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Self-propelled thiol functionalized SiO2@MnO2 microstructure for dynamic extraction of pollutants from the aquatic medium
Artificial intelligence enabled performance evaluation of an enhanced SPR biosensor for malaria diagnosis
Correction: Measuring dissolved oxygen in Miso for forensic medicine and semisolid food analysis
Insights into demographic and cultural influences on the oral microbiome from historical Japanese dental calculus
Proximity‐Induced Transfer of a Mass Tag Enables Direct Profiling of Active Matrix Metalloproteases
ABSTRACT Conventional activity‐based probes in activity‐based protein profiling (ABPP) require enrichment or reporter tags for detection, which limits sensitivity and multiplexing. Here, we present an enrichment‐free chemoproteomic approach that enables direct mass spectrometric detection by Matrix‐Assisted Laser Desorption/Ionization (MALDI) of active proteases. An active‐site–directed affinity probe transfers, through a proximity‐induced reaction, a MALDI‐detectable α‐cyano‐4‐hydroxycinnamic acid (CHCA) tag exclusively to catalytically active forms of matrix metalloproteases (MMPs). The CHCA label enhances ionization efficiency and markedly improves signal‐to‐noise ratios, allowing confident identification of CHCA‐labelled peptides under discriminating analytical conditions. Each active metalloprotease is thereby, associated with a distinct set of CHCA signature peptides, defining its activity fingerprint. This workflow achieves multiplexed and quantitative activity profiling of MMPs, directly in complex proteomes. This design expands ABPP into the mass spectrometry domain and establishes a robust platform for activity‐based enzyme detection.
Dynamical analysis and exact solitary wave solutions of $$(2+1)$$-dimensional integro-differential Jaulent-Miodek equation using two analytical schemes
Comparative analysis of the sputum microbiota in different COPD clinical states
Abstract Chronic obstructive pulmonary disease (COPD) is a well-known respiratory illness. COPD patients oscillate between a stable state and an exacerbated state that leads to disease deterioration. Studies suggest that respiratory microbiome dysbiosis plays a vital role in COPD pathogenesis. However, the exact microbial composition among different clinical states of COPD is still elusive. To determine and compare the respiratory microbiota composition in different COPD clinical states, namely, the stable state (S-COPD) and the acute exacerbated state (AE-COPD). In this prospective study, 74 samples were collected from COPD patients. The sputum microbiota was analyzed via 16 S rRNA gene sequencing, and only 35 samples were included due to bad reads or not in accordance with inclusion criteria: S-COPD patients ( n = 18), and AE-COPD patients ( n = 17). Bioinformatics analysis was used to determine changes in the microbiota among the comparison groups. The most abundant phyla among all the samples were Proteobacteria, Fusobacteria, Firmicutes, and Actinobacteria, with Paracoccus , Streptomyces Leptotrichia Fusobacterium and Ruminococcaceae being the most prevalent genera. Dissimilarity in abundance across the studied COPD states was observed, with significantly greater abundance of Proteobacteria and Fusobacteria in S-COPD patients and greater abundance of Firmicutes in AE-COPD patients at the phylum level. At the genus level, Paracoccus , Fusobacterium , Streptococcus , Haemophilus , and Moraxella were significantly different between the two groups and were more prevalent in S-COPD, whereas Cellulosilyticum , Streptomyces , Leptotrichia , Ruminococcaceae_UCG_014 , and Atopobium were more prevalent in exacerbated individuals. Alpha diversity revealed greater diversity in stable versus exacerbated patients, and a PCoA plot of Bray‒Curtis and weighted UniFrac distances revealed that stable patients were highly clustered, whereas exacerbated patients were more disseminated. At the genus level, LEfSe analysis revealed the dominance of Cellulosilytic , Liptotrichia , and Streptomyces in the AE-COPD group, whereas the S-COPD group microbiome was dominated by the genera Paracoccus , Fusobacterium , Streptococcus Haemophilus , and Moraxella ( p < 0.05). The results of the present study suggest that COPD patients have unique microbial profiles that differ across different states, with increased abundances of Proteobacteria, chiefly Paracoccus . These findings need more research to clarify the definite role of microbiome dysbiosis in COPD pathogenesis.
