Browse Articles

Variation in pathogen load and the pathogen load–infectiousness relationship broaden avian malaria’s distribution

Abstract Two aspects of host infectiousness shape pathogen transmission and distribution but are underappreciated: the relationship between pathogen load and infectiousness, and variability in pathogen load within species. We quantified the relationship between host pathogen load (parasitemia) for avian malaria ( Plasmodium relictum ) and infectiousness for biting Culex quinquefasciatus mosquitoes with experimental infections in canaries ( Serinus canaria ). Using this relationship, we estimated the infectiousness of 17 bird species in 11 communities in Hawaiʻi and quantified the relative contributions of infection stage (acute versus chronic) to transmission. We show that infectiousness to mosquitoes increased with parasitemia, temperature, and time since feeding. The relationship’s gradual (low) parasitemia slope resulted in a wide range of parasitemias being partly infectious, and high within-host species variability in parasitemia led to extensive overlap in infectiousness among hosts. Disproportionate mosquito host utilization (inferred from relative infection prevalence) elevated the importance of a few host species, yet broad overlap in species infectiousness resulted in similar total infectiousness across most bird communities. This similarity likely contributed to avian malaria’s widespread distribution throughout Hawaiʻi despite diverse host community assemblages. Our findings highlight the importance of both the shape of the pathogen load–infectiousness relationship and within-species variability in determining a pathogen’s host range, transmission intensity, and spatial spread.

Utilizing deep learning models for early detection and classification of fruit diseases: towards sustainable agriculture and enhanced food quality

A synthetic system for RNA-responsive pyroptosis based on type III-E CRISPR nuclease-protease

Abstract Pyroptosis plays a crucial role in immune defense against infections and endogenous threats by eliminating harmful cells and modulating the immune response through inflammation. However, the natural activation of pyroptosis involves intricate signaling pathways, posing significant challenges for its artificial manipulation in research and therapies. Here, we present DAMAGE ( D e a th Ma nipulation Ge ne), an innovative system that integrates gasdermins within the type III-E CRISPR framework, enabling the specific recognition of target RNA (tgRNA) and triggering pyroptosis. This mechanism allows DAMAGE to selectively identify and eliminate virus-infected, cancerous, and senescent cells, all of which exhibit altered RNA transcriptomes. Additionally, DAMAGE exhibits considerable promise as a platform for mRNA-LNP therapy. Our study highlights the potential of this CRISPR-based system in the controllable induction of pyroptosis, offering an innovative therapeutic strategy for treating RNA-heterogeneous diseases.

Normalized Caputo–Fabrizio SVIR modeling and bifurcation analysis

Aryl sulfur ligand-modulated silver catalysts with tailored binding affinity for selective nitrate-to-ammonia conversion

A multi-phase, multi-method assessment of national COVID-19 vaccination performance with equity analysis

Abstract The COVID-19 pandemic, originating in Wuhan, China, rapidly escalated into a global crisis, straining public health systems and economies. This study proposes a data-driven framework to evaluate the response performance of 143 countries across three phases: pre-vaccination, vaccination, and post-vaccination. Countries are clustered using K-means clustering based on pandemic-related indicators, and indicator importance is derived using an objective weighting method that accounts for variability and interdependence. Three Multi-Criteria Decision-Making (MCDM) methods (MACONT, COCOSO, and EDAS) are then applied to rank countries within clusters. The framework integrates diverse measures, including healthcare capacity, economic resilience, and the Risk INFORM COVID-19 index, enabling a holistic evaluation. Results reveal notable differences in country performance over time, with early vaccination rollout and strict public health policies associated with more stable rankings. Key determinants include the strength of the healthcare system, economic adaptability, and equitable vaccine distribution. An equity analysis using the Gini coefficient highlights significant disparities in global vaccine access, emphasizing the urgency of coordinated policy action. Finally, by tracking country movements across clusters, the study provides insights into evolving national responses. It concludes with recommendations for improving global preparedness and building more resilient, equitable systems for future health emergencies.

Simulating fluid vortex interactions on a superconducting quantum processor

Reconstructing subsurface fracture geometries in rock slope instabilities through ambient vibration-based numerical modelling inversion

Abstract Detailed engineering-geological models are crucial for assessing landslide hazards, yet their reliability is limited when poorly defined fracture networks control slope failure mechanisms. Traditional surveying techniques often fail to accurately constrain fracture extents, resulting in oversimplified and uncertain boundary conditions. We address these limitations by integrating array-based ambient vibration modal analysis with numerical modelling to invert for the subsurface geometry of fracture-controlled rock slope instabilities. We applied our approach at two case studies exhibiting similar toppling failure mechanisms. Linear seismic arrays were deployed to record ambient vibrations and derive resonance frequencies and 3D mode shapes using the Frequency Domain Decomposition technique. We then constructed 3D finite-element models representing the unstable rock volumes with their rear boundaries segmented into regular grids to simulate thousands of unique fracture configurations. Model results were compared with field-derived modal parameters using a multi-metric similarity ranking score evaluating resonance frequency and mode shape consistency. Results revealed ensembles of top-performing models that reproduced the observed resonance modes and converged toward fracture geometries consistent with field-estimated fracture depths. Inversion stability increased with the number of resonance modes considered, highlighting the need for multiple constraints. Our results demonstrate that integrating ambient vibration field surveys with numerical modal analysis can support quantitative description of subsurface boundary conditions in unstable rock slopes, providing a robust framework for improved landslide structural characterization and monitoring.

