Search Results (13,189)

Antibiotic resistance is considered to be one of the most complex health obstacles of our time. Methicillin-resistant Staphylococcus aureus (MRSA) represents a global health challenge due to its broad treatment resistance capacity, resulting in high mortality rates. The shikimate pathway (SP) is responsible for the biosynthesis of chorismate from glycolysis and pentose phosphate pathway intermediates. This pathway plays a crucial role in producing aromatic amino acids, folates, ubiquinone, and other secondary metabolites in bacteria. Notably, SP is absent in humans, which makes it a specific and potential therapeutic target to explore for discovering new antibiotics against MRSA. The present study characterized in vitro and in silico natural products as inhibitors of the shikimate dehydrogenase from methicillin-resistant S. aureus (SaSDH). The results showed that, from the set of compounds studied, phloridzin, rutin, and caffeic acid were the most potent inhibitors of SaSDH, with IC₅₀ values of 140, 160, and 240 µM, respectively. Furthermore, phloridzin showed a mixed-type inhibition mechanism, whilst rutin and caffeic acid showed non-competitive mechanisms. The structural characterization of the SaSDH–inhibitor complex indicated that these compounds interacted with amino acids from the catalytic site and formed stable complexes. In biological activity studies against MRSA, caffeic acid showed an MIC of 2.2 mg/mL. Taken together, these data encourage using these compounds as a starting point for developing new antibiotics based on natural products against MRSA. Full article

Tetrabromobisphenol A (TBBPA), a widely utilised brominated flame retardant, demonstrates toxicological effects in aquatic organisms. Tea polyphenols (TPs), natural compounds found in tea leaves, exhibit both antioxidant and anti-inflammatory activities. The kidney is one of the major metabolic organs in common carp and serves as a target organ for toxic substances. This study evaluated the therapeutic potential of TPs in mitigating TBBPA-induced nephrotoxicity in common carp. Common carp were exposed to 0.5 mg/L TBBPA in water and/or fed a diet supplemented with 1 g/kg TPs for 14 days. In vitro, primary renal cells were treated with 60 μM TBBPA and/or 2.5 μg/L TPs for 24 h. Methods included histopathology, TUNEL assay for apoptosis, ROS detection, and molecular analyses. Antioxidant enzymes (SOD, CAT) and inflammatory cytokines (IL-1β, IL-6, TNF-α) were quantified using ELISA kits. Results showed that TBBPA induced oxidative stress, and activated the ROS-PI3K/AKT-NF-κB pathway, thereby resulting in inflammatory responses. TBBPA upregulated apoptosis-related genes (Caspase-3, Bax, and Bcl-2) and induced apoptosis. TBBPA upregulated the expression of RIPK3/MLKL, thereby exacerbating necroptosis. TPs intervention significantly mitigated these effects by reducing ROS, suppressing NF-κB activation, and restoring antioxidant enzyme activities (SOD, CAT). Moreover, TPs attenuated apoptosis and necrosis in the carp kidney, thereby enhancing the survival ability and immunity of common carp. Full article

Beyond its crucial role as a tight barrier to protect the nervous system, the Blood–Brain Barrier (BBB) is increasingly being recognized for its physiological processes that affect brain function and behavior. In Drosophila melanogaster, the BBB expresses sex-specific transcripts, and a change in the sexual identity of adult BBB cells results in a significant reduction in male courtship behavior. The molecular nature of this BBB/brain interaction and the molecules that mediate it are unknown. Here we feminize BBB cells by targeted expression of the Drosophila female-specific master regulator TraF in otherwise normal males. We examined the effect on RNA expression in dissected brains by RNA sequenc-ing. We find that 283 transcripts change in comparison to normal control males. Tran-scripts representing cell signaling processes and synaptic communication are enriched, as are hormonal mediators. These transcripts provide a valuable resource for addressing questions about BBB and brain interaction. Full article

