Search Results (489)

Schwann cells (SC) myelinate peripheral axons and orchestrate nerve regeneration after injury by switching between myelinating, proliferative and repair states. Evidence suggests that Botulinum Neurotoxin Type A (BoNT/A) influences SC biology, potentially supporting nerve repair and pain relief in peripheral neuropathic pain (PNP) models. However, BoNT/A receptor and target expression in human SC (hSC) remains poorly explored. Here, this translational gap was addressed by transcriptionally profiling genes encoding BoNT/A-relevant receptors and targets in primary hSC and testing whether paclitaxel evokes hSC phenotype plasticity in vitro based on changes in gene expression. Primary hSC were isolated, cultured, and treated with paclitaxel or vehicle, followed by RT-qPCR profiling of hSC markers and BoNT/A receptor/targets genes. Untreated hSC expressed moderate NGFR and S100β, with low MBP levels, suggesting a non-myelinating state profile. Transcripts encoding the BoNT/A receptor machinery (SV2A, SYT1) and the target SNAP25 were detectable at moderate levels. Paclitaxel induced changes in gene expression: SV2A and SYT1 decreased (up to two-fold), whereas SNAP25 and MBP increased, accompanied by reduced NGFR, indicating a shift toward a more differentiated transcriptional state. These data indicate hSC transcriptional plasticity in vitro and provide transcriptional evidence for the expression of BoNT/A-related molecular components in non-neuronal human cells. Full article

Posterior axis development during mammalian embryogenesis is driven by transient progenitor states that give rise to neural and mesodermal lineages, including neuromesodermal progenitors (NMPs). In vitro derivation of posterior progenitor populations from human pluripotent stem cells (hPSCs) has relied on modulation of Wnt and FGF signaling; however, these approaches frequently generate heterogeneous and unstable cell populations. Here, we investigated whether sodium butyrate (NaB) supplementation could promote a posteriorly biased intermediate state without extensive extracellular signaling control. We show that NaB, a histone deacetylase inhibitor, promotes the induction of posterior pre-neural progenitors (PNPs) characterized by co-expression of CDX2 and SOX2, together with suppression of SOX1. Transcriptomic analyses revealed that NaB-treated cells exhibit a posteriorly enriched PNPs with restrained anterior neural differentiation, transient early TBXT induction, and progressive activation of posterior HOX genes, consistent with an incompletely caudalized intermediate rather than a fully specified NMP population. Importantly, these PNPs remained responsive to canonical neural tube patterning cues, including retinoic acid and smoothened agonists, enabling further differentiation toward ventral spinal cord lineages. Collectively, our findings demonstrate that NaB supplementation supports posterior PNPs from hPSCs, providing a simple and reproducible platform for modeling early posterior neural development in vitro. Full article

Pyridoxal 5′-phosphate (PLP), the catalytically active form of vitamin B6, is an enabling cofactor for synthetic biocatalysis, but PLP production remains difficult to compare across chemical, microbial, and cell-free routes. This review reframes PLP synthesis as a biocatalytic cascade-design problem. Chemical phosphorylation is used as a benchmark for selectivity, reagent burden, and purification, whereas de novo and salvage-pathway enzymes define the molecular constraints governing biological production. We focus on PdxK/PdxY, PdxH/PNPOx, PdxS/PdxT, engineered acid phosphatase, and polyphosphate kinase modules. Recent PPi-driven and PPK/polyP-supported cascades show that high PLP concentrations are attainable, but titer alone is not sufficient for route comparison: substrate identity, phosphate-donor equivalents, adenylate loading, whole-cell or cell-free format, oxygen and H₂O₂ management, catalyst reuse, salt burden, isolated recovery, and product purity must be evaluated separately. By distinguishing PN fermentation, intracellular cofactor supply, PNP intermediates, reaction-broth PLP concentration, and isolated PLP yield, this review proposes a route-readiness map for PLP-producing cascades. The most promising next-generation systems will couple product-tolerant phosphorylation, oxidase performance, cofactor regeneration, and downstream stabilization in a single process-aware design. Full article

