The capability to detect trace-level protein in complex biofluids is critical in various fields, from fundamental biological research to clinical disease diagnosis. Digital protein sensing by counting single molecules bound to probes is the technology of choice with exceptional sensitivity. However, label-free single-molecule sensing has been challenging so far because of the extremely weak signal and inherent quantum noise. Herein, we present an integral plasmonic imaging technique for the next generation of label-free digital protein sensing, offering in-plane plasmonic scattering and integral detection to maximize optical scattering signals and collection efficiency. This technology allows for high-sensitivity single-molecule detection, without the need for signal amplification, and provides the capability to simultaneously obtain mass and binding kinetics information. Leveraging the plasmonic waveguide mode, the system ensures low light-induced heating effects, making it highly suitable for biological applications. Experimental results demonstrate quantitative imaging and digital counting of individual unlabeled protein molecules, including representative globular proteins down to 25 kDa, and enable detection of protein concentrations at the sub-picomolar level in buffered solutions and biologically relevant fluid environments. Additionally, the system’s unique ability to perform digital counting of molecular binding events highlights its potential for in-depth analysis of binding kinetics. We anticipate that this label-free digital protein sensing could find wide applications in clinical low-abundance molecular detection.

Surface plasmon resonance microscopy (SPRM) is a highly sensitive, label-free optical imaging technique. However, its spatial resolution is limited by the directional propagation of surface plasmon waves (SPWs), resulting in anisotropic imaging. To address this limitation, we propose an isotropic super-resolution interferometric plasmonic microscopy (ISR-IPM) technique. This method combines multi-angle SPWs excitation, system transfer function theory, and frequency-domain reconstruction algorithms to expand spatial frequencies in all directions. The experimental results show that ISR-IPM effectively eliminates the anisotropy of traditional SPRM, fundamentally overcomes the optical diffraction limit, and achieves a spatial resolution of ~150 nm for nanoscale targets. This technology may further provide a powerful platform for nanoscale imaging, quantitative analysis of biomolecular dynamics, and comprehensive characterization of surface plasmon fields.

Van der Waals (vdW) crystals have emerged as promising candidates for polarization-sensitive optoelectronics due to their large structural anisotropy along the out-of-plane directions, while most of these crystals exhibit small in-plane anisotropy. In this work, we systematically investigate the steady-state and transient optical response of monoclinic NbOI2 crystals with large in-plane anisotropy. First-principles calculations combined with polarization-resolved absorption measurements demonstrate a pronounced linear dichroism (LD) with a maximum LD value of similar to 0.4 in the visible range, exceeding that of most 2D vdW materials. As revealed by ultrafast pump-probe spectroscopy, this large LD is associated with polarization-dependent exciton dynamics switching between photo-bleaching (PB) and photoinduced absorption (PA), which implies anisotropic carrier relaxation and allows for selective and ultrafast modulation of polarized absorption. The modulation depth as determined by nonlinear absorption measurement also exhibits the same polarization dependence, which manifests as two-photon absorption (TPA) for sub-bandgap photons with a maximum TPA coefficient of 172 cm/GW exceeding that of most 2D semiconductors. The strong linear and nonlinear anisotropic optical absorption with an ultrafast response time suggests NbOI2 could be an ideal candidate for polarization-tunable photonic and optoelectronic applications. (c) 2026 Chinese Laser Press

Objective Versican (VCAN), a prominent extracellular matrix component upregulated in inflammatory diseases, demonstrates context-specific regulatory mechanisms. Periodontitis, a chronic inflammatory disease leading to periodontal tissue destruction and tooth loss, the pathological role of it remains poorly defined. Our study aims to examine VCAN-mediated mechanisms in periodontitis. Methods We conducted a comprehensive analysis of bulk RNA sequencing and single-cell RNA sequencing data to examine VCAN expression level and source in periodontitis. Functional and correlation analyses were used to explore its biological functions. We then validated VCAN expression using quantitative real-time polymerase chain reaction, immunohistochemical staining, and immunofluorescence staining in animal models and investigated its biological functions in inflammation through in vitro experiments. Results Our findings reveal that VCAN is mainly generated by fibroblast in periodontitis, and its expression significantly upregulated at both mRNA and protein levels. Using VCAN-overexpressing L929 cells, we demonstrated enhanced proliferative capacity and inflammatory potential. Co-culture experiments with RAW264.7 cells showed promoted migration, adhesion, M1 polarization, and mitogen-activated protein kinase (MAPK) pathway activation. Conclusion VCAN enhances fibroblast proliferation and migration, and upregulates inflammatory cytokines expression. Furthermore, fibroblast-derived VCAN not only induces macrophage chemotaxis, migration, adhesion, and polarization toward the proinflammatory M1 phenotype, but also activates MAPK signaling of macrophage, which may amplify inflammatory cascades to exacerbate periodontal tissue destruction. Targeted regulation of VCAN expression may become a promising precision treatment strategy for periodontitis.

