Search Results (11,662)

Bovine milk represents a highly complex colloidal system whose physicochemical stability depends on the organization of milk fat globules, casein micelles, membrane-associated phospholipids, and somatic cellular components. Mechanical separation procedures such as centrifugation induce redistribution of dispersed colloidal fractions and structural perturbations within the milk matrix, potentially enabling fraudulent reduction of somatic cell count while preserving bulk compositional parameters. In the present study, we investigated whether advanced deep learning architectures could identify centrifugation-associated structural alterations in bovine milk using microscopy image representations. A total of 16,472 microscopy images obtained from centrifuged and non-centrifuged milk samples were analyzed using Swin Transformer V2 and ConvNeXt-Base architectures. Both models successfully detected centrifugation-associated structural perturbations and substantially outperformed the previously analyzed InceptionC baseline. ConvNeXt-Base achieved 87.30% classification accuracy together with 86.85% balanced accuracy and 86.59% harmonic average of recalls following totalogit aggregation. Importantly, Swin Transformer V2 demonstrated strong monotonic relationships between logit metrics and centrifugation ratio (r = 0.640–0.651, p < 0.01), indicating sensitivity to progressive image-level changes associated with increasing centrifugation ratio. Collectively, the obtained findings demonstrate that microscopy-derived deep learning representations capture structural information associated with centrifugation-induced changes in bovine milk, supporting the applicability of AI-assisted microscopy for detecting processing-related alterations in complex dairy systems. Full article

Pancreatic cancer is among the most aggressive malignancies, and late diagnosis remains a key challenge. For a systematic review of pancreatic cancer diagnosis and prognosis, Scopus and Web of Science databases were used for the period from 2016 to 2026. The search query included the following keywords and their combinations: pancreatic cancer, diagnosis, early detection, prognosis, biomarkers, metabolomic profiling, CA19-9, microbiome, metagenomic changes, circulating tumor DNA, genomic analysis. Inclusion criteria included only articles published in English. Exclusion criteria included case reports and studies that did not examine pancreatic cancer. Our analysis demonstrates that integrating multi-omics data, particularly combining traditional CA19-9 with circulating tumor DNA (ctDNA) and metabolomic profiles (lipids, amino acids, carbohydrates), significantly improves diagnostic accuracy. Microbiome composition and genomic alterations further refine risk stratification and prognostic assessment. The synergistic use of these biomarkers may facilitate the development of screening, early diagnosis, risk stratification, and treatment optimization. However, the introduction of new diagnostic approaches into clinical practice requires additional verification, standardization and prospective clinical studies. Full article

The dissemination of antibiotic resistance genes (ARGs) in riverine ecosystems poses a pressing public health threat, while the mechanisms governing the assembly of the gut resistome in wild fish remain poorly elucidated. This study aimed to elucidate the distributional patterns of ARGs across multiple environmental compartments and to identify factors associated with their variation, particularly the contributions of mobile genetic elements (MGEs) and metal resistance genes (MRGs) to gut resistome variation. Metagenomic sequencing was conducted on 60 samples, comprising water, sediment, and gut contents from three wild fish species (Megalobrama terminalis, Aristichthys nobilis, and Coilia nasus) with distinct feeding habits, collected from four reaches of the Qiantang River basin. A total of 305 ARG subtypes belonging to 23 classes were identified. ARG composition differed significantly across environmental media and host species (permutational multivariate analysis of variance, PERMANOVA; p < 0.01), with host species identity as the primary structuring factor. Variance partitioning analysis (VPA) revealed that MGEs independently explained the largest fraction of ARG variation in A. nobilis (33.8%, p = 0.006), whereas MRGs dominated in C. nasus (33.3%, p = 0.005); in M. terminalis, MGEs and MRGs together accounted for 47.9% of the variation. Metagenomic assembly recovered 2622 ARG-carrying contigs, of which 28.3% (743) were predicted as plasmid sequences; physical co-localization among ARGs, MGEs, and MRGs was detected on both chromosomes and plasmids. Metagenomic binning validated the physical co-localization of ARG-MGE-MRG modules in genera such as Morganella and Burkholderia at the genome level, while plasmid-borne high-risk ARGs were identified in Aeromonas. Risk ranking further revealed significant enrichment of Rank II potentially high-risk ARGs (e.g., mcr-7.1, blaZ) in fish guts, carried by potential pathogens. These findings suggest that horizontal gene transfer involving MGEs and co-selection related to MRGs are closely associated with the fish gut resistome composition in a manner dependent on host ecology, providing a scientific basis for shifting riverine resistance management from concentration-based control toward the interruption of dissemination pathways. Full article