Reactive oxygen species (ROS) are often seen solely as harmful byproducts of oxidative metabolism, yet evidence reveals their paradoxical roles in both promoting and inhibiting cancer progression. Despite advances, precise context-dependent mechanisms by which ROS modulate oncogenic signaling, therapeutic response, and tumor microenvironment dynamics remain unclear. Specifically, the spatial and temporal aspects of ROS regulation (i.e., the distinct effects of mitochondrial versus cytosolic ROS on the PI3K/Akt and NF-κB pathways, and the differential cellular outcomes driven by acute versus chronic ROS exposure) have been underexplored. Additionally, the specific contributions of ROS-generating enzymes, like NOX isoforms and xanthine oxidase, to tumor microenvironment remodeling and immune modulation remain poorly understood. This review synthesizes current findings with a focus on these critical gaps, offering novel mechanistic insights into the dualistic nature of ROS in cancer biology. By systematically integrating data on ROS source-specific functions and redox-sensitive signaling pathways, the complex interplay between ROS concentration, localization, and persistence is elucidated, revealing how these factors dictate the paradoxical support of tumor progression or induction of cancer cell death. Particular attention is given to antioxidant mechanisms, including NRF2-mediated responses, that may undermine the efficacy of ROS-targeted therapies. Recent breakthroughs in redox biosensors (i.e., redox-sensitive fluorescent proteins, HyPer variants, and peroxiredoxin–FRET constructs) enable precise, real-time ROS imaging across subcellular compartments. Translational advances, including redox-modulating drugs and synthetic lethality strategies targeting glutathione or NADPH dependencies, further highlight actionable vulnerabilities. This refined understanding advances the field by highlighting context-specific vulnerabilities in tumor redox biology and guiding more precise therapeutic strategies. Continued research on redox-regulated signaling and its interplay with inflammation and therapy resistance is essential to unravel ROS dynamics in tumors and develop targeted, context-specific interventions harnessing their dual roles. Full article

Understanding how energy use and economic activity shape carbon emissions is pivotal for achieving global climate targets. This study quantifies the dynamic nexus between disaggregated energy consumption, economic growth, and CO₂ emissions in India and China—two economies that together account for more than one-third of global emissions. Using annual data from 1990 to 2021, we implement Long Short-Term Memory (LSTM) neural networks, which outperform traditional linear models in capturing nonlinearities and lagged effects. The dataset is split into training (1990–2013) and testing (2014–2021) intervals to ensure rigorous out-of-sample validation. Results reveal stark national differences. For India, coal, natural gas consumption, and economic growth are the strongest positive drivers of emissions, whereas renewable energy exerts a significant mitigating effect, and nuclear energy is negligible. In China, emissions are dominated by coal and petroleum use and by economic growth, while renewable and nuclear sources show weak, inconsistent impacts. We recommend retrofitting India’s coal- and gas-plants with carbon capture and storage, doubling clean-tech subsidies, and tripling annual solar-plus-storage auctions to displace fossil baseload. For China, priorities include ultra-supercritical upgrades with carbon capture, utilisation, and storage, green-bond-financed solar–wind buildouts, grid-scale storage deployments, and hydrogen-electric freight corridors. These data-driven pathways simultaneously cut flagship emitters, decouple GDP from carbon, provide replicable models for global net-zero research, and advance climate-resilient economic growth worldwide. Full article

Aflatoxin contamination, primarily caused by Aspergillus flavus, poses a significant threat to peanut (Arachis hypogaea L.) production, food safety, and global trade. Despite extensive efforts, breeding for durable resistance remains difficult due to the polygenic and environmentally sensitive nature of resistance. Although germplasm such as J11 have shown partial resistance, none of the identified lines demonstrated stable or comprehensive protection across diverse environments. Resistance involves physical barriers, biochemical defenses, and suppression of toxin biosynthesis. However, these traits typically exhibit modest effects and are strongly influenced by genotype–environment interactions. A paradigm shift is underway with increasing focus on host susceptibility (S) genes, native peanut genes exploited by A. flavus to facilitate colonization or toxin production. Recent studies have identified promising S gene candidates such as AhS5H1/2, which suppress salicylic acid-mediated defense, and ABR1, a negative regulator of ABA signaling. Disrupting such genes through gene editing holds potential for broad-spectrum resistance. To advance resistance breeding, an integrated pipeline is essential. This includes phenotyping diverse germplasm under stress conditions, mapping resistance loci using QTL and GWAS, and applying multi-omics platforms to identify candidate genes. Functional validation using CRISPR/Cas9, Cas12a, base editors, and prime editing allows precise gene targeting. Validated genes can be introgressed into elite lines through breeding by marker-assisted and genomic selection, accelerating the breeding of aflatoxin-resistant peanut varieties. This review highlights recent advances in peanut aflatoxin resistance research, emphasizing susceptibility gene targeting and genome editing. Integrating conventional breeding with multi-omics and precision biotechnology offers a promising path toward developing aflatoxin-free peanut cultivars. Full article