To address the challenges of the large-scale georeferencing of tower-based cameras and the limited capability of video-based spatial analysis, we proposed a geospatial mapping method integrating 3D GIS and gradient descent optimization. Using a Digital Elevation Model (DEM), high-resolution remote sensing imagery, and tower-based video data as the primary data sources, the proposed method first estimates the intrinsic parameters of the tower-based camera by aligning a 3D GIS virtual camera with the video imagery. Subsequently, the initial camera extrinsic parameters are estimated using the PnP algorithm based on the previously estimated intrinsic matrix $K$ and the corresponding control point pairs. Building upon these initial estimates, the camera intrinsic and extrinsic parameters are jointly optimized using a constrained L-BFGS-B framework that incorporates prior knowledge of the tower planar location, explicit box constraints, and a semi-constrained parameterization scheme with bounded parameter ranges. Furthermore, an outlier-removal and re-optimization strategy is employed to further improve the accuracy of parameter estimation. Finally, the optimized parameters are employed to transform image coordinates into three-dimensional world coordinates, and video geospatial mapping is achieved through the integration of colored point clouds with the 3D GIS scene. The results showed the following: (1) The 3D GIS scene constructed from publicly available DEM and high-resolution remote sensing imagery met the requirements for the initial estimation of intrinsic and extrinsic camera parameters. (2) Compared with PnP, RANSAC-PnP, SQPnP, and DLT, the proposed method achieves lower reprojection and 3D spatial errors. For the independent check points, the RMSE of the reprojection error is reduced by 66.4%, 73.6%, 68.0%, and 48.3%, respectively, while the RMSE of the 3D spatial error is reduced by 84.6%, 86.2%, 83.1%, and 69.4%, respectively. These results demonstrate that the proposed method provides reliable camera parameter estimates for video geospatial mapping. (3) Using the estimated camera parameters, image coordinates are transformed into 3D world coordinates to generate a georeferenced colored point cloud, which facilitates integrated analysis with existing geospatial datasets. The proposed method provides a feasible solution for tower-based camera georeferencing and three-dimensional visualization under conditions without field calibration. It offers a theoretical and technical basis for geospatial monitoring and related applications. Full article

Neurodegenerative diseases (NDs) such as Alzheimer’s, Parkinson’s, and Huntington’s disease are progressive and currently incurable conditions characterized by the deterioration of neuronal structure and function. Its incidence is increasing, primarily driven by global aging, and it represents a significant public health concern. Traditional therapies offer only symptomatic relief and are unable to halt or reverse the underlying neurodegenerative processes. One of the key challenges in developing effective treatments is the presence of biological barriers, such as the blood–brain barrier (BBB), which limits drug delivery to the central nervous system (CNS), namely the brain. Nanotechnology has emerged as a promising tool to overcome these obstacles. Nanoparticles (NPs), due to their small size, biocompatibility, and versatility, can be engineered to cross the BBB, protect therapeutic agents from degradation, and deliver them precisely to target sites in the brain. This work explores the current advances in lipid and polymeric-based nanoparticle (LNPs and PNPs, respectively) drug delivery systems (DDS) and their application in preclinical studies for the treatment of the NDs previously mentioned. The presented studies suggest that this strategy holds great potential, offering new perspectives and emerging strategies to improve therapeutic outcomes for NDs, and promote neuroprotection of the brain. Full article

Assuming the permanent charge density is much larger than the ion concentrations on two boundaries of an open ion channel, this paper studies the impacts of large permanent charges which are positioned along the channel wall and play a significant role in channel functioning. Through this work, the quasi-one-dimensional Poisson–Nernst–Planck (PNP) system is utilized to analyze the electrodiffusion properties of ionic flows through an ion channel. The geometric singular perturbation theory is applied to derive the existence and local uniqueness of solutions to the corresponding PNP system containing large permanent charges and two monovalent cations. The matching asymptotic expansions are conducted to generate the expansions of state variables in terms of the permanent charge density, from which the permanent charge effects on individual fluxes can be discussed by further using the regular perturbation analysis. The mechanisms of the inhibited role played by large permanent charge in the co-ions’ flow are also elucidated from the perspective of internal dynamics. We hope this work could provide more comprehensive information on large permanent charge effects on ion channels and display more realistic interactions between related physical parameters such as channel geometry, diffusion coefficients, boundary potential and boundary ion concentrations. Full article