Germanium (Ge) is a high-tech critical metal typically hosted at trace levels in sphalerite, making its detection and characterization challenging in both primary ores and mine residues. This study presents a multi-scale analytical workflow combining laser-induced breakdown spectroscopy (LIBS), electron probe micro-analysis (EPMA), and multivariate statistics to detect, map and quantify Ge distribution in a representative Pb-Zn sample from the Les Malines deposit (France). µ-LIBS mapping enables rapid centimeter-scale screening at 15 µm resolution and identifies Ge-bearing domains over large areas, which are subsequently investigated at micrometer scale using EPMA chemical mapping and quantitative analyses. Results reveal a strong µm-scale heterogeneity of Ge distribution within sphalerite, with Ge systematically concentrated in an Fe-rich intermediate zonation associated with prismatic growth textures, while Cu/Cd/Ag are enriched in distinct collomorph domains. Multivariate statistical analyses (correlation matrices and PCA) confirm a strong geochemical structuring opposing an Fe/Ge association against a Cu/Cd/Ag pole. These findings demonstrate that Ge incorporation is controlled by localized growth conditions rather than bulk composition. The proposed workflow provides an efficient and scalable framework for exploration, enabling rapid targeting of critical metal enrichments and supporting their extension to multiple mineralization stages, Pb-Zn deposits, and other high-tech critical metals (HTCMs) such as Ga and In. Full article

Leaf litter decomposition is a key pathway for carbon transfer from forest ecosystems to soils and surface waters. Dissolved organic matter (DOM) released during early-stage leaching represents a potentially reactive fraction of this carbon pool; however, its molecular composition and short-term reactivity remain insufficiently characterised. This study provides a comparative characterisation of DOM released from composite leaf litter samples representing five common deciduous tree species (Betula pendula, Carpinus betulus, Alnus glutinosa, Populus tremula, and Quercus robur) under controlled laboratory conditions. Leaf material collected from multiple trees per species was pooled to obtain a single composite sample; therefore, replicate leaching experiments represent procedural rather than biological replication. DOM was isolated using solid-phase extraction (SPE) and analysed by gas chromatography–mass spectrometry (GC–MS) following trimethylsilyl (TMS) derivatisation, while chemical oxygen demand (COD) and biochemical oxygen demand (BOD₅) were used as indicators of oxidative reactivity and short-term biodegradability. The applied analytical approach captures a selective and operationally defined fraction of DOM, primarily low-molecular-weight and derivatisable compounds; therefore, the results are interpreted as semi-quantitative compositional fingerprints. Carbohydrates, phenolic compounds, and low-molecular-weight organic acids dominated the detected fraction of DOM, with differences observed among composite samples. The composite samples representing A. glutinosa and P. tremula contained higher relative proportions of carbohydrate-related compounds, whereas the composite samples representing B. pendula and C. betulus showed higher relative contributions of aromatic compounds. Apparent differences in BOD₅ were observed among composite samples; however, these observations likely reflect procedural variability rather than independent biological effects. The results indicate variability in DOM composition and apparent reactivity among composite litter samples under controlled laboratory conditions. Due to the lack of biological replication and the selective nature of the analytical approach, the findings should be interpreted as exploratory and not as evidence of generalised tree-species effects. Full article