Spatiotemporal characterization of a distinct nectin-3+ cell population in the developing and adult mouse retina
Abstract While Nectin-3 is a recognized marker for maintaining stem and progenitor “side populations” in non-neural epithelia, its role and distribution within the mammalian retina remain largely uncharacterized. Identifying such rare populations is a critical step toward unlocking the regenerative potential of the retina after injury. This study provides a comprehensive spatiotemporal characterization of Nectin-3 + cells from embryonic development through adulthood. Using fluorescence-activated cell sorting, a distinct Nectin-3 + population was identified across all examined stages, representing 1.27% ± 0.24 of viable cells at E18, peaking at P5 (3.62% ± 0.58), and persisting in the adult retina (1.43% ± 0.14). At this mature stage, triple-positive (Nectin-3 + /CD117 + /Sca-1 + ) cells displayed a robust molecular signature defined by the significant up-regulation of Chx10 , Nestin , Pax6 , Rax , and the RGC marker Rbpms , despite a lack of synaptic transcript ( Syp ) enrichment. Morphological analysis via confocal microscopy demonstrated that Nectin-3 + cells undergo dynamic changes during development. In the adult retina, these cells showed colocalization with RBPMS, while remaining distinct from the Müller glial scaffold. Principal Component Analysis highlighted that adult cells retain transcriptional features typically associated with early development, indicating a specialized neuronal identity that is molecularly distinct from the surrounding retinal environment. By defining this fraction, we provide a practical tool for studying retinal diversity and a foundation for investigating the potential regenerative capacity of these rare cells.
Explaining reported generative AI engagement in higher education: an extended TAM with ethical compatibility and reliance-based trust
Design and implementation of a traffic monitoring, collision awareness and advisory prototype for hot air balloons
A data-driven performance index for center forwards in the English Premier League
Abstract Despite the growing availability of performance data in professional soccer, existing player rating systems lack positional specificity and fail to capture the multidimensional demands of center forwards. Building on composite index and regularized regression approaches in sports analytics, this study develops a Soccer Performance Index (SPI) tailored to center forwards in the English Premier League (EPL) across the 2021–2024 seasons. Data from 194 player-season observations were analysed using 109 Wyscout performance metrics as predictors. Three SPI versions were constructed using Lasso and Ridge regression: one based on market value, one on the InStat Index, and a hybrid combining both. The hybrid model, weighting market value at 70% and the InStat Index at 30%, achieved the strongest explanatory fit (R 2 = 0.676, RMSE = 0.669), accounting for approximately 68% of variance in player valuation — a result consistent with the complexity inherent in behavioural and performance modelling contexts. xG per 90, shots, and key passes per 90 emerged as the strongest predictors. Methodological considerations include the use of a minimum participation threshold (≥ 20 matches), which may introduce survivorship bias; the treatment of player-season observations as independent units, which does not account for repeated-measures dependence; and the reliance on internal validation only. The SPI demonstrates ecological relevance by integrating both financial and on-field performance indicators, offering a structured framework for talent identification and recruitment support in applied professional contexts.
A shared-control ultrasound-guided robotic system for liver puncture under respiratory motion
A distribution-level statistical framework for reliable pipeline leak detection using multi-domain signal analysis
Predictive value of serum biomarkers for survival in melanoma: a systematic review and meta-analysis
Abstract Malignant melanoma is responsible for most skin cancer–related deaths due to its unpredictable behavior. Serum biomarkers have been widely investigated to improve prognostic assessment, yet their clinical utility remains inconclusive. This systematic review and meta-analysis evaluated the prognostic significance of serum biomarkers in melanoma. Following PRISMA guidelines and a registered protocol (PROSPERO: CRD42023486532), PubMed, EMBASE, and CENTRAL were searched for studies assessing biomarkers and survival outcomes. Univariate analyses revealed that elevated levels of LDH (HR 2.29, 95%-CI:1.97–2.67), S100B (HR 2.52, 95%-CI:1.59–3.99), circulating tumor DNA (ctDNA) (HR 2.61, 95%-CI:1.90–3.58), neutrophil-to-lymphocyte ratio (NLR) (HR 2.34, 95%-CI:1.86–2.93), and interleukin-6 (HR 3.11, 95%-CI:2.44–3.96) were significantly associated with reduced overall survival. Similarly, higher levels of LDH (HR 2.04, 95%-CI:1.68–2.47), S100B (HR 1.94, 95%-CI:1.39–2.70), ctDNA (HR 2.57, 95%-CI:1.95–3.39), and NLR (HR 2.38, 95%-CI:1.52–3.73) predicted shorter progression-free survival. These associations persisted in multivariable-adjusted analyses for LDH, ctDNA, NLR, IL-6, S100B, and CRP supporting their predictive relevance. LDH remains a reliable and cost-effective biomarker, while NLR may provide complementary prognostic information in patients receiving immune checkpoint inhibitors. Emerging biomarkers such as ctDNA demonstrate promising prognostic potential, but further evaluation is required before routine clinical implementation.