Sparse visual odometry (VO) is a core component of lightweight unmanned aerial vehicle (UAV) visual navigation, yet the isolated effect of adding aligned metric depth to a minimal frame-to-frame pipeline is easily obscured in full SLAM systems. This paper presents a UAV-relevant controlled synthetic ablation of RGB-only and RGB-D geometric backends under fixed sparse frontends. ORB matching and KLT tracking are evaluated on a 32-sequence TartanAir validation split of flight-like synthetic RGB-D scenes by routing identical 2D correspondences either to Essential Matrix estimation or, with aligned depth, to PnP with RANSAC. The study reports ATE, Sim(3)-aligned ATE, translational and rotational RPE, robustness under temporal subsampling and RGB degradations, and isolated solver latency on a workstation and Raspberry Pi 4. At stride 1, RGB-D PnP reduces ATE by 61.8% for KLT and 29.1% for ORB, with translational RPE reductions of 61.6% and 41.9%. Rotational RPE reductions are stronger and persist across all tested strides, reaching 85.9% for KLT and 77.3% for ORB at stride 1. Sim(3) analysis shows that only 7–16% of PnP ATE is metric-scale drift. At coarser strides, however, KLT-PnP no longer improves ATE, showing that depth assistance depends on stable frontend tracking and valid depth-supported correspondences. The contribution is a reproducible diagnostic benchmark and failure-mode analysis for UAV-relevant depth-assisted sparse VO under oracle aligned depth, providing component-level evidence rather than full onboard deployment validation. Full article

Nitrogen deposition continuously alters the invasibility of terrestrial ecosystems, but how the composition of local plant functional groups regulates this process by root-associated microbial during invasion, especially under the background of resource changes, remains unclear. This study focused on the invasive plant Cenchrus spinifex Cav. and conducted an interactive experiment using nitrogen addition and four different functional group combinations of local plant communities. The results show that the community with the closest phylogenetic distance (PD = 189) had the strongest resistance to invasion. Nitrogen addition was the core factor driving invasion (total effect 0.86), which promoted invasion by increasing soil nitrogen pools and altering microbial community structure. The role of leguminous plants changed fundamentally with nitrogen availability; they were competitors under low-nitrogen conditions, while under high-nitrogen conditions, they transformed into “synergistic invaders” by shaping the root-associated environment rich in microorganisms such as Proteobacteria that facilitate rapid nutrient turnover. Plant nitrogen and phosphorus content (PNP) is a key indicator reflecting the nutrient absorption capacity of invasive plants and is closely related to invasion success. It significantly promotes the ability of root resources acquisition. The study shows that invasion success depends on the dynamic balance among resource input, the phylogenetic background of the local community, and the microbial feedback regulated by it. Future ecological management should consider the coordinated regulation of aboveground functional group selection and underground microbial processes. Full article

With the rapid advancement of wearable technologies, high-performance flexible sensors have garnered significant research interest. This study presents a PAM-5 hydrogel characterized by exceptional tensile strain (425%), superior compressive modulus (325 kPa), and notable ionic conductivity (1.1 S/m), serving as a robust mechanical framework and electrical foundation for developing advanced sensors. The PNP-5 integrated hydrogel sensor fabricated from this material demonstrates an extensive sensing range (2–53 kPa), remarkable sensitivity, and rapid response time (~321 ms), with its outstanding performance attributed to the synergistic structural design. Furthermore, the sensor exhibits excellent durability, maintaining consistent voltage output (~6.5 mV) across 1000 compression cycles, confirming its long-term operational stability. Through real-time monitoring of physiological signals and biomechanical movements including finger bending, respiration, and grasping, combined with spatial pressure mapping experiments using a 5 × 5 array touchpad, the device’s potential applications in wearable sensing platforms and human–machine interface systems are effectively demonstrated. This self-powered hydrogel sensor not only advances the performance metrics of flexible electronic devices but also establishes a solid experimental basis for future development of intelligent materials in health monitoring and interactive technologies. Full article