Wireless body area networks (WBANs) for healthcare monitoring require age-of-information (AoI)-aware resource allocation under heterogeneous periodic and aperiodic traffic. Existing AoI-aware resource allocation methods can be broadly divided into centralized, decentralized, and hybrid approaches, but each has a structural limitation: centralized scheduling may allocate time slots to sources without newly generated samples, decentralized access may suffer from collision-induced delay under heavy contention, and fixed hybrid access may fail to adapt the scheduled and random access regions to the current traffic composition. To jointly address these limitations, this paper formulates a sample-wise weighted AoI minimization problem that accounts for source-specific sampling periods, transmission lengths, and priority weights, and proposes an online collaborative hybrid scheduler. The proposed method extracts traffic features at runtime, classifies sources as periodic or aperiodic, schedules periodic samples through contention-free access close to their sampling start times, and supports aperiodic samples through random access without pre-reserving slots. It further adapts the contention-free and random access regions according to the detected traffic composition. Simulation results show that the proposed scheduler reduces sample-wise weighted AoI compared with centralized and decentralized AoI schedulers by mitigating incorrect scheduling, reducing collision-induced delay, and improving superframe utilization. Full article

Background: Non-small cell lung cancer (NSCLC) remains the leading cause of cancer-related mortality worldwide despite advances in diagnosis and treatment. Increasing evidence suggests that alterations in the lung microbiome may contribute to NSCLC development and progression; however, findings remain inconsistent due to heterogeneous biological materials and methodological differences among studies. Therefore, this study aimed to characterize the lung tissue microbiome in NSCLC using a paired tissue-based approach. Methods: Thirty-two patients with NSCLC were enrolled. For each patient, two samples were collected: primary tumor tissue and matched macroscopically unchanged adjacent lung tissue. The V3-V4 region of the 16S rRNA gene was amplified and sequenced, followed by bioinformatic analysis using the QIIME2 pipeline. Results: Tumor tissues demonstrated lower alpha (Shannon H = 9.60, q = 0.001) and beta (Jaccard pseudo-F = 1.26, q = 0.015) diversity compared with adjacent controls, indicating reduced microbial complexity within the tumor microenvironment. Escherichia-Shigella was the most abundant detected genus (~12%) in both groups, although without a statistically significant difference. Analysis of microbiome variation in relation to spatial distance between sampled tissues revealed a strong trend toward significance (p = 0.07) with a substantial effect size (R² = 0.207). Conclusions: The observed microbiome alterations in NSCLC were more evident at the ecological level than in overall taxonomic composition, supporting a model of microbial community simplification rather than complete compositional replacement. Our findings also suggest that tumor-adjacent lung tissue may not represent a fully neutral control due to the local field effect. The relatively high abundance of Escherichia-Shigella indicates that this taxon may warrant further investigation in NSCLC microbiome studies. Full article

Epidermolysis bullosa (EB) comprises a group of rare inherited genodermatoses characterized by fragility and blistering of the skin and mucous membranes, chronic wounding, and significant morbidity including increased risk of squamous cell carcinoma in severe subtypes. Key unmet priorities include reducing diagnostic latency, establishing objective wound monitoring, enabling early detection of malignant transformation within chronic ulcerations, and developing therapies that durably modify disease progression. Artificial intelligence (AI) encompassing machine learning (ML), and deep learning (DL) is increasingly integrated into EB research and clinical practice to address these unmet needs. This structured narrative review synthesises current evidence on AI applications in EB spanning genetic diagnostics, wound assessment, inflammatory endotyping, drug repurposing, and emerging therapeutic technologies, and integrates evidence from registered clinical trials. In genomics, DL-based splicing prediction models and variant prioritisation frameworks accelerate pathogenic variant detection and reduce diagnostic latency. In wound care, convolutional neural networks-based platforms enable automated lesion segmentation and remote monitoring, while multimodal AI models predict healing trajectories and support stratification of wounds by chronicity. Computational transcriptomic analyses have identified candidate repurposing agents by reversing pathogenic gene expression signatures in EB tissue. Emerging convergence of AI with biosensors-integrated wound dressings and three-dimensional bioprinting of genetically corrected skin substitutes represents a transformative future direction. Translational barriers include limited EB-specific training datasets, algorithmic bias across diverse skin phototypes, the interpretability deficit of DL systems, and evolving regulatory frameworks for AI as a medical device. Expansion of internationally interoperable EB disease registries with standardised wound imaging protocols is identified as the single most impactful intervention to accelerate AI adoption. A minimum endpoint set for AI-assisted EB wound assessment, incorporating wound area trajectory, wound type classification, tissue composition, and paired patient-reported pain and itch scores, is proposed to standardise outcome reporting across future studies. Full article

