The high accuracy in surface-enhanced Raman scattering-lateral flow immunoassays (SERS-LFIAs) is critical for reliable point-of-care testing (POCT) in clinical diagnostics. Conventional approaches are often affected by sampling variability and uneven distribution of immunoprobes, leading to unreliable signal fluctuations. To address this challenge, we developed a high-performance SERS-LFIA strip based on gold nanostars (Au NSs) and integrated it with an artificial intelligence (AI)-powered diagnostic framework. Specifically, Au NSs with exceptional SERS enhancement were synthesized via an optimized "two-step" method and utilized as nanoprobes to construct an influenza B (FluB) SERS-LFIA strip for performance validation. A novel large-area Raman scanning technique was then employed to generate intensity maps depicting the immunoprobe distribution around the test (T) line. A deep residual neural network (ResNet-18) was subsequently applied to analyze these SERS images, minimizing subjective interpretation and significantly improving accuracy. The optimized framework achieved 100% training accuracy and 95% validation accuracy, significantly outperforming conventional peak intensity analysis and support vector machine (SVM)-based full-spectrum discrimination methods. The Au NSs-based SERS-LFIA platform and the optimized ResNet-18 model were integrated into a portable Raman spectrometer to create an automated diagnostic system. To further evaluate the stability and versatility of the system, the detection target was switched to influenza A (FluA) by altering the capture and detection antibodies. This reengineered system demonstrated a 95% accuracy rate in testing 40 simulated human clinical samples. Our work establishes a machine learning-enhanced, automated SERS-LFIA system that leverages Au NSs for superior signal enhancement and utilizes deep learning for robust image analysis. This integrated approach provides a scalable and high-performance POCT framework, paving the way for automated clinical diagnostics. (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)-(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(SERS-LFIA)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(POCT)(sic)(sic)(sic)(sic).(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic) (Au NSs) (sic)(sic)(sic)(sic)SERS-LFIA(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(AI)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)"(sic)(sic)(sic)"(sic)(sic)(sic)(sic)(sic)(sic)(sic)SERS(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(FluB) SERS-LFIA(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(T(sic))(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(ResNet-18)(sic)(sic)(sic)(sic)SERS(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)100%(sic)(sic)(sic)(sic)(sic)(sic)(sic)95%(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(SVM)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)SERS-LFIA(sic)(sic)(sic)(sic)(sic)(sic)ResNet-18(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(FluA).(sic)(sic)(sic)(sic)(sic)(sic)(sic)40(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)95%(sic)(sic)(sic)(sic).(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)SERS-LFIA(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)POCT(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).

Activity-dependent synaptic remodeling, essential for neural circuit plasticity, is orchestrated by central organizers within the postsynaptic density (PSD), including the scaffolding protein PSD95. However, the molecular mechanisms driving this process remain incompletely understood. Here, we identify cyclin-dependent kinase-like 5 (CDKL5), a protein associated with a severe neurodevelopmental condition known as CDKL5 deficiency disorder (CDD), as a critical regulator of structural plasticity at excitatory synapses. We show that CDKL5 undergoes liquid-liquid phase separation (LLPS) in vitro and in cultured neurons, forming cocondensates with PSD95. This LLPS-driven process spatially organizes synaptic components, specifically enabling the synaptic recruitment of Kalirin7 to promote dendritic spine enlargement. Pathogenic mutations disrupt condensate formation by impairing the LLPS capacity of CDKL5, directly linking phase separation defects to the pathogenesis of CDD. Our findings reveal a crucial role for CDKL5 in synaptic plasticity and establish LLPS as a fundamental mechanism by which CDKL5 coordinates molecular events to reorganize PSD architecture during synaptic remodeling.