Hyperspectral imaging (HSI) provides rich spatial and spectral information for remote sensing, mineral exploration, and biomedical analysis, but its limited spatial resolution and sensor imperfections lead to blurred, noisy, and mixed-pixel observations. Addressing these degradations jointly—rather than sequentially—has been shown to improve physical interpretability, yet existing joint deblurring–unmixing methods rely primarily on hand-crafted regularizers that do not fully exploit spatial–spectral structure. Meanwhile, recent plug-and-play (PnP) approaches applied to HSI leverage deep priors but focus solely on either deconvolution or unmixing in isolation. To bridge this gap, we formulate the joint inverse problem of hyperspectral deblurring and spectral unmixing and propose, to our knowledge, the first plug-and-play framework tailored for this coupled task using the Alternating Direction Method of Multipliers (ADMM) and a pretrained deep denoiser (DnCNN) as an implicit PnP prior. Our method uses the natural splitting properties of ADMM to separate a physics-driven subproblem that enforces fidelity to the hyperspectral forward model, which includes linear mixing and blur under a linear, space-invariant convolution approximation, from the data-driven prior step. This synergy of model-based fidelity and learned spatial prior enables more accurate abundance estimates than those obtained with approaches relying solely on analytical regularizers. Experimental results on real hyperspectral datasets demonstrate that the proposed Plug-and-Play Joint Deconvolution and Unmixing (PnP-JDU) method outperforms conventional unmixing baselines, stand-alone PnP unmixing methods, and the Deblurring and Sparse Unmixing via the Alternating Direction Method with Total Variation (DSUnADM-TV) baseline in reconstruction and abundance accuracy metrics. Across the tested datasets and imaging conditions, PnP-JDU achieves lower RMSE, higher PSNR, lower reconstruction and abundance errors, and lower SAD values, while preserving fine spatial details and producing physically meaningful abundance maps. Full article

Ischemia-reperfusion injury (IRI) remains a primary driver of primary graft dysfunction (PGD) following lung transplantation, yet effective therapeutic strategies are currently limited. Early IRI is driven by coordinated oxidative and inflammatory responses, highlighting the need for therapeutic strategies capable of targeting both processes simultaneously. Using integrated human multi-omics analysis, in silico target prediction, and experimental validation, we identified barbaloin as a dual-target lead compound acting on interleukin-6 (IL-6) and purine nucleoside phosphorylase (PNP). In vitro, barbaloin suppressed IL-6 and PNP expression, inhibited PNP activity, reduced reactive oxygen species (ROS) accumulation, and attenuated NF-κB/NLRP3 inflammatory signaling. Crucially, in vivo validation in a C57BL/6J mouse model demonstrated that barbaloin (15 mg/kg) attenuated pulmonary edema and histological injury, partially restored respiratory mechanics, and reduced IL-6 and PNP expression. Collectively, these findings support the IL-6/PNP axis as a critical mediator of early lung IRI and identify barbaloin as a promising dual-target therapeutic candidate for mitigating oxidative and inflammatory injury during lung transplantation. Full article

Background: Conventional drug delivery systems often lead to fluctuating plasma concentrations (“Peak and Trough” phenomenon), causing toxicity or inefficacy. Microfluidics has emerged as a revolutionary tool to overcome, among other applications, the limitations of conventional bulk encapsulation methods, such as polydispersity and poor reproducibility. Methods: A systematic review of the literature published between 2020 and 2025 was conducted to evaluate the application of microfluidics in the synthesis of advanced nanomedicines. The review focused on Lipid Nanoparticles (LNPs), Polymeric Nanoparticles (PNPs), and Hydrogel Microspheres. Results: Microfluidics enables the production of monodisperse particles with precise control over geometry and drug loading stoichiometry. Key therapeutic applications include oncology (passive and active targeting), gene therapy (mRNA vaccines), and regenerative medicine (diabetic wound healing). Conclusions: While microfluidics offers superior quality control compared to bulk methods, industrial scalability remains the primary challenge, currently addressed through parallelization and continuous flow strategies. Full article