Paper-based colorimetric biosensors represent a promising class of low-cost diagnostic tools that do not require external instrumentation. However, their broader applicability is limited by the environmental concerns associated with conventional metal-based nanomaterials and the subjectivity of visual interpretation. To address these challenges, this study introduces a proof-of-concept platform—using CA19-9 as a model biomarker—that integrates naturally derived melanin nanoparticles (MNPs) with machine learning-based image analysis to enable environmentally sustainable and analytically robust colorimetric quantification. Upon target binding, MNPs induce a concentration-dependent color transition from yellow to brown. This visual signal was quantified using a machine learning pipeline incorporating automated region segmentation and regression modeling. Sensor areas were segmented using three different algorithms, with the U-Net model achieving the highest accuracy (average IoU: 0.9025 ± 0.0392). Features extracted from segmented regions were used to train seven regression models, among which XGBoost performed best, yielding a Mean Absolute Percentage Error (MAPE) of 17%. Although reduced sensitivity was observed at higher analyte concentrations due to sensor saturation, the model showed strong predictive accuracy at lower concentrations, which are especially challenging for visual interpretation. This approach enables accurate, reproducible, and objective quantification of colorimetric signals, thereby offering a sustainable and scalable alternative for point-of-care diagnostic applications. Full article

Background/Objectives: Metabolic syndrome (MetS) is a multifactorial condition characterized by abdominal obesity, dyslipidemia, insulin resistance, hypertension, and chronic inflammation. As its global prevalence rises, there is increasing interest in natural, multi-targeted approaches to manage MetS. Curcuma longa Linn. (turmeric), especially its active compound curcumin, has shown therapeutic promise in preclinical studies. This systematic review and meta-analysis evaluated the effects of Curcuma longa and its derivatives on MetS-related outcomes in rodent models. Methods: A comprehensive search was conducted across six databases (PubMed, Scopus, AMED, LILACS, MDPI, and Google Scholar), yielding 47 eligible in vivo studies. Data were extracted on key metabolic, inflammatory, and oxidative stress markers and analyzed using random-effects models. Results were presented as mean differences (MD) with 95% confidence intervals (CI). Results: Meta-analysis showed that curcumin significantly reduced body weight (rats: MD = −42.10; mice: MD = −2.91), blood glucose (rats: MD = −55.59; mice: MD = −28.69), triglycerides (rats: MD = −70.17; mice: MD = −24.57), total cholesterol (rats: MD = −35.77; mice: MD = −52.61), and LDL cholesterol (rats: MD = −69.34; mice: MD = −42.93). HDL cholesterol increased significantly in rats but not in mice. Inflammatory cytokines were markedly reduced, while oxidative stress improved via decreased malondialdehyde (MDA) and elevated superoxide dismutase (SOD) and catalase (CAT) levels. Heterogeneity was moderate to high, primarily due to variations in curcumin dosage (ranging from 10 to 500 mg/kg) and treatment duration (2 to 16 weeks) across studies. Conclusions: This preclinical evidence supports Curcuma longa as a promising functional food component for preventing and managing MetS. Its multi-faceted effects warrant further clinical studies to validate its translational potential. Full article

