Search Results (971)

Large Language Models (LLMs) promise to enhance clinical decision-making, yet empirical studies reveal a paradox: physician performance with LLM assistance shows minimal improvement or even deterioration. This failure stems from an “acquiescence problem”: current LLMs passively confirm rather than challenge clinicians’ hypotheses, reinforcing cognitive biases such as anchoring and premature closure. To address these limitations, we propose a Dialogic Reasoning Framework that operationalizes Dialogical AI principles through a prototype implementation named “Diagnostic Dialogue” (DiDi). This framework operationalizes LLMs into three user-controlled roles: the Framework Coach (guiding structured reasoning), the Socratic Guide (asking probing questions), and the Red Team Partner (presenting evidence-based alternatives). Built upon Retrieval-Augmented Generation (RAG) architecture for factual grounding and traceability, this framework transforms LLMs from passive information providers into active reasoning partners that systematically mitigate cognitive bias. We evaluate the feasibility and qualitative impact of this framework through a pilot study (DiDi) deployed at Centro Chirurgico Toscano (CCT). Through purposive sampling of complex clinical scenarios, we present comparative case studies illustrating how the dialogic approach generates necessary cognitive friction to overcome acquiescence observed in standard LLM interactions. While rigorous clinical validation through randomized controlled trials remains necessary, this work establishes a methodological foundation for designing LLM-based clinical decision support systems that genuinely augment human clinical reasoning. Full article

Background/Objectives: Functional lumen imaging probe (FLIP) panometry allows real-time assessment of the esophagogastric junction opening and esophageal body contractile activity during an endoscopic procedure. Despite the development of the Dallas Consensus, FLIP panometry analysis remains complex. Artificial intelligence (AI) models have proven their benefit in high-resolution esophageal manometry; however, data on their role in FLIP panometry are scarce. This study aims to develop an AI model for automatic classification of motility patterns during a FLIP panometry exam. Methods: A total of 105 exams from five centers from both the European and American continents were included. Several machine learning models were trained and evaluated for detection of FLIP panometry patterns. Each exam was classified with an expert consensus-based decision according to the Dallas Consensus, with division into a training and testing dataset in a patient-split design. Models’ performance was evaluated through their accuracy and area under the receiver-operating characteristic curve (AUC-ROC). Results: Pathological planimetry patterns were identified by an AdaBoost Classifier with 84.9% accuracy and a mean AUC-ROC of 0.92. Random Forest identified disorders of the esophagogastric junction opening with 86.7% accuracy and an AUC-ROC of 0.973. The Gradient Boosting Classifier identified disorders of the contractile response with 86.0% accuracy and an AUC-ROC of 0.933. Conclusions: In this study, integrating exams with different probe sizes and demographic contexts, a machine learning model accurately classified FLIP panometry exams according to the Dallas Consensus. AI-driven FLIP panometry could revolutionize the approach to this exam during an endoscopic procedure, optimizing exam accuracy, standardization, and accessibility, and transforming patient management. Full article

The increasing sophistication of social bots demands advanced simulation frameworks to model potential vulnerabilities in detection systems and probe their robustness.While existing studies have explored aspects of social bot simulation, they often fall short in capturing key adversarial behaviors. To address this gap, we propose a simulation framework that jointly incorporates both realistic behavioral mimicry and adaptive inter-bot coordination. Our approach introduces a human-like behavior module that reduces detectable divergence from genuine user activity patterns through distributional matching, combined with a coordination module that enables strategic cooperation while maintaining structural stealth. The effectiveness of the proposed framework is validated through adversarial simulations against both feature-based (Random Forest) and graph-based (BotRGCN) detectors on a real-world dataset. Experimental results demonstrate that our approach enables bots to achieve remarkable evasion capabilities, with the human-like behavior module reaching up to a 100% survival rate against RF-based detectors and 99.1% against the BotRGCN detector. This study yields two key findings: (1) The integration of human-like behavior and target-aware coordination establishes a new paradigm for simulating botnets that are resilient to both feature-based and graph-based detectors; (2) The proposed likelihood-based reward and group-state optimization mechanism effectively align botnet activities with the social context, achieving concealment through integration rather than mere avoidance. The framework provides valuable insights into the complex interplay between evasion strategies and detector effectiveness, offering a robust foundation for future research on social bot threats. Full article