Frankliniella occidentalis is a major threat to lettuce production due to its role in transmitting Impatiens necrotic spot virus (INSV). To better understand the potential risk of INSV outbreaks in Florida lettuce production areas, this study aimed to assess thrips population abundance and species composition. Five methods, whole plant sampling, yellow sticky traps, blue sticky traps, yellow pan traps, and blue pan traps, were evaluated for thrips collection. Traps were placed in one research and two commercial lettuce farms for two growth cycles, Fall 2023 and Spring 2024. Sticky traps collected the highest number of thrips followed by pan traps, and whole plant sampling. Although sticky traps collected the highest number of thrips, specimens were in poor condition and not easily identifiable to species, which is an important step in determining the risk of INSV outbreaks. In contrast, pan traps collected the second highest number of thrips, and preserved specimens well for later identification. Overall, at least eleven species were identified using these sampling methods, the abundant species were Frankliniella bispinosa, Microcephalothrips abdominalis, and Leucothrips piercei. Vectors of orthotospoviruses, F. occidentalis, F. fusca, and F. schultzei, were also detected, underscoring the importance of continued thrips surveillance to safeguard Florida lettuce from thrips and thrips-transmitted viruses. Full article

Improving soybean plant architecture is critical for enhancing yield potential. To dissect the genetics of related traits, a recombinant inbred line population of 175 F₉:₁₂ families (derived from Glycine max cultivars Jidou 12 [female] × Ji NF58 [male]) was used for quantitative trait locus (QTL) mapping. Four key traits—plant height, bottom pod height, node number on main stem, and branch number—were analyzed across six environments (two growing seasons × three locations) via two methods: composite interval mapping (CIM, QTL Cartographer v2.5) and mixed-model-based composite interval mapping (MCIM, QTLNetwork 2.0). A total of 22 stable QTLs were detected, with phenotypic variation explained (PVE) of 1.2–52.5%. Co-localized QTLs (due to significant trait correlations) concentrated in three genomic intervals: Satt286-Sat_251 (LG C2/chromosome 06), Satt156-Satt229 (LG L/chromosome 19), and Satt581-Sat_190 (LG O/chromosome 10). A novel QTL (qBPH-O-2) for bottom pod height was identified on LG O. Major QTLs with QTL-by-environment (QE) interactions were found on LG A1 (plant height, node number on main stem) and qBN-C2-1 (branch number, high additive effects + QE interactions). These findings support marker-assisted selection (MAS), targeted plant architecture improvement, and gene pyramiding in soybean breeding. Full article

Gamma irradiation is one of the techniques widely authorized for the decontamination of dried herbs and spices. Its effect on the functional properties of essential oils, however, remains incompletely characterized. In this study, we examined the impact of gamma irradiation (at 5, 15, and 25 kGy) on the phytochemical composition, antimicrobial activity, antioxidant capacity, and insect-repellent activity of Cymbopogon citratus essential oil. The GC-MS analysis revealed that the citral-dominant chemotype remained stable across all irradiation doses, with geranial and neral constituting approximately 62–63% of the volatile profile. The antibacterial assays were done on five bacterial strains (Staphylococcus aureus, Bacillus subtilis, Streptococcus spp., Pseudomonas aeruginosa, and Klebsiella pneumoniae). Inhibition zones showed no statistically significant differences across irradiation doses (p ≥ 0.05), while MIC (75–100 µg/mL) and MBC (125–150 µg/mL) values remained constant across all doses. DPPH, ABTS, and FRAP antioxidant assays revealed no dose-dependent changes (DPPH IC₅₀: 688–703 µg/mL; ABTS IC₅₀: 18–22 µg/mL; FRAP: 505–517 µg/mL ascorbic-acid equivalents). The essential oil exhibited pronounced repellent activity (87–99%) against adult Tribolium confusum beetles at 0.125 µL/cm², persisting for 24 h and unaffected by irradiation. Molecular docking of the major constituents (geranial, neral, geraniol, and β-myrcene) against key target proteins (3N7H, 3NVY, 4URM, and 8BN6) provided predictive support consistent with the observed activities, indicating plausible molecular interactions rather than confirmed target engagement. In silico ADME and toxicity profiling indicated favorable predicted pharmacokinetic properties and no major in silico toxicity alerts for the four modeled constituents. Taken together, these findings indicate that, under the conditions tested, gamma irradiation at food-decontamination doses produced no major shifts in composition and no statistically detectable changes in the measured bioactivities of C. citratus essential oil. Full article