The Mapleton Falls National Park transmission line corridor in Queensland, Australia, has received a number of vegetation management treatments over the last decade to maintain and protect the infrastructure and to ensure continuous electricity supply. Recent treatments have included ‘mega-mulching’ (mechanical mastication of vegetation to a mulch layer) in 2020 and targeted herbicide treatment of woody vegetation, with the aim of reducing vegetation height by encouraging a native herbaceous groundcover beneath the transmission lines. We measured vegetation structure (cover and height) and composition (species presence in 15 × 2 m plots), at 12 transects, 90 m in length on the transmission line corridor, to determine if management goals were being achieved and to determine how the vegetation and fire hazard (based on the overall fuel hazard assessment method) varied among the treated corridor, the forest edge environment, and the natural forest. The results showed that vegetation structure and composition in the treated zones had been modified to a state where herbaceous plant species were dominant; there was a significantly (p < 0.05) higher native grass cover and cover of herbs, sedges, and ferns in the treated zones, and a lower cover of trees and tall woody plants (>1 m in height) in these areas. For example, mean native grass cover and the cover of herbs and sedges in the treated areas was 10.2 and 2.8 times higher, respectively, than in the natural forest. The changes in the vegetation structure (particularly removal of tall woody vegetation) resulted in a lower overall fuel hazard in the treated zones, relative to the edge zones and natural forest. The overall fuel hazard was classified as ‘high’ in 83% of the transects in the treated areas, but it was classified as ‘extreme’ in 75% of the transects in the adjacent forest zone. Importantly, there were few introduced species recorded. The results suggest that fuel management has been successful in reducing wildfire risk in the transmission corridor. Temporal monitoring is recommended to determine the frequency of ongoing fuel management. Full article

Background: Nasopharyngeal carcinoma (NPC) is an epithelial malignancy arising from the nasopharyngeal mucosa. Despite treatment advances such as the use of intensity-modulated radiotherapy and immune checkpoint inhibitors, resistance remains a significant clinical challenge. Many tumours are also diagnosed at an advanced stage associated with poor prognosis. Objective: This review aims to explore the biological roles of autophagy in NPC, primarily highlighting its involvement in disease pathogenesis and treatment resistance. Methods: We performed a review of the recent literature examining the role of autophagy-related pathways in NPC pathogenesis, biomarker discovery, and therapeutic targeting. Results: Autophagy plays a dual role in NPC as it contributes to both tumour suppression and progression. It is involved in tumour initiation, metastasis, immune modulation, and treatment resistance. Autophagy-related genes such as SQSTM1, Beclin-1, and AURKA may serve as prognostic and therapeutic biomarkers. Various strategies are being investigated for their role to modulate autophagy using pharmacologic inhibitors, RNA interventions, and natural compounds. Conclusions: Further research into autophagy’s context-dependent roles in NPC may inform the development of personalised therapies and allow progress in translational and precision oncology. Full article

Cancer remains a major global health challenge requiring the development of diagnostic and therapeutic strategies. Liquid biopsy is considered a promising minimally invasive tool for cancer screening, prognosis and treatment monitoring. Recent studies suggest that neutrophil extracellular traps (NETs) may also be potential liquid biopsy markers. NETs are web-like chromatin structures released by neutrophils in response to various stimuli to trap and neutralize pathogens. However, excessive or dysregulated NET formation has been implicated in tumor progression and metastasis. Elevated levels of NETs have been observed in patients with various types of cancer and correlate with disease stage and prognosis. The presence of NET markers such as citrullinated histone H3 (H3Cit), neutrophil elastase (NE) and myeloperoxidase (MPO) has been associated with higher tumor burden and poorer clinical outcomes. Several studies have shown a positive correlation between NET markers and circulating free DNA (cfDNA) levels, suggesting that NETs may increase the sensitivity of liquid biopsy in detecting and monitoring cancer progression. This review examines the role of NETs in the tumor microenvironment, their contribution to cancer progression and metastasis, and their potential use in liquid biopsy and cancer therapy. Full article

This study explores the role of egoistic and altruistic values on green real estate purchase intention among young consumers in Canada aged between 20 and 40 years. In addition, this study examines the mediating effects of pro-environmental self-identity between social consumption motivation and green real estate purchase intention. A quantitative cross-sectional research design with an explanatory nature is employed. A total of 432 participating consumers in Canada, comprising 44% men and 48% women, with a graduate educational background accounting for 46.7%, and the ages between 24 and 35 contributing 75.2%, were part of the study, and the data collection used a survey method with a purposive sampling, followed by a respondent-driven method. Descriptive and inferential statistics were performed on the scales used for the study variables. A structural equational model and path analysis were conducted to derive the results, and the relationships were positive and significant. The study results infer the factors contributing to green real estate purchase intention, including altruistic value, egoistic value, social consumption motivation, and pro-environmental self-identity, with pro-environmental self-identity mediating the relationship. This study emphasizes the relevance of consumer values in real estate purchasing decisions, urging developers and marketers to prioritize ethical ideas, sustainable practices, and building a feeling of belonging and social connectedness. Offering eco-friendly amenities and green construction methods might attract clients, but creating a secure area for social interaction is critical. To the best of the authors’ knowledge, this research is the first to explore the role of egoistic and altruistic values on purchase intention, mainly in the housing and real estate sector, with the target consumers being young consumers in Canada. Full article