Defect engineering has been demonstrated to be an attractive strategy to improve the catalytic performance of g−C₃N₄−based catalysts. Herein, three graphite carbon nitrides (labeled “CN”) containing a certain number of cyano groups and nitrogen vacancies are prepared successfully by calcination of the dicyandiamide−based CN in the presence of KOH, and the performances of the corresponding Pd−based catalysts are evaluated by using the formic acid (FA) dehydrogenation as a probe reaction. The characterizations of X−ray diffraction (XRD), scanning transmission electron microscopy (STEM), X−ray photoelectron spectra (XPS), hydrogen temperature−programmed desorption (H₂−TPD), and hydrogen spillover experiments indicate that the high catalytic activity of Pd/CNK−0.5 is mainly attributed to its high efficient hydrogen spillover, relatively high dispersity of Pd species, and basicity due to the introduction of a proper amount of cyano groups and nitrogen vacancies. The low initial activity of Pd/CNK−0.75 may mainly be ascribed to its low hydrogen spillover ability and the strongly chemisorbed hydrogen on Pd single atoms or small clusters. Full article

Essential oils (EOs) are complex volatile mixtures that exhibit antioxidant activity through both chemical and biological pathways. Phenolic constituents act as efficient chain-breaking radical-trapping antioxidants, whereas some non-phenolic terpenes operate through distinct mechanisms. Notably, γ-terpinene functions via a “radical export” pathway, generating hydroperoxyl radicals that intercept lipid peroxyl radicals and accelerate chain termination. Recent methodological advances, such as inhibited autoxidation kinetics, oxygen-consumption assays, and fluorescence-based lipid peroxidation probes, have enabled more quantitative evaluation of these activities. Beyond direct radical chemistry, EOs also regulate redox homeostasis by modulating signaling networks such as Nrf2/Keap1, thereby activating antioxidant response element–driven enzymatic defenses in cell and animal models. Phenolic constituents and electrophilic compounds bearing an α,β-unsaturated carbonyl structure may directly activate Nrf2 by modifying Keap1 cysteine residues, whereas non-phenolic terpenes likely depend on oxidative metabolism to form active electrophilic species. Despite broad evidence of antioxidant efficacy, molecular characterization of EO–protein interactions remains limited. This review integrates radical-chain dynamics with redox signaling biology to clarify the mechanistic basis of EO antioxidant activity and to provide a framework for future research. Full article

Development of new biologically active materials based on natural products has, over the years, attracted considerable attention due to their effectiveness in human health and disease. Polyphenolic compounds, particularly flavonoids, provide a wide range of health benefits, including antioxidant, anti-inflammatory, anticancer, and antibacterial properties. A series of novel Schiff base derivatives of flavonoids with amino-containing linkers was successfully designed and synthesized through condensation reactions. Naringin and naringenin derivatives with diamines, including ethylenediamine (EDA), 1,3-diamino-2-propanol (DA-2-PrOH), tetramethylenediamine (TMEDA), pentamethylenediamine (PMEDA), as well as polyamines spermidine (SPD) and spermine (SPM), were synthesized and well-characterized through FT-IR, UV–Visible, ESI–MS, ¹H and ¹³C NMR spectroscopy, and elemental analysis. The so confirmed and well-characterized derivatives were subjected to photoluminescence studies, exhibiting enhanced activity, especially for naringin-based derivatives, and quenching in some others, thus verifying the significance of chemically modifying the conjugated systems of these molecules. Their biological activity was examined in the case of their antimicrobial efficacy against two Gram (+) (Staphylococcus aureus and Bacillus cereus) and two Gram (−) (Escherichia coli and Xanthomonas campestris) bacterial strains. Antibacterial screening projected selectivity of modified flavonoids against E. coli, proposing new “dense” flavonoid-(poly)amine materials as multifunctional antimicrobial agents and fluorescent probes. Full article