Solid-state fermentation of burdock roots with Eurotium cristatum was performed to enhance their functional properties. Fermentation induced marked compositional remodeling, resulting in a 1.37-fold increase in protein content compared to unfermented controls. Antioxidant capacities were markedly enhanced. DPPH and ABTS radical-scavenging activities both exceeded 90%, and intracellular ROS levels in Caenorhabditis elegans were reduced by 62.7%. Phenolic profiling via UPLC-ESI-MS/MS identified and quantified 74 phenolic compounds across samples; notably, 10 flavonoids were exclusively detected in fermented burdock roots, indicative of microbial biotransformation. Correlation analysis integrating phenolic abundance with all three antioxidant endpoints revealed 11 compounds significantly associated with enhanced bioactivity. Among these, sinapic acid, 3-hydroxyflavone, liquiritigenin, and sakuranetin exhibited positive correlations with all three antioxidant measures. Prostaglandin G/H synthase 1 (PTGS1) and PTGS2 were identified as shared antioxidant-relevant targets, with PTGS1 highlighted due to its constitutive role in prostaglandin biosynthesis. Importantly, 3-hydroxyflavone, liquiritigenin, and sakuranetin were newly emerged following fermentation, providing direct evidence that E. cristatum mediates the synthesis or structural modification of key flavonoids, thereby augmenting the antioxidant chemical profile and functional efficacy of burdock roots. Full article

To address prevalent industrial challenges, including the high cost of fabricating microstructures via photolithography and 3D printing, impurity residues easily generated by conventional physical/chemical pore-forming techniques, and the limited sensitivity of regular capacitive sensors, this paper innovatively proposes an integrated low-temperature in situ gas foaming strategy using ammonium bicarbonate for the fabrication of porous TPU-based ionic gels. Relying on the complete gaseous decomposition property of ammonium bicarbonate upon heating, a three-dimensionally interconnected continuous porous network is spontaneously constructed inside the polymer matrix. Thermoplastic polyurethane (TPU) is selected as the continuous polymer phase, and [EMIM][TFSI] imidazolium ionic liquid is blended as the ion source to synthesize composite ionic gel substrates. A PDMS composite slurry filled with graphene is employed to prepare flexible substrates, followed by low-temperature oxygen plasma surface modification to introduce polar functional groups such as hydroxyl and carboxyl onto electrode surfaces. A standard sandwich-structured ionic pressure sensor with the configuration of “top modified electrode—porous ionic gel dielectric layer—bottom modified electrode” is finally assembled. The porous framework and modified electrodes constitute a dual synergistic enhancement system: the porous structure markedly reduces the equivalent elastic modulus of the gel and improves its compressive deformation capacity; polar-modified electrodes optimize the interfacial compatibility between electrodes and gels, shorten ion migration paths and lower interfacial contact resistance. Systematic calibration of multiple batches of parallel samples reveals that the as-fabricated sensor achieves a high sensitivity of 25.3 kPa⁻¹ across the full measuring range from 0 to 1000 kPa with a linear fitting coefficient R² = 0.992. The loading response time and unloading recovery time of the device are 60 ms and 80 ms respectively, with a performance degradation of less than 3% after 1000 consecutive loading–unloading cycles, featuring low hysteresis error and excellent signal repeatability. Multi-scenario in vivo wearable tests on human subjects verify that the device can precisely capture subtle fluctuations of radial artery pulse and periodic laryngeal deformation during swallowing, distinguish characteristic waveform patterns of various English words according to differences in vocal cord vibration, and accurately detect bending motions when attached to finger joints. The entire fabrication process adopts common chemical raw materials and standard laboratory equipment without expensive micro-nano processing facilities, featuring convenient raw material procurement and high process fault tolerance, which enables large-area coating-based mass production. This work delivers a novel technical route for the low-cost large-scale production of high-performance ionic flexible sensors and bears significant industrialization reference value for applications in wearable medical monitoring, bionic robotic electronic skin, flexible human–machine interactive touch panels and other related fields. Full article