S100 proteins, a family of Ca²⁺-binding proteins, play numerous roles in cellular processes such as proliferation, differentiation, and apoptosis. Recent evidence has highlighted their critical involvement in neuroinflammation, a pathological hallmark of various neurodegenerative disorders including Alzheimer’s disease, multiple sclerosis, and Parkinson’s disease. Among these proteins, S100B and S100A8/A9 are particularly implicated in modulating inflammatory responses in the CNS. Acting as DAMPs, they interact with pattern recognition receptors like RAGE and TLRs, triggering pro-inflammatory signaling cascades and glial activation. While low concentrations of S100 proteins may support neuroprotective functions, increased levels are often associated with exacerbated inflammation and neuronal damage. This review explores the dualistic nature of S100 proteins in neuroinflammatory processes, their molecular interactions, and their potential as biomarkers and therapeutic targets in neurodegenerative disease management. Full article

Allicin (ALC), a naturally occurring organosulfur compound derived from garlic (Allium sativum), exhibits potential neuroprotective properties. Parkinson’s disease (PD) is a progressive neurodegenerative disease characterized by degeneration of dopaminergic neurons and motor dysfunction. This study utilized bioinformatics and network pharmacology methods to predict the anti-PD mechanism of ALC and established in vivo and in vitro PD models using 6-hydroxydopamine (6-OHDA) for experimental verification. Network pharmacological analysis indicates that apoptosis regulation and the PKA/p-CREB/BDNF signaling pathway are closely related to the anti-PD effect of ALC, and protein kinase A (PKA) and dopamine transporter (DAT) are key molecular targets. The experimental results show that ALC administration can alleviate the cytotoxicity of SH-SY5Y induced by 6-OHDA and simultaneously improve the motor dysfunction and dopaminergic neuron loss in PD mice. In addition, ALC can also activate the PKA/p-CREB/BDNF signaling pathway and increase the DAT level in brain tissue, regulate the expression of BAX and Bcl-2, and reduce neuronal apoptosis. These results indicate that ALC can exert anti-PD effects by up-regulating the PKA/p-CREB/BDNF/DAT signaling pathway and inhibiting neuronal apoptosis, providing theoretical support for the application of ALC in PD. Full article

Rock bolts are crucial components in the subterranean support systems in underground mines that provide adequate structural reinforcement to the rock mass to prevent unforeseen hazards like rockfalls. This makes frequent assessments of such bolts critical for maintaining rock mass stability and minimising risks in underground mining operations. Where manual surveying of rock bolts is challenging due to the low-light conditions in the underground mines and the time-intensive nature of the process, automated detection of rock bolts serves as a plausible solution. To that end, this study focuses on the automatic identification of rock bolts within medium- to large-scale 3D point clouds obtained from underground mines using mobile laser scanners. Existing techniques for automated rock bolt identification primarily rely on feature engineering and traditional machine learning approaches. However, such techniques lack robustness as these point clouds present several challenges due to data noise, varying environments, and complex surrounding structures. Moreover, the target rock bolts are extremely small objects within large-scale point clouds and are often partially obscured due to the application of reinforcement shotcrete. Addressing these challenges, this paper proposes an approach termed DeepBolt, which employs a novel two-stage deep learning architecture specifically designed for handling severe class imbalance for the automatic and efficient identification of rock bolts in complex 3D point clouds. The proposed method surpasses state-of-the-art semantic segmentation models by up to 42.5% in Intersection over Union (IoU) for rock bolt points. Additionally, it outperforms existing rock bolt identification techniques, achieving a 96.41% precision and 96.96% recall in classifying rock bolts, demonstrating its robustness and effectiveness in complex underground environments. Full article