Digital assays hold great potential for biomarker analysis in early disease diagnosis, and a key method to achieve digital detection is through the single-molecule pull-down (SiMPull) technique. However, the SiMPull assay frequently encounters challenges in field-of-view, detection throughput and ease of use. Lensless holographic microscopy (LHM) is a compact imaging technique that, when combined with aggregation assays, enables the detection of aggregation events of thousands of particles in a 3-D liquid-phase environment. This makes it a highly promising digital biosensing approach. Herein, we propose a rapid and ultrasensitive microbead aggregation assay based on LHM imaging for the digital detection of nucleic acid and protein biomarkers in solution. After an incubation period of less than one hour, the solution is directly imaged using LHM. The localization of microbeads within an extensive 3-D liquid mixture is determined via an automated bead locating and focusing procedure. This process eliminates the need for complex microfluidic channels and circumvents the requirement to wait for particles to settle to the sensing plane, which greatly simplifies the sensing platform and reduces the detection time. Finally, feature-based automatic classification is proposed to identify the aggregation rates, which reflect the concentration of analytes. Experiments indicate that the assay can be performed within 60 min and reach a limit of detection (LOD) of 10 fM and dynamic range of at least 4 orders of magnitude for nucleic acid; for interleukin-6, the LOD is 2 pg/mL, with a dynamic range of 2-3 orders of magnitude. The proposed approach is expected to enable point-of-care applications in settings where resource scarcity and protocol simplicity are of great importance.

Objective: Periodontitis, a highly prevalent chronic inflammatory disease caused by bacteria. Cardiovascular disease (CVD) is responsible for more than 17 million deaths globally each year. Both periodontitis and CVD are global noncommunicable diseases that share common risk factors. This review aimed to provide a guide for dentists and physicians, and improved treatment regimens for patients with both periodontitis and CVDs. Method: This present review reports existing evidence in the literature to summarize the correlation between periodontitis and six types of CVD, discusses the existing epidemiological evidence, intermechanism connections, and the impact of periodontal therapy on cardiovascular health. Results: Current research has emphasized the potential importance of periodontitis as a risk factor for CVD and has revealed various mechanisms of interaction between the two conditions. These mechanisms include oxidative stress, immune-inflammatory responses, and dysbiosis of the oral microbiota. Periodontitis may directly or indirectly induce systemic inflammation and oxidative stress by altering the circulation of oral microbiota, thereby affecting the occurrence and development of CVD. Conclusion: By strengthening prevention strategies of periodontitis, we have the potential to prevent or ameliorate cardiovascular conditions. The review provides new perspectives and an indication of future directions for the prevention of CVD.

Periodontitis, a common dental illness, causes periodontal tissue inflammation and irreversible bone loss, inevitably resulting in tooth loss. Hyperhomocysteinaemia (HHcy), defined as blood total homocysteine (Hcy) levels greater than 15 µmol/L, is linked to increased cardiovascular disease risk. Mounting evidence indicates a connection between HHcy and periodontitis; however, the underlying processes remain unknown. Herein, we explored the mechanisms by which HHcy exacerbates periodontal tissue inflammation and osteoclast formation. In an animal model of periodontitis treated with HHcy, periodontal attachment loss was aggravated, and both systemic and gingival tissue inflammation levels tended to increase; additionally, antioxidant-related proteins were suppressed and expressed at low levels, whereas oxidative damage-related protein expression increased. In RAW264.7 cells treated with LPS or LPS + Hcy, the LPS + Hcy group presented increased reactive oxygen species (ROS) fluorescence intensity, and Nrf2/HO-1 signalling pathway suppression was associated with inflammatory cytokine (TNF-α) expression. In monocyte osteoclasts treated with Rankl or Rankl + Hcy, the Rankl + Hcy group presented Nrf2/HO-1 signalling pathway suppression, an increase in osteoclast-related proteins (NFATc-1 and CTSK), and a more pronounced osteoclastic phenotype. Therefore, HHcy may exacerbate inflammation severity and osteoclast generation in periodontitis by promoting ROS production and inhibiting the Nrf2/HO-1 signalling pathway.