Nanomaterials with multiple enzyme-like activities offer significant opportunity for constructing multifunctional sensing methods. In this work, a hydrangea flower-like cerium dioxide nanomaterial (CeO₂ NF) with both peroxidase (POD)- and hydrolase-like activities, which was surface-modified by polyvinylpyrrolidone (PVP) in situ, was prepared through an oil bath method. Based on the POD-like activity of CeO₂ NFs, an “on-off” fluorescence method was established for chiral recognition of arginine (Arg) enantiomers. Meanwhile, utilizing the hydrolase-like activity of CeO₂ NFs and their synergistic interaction with alkaline phosphatase (ALP), an “on-off-on” fluorescence method was developed for the detection of p-nitrophenol (p-NP) and ALP. The sensor demonstrated excellent chiral selectivity for Arg enantiomers, with a high enantiomeric factor (ef) of up to 2.48, allowing for the quantitative detection of L-Arg in the range of 770–940 μM, with a limit of detection (LOD) of 26.00 μM. Furthermore, it exhibited high sensitivity for p-NP and ALP detection, with linear ranges of 10.0–84.3 μM and 300–2000 mU/mL, and LODs of 7.07 μM and 200 mU/mL, respectively. Through an enzyme kinetic analysis, fluorescence lifetime measurement, zeta potential analysis, and density functional theory (DFT) calculations, the underlying catalytic and chiral recognition mechanisms were proposed. Finally, the method was validated through the accurate detection of L-Arg, p-NP, and ALP in real samples (rabbit plasma, food-grade amino acid, and water samples). Full article

In modern reliability engineering, modeling bounded lifetime data under realistic experimental conditions is still challenging, especially when censoring schemes and unit removals are random. This study proposes a new and unified reliability framework by combining the flexible powering new power (PNP) distribution with a grouping-based progressive first-failure mechanism using a beta-binomial random design. The proposed approach explicitly accounts for the randomness in group removals, providing a more realistic description of practical life-testing experiments. Classical estimation is carried out using maximum likelihood methods with the Newton-Raphson algorithm, along with confidence intervals constructed under both standard and log-transformed parameterizations. To increase flexibility in inference, a Bayesian approach is developed based on a joint gamma and shifted log-normal prior, which respects parameter constraints and incorporates prior uncertainty. Since the posterior distributions cannot be obtained in closed form, a Metropolis-Hastings Markov chain Monte Carlo algorithm is used to generate reliable posterior estimates and credible intervals. Additionally, beyond sensitivity analysis, multiple prior robustness diagnostics are incorporated to ensure reliable hyperparameter calibration and to safeguard against prior misspecification. The performance of the proposed estimators is carefully examined through extensive Monte Carlo simulations under different censoring schemes and parameter settings. The simulation results indicate that the proposed Bayesian procedures often provide more stable estimation and shorter interval estimates with competitive coverage probabilities compared with the corresponding classical methods, particularly under moderate-to-heavy censoring settings. To demonstrate its practical usefulness, the proposed model is applied to two real datasets on tensile strength of carbon and polyester fibers, where it provides a good fit and useful insights into material reliability and failure behavior. In the same applications, the practical relevance and superior performance of the proposed distribution are demonstrated, where it outperforms existing bounded versions of several well-known models, including the gamma, Weibull, and Birnbaum-Saunders distributions. Overall, this work contributes to reliability analysis by offering a flexible and computationally efficient framework that accounts for both random censoring and complex lifetime patterns, with potential applications in engineering, materials science, and applied reliability studies. Full article

p-nitrophenol (p-NP) is a pollutant with environmental persistence, bioaccumulation potential, and significant health risks, and is widely dispersed in wastewater, so efficient removal of p-NP is imperative. Among the various methods, the catalytic reduction of p-NP to p-aminophenol (p-AP) using sodium borohydride (NaBH₄) is a particularly promising one and, herein, catalysts play a crucial role. Among the various metals, Au shows unique catalytic activity for p-NP reduction. However, nanosized Au often exhibit limited activity and stability due to their high surface free energy. To address this challenge, we designed and synthesized Au-SnO_x hybrid nanoparticles confined within hollow mesoporous silica nanoreactors (Au-SnO_x@hm-SiO₂) via a soft-template-assisted co-adsorption strategy. The resulting bimetallic Au-SnO_x@hm-SiO₂ nanoreactor showed significantly enhanced catalytic activity toward the NaBH₄-mediated reduction of p-nitrophenol (p-NP) compared with its monometallic Au@hm-SiO₂ counterpart, owing to the synergistic effect between Au and SnO_x. Among various Au/Sn ratios, the catalyst with an Au/Sn molar ratio of 1:0.1 demonstrated the highest activity, achieving complete conversion of p-NP within 5 min at a p-NP/Au molar ratio of 529:1—a tenfold improvement over Au@hm-SiO₂. Moreover, the catalyst maintained high efficiency over six consecutive cycles, with only slight deactivation, benefiting from the protective silica shell. Full article