Exosomes have emerged as potential biomarkers for non-invasive diagnosis and real-time monitoring of disease due to their ability to carry bio-molecular information from their source cells and stability in various body fluids. However, existing detection methods face challenges of insufficient specificity, limited sensitivity, and difficulty in distinguishing exosomes from different cellular origins. To address this, this study developed a fluorescent biosensing platform based on silica-coated upconversion nanoparticles (UCNPs@SiO2) for characterization of exosomes derived from pancreatic cancer cells that simultaneously overexpress epidermal growth factor receptor (EGFR) and epithelial cell adhesion molecule (EpCAM). The platform employed a cascade signal amplification strategy. Firstly, exosomes were captured using magnetic beads conjugated with anti-CD63 antibodies. Subsequently, specific recognition of EGFR and EpCAM was achieved via DNA aptamers, triggering rolling circle amplification (RCA) to generate long-chain DNA products containing repetitive sequences. Finally, UCNPs-labeled DNA probes complementary to RCA products were added. After incubation and magnetic separation, the fluorescent signal of free UCNPs in the supernatant was detected. Experimental results showed that the supernatant fluorescence intensity decreased with increasing exosome concentration, exhibiting a negative correlation response. Additionally, semi-quantitative naked-eye analysis could be performed by observing the degree of magnetic bead aggregation, with the lowest visually distinguishable concentration for EGFR-and EpCAM-positive exosomes being 3.0 & times;10(3) particles/mL and 6.0 & times;10(3) particles/mL, respectively. The analytical method established in this study enabled high-sensitivity, dual-protein phenotyping of exosomes from specific cellular origins, providing a novel methodological support for non-invasive diagnosis and therapeutic monitoring of pancreatic cancer.

Exosomes, as extracellular vesicles, represent ideal biomarkers for liquid biopsy, enabling non-invasive, sensitive, and dynamic disease monitoring. Detection or profiling of exosomes are essential for advancing exosome-based cancer diagnostics or prognostic prediction. Here, we developed an enhanced electrochemiluminescence (ECL) biosensing platform that combines magnetic enrichment with catalytic hairpin assembly (CHA), an enzyme-free DNA amplification circuit triggered by the complementary sequences of CD63 aptamer (Apt), to achieve sensitive exosome detection. The enhanced ECL signal relies on a nanocomposite of a zirconium-based metal-organic framework (Zr-MOF) and water soluble gold nanoclusters (AuNCs), denoted as Zr-MOF/AuNCs. The platform exhibits a wide linear range (6.12 × 10² - 6.12 × 10⁷ particles/mL) and a low detection limit (187.4 particles/mL) for tumor-derived exosomes. Spike-recovery assays in complex serum matrices and high specificity confirm its robustness and clinical potential. By combining magnetic preconcentration, signal amplification via Zr-MOF and CHA, and Apt-mediated recognition, this work establishes a new paradigm for non-invasive cancer diagnostics.

Exosomes play a pivotal role in cancer diagnosis and therapy, yet their clinical application faces significant technical challenges. To address this, we developed a sensitive, aptamer-based platform that integrates nucleic acid amplification and a G-quadruplex (G4) with two-signal output for exosome detection. Our approach employs CD63 antibody-conjugated magnetic beads (MBs) for efficient exosome capture and purification. The captured exosomes are then bound by CD63-specific aptamers embedded in rolling circle amplification (RCA) products, forming a sandwich complex with the MBs. The RCA products also contain G4 repeats, enabling two-signal output through interaction with Fe(III)-protoporphyrin IX (Hemin) and N-methylporphyrin-dipropionic acid IX (NMM). The G4-Hemin complex catalyzes the oxidation of 3,3′,5,5′-tetramethylbenzidine (TMB), producing a colorimetric signal, while the G4-NMM enhances fluorescence emission. This detection system achieves high sensitivity, with detection ranges of 4.0 × 103 to 4.0 × 107 particles/mL (colorimetric) and 4.0 × 102 to 4.0 × 106 particles/mL (fluorescence), and low detection limits of 1.78 × 103 particles/mL (colorimetric) and 96 particles/mL (fluorescence). Demonstrating high performance in complex media (10

In the field of brain decoding research, reconstructing visual perception from neural recordings is a challenging but crucial task. With the use of superior algorithms, many methods have been dedicated to enhancing decoding performance. However, these models that map neural activities onto semantically entangled feature space are difficult to interpret. It is hard to understand the connections between neural activities and these abstract features. In this paper, we propose an interpretable neural decoding model that projects neural activities onto a semantically disentangled feature space with each dimension representing distinct visual attributes, such as gender and facial pose. A two-stage algorithm is designed to achieve this goal. First, a deep generative model learns semantically-disentangled image representations in an unsupervised way. Second, neural activities are linearly embedded into the semantic space, which the generator uses to reconstruct visual stimuli. Due to modality heterogeneity, it is challenging to learn such a neural embedded high-level semantic representation. We induce pixel, feature, and semantic alignment to ensure reconstruction quality. Three experimental fMRI datasets containing handwritten digits, characters, and human face stimuli are used to evaluate the neural decoding performance of our model. We also demonstrate the model interpretability through a reconstructed image editing application. The experimental results indicate that our model maintains a competitive decoding performance while remaining interpretable.