Exploiting human fucosyltransferase 8 allostery with a covalent inhibitor for core fucosylation suppression

Abstract Core fucosylation, catalyzed by fucosyltransferase 8 (FUT8), plays critical roles in cancer progression, immune evasion, and drug resistance, making it a compelling therapeutic target. However, development of selective FUT8 inhibitors has been hindered by shared substrate specificity of fucosyltransferases. Here, we report the discovery of a previously unrecognized allosteric site on FUT8 and the development of a low-toxicity covalent inhibitor, CAIF (stearic acid-N-hydroxysuccinimide ester-dimethylimidazolium bromide), through structure-based drug design. High-throughput screening and crystallographic studies reveal that small molecules such as NH125 bind to a channel-like allosteric pocket, inducing conformational changes that disrupt FUT8 activity. Leveraging these insights, we design CAIF to covalently target lysine K216 within the allosteric site. CAIF exhibits minimal cytotoxicity and significantly inhibits core fucosylation and cancer cell invasion in cellular assays. This work establishes CAIF as a lead compound for further optimization and development, offering a framework for targeting glycosyltransferases through allosteric and covalent inhibition strategies.

Respiratory physiology after resupination following prone ventilation to predict 28-day mortality in mechanically ventilated patients: a machine learning analysis

Abstract The clinical significance of resupination parameters following prone positioning remains largely unknown. This study employed machine learning to predict the survival of patients receiving mechanical ventilation (MV) by analyzing oxygenation and respiratory mechanics after resupination. Data were extracted from the COVID-Predict Dutch Data Warehouse. Patients receiving MV who underwent the supine–prone–supine sequence were selected, and the variables related to respiratory physiology within 4 h before proning and after resupination were recorded. Machine learning models were trained on the features selected using LASSO regression to predict the 28-day mortality. Patients who did not survive (157/522, 30.1%) had lower PaO 2 /FiO 2 values, higher ventilatory ratios, increased physiological dead space, higher driving pressure, and lower static and dynamic lung compliance values at resupination. The predictive performance of the individual clinical parameters for 28-day mortality was generally modest, and FiO 2 , PaO 2 /FiO 2 , physiological dead space, and dynamic lung compliance were the best predictors of mortality. Overall, XGBoost showed the best recall (0.732) and maintained the highest AUC (0.719), while its F1-score (0.500) was the best for predicting mortality despite a low precision (0.380). Survival after prone positioning in patients receiving MV can be stratified by physiological responses after resupination.

Cellular and transcriptional trajectories of neural fate specification in sea anemone uncover two modes of adult neurogenesis

Proteomic profiling of equine airway mucus reveals compositional changes in asthmatic phenotypes

Abstract Mucus hypersecretion and accumulation are hallmark features of equine asthma (EA), a meaningful respiratory disorder in horses occurring in mild to moderate (MEA) and severe (SEA) forms. Changes of the proteomic composition of airway mucus in EA are poorly understood. Using label-free quantitative liquid chromatography-mass spectrometry, we analyzed airway mucus from SEA ( n  = 10), MEA ( n  = 6), and healthy ( n  = 8) horses. We identified and quantified 2,275 proteins including gel-forming mucins MUC5AC and MUC5B and membrane-bound mucins MUC1 and MUC4. Compared with healthy controls, 130 proteins (SEA) and 103 (MEA) were significantly increased. 38 were elevated in SEA relative to MEA, 10 were higher in MEA. MUC4 was markedly increased in both, correlated with bronchoalveolar lavage neutrophils (ρ = 0.790, p  = 4.9E-06), and distinguished excellently between healthy and asthmatics (AUC = 1.0, 95% CI: 1–1), similar to 23 other proteins. MUC5AC was elevated in both, whereas MUC5B only in SEA. MUC1 did not differ between groups. Changes in mucus-modifying proteins, including glycosyltransferases and aquaporins, suggest altered mucus properties in EA. Functional enrichment analyses revealed inflammation-, tissue remodeling- and coagulation-linked GO terms and pathways in EA. The distinct proteomic profiles add to the understanding of EA and may offer novel targets for phenotype-specific biomarkers and therapy.

