Search Results (3,676)

Plant-derived saponins have attracted significant interest for their potential to promote apoptotic cell death and enhance antitumor immune responses through immunogenic cell death (ICD). Akebia quinata, a saponin-rich medicinal plant, exhibits diverse pharmacological properties; however, studies on its seeds are limited, and their immunomodulatory activity in cancer remains largely unexplored. In this study, A. quinata seeds were extracted using 70% ethanol, and the phytochemical profile was characterized using UHPLC–QTOF MS/MS. We investigated the anticancer properties of A. quinata seed extract (AQSE), focusing on its role in inducing apoptosis and ICD in non-small cell lung cancer (NSCLC). In human NSCLC cell lines (A549 and H460), AQSE exhibited potent cytotoxic effects in a dose-dependent manner. Flow cytometric analysis confirmed the induction of apoptosis, evidenced by a significant increase in Annexin V-positive cells and an elevated sub-G1 population. Mechanistically, AQSE treatment induced cell death by simultaneously inhibiting the survival-promoting MEK/ERK/CREB axis and activating the stress-responsive JNK pathway. Furthermore, AQSE triggered hallmark features of ICD, characterized by surface exposure of calreticulin and the release of extracellular HMGB1 and ATP. Most importantly, an in vivo vaccination assay using a syngeneic mouse model demonstrated that immunization with AQSE-treated dying cells significantly suppressed tumor growth upon rechallenge, confirming the establishment of antitumor immunological memory. Additionally, bioassay-guided fractionation revealed that the anticancer activity was primarily concentrated in the ethyl acetate fraction. These findings suggest that AQSE exerts anticancer effects via the induction of apoptosis and ICD, highlighting its potential as a promising natural candidate for the development of novel therapeutic strategies against NSCLC. Full article

Antibiotic therapy represents one of the strongest ecological perturbations of the human gut microbiota, inducing rapid and often prolonged alterations in community structure, metabolic activity, and functional resilience. While the use of probiotics to mitigate antibiotic-associated dysbiosis is widely adopted in clinical practice, probiotic selection is still largely empirical and insufficiently grounded in biological compatibility with specific antibiotic pressures. In this conceptual review, antibiotics are reframed not merely as antimicrobial agents, but as ecological forces that shape microbial survival, quiescence, and recolonization dynamics. We propose a biologically informed framework that distinguishes genetic antibiotic resistance from functional or ecological insensitivity, highlighting how microbial traits, such as the absence or inaccessibility of the antibiotic target, metabolic state, sporulation, and cellular architecture, influence the persistence of probiotics during antibiotic exposure. By integrating the mechanisms of action of antibiotics with key physiological and structural features of probiotic microorganisms, we develop a conceptual framework aimed at rationalizing the compatibility of probiotics and antibiotics. This framework does not imply clinical efficacy but provides an interpretative tool to guide hypothesis generation, experimental validation, and the design of future targeted probiotic strategies. A more ecologically grounded approach to probiotic selection may ultimately improve microbiota support during antibiotic therapy and advance personalized microbiome modulation. Full article

The heavy metal pollution linked to extractive activities has attracted broad public attention. To examine the current state of heavy metal pollution in river sediments around iron tailing zones, this study was carried out to evaluate the distribution features, potential sources, and environmental hazards of heavy metals (HMs, Cr, Cd, Ni, Cu, Zn, Pb, As, and Hg) in the surface sediments of rivers in the Xiangshan area of Ma’anshan City. Results indicated that, except for Cr, the mean heavy metal concentrations exceeded the soil background levels in Anhui’s Huaihe River Basin. Variability in metal concentrations among the sediments was moderate, exhibiting an uneven spatial distribution. Significant positive correlations were detected between various HMs in the sediments, suggesting a common pollution source. Source analysis findings revealed that the HMs primarily originate from agricultural fertilization, mining, and smelting activities. Evaluation results from both the single-factor pollution index and the Nemerow comprehensive index indicated that the upstream section of the Caishi River is severely polluted by HMs. The potential ecological risk index evaluation results demonstrated that 85% of sediment samples from sampling points achieved a high comprehensive potential ecological risk level for HMs, with Cd, Cu, and Hg identified as the key contributors. The human health risk assessment demonstrated that both adults and children are subjected to carcinogenic risks from heavy metal exposure, with children exhibiting a higher risk level. This study offers valuable insights into managing heavy metal contamination in river sediments adjacent to iron tailings regions. Full article