Exosomes carry various biological information and are abundant in body fluids, making them a promising noninvasive biomarker for disease diagnosis and prognosis. However, current detection methods have limitations in sensitivity, specificity, and cost effectiveness, hindering their clinical application. To address these challenges, we have developed a fast, accurate, and cost-effective method for detecting exosomes with high sensitivity and specificity, making it ideal for clinical applications. Clusters of differentiation 63 (CD63) aptamer with its complementary DNA (CD63 aptamer/cDNA) linked to streptavidin-coated magnetic beads (SA-MBs) are used as a capture probe. Exosomes with CD63 proteins can bind to the aptamer and release the cDNA, which initiates rolling circle amplification (RCA) to magnify the cDNA copies. The negatively charged RCA products induce the aggregation of positively charged spermine-modified silver nanoparticles (AgNPs) through electrostatic attraction. The aggregation of AgNPs can be observed visually with the naked eye or quantitatively analyzed using ultraviolet-visible (UV-vis) spectroscopy to determine the concentration of exosomes, with limits of detection of 4.0 × 104 particles/mL for visual observation and 800 particles/mL for UV-vis spectroscopy, respectively. The method has also been demonstrated for detecting the exosomes in serum samples, indicating its potential for clinical use in liquid biopsy.

Ponicidin (Pon), a diterpenoid isolated from Rabdosia rubescens, exhibits a broad range of pharmacological activities, including anti-inflammatory effects. However, its therapeutic potential in Alzheimer's disease (AD), particularly in modulating receptor-interacting protein kinase 1 (RIPK1)-mediated neuroinflammation and necroptosis, remains underexplored. This study aims to investigate the mechanism through which Pon targets RIPK1 to alleviate AD pathogenesis. The interaction between Pon and RIPK1 was confirmed using bio-layer interferometry (BLI) and drug affinity responsive target stability (DARTS) assays. In vitro, the effects of Pon on inflammatory responses and necroptosis were evaluated in BV2 microglial cells (BV2 cells) and HT22 hippocampal neuronal cells (HT22 cells) using Enzyme-linked immunosorbent assay (ELISA), Reverse transcription quantitative real-time polymerase chain reaction (RT-qPCR), Western blotting (WB), and flow cytometry. In vivo, Pon's therapeutic efficacy was assessed in the 5 × FAD transgenic mouse model of AD through behavioral tests, histological analysis, and biochemical assays. Pon was found to bind RIPK1 with high affinity (KD = 135 nM) and enhance RIPK1's resistance to proteolytic degradation. In microglial cells, Pon effectively inhibited the release of pro-inflammatory cytokines interleukin-6 (IL-6) and tumor necrosis factor alpha (TNF-α) by disrupting the RIPK1-janus kinase 1 (JAK1)-signal transducer and activator of transcription 1 (STAT1) signaling pathway. In neurons, Pon suppressed RIPK1-mediated necroptosis by blocking the RIPK1-RIPK3-mixed lineage kinase domain-like protein (MLKL) cascade. Behavioral analysis of 5 × FAD mice revealed that Pon treatment significantly improved cognitive function, reduced amyloid-beta (Aβ) plaque deposition, and alleviated neuroinflammation and necroptosis in the brain. Pon exerts dual neuroprotective effects by targeting RIPK1, mitigating both neuroinflammation and necroptosis, two critical pathological processes in AD. These findings underscore Pon's potential as a disease-modifying therapy for AD and provide a foundation for the clinical development of natural product-derived RIPK1 inhibitors in neurodegenerative diseases.