Reactive oxygen species-activated bioorthogonal chemistry in living systems enabled by boronate-caged dihydrotetrazines

Highly sensitive profiling somatic mutations of thyroid cancer by nucleotide-enrichment-based MALDI-TOF MS assay

Granulomas microenvironment-guided sono-immunotherapy to treat and prevent recurrence of tuberculosis

Miniaturized flexible skin moisture sensor with optimized coil for enhanced wireless power efficiency

Abstract Efficient wireless power transfer is essential for the stable operation of battery-free wearable sensors. Especially for Near Field Communication (NFC)-based sensors, the performance of the antenna coil is a critical factor in determining power reception efficiency and data communication reliability. However, as sensors become smaller, reducing coil size drastically reduces communication sensitivity, making it crucial to design a coil that delivers optimal performance within a limited area. This study focuses on the optimal design of a miniature antenna coil for a wearable sensor capable of measuring skin hydration. Considering the characteristics of wearable devices, the design and experimental validation were conducted to maintain a stable resonant frequency and robust power reception and data communication even under mechanical deformation, such as bending of the skin surface. Consequently, a compact, battery-free sensor platform integrated with the optimized antenna coil enables real-time monitoring of patient skin hydration, and its durability has been proven through rigorous environmental testing. Furthermore, polydimethylsiloxane (PDMS) encapsulation ensures mechanical durability and long-term stability. This study highlights the importance of coil optimization in the development of next-generation wearable healthcare devices and provides a basic design framework for miniature sensor systems.

Enhanced forest carbon gains from stronger protection in China’s protected areas

Green-Synthesized titanium dioxide Nanoparticle–Modified glass ionomer cement: in vitro and in Silico assessment of Mechanical, Physical, and safety properties performance

Abstract Recurrent caries continues to pose a major problem in restorative dentistry. Although numerous antimicrobial agents have been added to restorative materials, their effectiveness and their influence on the materials’ mechanical properties are still uncertain. This study investigated the physicomechanical performance of glass ionomer cement (GIC) modified with green-synthesized titanium dioxide nanoparticles prepared using Citrus aurantium seed extract (CA-TiO₂NPs). The novelty of this work lies in integrating a plant-mediated synthesis approach with systematic mechanical evaluation and preliminary in silico safety screening within a single proof-of-concept framework. CA-TiO₂NPs were synthesized via phytoreduction using C. aurantium seed extract and added to the powder phase of a conventional GIC at 5% and 10% w/w. Flexural strength was evaluated according to ISO 9917-2:2017, water sorption and solubility according to ISO 4049:2009, and Vickers microhardness according to ASTM E384. The major bioactive constituents identified by Gas Chromatography-Mass Spectrometry (GC–MS) were profiled. In addition, computational toxicological and ADME predictions were performed to provide early-stage safety indicators. While nanoparticle incorporation influenced selected mechanical parameters, flexural strength differences were not statistically significant. The 10% CA-TiO₂ NPs group exhibited the highest flexural modulus and microhardness (P < 0.0001) and lowest water sorption and solubility. Additionally. In silico pharmacokinetic and toxicological analyses suggested a favorable preliminary safety profile of the phytochemical constituents, supporting proof-of-concept evaluation rather than clinical validation. The combination of green synthesis and mechanical testing, together with screening-level toxic informatics of major extract-associated metabolites, supports a proof-of-concept for improving CA TiO₂ NPs –modified GIC properties while prioritizing safety-relevant endpoints for subsequent leachate and cytotoxicity testing. Clinical Significance: Computational integration strengthens the translational validity of green nanomaterial development by providing preliminary molecular-level safety indicators for phytochemical constituents potentially associated with the modified material.

Rational design and in vivo validation of capsid inhibitors for enterovirus D68

Altered functional connectivity density in the prefrontal-limbic-visual networks of vestibular migraine

Abstract This study aimed to explore abnormal patterns of functional connectivity density (FCD) and functional connectivity (FC) in patients with vestibular migraine (VM) and their associations with clinical symptoms. Resting-state functional magnetic resonance imaging (rs-fMRI) data from 49 VM patients and 61 healthy controls (HCs) were analyzed using Global FCD (GFCD), long-range FCD (LRFCD), and seed-based FC. Compared with HCs, VM patients demonstrated decreased GFCD and LRFCD in the bilateral medial prefrontal cortex (mPFC), along with increased GFCD in the right lingual gyrus (LING), right middle occipital cortex (MOC), left precuneus (preCUN), and elevated LRFCD in the middle cingulate cortex (MCC) and bilateral MOC. Seed-based FC analysis revealed significantly reduced connectivity between the mPFC and multiple regions, including the right cuneus/precuneus (CUN/preCUN), bilateral posterior cingulate cortex (PCC), bilateral hippocampus/parahippocampus (HIPP/ParaHIPP), and left calcarine cortex (CAL) in VM patients. Correlation analysis identified a positive association between GFCD in the left preCUN and Dizziness Handicap Inventory (DHI) scores ( r  = 0.370, p  = 0.011). These findings highlight disrupted prefrontal-limbic-visual network integration in VM, with precuneus dysfunction potentially linked to dizziness severity. This study provides novel insights into the neural mechanisms underlying VM, highlighting the role of altered functional integration in symptom manifestation.