The practical deployment of Orthogonal Frequency Division Multiplexing Passive Optical Networks (OFDM-PONs) is hindered by the lack of a Medium Access Network (MAC) protocol capable of managing their flexible, distance-dependent data rates, despite their high spectral efficiency. This paper proposes and validates a novel rate-adaptive, Time Division Multiplexing (TDM)-based MAC protocol for OFDM-PON systems. A key contribution is the design of a three-layer header frame structure that supports multi-ONU data scheduling with heterogeneous rate profiles. Furthermore, the protocol incorporates a unique channel probing mechanism to dynamically determine the optimal transmission rate for each Optical Network Unit (ONU) during activation. The proposed Optical Line Terminal (OLT) side MAC protocol has been fully implemented in hardware on a Xilinx VCU118 FPGA platform, featuring a custom-designed ring buffer pool for efficient multi-ONU data management. Experimental results demonstrate robust upstream and downstream data transmission and confirm the system’s ability to achieve flexible net data rate switching on the downlink from 8.1 Gbit/s to 32.8 Gbit/s, contingent on the assigned rate stage. Full article

MicroRNAs, pivotal regulators of gene expression and physiology, serve as reliable biomarkers for early cancer diagnosis and therapy. As one of the earliest discovered miRNAs in the human genome, miRNA-21 provides critical information for early cancer diagnosis, drug therapy, and prognosis. In this work, we harness CRISPR as a bridge to integrate target-induced self-priming hairpin isothermal amplification (SIAM) with terminal transferase (TdT) polymerization labeling, constructing a facile, straightforward electrochemical biosensor for sensitive miRNA-21 detection. Unlike conventional single-strand template-based exponential amplification (EXPAR), the SIAM hairpin undergoes target triggered intramolecular conformational change, initiating extension and strand displacement reactions that suppress nonspecific dimer formation and lower background current. Notably, the assay requires only a single probe, enabling unidirectional signal amplification while nonspecific reactions caused by system complexity. The generated SIAM products activate the Cas12a/crRNA complex to trans-cleave PO₄³⁻ modified single-stranded DNAs (ssDNAs); the resulting 3′ hydroxyl ssDNAs are subsequently labeled by TdT, with the assistance of SA-HRP catalyzing hydrogen peroxide, achieving robust signal amplification. Under optimized conditions, the cathodic current exhibits a logarithmic relationship with miRNA concentrations from 20 fM to 5.0 × 10⁸ fM, with a detection limit of 9.2 fM. The biosensor successfully quantified miRNA-21 in commercial serum samples and biological lysates, demonstrating its potential for cancer diagnostics and therapy. Full article

Alkaline water electrolysis is a widely researched method for hydrogen generation due to its low cost, scalability and its advantage of being able to produce hydrogen using only renewable energy. Enhancing the efficiency of electrolysis systems relies mainly on the development of high-performance ion-conductive membranes. The incorporation of ceramic fillers into polyvinyl alcohol (PVA) membranes as a composite material has shown considerable promise in enhancing the performance of electrolyzers. In this work, novel composite separator membranes for use in alkaline electrolyzers were developed from aqueous PVA solutions and physically crosslinked through a freeze–thawing process. To enhance the membrane properties, two types of ceramic fillers—titanium dioxide (TiO₂) and zirconium dioxide (ZrO₂)—were incorporated into the starting crosslinking solution. The thermal stability of these membranes was studied by a Differential Scanning Calorimetry (DSC) technique where we can conclude that addition of TiO₂ and ZrO₂ significantly influences the thermal properties of PVA membranes. These metal oxides enhance thermal stability, as shown by the shift in exothermic peaks toward higher temperatures and alterations in the degradation mechanism, evidenced by changes in the intensity and number of DSC peaks. The effect is concentration-dependent for TiO₂, where higher contents produce more pronounced yet increasingly complex thermal behavior. Compared with commercial membrane (Zirfon Perl), these types of membranes exhibit better electrochemical performance at ambient temperature and pressure; however, the process of preparation is simpler, reducing the cost of the hydrogen production process. The polarization curves (U-I curves) indicated a decrease in voltage with the addition of an ionic activator based on cobalt and molybdenum. Conductivity measurements performed using electrochemical impedance spectroscopy utilizing a two-probe method revealed that PVA membranes with TiO₂ exhibit ionic conductivity comparable to that of the commercial membrane. Compared to the commercial membrane, these types of membranes demonstrated similar mechanical properties and improved electrochemical performance at ambient temperature and pressure, along with a simplified production process and lower cost of hydrogen production. Full article