The worldwide spread of semiconductor devices has driven a surge in electronic waste (e-waste), which reached 62 million metric tons in 2022 and is projected to exceed 80 million metric tons by 2030. E-waste contains hazardous substances such as cadmium and mercury, yet also represents a $57 billion annual opportunity through the recovery of valuable and critical raw materials (CRMs). However, formal recycling rates remain stagnant at 22.3%, largely due to limitations of current automated sorting methods. These systems primarily rely on visible-light (RGB) imaging, which lacks the spectral resolution needed to distinguish chemically similar polymers, complex metal alloys, and composite substrates on printed circuit boards (PCBs). This paper presents a multidisciplinary synthesis of AI-driven detection and classification for e-waste, bridging materials science and computer vision through three interconnected themes. 1. Material and Economic Context: The toxicological risks and economic drivers of semiconductor recycling are characterized, framing fine-grained material identification as essential for a circular economy. 2. Multispectral Sensing & Fusion: Sensing modalities such as near-infrared (NIR), hyperspectral imaging (HSI), and X-ray fluorescence (XRF) are assessed, and sensor fusion strategies, including early, late, and intermediate fusion, are reviewed for high-throughput industrial settings. 3. Deep Learning Benchmarking: 11 publicly available PCB datasets are analyzed, and the YOLO series (YOLOv3–YOLOv12) is compared with leading non-YOLO detectors, including Faster R-CNN, RT-DETR-L, and RetinaNet. The results show that while YOLOv9s achieves a peak mAP@0.5 of 56.5% and YOLOv11s offers an optimal industrial profile (37.2% mAP@0.5:0.95 at 115 ms edge inference), all RGB-based models fail to detect visually ambiguous surface-mount devices (SMDs), with mAP values below 12%. This confirms a performance ceiling for purely visual systems. The review concludes that transitioning from RGB-centric to multispectral fusion architectures is the primary research frontier and proposes a roadmap for standardized multimodal datasets and edge-deployable fusion models to enable next-generation, high-recovery automated recycling. Full article

Rickettsia spp. is ubiquitous in nature and capable of causing diseases of varying severity. The most extensive group comprises the spotted fever group (SFG) Rickettsiae, the members of which are predominantly transmitted by ticks. The expansion of tick habitats observed in recent decades poses an increasing threat of dissemination of tick-borne infections into regions previously considered non-endemic. The aim of this study was to determine the prevalence of SFG Rickettsiae in ixodid ticks collected in Northwest Russia and to characterize the species diversity of these pathogens within the study area. Questing adult ixodid ticks (n = 4566) were collected from eight regions of Northwest Russia (Arkhangelsk, Kaliningrad, Leningrad, Novgorod, Pskov and Vologda Regions, as well as the Republic of Karelia and St. Petersburg) in 2023 to 2025 (from April to September). The species composition included Ixodes ricinus (n = 1683), Ixodes persulcatus (n = 2404), and Dermacentor reticulatus (n = 479). Genomic DNA was extracted from individual ticks and screened for SFG Rickettsiae using real-time PCR, followed by conventional PCR targeting the gltA, ompA, ompB, and sca4 (gene D) genes. Nucleotide sequences obtained for a subset of positive samples for the various genes were analyzed. The overall prevalence of SFG Rickettsiae was 12.6% (95% CI: 11.7–13.6). Circulation of the following species was detected: Rickettsia helvetica, Rickettsia conorii subsp. raoultii, Candidatus Rickettsia tarasevichiae, Rickettsia monacensis, and Rickettsia felis. The findings indicate considerable species diversity of SFG Rickettsiae in natural foci of Northwest Russia. Rickettsia monacensis was detected in ixodid ticks within the study area for the first time, and R. felis was identified in Russia for the first time. Full article