Emotion recognition from physiological signals has immense applications in healthcare and human–computer interaction. We developed an electrodermal activity (EDA)-graph signal processing pipeline that produces highly sensitive features for detecting the affective dimensions (arousal and valence) of emotions. Using the Continuously Annotated Signals of Emotion dataset, we compared our graph-based EDA features (EDA-graph) with traditional time- and frequency-domain EDA features and features derived from other signals (heart rate variability, pulse transit time, electromyography, skin temperature, and respiration) for detecting affective dimensions using machine learning regression models. The EDA-graph features showed superior performance in continuous affective dimension recognition compared to the most accurate state-of-the-art models, achieving RMSE values of 0.801 for arousal and 0.714 for valence. Furthermore, we used a variety of traditional and recently published datasets collected in laboratory and ambulatory settings to perform a comprehensive evaluation of the robust generalization capabilities of our approach across different emotional contexts. The models demonstrated exceptional performance in classifying emotional states across the datasets, achieving 98.2% accuracy in detecting positive, negative, and mixed emotions; 92.75% in discriminating between emotions (relaxed, amused, bored, scared, and neutral); and 86.54% in detecting stress vs. no stress. These results highlight the potential of a graph-based analysis of EDA in emotion recognition systems in different contexts, especially for real-world applications. Full article

Background: In an earlier murine model of myocardial infarction (MI), we showed that CD8 cells and myeloid dendritic cells (mDCs) infiltrate the infarcted myocardium within the first week. However, in humans, the spatial interplay between CD8⁺ T cells and dendritic cells in the spatial context of human myocardial infarction remains underexplored. Objective: In the present study, we applied spatial transcriptomics and functional assays to characterize immune–stromal dynamics in infarcted myocardium and peripheral blood. Methods & Results: Spatial transcriptomics analysis of infarcted human myocardium at days 2 and 6 post-MI, combined with peripheral blood flow cytometry and EPC colony-forming assays, was performed. Cell composition, pathway enrichment, and cell-to-cell communication analyses were conducted to map immune–stromal cells’ dynamics across time points. Spatial mapping identified dynamic shifts in immune, fibroblast, and endothelial populations, with fibroblasts and endothelial cells remaining abundant throughout. CD8⁺ T cells accumulated in ischemic regions while their circulating levels declined. Gene Ontology and pathway analyses of CD8A⁺ transcripts revealed enrichment of proinflammatory and NF-κB survival programs. ITGAX/CD33/THBD⁺ APCs progressively increased within infarct zones, activating antigen-presentation and leukocyte chemotaxis pathways. Early (day 2) APC–endothelial crosstalk showed the strongest predicted recruitment signals for CD8⁺ T cells, which diminished by day 6. Finally, EPC colony-forming capacity showed a tendency for reduction in MI patients and inversely correlated with coronary lesion burden, indicating impaired vascular repair potential. Conclusions: This integrative spatial and functional study demonstrates that APC-driven CD8⁺ recruitment and EPC dysfunction are key features of human MI. Immune–endothelial niches facilitate early cytotoxic T-cell infiltration, while progenitor depletion limits vascular regeneration. These findings provide mechanistic insight into immune–vascular imbalance during infarct healing and highlight potential therapeutic targets to modulate inflammation and restore vascular repair. Full article

Background/Objectives: Understanding the mechanisms of drug response plays an essential role in predicting effects prior to drug administration and advancing personalized medicine by optimizing treatment strategies. This study aimed to identify gene combinations that can predict the antitumor activity of lenvatinib, which is a multi-targeted tyrosine kinase inhibitor. Methods: Cancer- and drug-response-related gene sets were identified by mapping gene expression profiles of previously reported syngeneic mouse tumor models onto a protein–protein interaction network and extracting subnetworks comprising nodes where high expression levels were clustered. The scores for these network modules were calculated using the expression data of mouse tumor models prior to drug administration. These scores were used to train a machine learning (ML) model of drug response to lenvatinib by narrowing down the parameter space using hepatocellular carcinoma patient-derived xenograft (HCC PDX) models acquired in this study. Results: Using this integrative framework, we identified several network modules including those involved in the nerve growth factor signaling pathway, Wnt signaling pathway, and interleukin signaling pathways, that were consistently prioritized as informative features across PDX models and human patient data from The Cancer Genome Atlas. Conclusions: These network modules exhibit biological functions that are linked to the known targets of lenvatinib in the cancer cells or the tumor microenvironment, highlighting their potential relevance as determinants of drug response. Full article

