Browse Articles

Deterministic, dynamically reconfigurable single quantum emitters enabled by tip-enhanced nano-optical trapping spectroscopy

Correction: Serum CCL1 discriminates infectious and sterile systemic inflammation in sepsis and acute pancreatitis

Carbapenemase-producing carbapenem-resistant Enterobacterales in surgical intensive care unit patients in a tertiary hospital in India

Davide Blasi

Blockchain-based solution for secure and transparent pharmaceutical supply chain management using drugledger

Abstract The pharmaceutical supply chain continues to face significant challenges, including the circulation of counterfeit medicines, limited traceability, and insufficient transparency among stakeholders. To address these issues, this study presents a blockchain- and IPFS-based traceability framework designed to improve the secure tracking and verification of pharmaceutical products. The proposed system combines role-based smart contracts with decentralised off-chain storage to maintain product history and enable integrity validation using unique digital identifiers. The prototype was implemented in Ethereum-compatible environments. Repeated workflow measurements were obtained in the Remix VM environment, while Sepolia was used for deployment-level validation and public testnet verification. The system demonstrated the complete workflow, including participant registration, product enrollment, manufacturing, multi-stage transfer of ownership, and authenticity verification. Analysis of the execution outputs indicates that the core operations exhibit consistent gas consumption across repeated runs, offering clear insight into computational cost and system behaviour. The findings suggest that the proposed framework can serve as a viable foundation for improving traceability in pharmaceutical supply chains at the prototype level. However, further investigation is required to assess system performance under real-world deployment conditions, particularly with respect to scalability, latency, and large-scale operational constraints.

Author Correction: Pentavalent and tetravalent uranium formation via glycerol-stimulated bacteria in mine water

Self-healing neural networks via modular patch layers for diverse structural and adversarial damages

Abstract The growing use of modern deep neural networks (DNNs) in safety-critical and continuously operating systems exposes them to safety-centric concerns and raises the usual challenge of maintaining their continued operation. Internal structural flaws or adversarial attacks may lead to performance degradation that may severely affect reliability. This paper presents a Self-Healing Neural Network (SHNN) model which can enhance recovery in post-deployment by automatically identifying, localizing, and repairing damaged components of networks with lightweight and modular patch layers. SHNN isolates faults without requiring full retraining or global fine-tuning by performing activation discrepancy analysis at each layer and retraining the afflicted layer selectively, which causes little computational and memory overhead. Experiments of MNIST, Fashion-MNIST, Sign Language MNIST, and CIFAR-10 indicate SHNN restores performance after structural damage close to baseline (97.33% and 97.22% vs. 97.76% test accuracy of the zeroed and randomised weights) and restores a lost accuracy of 74–84% in FGSM and PGD adversarial attacks. Post-healing activation deviation scores decrease significantly, confirming effective fault localization as the reduced value confirms. The framework offers a self-repair mechanism based on modules, interpretable, and dataset adaptive. with a live Streamlit interface, and allows building resilient and fault-tolerant AI systems to operate in high-reliability environments.

SECmeres outperform extracellular vesicles as potential blood RNA biomarkers for Alzheimer’s disease

Abstract Cells release heterogeneous extracellular vesicles and particles (EVPs) into circulation, carrying RNA and proteins that reflect their origin. Recently, brain-derived EVs have gained significant attention as non-invasive biomarkers for Alzheimer’s disease (AD). Here, we identified sub-50nm extracellular nanoparticles in human brain and blood that lack the hallmarks of small EVs, exosomes, exomeres, and supermeres but are enriched for brain-specific markers, hereafter termed small EPs or ‘SECmeres’. We discovered that RNAs associated with SECmeres discriminated AD cases from controls with higher significance than small EVs, large EVs showed no differences. Discriminating RNAs were enriched in small EVs (Synaptotagmin, Alpha-synuclein, MAPT) or SECmeres (L1CAM, Syntaxin, Neurogranin), indicating distinct brain-derived signatures. Single-cell RNAseq deconvolution shows small EVs contain RNAs from diverse brain cells, whereas SECmeres enrich brain endothelial transcripts, lining cerebral blood vessels and forming the blood–brain barrier (BBB). These findings challenge the prevailing view that small EVs are the primary carriers of biomarkers. Collectively, our study shows that blood EVPs carry brain-specific information for liquid biopsy, pending validation in larger blinded clinical trials.

Advanced energy-based and two-degree-of-freedom analytical modeling of reinforced concrete slabs subjected to low-velocity impact and equivalent blast loading

Enhanced energy storage performance of confined co-doping dielectric films via nanoparticle self-assembly in ferroelectric phases

Reduced Symmetry Metal–Organic Cage‐to‐Framework Materials

ABSTRACT Metal–organic frameworks (MOFs) are typically assembled from inflexible, 2D aromatic linker units to provide structural predictability, rigidity and prevent architectural collapse. Limiting the pool of structural units from which these materials are derived, however, inevitably restricts the diversity of architectures that can be realised. In this work, we have explored organic cages with 1,2,3‐triazole struts as 3D linkers for Ag(I)‐based MOFs. These linkers are unusual in two key facets. First, the cage structure is semi‐rigid, providing both shape persistence and (limited) conformational freedom. Second, in contrast to the reticular design concepts of traditional MOFs, minor structural modifications at locations remote from the coordinating units were found to induce profound changes to the resultant MOF architectures, which included 2D honeycomb structures, 2D corrugated sheets and an interpenetrated 3D network. This is the first report of the incorporation of reduced symmetry cage linkers into metal–organic cage‐to‐framework structures, providing a blueprint for the introduction of low‐symmetry and chiral intrinsic porosity into framework materials.

