternized triethanolammonium 2-acrylamido-2-methylpropane sulfonate to form LQAA, while crosslinked copolymerization with N , N ′-methylenebisacrylamide yielded CQAA hydrogels. For comparison, acrylic acid-co-2-acrylamido-2-methylpropane sulfonic acid (CAA) hydrogels were also fabricated. The resulting MNCs were comprehensively characterized to elucidate their chemical structure, thermal stability, magnetic behavior, surface morphology, and crystallographic features. The nanocomposites exhibit superparamagnetic behavior, high structural stability, and uniform dispersion of ferrite nanoparticles within the ionic and hydrogel matrices. Their adsorption performance toward Cr ions removal from aqueous solutions was systematically investigated, including the effects of pH, contact time, and adsorbent dosage. The fate of Cr 6+ in the aqueous solution, as well as the surface of the material, was determined; the results demonstrated not only the successful adsorption of Cr 6+ from solution, but also confirmed the presence and in situ reduction of Cr 6+ to Cr 3+ on the surface. Kinetic and isotherm analyses reveal rapid adsorption governed by surface chemisorption and diffusion processes. Remarkably, the MNCs demonstrate exceptionally high equilibrium Cr ions adsorption capacities (800–950 mg g −1 ), exceeding those of most reported magnetic adsorbents. This superior performance arises from the synergistic combination of functionalized Fe 3 O 4 and NiFe 2 O 4 surfaces with highly charged PIL networks, which provide a high density of accessible binding sites. Competitive adsorption studies using simulated industrial wastewater containing Cr 6+ , Cu 2+ , and Ni 2+ (up to 1000 mg L −1 each) further confirm the strong affinity, selectivity, and stability of the developed materials, particularly toward Cr ions. These findings highlight the potential of PIL-based magnetic nanocomposites as efficient, magnetically recoverable, and reusable adsorbents for advanced treatment of heavy metal contaminate.
Solvent-assisted running-in strategy enables improved triboelectric nanogenerator output
Abstract The challenge of low electric outputs of triboelectric nanogenerators limits their large-scale practical applications. Although considerable efforts have been focused on improving the output, e.g., enhancing the surface charge densities of tribo-materials, the improvements are either too weak or too complicated. Here, we show that optimizing the running-in process with dimethyl sulfoxide solvent can increase the charge density of polyimide-based triboelectric nanogenerators to 2.5 mC m −2 , a fourfold enhancement compared with untreated devices. The solvent-assisted running-in process removes the worn radicals, debris, or transferred materials on the contact surface, reducing electron transfer hindrance issues. Through time-of-flight secondary ion mass spectrometry, molecular dynamics simulations and density functional theory analyses at the molecular and electronic levels, the results indicate that running-in friction further induces the breakage of the N–C bonds in polyimide, resulting in the freedom to release amide groups. Together with the function of dimethyl sulfoxide-driven extraction, the amide-containing chains rearrange into “molecular brushes” towards contact surfaces, among which the highly electron-withdrawing C = O bonds are thus exposed and capture electrons from the counter tribolayer. This solvent-assisted running-in strategy improves electrical output in engineering polymers without material modification and clarifies how tribological running-in can be used to regulate triboelectric performances.