Aging represents a significant global challenge characterized by persistent oxidative stress and dysregulated lipid metabolism. Crocin, the primary bioactive constituent of saffron (Crocus sativus L.), is widely utilized as a natural food colorant and exhibits potent anti-inflammatory and antioxidant properties. Previous studies have demonstrated crocin's antioxidative, neuroprotective and memory-enhancing effects in aged rats; however, its direct impact on aging and the underlying mechanisms remain unexplored. In this study, we demonstrated that crocin treatment extended lifespan, enhanced survival under heat and juglone-induced oxidative stress, and reduced lipofuscin accumulation in the model organism C. elegans. Mechanistically, crocin activated DAF-16, the C. elegans homolog of human FOXO, resulting in the upregulation of key antioxidant genes (gst-4, sod-3 and hsp-16.2). Notably, the lifespan-extension effect of crocin was abolished in a daf-16 mutant, and its antioxidant effects were significantly attenuated in daf-16 RNAi experiments conducted in N2, CL2166, CF1553 and TJ375 strains. Furthermore, crocin specifically reduced fat accumulation, and upregulated the expression of genes involved in lipid mobilization (lipl-3, lipl-4, atgl-1 and acs-2) and unsaturated fatty acid synthesis (fat-6 and elo-2) in aged nematodes. GC-MS analysis further demonstrated that crocin treatment elevated the levels of unsaturated fatty acids (C18:1n9, C20:4n-6, C20:4n-3 and C20:5n-3), an effect that was completely abolished under daf-16 knockdown conditions. Collectively, these findings suggest that crocin promotes longevity in C. elegans by mitigating oxidative stress and modulating lipid metabolism through the DAF-16/FOXO pathway. These results highlight the potential of crocin as a promising strategy for treating aging and age-related diseases.

Multimodal learning has been a popular area of research, yet integrating electroencephalogram (EEG) data poses unique challenges due to its inherent variability and limited availability. In this paper, we introduce a novel multimodal framework that accommodates not only conventional modalities such as video, images, and audio, but also incorporates EEG data. Our framework is designed to flexibly handle varying input sizes, while dynamically adjusting attention to account for feature importance across modalities. We evaluate our approach on a recently introduced emotion recognition dataset that combines data from three modalities, making it an ideal testbed for multimodal learning. The experimental results provide a benchmark for the dataset and demonstrate the effectiveness of the proposed framework. This work highlights the potential of integrating EEG into multimodal systems, paving the way for more robust and comprehensive applications in emotion recognition and beyond.

Immunotherapy offers a promising avenue for reducing tumor metastasis and recurrence but faces challenges from the tumor immunosuppressive microenvironment (TIME) and restricted antigen presentation. To address these challenges, this study have developed an innovative approach utilizing molybdenum (Mo)-doped Prussian blue nanoparticles coated with a cancer cell membrane (CCM), referred to as PMo@CCM. This novel nanoplatform excels in performing photothermal therapy (PTT), while the Mo and Fe components effectively deplete glutathione (GSH) and generate reactive oxygen species (ROS), thereby significantly enhancing chemodynamic therapy (CDT) and remodeling the TIME. The synergistic PTT/CDT approach not only induces tumor immunogenic cell death (ICD) but also facilitates antigen presentation. The CCM coating further supplies antigens and prompts dendritic cell (DC) maturation. This comprehensive strategy markedly enhances the effectiveness of immunotherapy, as evidenced by a significant increase in T cell activation. Moreover, the use of programmed cell death protein 1 antibodies (anti PD-1) effectively blocks the PD-1 immune checkpoint pathway. RNA sequencing analysis has identified genes associated with the observed substantial reduction in tumor growth. In conclusion, the PMo@CCM nanoplatform enables homologously targeted tumor synergistic therapy, guided by photothermal and magnetic resonance imaging (PTI&MRI), significantly impeding the progression of both primary and metastatic tumors. A novel Prussian blue variant, PMo@CCM, which is coated with a cancer cell membrane (CCM) and doped with molybdenum (Mo), is designed to activate immunotherapy through photothermal therapy (PTT) and chemodynamic therapy (CDT). The integration of anti programmed cell death protein-1 (PD-1) with PMo@CCM further enhances immunotherapy by modulating the tumor immunosuppressive microenvironment (TIME), offering a comprehensive strategy for cancer treatment. image