Computational screening of antimicrobial peptide analogs targeting AdeB efflux transporter protein in Acinetobacter baumannii associated with multidrug resistance

Persistence of memory: lifespan dynamics of the human antiviral antibody reactome

Abstract The human antiviral antibody reactome provides a cumulative molecular record of immune exposures. Using high-resolution VirScan profiling, we compared epitope-level antibody responses across early childhood and adulthood. Infants are born with maternal IgG antibodies, but these antibodies decay rapidly and are replaced by endogenous responses to ~22 new viral exposures within three years. Pediatric antibody reactivities remain highly dynamic until about age 7 and are broad in epitope specificity but largely short-lived. In contrast, adult reactomes are remarkably stable and individualized, enabling accurate longitudinal donor identification (Immunoprint, > 99.99% accuracy). Stability varies by viral family, with Pneumoviridae and Picornaviridae persisting more robustly than Coronaviridae or Orthomyxoviridae . Across ages, immunodominant epitopes and initial binding strength predict response persistence. Longitudinal profiling highlights biological and epidemiological drivers of reactome change. This population-level, age-stratified atlas informs our understanding of immune memory and development with applications to vaccine design, surveillance, and precision public health.

Avena sativa-derived avenanthramides suppress 12-lipoxygenase activity and downstream arachidonic acid metabolites in letrozole-induced PCOS rats

Abstract Polycystic ovary syndrome (PCOS) implicates hormonal imbalance, ovulation disorders, metabolic disturbances, and chronic low-grade inflammation. In this study, we investigated the inflammation driven by 12-lipoxygenase (12-LOX)-mediated conversion of arachidonic acid to pro-inflammatory 12-hydroxyeicosatetraenoic acid (12-HETE) and the therapeutic potential of avenanthramide (AVA)-enriched oat extract and trans-resveratrol (RSV) as natural 12-LOX inhibitors. AVA-enriched extract was obtained from oats using 80% methanol, dried, and analyzed by HPLC. Fifty-six rats (3-week-old females) were divided into 8 groups: four received letrozole 1 mg/kg in 0.5% carboxymethyl cellulose (CMC) for 21 days to induce PCOS, while four received only CMC. Each pair (PCOS and non-PCOS) was treated for 2 weeks with either AVA (100 and 300 mg/kg), trans-resveratrol (20 mg/kg), or left untreated. Serum hormonal profile and 12-HETE levels were assessed via ELISA. Ovarian 12-LOX expression was evaluated by Western blotting. Histopathological analysis was performed on the liver and reproductive organs. Treating PCOS-induced rats with 100 mg/kg AVA, 300 mg/kg AVA, and 20 mg/kg RSV significantly restored their hormonal profile to normal levels and significantly reduced ovarian 12-LOX expression and subsequently their serum 12-HETE levels compared to the untreated PCOS rats ( P  < 0.001, P  < 0.0001, and P  < 0.0001, respectively). The 300 mg/kg AVA and 20 mg/kg RSV treatments restored normal ovarian and uterine architecture compared to the untreated PCOS group. No histopathological alterations were observed in the liver or oviduct. Avenanthramides from Avena sativa (oats) restore endocrine and ovarian function in letrozole-induced PCOS by directly inhibiting the redox-sensitive 12-lipoxygenase–12-HETE lipid signaling pathway. These findings suggest that avenanthramides may represent a promising therapeutic approach for restoring endocrine balance and ovarian function in PCOS.

Generating synthetic multi-national longitudinal cohorts for clinically grounded HIV research

Physics-based data augmentation to enhance deep learning performance in tropical cyclone and storm surge modelling

Abstract Accurate modelling of tropical cyclones (TCs) is essential for reliable storm surge simulations, as TCs provide the primary external forcing. While deep learning models have demonstrated effectiveness in TC modelling, a challenge remains regarding the limited diversity of TCs in reanalysis data used for training—specifically, the scarcity of extreme TC events. This study proposes a physics-based data augmentation method that utilizes a numerical weather prediction (NWP) model to physically generate TCs lying beyond the range of reanalysis data. Subsequently, focusing on small-sized yet intense TCs as extreme cases, a model—originally pre-trained solely on reanalysis data—was fine-tuned using this augmented dataset to convert a parametric TC model (PM) field into an NWP-like field. Validation using test data mimicking extreme TCs and a storm surge hindcast of TC Faxai (a compact, intense TC that struck Tokyo Bay in 2019) revealed that the PM failed to simulate storm surges where topographic effects on the wind field are significant, and the pre-trained model underestimated wind speeds and storm surges. In contrast, the fine-tuned model successfully captured the spatiotemporal features of the extreme TCs and the peak storm surges upon TC landfall, achieving the lowest RMSE for storm surges across all TCs and tide gauges. These results suggest that physics-based data augmentation can effectively extend the applicability of deep learning models for TC and storm surge modelling to extreme events.