Distributed fiber-optic photoacoustic non-destructive testing (DFP-NDT) represents a paradigm shift from passive sensing to active probing, fundamentally transforming structural health monitoring through integrated fiber-based ultrasonic generation and detection capabilities. This review systematically examines DFP-NDT’s evolution by following the technology’s natural progression from fundamental principles to practical implementations. Unlike conventional approaches that require external excitation mechanisms, DFP-NDT leverages photoacoustic transducers as integrated active components where fiber-optical devices themselves generate and detect ultrasonic waves. Central to this technology are photoacoustic materials engineered to maximize conversion efficiency—from carbon nanotube-polymer composites achieving 2.74 × 10⁻² conversion efficiency to innovative MXene-based systems that combine high photothermal conversion with structural protection functionality. These materials operate within sophisticated microstructural frameworks—including tilted fiber Bragg gratings, collapsed photonic crystal fibers, and functionalized polymer coatings—that enable precise control over optical-to-thermal-to-acoustic energy conversion. Six primary distributed fiber-optic photoacoustic transducer array (DFOPTA) methodologies have been developed to transform single-point transducers into multiplexed systems, with low-frequency variants significantly extending penetration capability while maintaining high spatial resolution. Recent advances in imaging algorithms have particular emphasis on techniques specifically adapted for distributed photoacoustic data, including innovative computational frameworks that overcome traditional algorithmic limitations through sophisticated statistical modeling. Documented applications demonstrate DFP-NDT’s exceptional versatility across structural monitoring scenarios, achieving impressive performance metrics including 90 × 54 cm² coverage areas, sub-millimeter resolution, and robust operation under complex multimodal interference conditions. Despite these advances, key challenges remain in scaling multiplexing density, expanding operational robustness for extreme environments, and developing algorithms specifically optimized for simultaneous multi-source excitation. This review establishes a clear roadmap for future development where enhanced multiplexed architectures, domain-specific material innovations, and purpose-built computational frameworks will transition DFP-NDT from promising laboratory demonstrations to deployable industrial solutions for comprehensive structural integrity assessment. Full article

Singlet oxygen is a type of reactive oxygen species that is typically generated via type II photosensitization reactions. Since 1,3-diphenylisobenzofuran (DPBF), a commonly used chromogenic probe, exhibits peak absorbance at 410 nm for singlet oxygen detection, it severely interferes with blue light irradiation and compounds that absorb in this wavelength region. This study investigated developing and validating a fluorescence-based method using DPBF to quantitatively analyze the type II photosensitizing property of riboflavin (RF) and its heterocyclic flavin derivatives. DPBF fluorescence-based analysis provided more sensitive and practical results than traditional colorimetric methods. It effectively overcomes spectral interference from colored photosensitizers, such as RF and its derivatives, under blue light irradiation (λ peak 447 nm). This method permitted more effective measurement of their activity without interference from their intrinsic color and maintained high linearity and low variation across different sample concentrations, even with short irradiation times. The type II photosensitizing potency of the tested compounds under blue light was consistently ranked as follows: RF > flavin mononucleotide > flavin adenine dinucleotide > lumiflavin > lumichrome. The results suggest that the DPBF fluorescence-based assay is a more effective approach than colorimetric analysis, making it a practical and reproducible tool for assessing the type II photosensitizing properties of diverse compounds. This study also provides a refinement of an existing probe-based assay for relative comparisons under visible light conditions. Full article

A novel, label-free chemiluminescence sensing platform for CpG methylation was developed, leveraging the G-quadruplex (G4) structural sensitivity of G4–protein interactions to eliminate bisulfite conversion. This sensing system is based on the enhancement of luminol chemiluminescence generated from myoglobin upon binding to the G4-forming DNA. At the core of this biosensor is the G4-structure-dependent modulation of the peroxidase-like activity generating luminol chemiluminescence of myoglobin. The structural change by CpG methylation within the G4-forming sequence of the B cell lymphoma 2 (BCL2) gene promoter altered its binding to myoglobin, transducing the methylation state into a measurable signal catalyzed by myoglobin. This principle was validated in a practical assay using immobilized probes to capture the target DNA for methylation analysis. This system demonstrated the capability to distinguish methylation differences of 50% when the target DNA concentration was over 25 nM. Versatility was further confirmed using the sequence from the dopamine receptor D2 (DRD2) gene promoter, where the methylation similarly induced distinct topological and functional changes. This is the first study to directly link the epigenetic state of a G4-forming DNA sequence to a protein-mediated enzymatic output, offering a framework for simple, rapid, and highly adaptable biosensors for research and clinical applications. Full article