Wildfires at the wildland–urban interface (WUI) have increased in frequency and severity under global warming and intensified human activities. As a representative high-altitude mountainous region in southwestern China, Yunnan features complex topography, steep climatic gradients, and dispersed settlements interwoven with wildlands, making it a fire-prone area where wildfire management is particularly challenging. However, a fine-scale WUI dataset is currently lacking for this region. To address this gap, we refined WUI classification thresholds using a one-factor-at-a-time (OFAT) method and generated the first fine-resolution WUI map of Yunnan. Seasonal wildfire driving factors from 2004 to 2023 were quantified, and machine learning models were applied to produce seasonal susceptibility maps. SHapley Additive exPlanations (SHAP) were employed to interpret the dominant contributing factors. The resulting WUI covers 25,730.67 km², accounting for 6.5% of Yunnan’s land area. Random forest models effectively captured seasonal wildfire susceptibility patterns, with AUC values exceeding 0.83 across all seasons. High susceptibility zones (>0.5) comprised 30.09% of the WUI in spring, 25.74% in winter, 22.61% in autumn, and 13.74% in summer. SHAP analysis revealed that anthropogenic factors consistently drive wildfire occurrence, while climatic conditions in the preceding season influence vegetation status and subsequently affect wildfire likelihood in the current season. By integrating static “where” mapping with dynamic “when” susceptibility analysis, this study establishes a comprehensive “When–Where” framework that supports both long-term WUI planning and short-term seasonal early warning. The integration of fine scale WUI mapping with seasonal susceptibility modeling enhances wildfire risk management in complex high-altitude regions. These findings provide a scientific basis for location-specific, time-sensitive, and full-chain wildfire management in mountainous landscapes and contribute to cross-border ecological security governance in the Indo-China Peninsula. Full article

Visual saliency modeling has achieved high predictive performance in natural image domains, yet its generalization to abstract art remains limited by the lack of explicit semantic structure and the scarcity of eye-tracking data. In such semantically ambiguous contexts, understanding the underlying drivers of attention is as critical as predictive accuracy. This paper presents an interpretable, ’white-box’ saliency framework tailored to abstract art, which constructs predictions through a weighted combination of 35 modular heuristics grounded in perceptual psychology and art theory, including contrast, grouping, isolation and symmetry. Heuristic weights are optimized via a genetic algorithm and refined by a context-aware modulation mechanism that adapts to image-level visual features. Evaluation against eye-tracking data from 40 abstract paintings demonstrates that the model with the expanded activation variant produces stable, meaningful predictions while achieving a competitive KL-divergence score (1.11 ± 0.55), which is comparable to the SalGAN baseline (1.11 ± 0.53). Analysis of the optimized weights reveals strong contributions from contrast, texture, and grouping mechanisms, while nearly half of the heuristics, including most horizontal symmetry heuristics are systematically pruned by the model. Moreover, context-aware modulation reveals that these weights are not static but shift dynamically based on image-level features such as edge density and intensity variation. By prioritizing transparency over raw predictive performance, this study demonstrates that explainable saliency models can function as robust investigative tools for decoding the principles of human visual perception in data-scarce domains. Full article