Spatiotemporal modulation of surface texture for information encoding and object manipulation

Hybrid analysis of photovoltaic energy data containing excessive structural zeros

Abstract Photovoltaic (PV) power generation plays a critical role in the global transition toward sustainable energy systems. However, accurate PV power forecasting remains challenging due to the non-stationary nature of PV power time series and the presence of structural zeros. Most existing studies rely on parametric models or treat zero inflation as a secondary issue. In this study, a hybrid hurdle modeling framework is implemented to explicitly account for both structural zeros and continuous positive power values through a two-part structure. The zero component is modeled using Bayesian logistic regression (BLR), random forest classifier (RFC), and support vector classifier (SVC). The positive values are analyzed using the parametric models Gamma regression (GR), Log-normal regression (LNR), and Weibull regression (WR) and the non-parametric machine learning (ML) techniques random forest regression (RFR), extreme gradient boosting (XGBoost), and support vector regression (SVR). The proposed framework is validated using the target variable active power (AP, kW) based on a real-world data from a 110 kWe (129.6 kWp) PV plant located in Çaycuma, Zonguldak, Türkiye. Four metrics were used to compare the predictive performances of the models: mean squared error (MSE), mean absolute (scaled) error (MAE and MASE), and the coefficient of determination ( $$\:{R}^{2}$$ ). Among the 18 distinct hurdle models, the lowest error values and the highest $$\:{R}^{2}$$ were obtained when the positive component was modeled by either RFR, such as BLR-RFR (MSE = 67.425 kW, MAE = 3.974 kW, MASE = 0.615, $$\:{R}^{2}$$ = 0.899) or XGBoost, such as RFC-XGBoost (MSE = 69.252 kW, MAE = 4.075 kW, MASE = 0.630, $$\:{R}^{2}$$ = 0.898). The findings indicate that hybrid modeling approaches where the zero component is modeled using BLR, RFC, or SVC, and the positive component is modeled with either RFR or XGBoost provide a reliable framework for PV power forecasting, when predicting continuous data characterized by excessive structural zeros.

A chemoproteomic biotechnological toolkit for resolving xylanase specificity in decorated xylan

Abstract Xylanases are central to lignocellulosic biomass degradation, yet current methods lack the specificity to resolve how enzymes distinguish complex xylan structures decorated with arabinofuranose (Ara f ) and 4- O -methyl-glucuronic acid (MeGlcA). Here, we report a suite of chemically-defined activity-based probes (ABPs) that enable the selective detection of arabinoxylan- and glucuronoxylan-specific xylanases (AXXs and GXXs). These cyclophellitol-derived ABPs covalently label retaining xylanases at their active sites, allowing precise mapping of substrate specificity across diverse glycoside hydrolase families. Crystallographic and mass spectrometric analyses reveal the molecular basis of probe selectivity, while in-gel and pull-down assays demonstrate their effectiveness in profiling xylanase activities in complex bacterial and fungal proteomes, including cellulosomes. By integrating activity-based protein profiling (ABPP) with sequence similarity networks (SSNs), we further show that xylanase specificity can be predicted from sequence alone, enabling rapid functional annotation of uncharacterized xylanases. This chemoproteomic strategy provides a powerful platform for discovering and engineering substrate-specific enzymes for biomass valorisation, microbial ecology, and biotechnological applications.

A deep learning framework for histopathological analysis of pixel-level extracellular matrix variation in standard H&E-stained images

Abstract Artificial intelligence-driven image analysis has enabled significant advances in digital pathology. However, most approaches have focused on cell or organ structures. This manuscript presents a reproducible deep learning methodology for pixel-level analysis of amorphic patterns in haematoxylin and eosin-stained whole-slide histological images. This study analysed the pixel patterns in the extracellular matrix (ECM) part of connective tissue to identify differences in airway wall ECM compartments and their heterogeneity, which are microscopically similar and difficult to discern with the human eye. Through a targeted preprocessing pipeline, the deep learning model is guided to emphasise learning from pixel-level patterns in non-cellular tissue components while reducing the influence of cellular structures and artefacts. Combined with transfer learning, the model accurately distinguishes the characteristics of the airway submucosa and adventitia, achieving a test area under the curve of 0.84. Using visualisation techniques and statistical analysis, we demonstrate that random pixel imputation successfully reduces the effects of cellular structures on model learning. The framework is applied in a proof-of-principle study of lung tissue from patients with chronic obstructive pulmonary disease, illustrating how this quantitative approach can study population heterogeneity and inform novel research directions. Ultimately, this study provides an innovative and adaptable framework that unlocks the analytical potential of often-overlooked amorphic components in AI-empowered histopathology.