Sepsis is defined as a condition related to infection that manifests with multiorgan dysfunction, representing a life-threatening state. Consequently, severe complications frequently occur, with liver injury being one of the most prevalent serious complications of sepsis. Liver dysfunction during sepsis serves as an independent predictor of mortality. This review provides a comprehensive overview of current research on sepsis-induced liver injury (SILI), encompassing the clinical manifestations, diagnostic criteria, pathogenesis and therapeutic strategies associated with this condition. SILI may manifest as hypoxic hepatitis due to ischemia and shock, cholestasis resulting from abnormal bile metabolism, or bile duct sclerosis. The pathophysiology of sepsis involves intricate interactions among the inflammatory response, oxidative stress, and cell death. All of these factors complicate treatment and represent potential targets for therapeutic intervention. Furthermore, this review addresses the limitations inherent in conventional therapies currently employed for managing SILI and emphasizes the potential of novel targeted strategies aimed at addressing the fundamental mechanisms underlying this condition.

Dynamic single-molecule sensing (DSMS) enables real-time monitoring of molecular interactions with exceptional sensitivity and kinetic resolution, offering significant potential for ultrasensitive biomarker detection. However, existing DSMS platforms often require probe redesign or external force modulation to tune binding kinetics, which limits system simplicity and scalability. Here, we report an intrinsically regulated DSMS platform engineered through systematic optimization of nanoparticle size and buffer ionic strength. We first established a theoretical model describing two dominant kinetic regimes─damping-dominated and entropic-confinement-dominated dynamics─and identified a critical inflection point where sensitivity and specificity are balanced. While this model provides insights into kinetic tuning, practical challenges such as nanoparticle heterogeneity and matrix complexity limit its direct application for sensor design. To address this, we empirically optimized a previously developed DSMS system using average binding dwell time and total binding events as two key performance indicators. The optimized platform, featuring 150 nm polystyrene nanoparticles under 150 mM NaCl, achieved femtomolar detection of thrombin and HIV-1 p24 antigen with limits of detection of 213.9 fM and 4.3 fM, respectively. Notably, the platform maintained excellent specificity in diluted serum through dwell-time filtering, highlighting its robustness in complex biological matrices. This work establishes a novel DSMS strategy that enables efficient single-molecule sensing without probe modification or external actuation, paving the way for scalable, high-performance biomarker detection in clinical diagnostics and point-of-care applications.

Analyzing single-molecule binding kinetics offers an effective way to reduce the disturbance from nonspecific bindings in biosensors. Here we present a dual-parameter lifetime distribution modeling approach to detect specific binding signals in single-molecule sensors accurately. A proof of concept was demonstrated in the dynamic single-molecule sensing of microRNA using gold-nanoparticle labeled sandwiched assay with low-affinity probes. In this assay, a single molecule binding process was recorded with a large field-of-view plasmonic scattering microscope, and the lifetime distribution was quantified. A model involving two different exponential decaying constants was used to fit the lifetime distribution, providing accurate information about the number of nonspecific binding and specific binding events as well as their dissociation rates. We show both in simulations and experiments that by establishing the calibration curve with the number of specific binding events against different analyte concentrations, an ultra-low limit of detection at the femtomolar level could be achieved. The high sensitivity makes this approach a potential solution to detect low abundance biomarkers for disease diagnosis.

Layered ordered multianion materials exhibit remarkable structural flexibility and unique chemical and physical properties, which arise from the interplay of intralayer and interlayer interactions, as well as distinct local chemical environments originating from different anionic sublattices. Here, we report a novel layered quaternary compound, Bi18O21.6Se1.8Cl7.2, which features three different anionic sublattices. Bi18O21.6Se1.8Cl7.2 consists of [Bi6O9.6Cl6] and [Bi12O12Se1.8Cl1.2] slabs that stack along the c axis alternatively. Comprehensive physical properties and electronic properties of Bi18O21.6Se1.8Cl7.2 single crystals reveal semiconducting behavior with an indirect band gap of ∼1.51 eV and the dominant electron-type carriers. Notably, Bi18O21.6Se1.8Cl7.2 exhibits exceptionally low c-axial thermal conductivity κc (∼0.261-0.307 W m-1 K-1) at room temperature, significantly expanding the phase space of the ultralow-thermal-conductivity Bi-O-Se-Cl system. Our findings highlight that the strategic combination of distinct two-dimensional building blocks with varied structural and anionic coordination environments offers an effective approach for designing new layered materials with tunable physical properties.