Electrochemical sensors are user-friendly devices designed for the rapid and straightforward detection of target analytes. Serotonin (5-hydroxytryptamine, 5-HT) is a key neurotransmitter and neuromodulator that regulates diverse neuronal processes. Using a custom-designed screen-printed carbon electrode (SPCE) incorporating ordered mesoporous carbon–bimetal oxides of Cu and Ni (CuO–NiO–OMC), rapid and real-time detection of 5-HT was achieved. The CuO–NiO–OMC structure featured highly active CuO and NiO catalytic sites that effectively promoted the irreversible oxidation of 5-HT (vs. Ag/AgCl reference electrode). The CuO–NiO–OMC/SPCE sensor, connected to a portable potentiostat, exhibited exceptional electrocatalytic performance for the oxidation of 5-HT, with a detection limit of 42.5 nM. The sensitivity was 1.56 A M⁻¹ cm⁻², and the linear dynamic range was 0.0–80.0 µM. The CuO–NiO–OMC/SPCE sensor also demonstrated outstanding selectivity in the presence of competing neurochemicals, including norepinephrine, epinephrine, dopamine, and glutamate, as well as high concentrations of tested biomolecules and inorganic ions. Furthermore, the practicality of the sensor was demonstrated using human serum and urine samples, with recovery percentages ranging from 91.1% to 98.3%. Thus, the CuO–NiO–OMC/SPCE sensor offers an effective approach for 5-HT sensing, thereby permitting molecular-level understanding of brain function. Full article

People with special needs often face barriers to participating in adapted outdoor leisure physical activities. A participatory action research project involving a nonprofit organization, a citizen with motor disabilities, and researchers aimed to co-develop a digital platform connecting people with special needs interested in outdoor leisure physical activities with trained volunteers. The adopted co-design methodology followed four stages: (1) Exploration (identifying users’ needs and preferences), (2) Co-design (defining key information and platform features), (3) Validation (prioritizing features), and (4) Development (implementing and testing the platform). This article focuses on stages 2, 3, and 4. During stage 2, key information and features were identified to support matching people with special needs and volunteers and informing users about adapted outdoor leisure physical activities. In stage 3, these elements were prioritized using eight key considerations, including technological (e.g., ease of use), environmental (e.g., avoiding redundancy with existing initiatives), organizational (e.g., availability of human resources), and financial factors (e.g., grant planning). Stage 4 resulted in the launch of Tandem Actif, followed by user testing to document user experience and guide improvements. This article details the application of co-design within a participatory action research project aimed at promoting safe, ethical, and accessible participation in outdoor leisure physical activities for people with special needs. Full article

Curvularins, a class of macrocyclic lactones, have cytotoxic, antimicrobial, and anti-inflammatory properties. Curvularin, a 12-membered macrolactone, was used as a scaffold to design and synthesize structurally modified analogues to investigate structure–activity relationships and improve biological efficacy. Three series of curvularin-based analogues, Cur-5H-OMe, Cur-4P-OMe, and Cur-OMe, were synthesized with the same core structure but different substituent sizes and positions. Nine representative derivatives were evaluated for anti-inflammatory, anticancer, antibacterial, and antifungal activities. In LPS-stimulated RAW 264.7 macrophages, most compounds inhibited nitric oxide (NO) production in a concentration-dependent manner but exhibited cytotoxicity at high concentrations. Cytotoxicity assays against HaCaT cells and human cancer cell lines (HCT116, HeLa, and A375) revealed limited selectivity toward cancer cells. Antimicrobial evaluation indicated selective activity against the Gram-positive bacteria, Staphylococcus aureus. Compound 23 exhibited superior antibacterial potency compared with kanamycin and notable antifungal activity against Candida albicans. This study provides a versatile synthetic platform and identifies key structural features of curvularin derivatives, demonstrating their potential as anti-inflammatory and antimicrobial lead compounds. Full article

Canine cognitive dysfunction (CCD) is a common age-related neurodegenerative disorder in dogs that shares several pathological and clinical features with human Alzheimer’s disease (AD). In both species, β-amyloid (Aβ) accumulates within the brain parenchyma and cerebral vessel walls and is associated with synaptic loss, oxidative stress, mitochondrial dysfunction, and chronic neuroinflammation, ultimately leading to progressive cognitive decline. Increasing evidence indicates that impairment of brain clearance mechanisms, particularly the glymphatic system, represents a central pathogenic mechanism in both CCD and AD. The glymphatic system is a glia-dependent perivascular network involved in the clearance of Aβ and other metabolic waste products from the brain. Its function declines with aging, vascular disease, and astrocytic alterations, including changes in aquaporin-4 distribution. Reduced glymphatic and periarterial drainage promotes the retention and aggregation of Aβ and tau proteins. Compared with AD, tau pathology in CCD is generally less extensive, supporting the interpretation of CCD as an Aβ-predominant condition and a partial pathological analog of Alzheimer’s disease. Clinically, CCD is characterized by a constellation of behavioral changes including, disorientation, altered social interactions, sleep–wake cycle disturbances, a loss of housetraining, changes in activity levels, and increased anxiety, commonly summarized by the DISHAA acronym. Overall, CCD represents a valuable spontaneous large-animal model for investigating neurodegenerative mechanisms and clearance-related therapeutic targets relevant to both veterinary and human medicine. Full article