Mothers’ attitudes toward exclusive breastfeeding explored using a questionnaire based on the transtheoretical model in an observational study
Factors influencing clinical research participation in ophthalmology: a mixed-methods study in keratoconus
Abstract Keratoconus (KC) disproportionately affects young and underserved populations, yet clinical research participation remains unevenly distributed. To inform equitable recruitment strategies, we investigated demographic, behavioral, and perceptual factors influencing engagement in a non-interventional KC study. We compared demographic and socioeconomic profiles of patients solicited for participation in a non-interventional clinical study involving the laboratory analysis of excess tissue produced during a corneal cross-linking procedure. We conducted structured telephone surveys of participants and non-participants to assess barriers, facilitators, and attitudes toward clinical research. Here we show that, compared to participants, non-participants of the non-interventional KC study were older (33.2 ± 10.3 vs. 27.6 ± 7.3, p = 0.001) and predominantly male (90.2% vs. 64.8%, p = 0.003), while socioeconomic indices did not differ significantly. Prior clinical research participation strongly predicted future willingness to participate ( p = 0.01). Key barriers included concerns about side effects, insurance copays, and job flexibility. Facilitators included transportation assistance, provider-led information sessions, and work absence documentation. Despite participants and non-participants sharing similar preferred language profiles, non-participants were significantly more likely to report availability of translated materials as important interventions ( p = 0.04). Demographic variables alone did not predict participation. Behavioral and contextual factors, rather than fixed demographics, shaped clinical research engagement. Strategies such as personalized provider outreach, logistical support, and culturally tailored materials may improve recruitment and retention in KC clinical research; supporting participant-centered frameworks that address practical needs and readiness to engage, particularly in underserved populations.
Heterodera schachtii enolase does not elicit canonical immune responses in Arabidopsis thaliana
Abstract Enolase (2-phospho-D-glycerate hydrolase, EC 4.2.1.11) is a conserved glycolytic enzyme that catalyzes the reversible dehydration of 2-phosphoglycerate to phosphoenolpyruvate. In animal- and entomopathogenic nematodes, enolase has been shown to interact with host organisms and activate immune responses. In the plant-parasitic cyst nematode Heterodera schachtii , an established model species, enolase is hypothesized to be similarly exposed to host plant tissues during infection, based on its identification in the secretomes of related plant-parasitic nematodes. Unlike animals, plants lack an adaptive immune system and instead rely on innate immune responses to detect pathogen-derived molecules. Therefore, we investigated whether H. schachtii enolase can activate plant immune responses and influence growth and development in its host, Arabidopsis thaliana . Recombinant H. schachtii enolase was heterologously expressed in Escherichia coli and confirmed to be enzymatically active. Treatment of A. thaliana seedlings with purified enolase did not induce reactive oxygen species production, a hallmark of early plant immune activation. A concentration of 85 $$\upmu$$ g/ml had no effect on plant growth; only at the highest applied concentrations (425–850 $$\upmu$$ g/ml) was shoot growth reduced, while root growth remained unaffected. Consistently, quantitative PCR analysis of canonical defense marker genes ( FRK1 , NHL10 , PAD3 , CYP81F2 , and JAZ10 ) showed no robust transcriptional activation of plant immune pathways. Together, these results indicate that H. schachtii enolase does not function as an elicitor of canonical plant immune responses.