Single nanoparticle analysis is crucial for various applications in biology, materials, and energy. However, precisely profiling and monitoring weakly scattering nanoparticles remains challenging. Here, it is demonstrated that deep learning-empowered plasmonic microscopy (Deep-SM) enables precise sizing and collision detection of functional chemical and biological nanoparticles. Image sequences are recorded by the state-of-the-art plasmonic microscopy during single nanoparticle collision onto the sensor surface. Deep-SM can enhance signal detection and suppresses noise by leveraging spatio-temporal correlations of the unique signal and noise characteristics in plasmonic microscopy image sequences. Deep-SM can provide significant scattering signal enhancement and noise reduction in dynamic imaging of biological nanoparticles as small as 10 nm, as well as the collision detection of metallic nanoparticle electrochemistry and quantum coupling with plasmonic microscopy. The high sensitivity and simplicity make this approach promising for routine use in nanoparticle analysis across diverse scientific fields.

Imatinib is crucial for treating chronic myelogenous leukemia (CML). However, analyzing such a small molecule presents challenges due to the low signal levels in most existing biosensors, which are typically more sensitive to the mass or size of the analyte. Herein, we report a WS2-based plasmonic biosensor for the sensitive detection of imatinib by charge-induced impedance change. Leveraging plasmonic-based electrochemical impedance microscopy (P-EIM) imaging, we observed a remarkable charge sensitivity in monolayer WS2 single crystals. Imatinib binding to the capturing probes on the sensor's surface influences the charge distribution, leading to a distinct impedance imaging contrast signal. The results indicate the limit of detection at the sub-nanomolar level, and outstanding selectivity in detecting the specific binding of imatinib and c-Abl molecules.

Interference from nonspecific binding imposes a fundamental limit in the sensitivity of biosensors that is dependent on the affinity and specificity of the available sensing probes. The dynamic single-molecule sensing (DSMS) strategy allows ultrasensitive detection of biomarkers at the femtomolar level by identifying specific binding according to molecular binding traces. However, the accuracy in classifying binding traces is not sufficient from separate features, such as the bound lifetime. Here, we establish a DSMS workflow to improve the sensitivity and linearity by classifying molecular binding traces in surface plasmon resonance microscopy with multiple kinetic features. The improvement is achieved by correlation analysis to select key features of binding traces, followed by unsupervised k-clustering. The results show that this unsupervised classification approach improves the sensitivity and linearity in microRNA (hsa-miR155-5p, hsa-miR21-5p, and hsa-miR362-5p) detection to achieve a limit of detection at the subfemtomolar level.

The simultaneous detection of dopamine (DA) and acetaminophen (AP) is crucial for diagnosing and treating related mental disorders. However, accurately determining DA and AP in biological samples remains challenging due to the interference from other biomolecules, such as ascorbic acid (AA), uric acid (UA), epinephrine (EP), etc. Here, we present a dual-template molecularly imprinted electrochemical sensor for the selective recognition of DA and AP. Reduced graphene(rGO)-gold nanoparticles(AuNPs) composite were utilized to enhance the effective area and increase the electron transfer rate of glassy carbon electrode(GCE), thus the electrochemical response signals were amplified. This sensor combined the advantages of nanocomposites and molecularly imprinted polymer (MIP) to achieve highly sensitive and selective detection of DA and AP. However, decreases in the current response of some analytes and variations in relationships of the peak current versus analyte concentration will occur due to adsorption competition on active sites in multianalyte mixtures, rendering the detection range constrained and traditional linear fitting methods inaccurate for predicting analyte concentrations. Therefore, a concentration prediction model based on XGBoost was developed for the sensor to achieve the reliable multi-component determination of DA and AP within the identical measurement range as individual detection. In this model, nine characteristic parameters were extracted from the differential pulse voltammetry (DPV) response curve and Bayesian optimization (BO) was adopted for automatic hyperparameter search, thereby the prediction errors were reduced and the generalization ability was improved. Experiments indicated that the MIP/AuNPs/rGO/GCE exhibited a wide detection range of 2-240 mu M and 3-240 mu M for DA and AP, with detection limits down to 0.26 mu M and 0.33 mu M, respectively. In addition, the intelligent MIP/AuNPs/rGO/GCE sensor based on BO-XGBoost model provides more accurate prediction results for untrained concentration combinations obtained from fetal bovine serum samples, indicating that the proposed detection scheme holds potential in future clinical diagnosis.