Rapid post-disaster building damage assessment requires recognizing explicit structural failures and interpreting implicit situational cues in visually complex scenes. Whereas conventional automated methods are often confined to detecting explicit damage patterns, human perception naturally integrates both types of information into a holistic risk judgment. This study presents an exploratory investigation into the neural signatures underlying this integrated judgment process using electroencephalography. A modified paradigm was employed to probe the cognitive dynamics of risk evaluation in participants with civil engineering backgrounds. Although participants were instructed only to identify damaged buildings without explicit severity grading, event-related potential analysis revealed systematic, graded neural responses that scaled with damage severity. This suggests that the brain encodes damage-related information not as a binary state but as a continuous spectrum of perceived risk, implicitly processing severity, even in the absence of explicit instructions. Furthermore, single-trial analysis demonstrated that time-domain features contain robust discriminative information, verifying the feasibility of decoding these latent judgments from brain activity. These findings provide a physiological basis for developing future cognition-informed algorithms and human-in-the-loop frameworks, bridging the semantic gap to enhance the reliability of automated disaster assessment. Full article

Glycosphingolipids are amphiphilic compounds that feature sugar or glycan moieties installed onto a ceramide lipid. The synthesis of glycosphingolipids by members of the human gut microbiome, and their known immune stimulating activity, have made them of interest for potential pharmaceutical roles. However, the known diversity of glycosphingolipid glycans in bacteria remains limited, highlighting the need to isolate novel glycosphingolipid-producing organisms as a source of these compounds. The order Sphingomonadales, one of the major clades of sphingolipid producing bacteria, conserves a serine palmitoyltransferase (SPT) enzyme needed for the initial biosynthetic step in sphingolipid production which can be targeted as part of isolation efforts. With these bacteria known to live in diverse environments such as soil microbiomes, soap scum biofilms, and cyanobacterial microbiomes, there are many environments to target for the isolation of these bacteria. In this work, we designed a set of polymerase chain reaction (PCR) primers for the isolation of diverse Sphingomonadales strains by targeting the SPT gene (spt), which we used to isolate strains from the genera Sphingomonas and Novosphingobium in soil, soap scum biofilms, and xenic cyanobacterial cultures. In these efforts, streptomycin improved the encounter rate, as represented by the SPT assay true-positive rate. Our isolates represent novel genomic space: with genomes from both genera that have low similarity to known genomes, suggestive of novel species, while several novel plasmids were also missing known marker sequences. Full article

The human cerebral cortex is organized into distinct area-specific regions along the rostral–caudal axis, yet current human brain organoid models incompletely recapitulate this regional diversity. Here, we establish an area-specific brain organoid platform by leveraging transcription factors (TFs) identified through re-analysis of in vivo human cortical transcriptomic datasets. Publicly available single-cell RNA sequencing datasets from human developing cortex were re-analyzed to identify differentially expressed genes associated with rostral and caudal cortical identities. From this analysis, we identified SP9 (rostral-enriched) and DMRTA2 (caudal-enriched) as candidate TFs governing regional specification. To model cortical area identity, these TFs were overexpressed in an inducible manner during human cerebral organoid (hCO) generation. Overexpression of SP9 resulted in hCOs exhibiting rostral cortical characteristics, whereas DMRTA2 overexpression promoted caudal cortical features. The resulting hCOs showed distinct regional identities, reflected by differential expression of area-specific markers. In addition, these regional identities were accompanied by distinct functional phenotypes, as calcium imaging revealed divergent patterns of spontaneous neural activity between rostral and caudal hCOs. Altogether, our findings demonstrate that overexpression of TFs enables the controlled generation of area-specific hCOs. This approach provides a scalable and reproducible platform for studying human cortical regionalization and offers a framework for investigating region-specific mechanisms underlying neurodevelopmental and neurological disorders. Full article