The strategic engineering of metal–organic frameworks (MOFs) through integration with clay minerals offers a promising route to tailor their functional properties and expand their application scope. In this study, a series of clay-MOF composites was constructed by introducing MOFs onto the surfaces of different clay minerals. By varying the type of clay mineral, the nature and strength of surface-active sites could be effectively modulated. Notably, the Kaolinite-based MOFs (Ka-MOF) composite exhibited superior sensitivity for the detection of aspartic acid (AA), outperforming other composite nanozymes using o-phenylenediamine (OPD) and hydrogen peroxide (H₂O₂) as substrates, with a linear detection range of 0–37.56 μM and a low detection limit of 55.7 nM. The enhanced peroxidase-like activity is attributed to the substitution of silicon in the kaolinite structure by MOF components, which increases the density of Lewis acid–base sites. These sites facilitate H₂O₂ adsorption and promote its decomposition to generate singlet oxygen (¹O₂), thereby enhancing the catalytic oxidation process. Furthermore, the probe yielded satisfactory recoveries of aspartic acid (94.2% to 98.5%) in different real water samples through spiking recovery experiments. This work not only elucidates the influence of crystal surface engineering on the optical and catalytic properties of nanozymes but also provides a robust platform for tracing amino acids and studying their environmental chemical behaviors. Full article

This paper presents the design and evaluation of a fluorescent probe based on fluorescein hydrazide for the selective detection of hypochlorite (ClO⁻), bromide (Br⁻), and iodide (I⁻) ions in solution. The starting chemosensor, fluorescein hydrazide, is suitable for detecting hypochlorite anions in solution, as observed for the first time. The Br⁻ and I⁻ ions could be discovered after activating the probe with hypochlorite. Upon interaction with ClO⁻ ions, the proposed probe exhibits a significant increase in fluorescence emission, a sharp rise in absorbance, and a distinct color change, which is attributed to the conversion from the spirolactam closed form to the open form of the fluorescein ring. ClO⁻ and Br⁻ ions added together were found to brominate the probe in an acetonitrile–water mixture, resulting in a pronounced bathochromic shift in both absorption and emission spectra. Notably, the combination of ClO⁻ and I⁻ was more effective in cleaving the spirolactam ring than hypochlorite alone. Quantum chemical calculations were used to understand the detection mechanism of Br and I ions in a probe–hypochlorite mixture. The probe demonstrated exceptional selectivity and rapid response towards the target analytes, with detection limits determined to be 2.61 μM for ClO⁻, 66 nM for Br⁻, and 13 nM for I⁻. Furthermore, it successfully monitored fluctuations in ClO⁻, Br⁻, and I⁻ concentrations within complex systems, highlighting its potential application in environmental and biological monitoring. Full article

The promise of intracortical neural interfaces—to restore lost sensory and motor function and probe the brain’s activity—has long been constrained by device instability over chronic implantation. Conventional silicon-based probes, composed of heterogeneous materials, often fail due to mechanical mismatch, inflammatory responses, and interface-driven degradation, where stress can induce cracking, swelling, and exposure of cytotoxic elements to neural tissue. Silicon carbide (SiC) offers a compelling solution, combining chemical inertness, structural strength, and biocompatibility in both amorphous and crystalline forms. In this review, we discuss advances in SiC neural interfaces, highlighting contributions from multiple laboratories and emphasizing our own work on monolithic devices, constructed entirely from a single, homogeneous SiC material system. These devices mitigate interface-driven failures and show preliminary indications of magnetic resonance imaging (MRI) compatibility, with minimal image artifacts observed compared to conventional silicon probes, though further in vivo studies are needed to confirm thermal safety at high-field conditions. Collectively, SiC establishes a versatile platform for next-generation, durable neural interfaces capable of reliable, long-term brain interaction for both scientific and clinical applications. Full article