A lensless holographic microscope based on in-line holograms and optical diffraction tomography is an ideal imaging system for label-free 3D biological samples and can achieve large-volume imaging with single-cell resolution in a convenient way. However, due to the phase information loss and the missing cone problem, the imaging quality is significantly degraded by the reconstructed artifacts of twin images and out-of-focus images, which severely hinders the identification and interpretation of the objects. We propose an artifacts-free lensless on-chip tomography certified by three-dimensional deconvolution, which facilitates the extraction of real object morphology through straightforward yet effective computation. Initially, a globally valid systemic point spread function (PSF) is generated by simulating the imaging output of an ideal point light source positioned at the origin of the object space coordinate. Subsequently, an iterative three-dimensional deconvolution process is applied to the primitive imaging outcome of the lensless on-chip tomography using this PSF. Through rapid iterations, the optimized imaging result is swiftly obtained. Both the simulated and experimental results indicate that the artifacts-free lensless on-chip tomography can effectively circumvent the reconstructed artifacts and retrieve the real object morphology, which is critical for detailed observation and further quantitative analysis. In addition, we anticipate that the proposed approach has the potential to be transferred to other 3D imaging systems in systemic artifacts removal after corresponding modifications.

Iron metabolism refers to the process of absorption, transport, excretion and storage of iron in organisms, including the biological activities of iron ions and iron-binding proteins in cells. Clinical research and animal experiments have shown that iron metabolism is associated with the progress of periodontitis. Iron metabolism not only enhances the proliferation and toxicity of periodontal pathogens, but also activate host immune-inflammatory response mediated by macrophages, neutrophils and lymphocytes. In addition, iron metabolism is also involved in regulating cellular death sensitivity of gingival fibroblasts and osteoblasts and promoting the differentiation of osteoclasts, which plays a regulatory role in the regeneration and repair of periodontal tissue. This article reviews the research progress on the pathogenesis of periodontitis from the perspective of iron metabolism, aiming to provide new ideas for the treatment of periodontitis.

Angiogenesis is an important and necessary process in tissue regeneration and recovery, but suppressing such process is also a critical therapeutic consideration in some diseases including tumor and corneal neovascularization. Currently, pathological angiogenesis is mainly inhibited through the blockade of the interaction between vascular endothelial growth factor receptor (VEGFR) and its ligands. Compared to traditional anti-angiogenic agents such as monoclonal antibody and tyrosine kinase inhibitor, polypeptide has gained growing interests for high unit activity, few adverse reactions, and low costs. Among them, anti-Flt1 (AF) peptide could inhibit VEGFR1-induced endothelial cell migration and tube formation, showing excellent anti-angiogenic activity. Addressing the issues of low water solubility, instability, and poor bioavailability would significantly hinder the applications of AF. Herein, we prepared an AF functionalized tetrahedral framework nucleic acid (tFNA@AF) to overcome the abovementioned limitations of AF. The tFNA@AF presented enhanced structural stability and cellular uptake of AF, and exhibited superior bioavailability and satisfying anti-angiogenic effectiveness in vitro. Furthermore, tFNA@AF also demonstrated high-efficiency pathological angiogenesis inhibition on tumor-bearing mice of triple-negative breast cancer and corneal neovascularization of cornea cauterized rats, two typical models of tumor and traumatic cornea where angiogenesis is not anticipant. Our tFNA@AF as a novel polypeptide-nucleic acid conjugated nanomaterials showed superior bioavailability and high anti-angiogenic effectiveness, suggesting that with incremental improvements the nanomedicine may offer potentially useful treatment aid for anti-angiogenic therapy to prevent tumor growth and corneal neovascularization.