Currently, the application rate of virtual reality technology in the fields of medical education and medical treatment is relatively low. In this study, based on the structure of the human stomach, virtual reality technology, sensing technology, big data technology, and cloud computing technology were integrated. A new form of medical education was established to include the online website platform for data storage and analysis and the offline VR glasses for the physical operation. Through the use of 3d Max technology, Unity3D technology, and C# language, we constructed a three-dimensional model of the human stomach to present the stomach in three dimensions. This enables the users to immerse in it to achieve true human-computer interactive learning. This study updates the concept of medical education, effectively improves the quality and efficiency of medical education, and facilitates the development of surgical programs and reduction in the risk of surgery, as well as provides experimental materials for scientific research. In the future, it can be further developed to include the three-dimensional structures of the circulatory system and other major systems in the human body.

Abstract Influenza A virus (H1N1) poses a significant threat to global human health that imperative demands the development of sensitive and accurate point‐of‐care testing (POCT) methods. Here, for the first time, Fe‐MoS2 nanosheets were employed as a multifunctional nanotag in the development of catalytic colorimetric‐photothermal dual‐mode lateral flow immunoassay (dLFIA) strips for the sensitive detection of H1N1 inactivated virus. The Fe‐MoS2 nanosheets featuring large size, high specific surface area, and ultrathin structure could flow smoothly on the strips and thus quickly produce an ideal colorimetric signal for qualitative analysis. Both the limit of detection (LOD) of catalytic colorimetric and photothermal signals reached 1000 copies/mL and the corresponding calculated LOD was 550 and 691 copies/mL, respectively, which were about 50–90‐fold more sensitive than traditional gold nanoparticles based‐LFIA (5 × 104 copies/mL). The developed assay could correctly identify eight positive clinical samples with Ct values less than 35 and 10 negative actual samples, proving significant promise for rapid, sensitive, and accurate detection of H1N1, especially in resource‐limited areas.

Spike (S) protein, a homotrimeric glycoprotein, is the most important antigen target for SARS-CoV-2 vaccines. A complete simulation of the advanced structure of this homotrimer during subunit vaccine development is the most likely method to improve its immunoprotective effects. In this study, preparation strategies for the S protein receptor-binding domain, S1 region, and ectodomain trimer nanoparticles were designed using ferritin nanoparticle self-assembly technology. The Bombyx mori baculovirus expression system was used to prepare three nanoparticle vaccines with high expression levels recorded in silkworms. The results in mice showed that the nanoparticle vaccine prepared using this strategy could induce immune responses when administered via both the subcutaneous administration and oral routes. Given the stability of these ferritin-based nanoparticle vaccines, an easy-to-use and low-cost oral immunization strategy can be employed in vaccine blind areas attributed to shortages of ultralow-temperature equipment and medical resources in underdeveloped areas. Oral vaccines are also promising candidates for limiting the spread of SARS-CoV-2 in domestic and farmed animals, especially in stray and wild animals.

The development of smart theranostic nanoplatforms has gained great interest in effective cancer treatment against the complex tumor microenvironment (TME), including weak acidity, hypoxia, and glutathione (GSH) overexpression. Herein, a TME-responsive nanoplatform named PMICApt/ICG, based on PB:Mn & Ir@CaCO3Aptamer/ICG, is designed for the competent synergistic photothermal therapy and photodynamic therapy (PDT) under the guidance of photothermal and magnetic resonance imaging. The nanoplatform's aptamer modification targeting the transferrin receptor and the epithelial cell adhesion molecule on breast cancer cells, and the acid degradable CaCO3 shell allow for effective tumor accumulation and TME-responsive payload release in situ. The nanoplatform also exhibits excellent PDT properties due to its ability to generate O-2 and consume antioxidant GSH in tumors. Additionally, the synergistic therapy is achieved by a single wavelength of near-infrared laser. RNA sequencing is performed to identify differentially expressed genes, which show that the expressions of proliferation and migration-associated genes are inhibited, while the apoptosis and immune response gene expressions are upregulated after the synergistic treatments. This multifunctional nanoplatform that responds to the TME to realize the on-demand payload release and enhance PDT induced by TME modulation holds great promise for clinical applications in tumor therapy.

Detailed phytochemical investigation on the traditional Chinese medicine Swertia pseudochinensis Hara led to the isolation of ten undescribed secoiridoids and fifteen known analogs. Their structures were elucidated by extensive spectroscopic analysis (including 1D and 2D NMR, and HRESIMS). Selected isolates were assayed for their anti-inflammatory and antibacterial activities, and moderate anti-inflammatory activity via inhibiting the secretion of cytokines IL-6 and TNF-α in macrophages RAW264.7 induced by LPS were observed. Antibacterial activity against Staphylococcus aureus was not found at 100 μM.

Most nanozyme research is limited to oxidase and peroxidase. Here, we reported the N, P, or S doped carbon nanotubes (CNTs) for enzyme mimics of nicotinamide adenine dinucleotide (NADH) oxidase and cytochrome c (Cyt c ) reductase. Through the doping of N element, the NADH oxidase-like activity of CNTs is highly improved, and the maximum initial velocity for N doped CNT (N-CNT) is increased by 4.28 times compared to that before the modification. Through the analysis of NADH oxidation products, we found that biologically active NAD + was produced, and the oxygen was selectively reduced to water or hydrogen peroxide, which is consistent with natural NADH oxidase. Furthermore, we found for the first time that carbon nanotubes can promote the transfer of electrons from NADH to Cyt c , thereby can mimic the properties of Cyt c reductase.

Systematic phytochemical investigation on the Mongolian medicinal herb Lomatogonium carinthiacum led to the isolation of 12 monoterpenoids including three new secoiridoids (1, 2 and 4) and one new iridoid glycoside (13), one new monoterpenoid alkaloid (3), and three new sesquiterpenoids (14–16). Comprehensive spectroscopic analysis (including 1D and 2D NMR, and HRESIMS) and quantum chemistry computations (including ECD and NMR calculations) were applied to elucidate their structures. Weak immunosuppressive activities were observed for the new isolates via inhibiting T cell proliferation and cytokine IFN-γ secretion in vitro.

Deep image inpainting research mainly focuses on constructing various neural network architectures or imposing novel optimization objectives. However, on the one hand, building a state-of-the-art deep inpainting model is an extremely complex task, and on the other hand, the resulting performance gains are sometimes very limited. We believe that besides the frameworks of inpainting models, lightweight traditional image processing techniques, which are often overlooked, can actually be helpful to these deep models. In this paper, we enhance the deep image inpainting models with the help of classical image complexity metrics. A knowledge-assisted index composed of missingness complexity and forward loss is presented to guide the batch selection in the training procedure. This index helps find samples that are more conducive to optimization in each iteration and ultimately boost the overall inpainting performance. The proposed approach is simple and can be plugged into many deep inpainting models by changing only a few lines of code. We experimentally demonstrate the improvements for several recently developed image inpainting models on various datasets.

Peste des Petits Ruminants (PPR) is a highly pathogenic disease that is classified as a World Organization for Animal Health (OIE)-listed disease. PPRV mainly infects small ruminants such as goats and sheep. In view of the global and high pathogenicity of PPRV, in this study, we proposed a novel nanoparticle vaccine strategy based on ferritin (Fe) self-assembly technology. Using Helicobacter pylori (H. pylori) ferritin as an antigen delivery vector, a PPRV hemagglutinin (H) protein was fused with ferritin and then expressed and purified in both Escherichia coli (E. coli) and silkworm baculovirus expression systems. Subsequently, the nanoparticle antigens’ expression level, immunogenicity and protective immune response were evaluated. Our results showed that the PPRV hemagglutinin–ferritin (H-Fe) protein was self-assembled in silkworms, while it was difficult to observe the correctly folded nanoparticle in E. coli. Meanwhile, the expression level of the H-Fe protein was higher than that of the H protein alone. Furthermore, the immunogenicity and protective immune response of H-Fe nanoparticle antigens expressed by silkworms were improved compared with the H antigen alone. Particularly, the protective immune response of H-Fe antigens expressed in E. coli did not change, as opposed to the H antigen, which was probably due to the incomplete nanoparticle structure in E. coli. This study indicated that the use of ferritin nanoparticles as antigen delivery carriers could increase the expression of antigen proteins and improve the immunogenicity and immune effect of antigens.