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Protan triggered colorimetric and fluorometric responsive coumarin coupled imidazole as Co2+ sensor, DFT and zebrafish bioimaging studies
1,2-Oxygen Transposition on Arenes Enabled by Palladium/Norbornene Cooperative Catalysis
Effect of zinc oxide or selenium nanoparticles on body weight, growth related genes and physiology in Baladi goats
Abstract Enhancing meat production is essential to meet the rising global demand for animal protein, improve food security, and support sustainable agriculture. This study evaluated the effects of zinc oxide (ZnO) and selenium (Se), each administered individually in conventional or nanoparticle (NP) forms on growth of Egyptian Baladi goats. Twenty-two pregnant Egyptian Baladi goats were divided into five groups, each receiving a different treatment via drinking water: 150 mg ZnO, 15 mg ZnO-NPs, 0.3 mg Se, or 0.03 mg Se-NPs. The control group received unsupplemented water. Treatments began 30 days before parturition and continued until weaning (90 days postpartum). Body weight, expression of GH, IGF-1, and leptin genes, along with physiological parameters, were evaluated. Goats receiving either ZnO-NPs or Se-NPs had significantly higher body weights at parturition and greater weight gain from birth to weaning than those in the conventional elements and control groups. Suckling kids from ZnO-NP or Se-NP-treated goats showed significantly higher birth and weaning weights, total body gain, and daily weight gain ( P < 0.001), particularly in the ZnO-NP group. Gene expression analysis revealed upregulated GH and IGF-1 and leptin expression in ZnO-NP- and Se-NP-treated goats, with the highest levels observed in the Se-NPs group. Physiological analysis showed protein and esterase isoenzyme pattern changes in ZnO-treated goats, while Se caused no alterations. Neither ZnO nor Se affected catalase, peroxidase or α-amylase activity. These findings highlight the potential of ZnO-NPs and Se-NPs as effective and safe nutritional supplements for improving livestock productivity.
Engineering Polyketide Stereocenters with Ketoreductase Domain Exchanges
The therapeutic potential of Zuogui Wan in oligoasthenozoospermia: insights from network pharmacology, molecular docking, molecular dynamics simulation, and experimental validation
Abstract Oligoasthenozoospermia (OAS) is a major cause of male infertility, with limited effective treatments. Chinese patent medicine Zuogui Wan (ZGW) has been traditionally used to improve sperm quality, but its molecular mechanisms remain unclear. This study integrates network pharmacology, molecular docking, molecular dynamics (MD) simulation, and in vivo and in vitro experiments to explore ZGW’s therapeutic effects in OAS. Active compounds and targets of ZGW were identified using network pharmacology, and intersecting OAS-related targets underwent enrichment and protein-protein interaction (PPI) analysis. Molecular docking and MD simulations assessed compound-target binding affinity and stability. In vitro , CCK-8 assays measured cell proliferation, while qPCR and Western blot analyzed key gene and protein expression. In vivo , a rat OAS model was used to evaluate ZGW’s therapeutic effects through transmission electron microscopy (TEM), hematoxylin & eosin (HE) staining, and TUNEL assays. The expression of key molecular targets was further validated by qPCR and Western blot. A total of 182 potential targets were identified, with TP53, NF-κB1, and PKC as key hub genes. KEGG pathway analysis highlighted the involvement of the PI3K-AKT and MAPK signaling pathways.Four core bioactive compounds—Cyasterone, Betavulgarin, Kaempferol, and Quercetin—were identified, with Cyasterone exhibiting the strongest binding affinity and highest stability.In vitro experiments demonstrated that ZGW significantly promoted cell proliferation and regulated apoptosis-related gene expression, indicating its potential in enhancing sperm function. In vivo , ZGW improved testicular structure, enhanced sperm quality, and reduced spermatogenic cell apoptosis, as evidenced by TEM, HE, and TUNEL assays. Molecular validation further confirmed ZGW’s modulation of key signaling pathways involved in OAS. ZGW modulates apoptosis, oxidative stress, and key pathways (PI3K-AKT, MAPK) while regulating TP53, NF-κB, and PKC expression. Cyasterone exhibits strong binding and stability with core targets. This study supports ZGW as a potential treatment for male infertility.
Enantioselective Synthesis of (+)-Auriculatol A
Variational quantum enhanced deep transfer learning for small underwater aqua species image classification
Abstract Precise underwater classification of small aquaculture species is essential for sustainable fisheries management, biodiversity monitoring, and automated marine ecosystem analysis. But it is still a challenging task owing to underwater image distortions from poor visibility, lighting changes, occlusions, and the high computational complexity of traditional deep learning models. To address these issues, we propose a Lightweight Variational Quantum Enhanced Deep Transfer Learning framework. This hybrid deep transfer learning model integrates pretrained classical convolutional neural networks with variational quantum circuits to improve feature representation and classification efficiency. The framework is designed to reduce computational complexity while enhancing accuracy by leveraging quantum feature extraction techniques. Experimental evaluations on curated small aquafarming species dataset demonstrate that the proposed approach achieves high classification accuracy (up to 99.25%) with significantly fewer parameters and floating-point operations, indicating its potential for resource-constrained applications. Ablation studies further validate the impact of quantum layers on model performance. These results suggest that quantum deep transfer learning models can offer a promising direction for robust and efficient underwater species classification.
Dynamic Atomistic Polar Structure Underpins Ultrahigh Linear Electro-Optic Coefficient in Transparent Ferroelectric Ceramics
The joint effects of multiple air pollutants, genetic susceptibility, and their interactions on incident cardiovascular disease, arrhythmia, and mortality
Red and near-infrared light treatment can change the intensity of biophoton emissions in cell culture
Nonstandard power grid frequency statistics across continents
Abstract Power-grid frequency reflects the balance between electricity supply and demand in a power system. Measuring the frequency and its variations allows monitoring of the power balance in the system and, thus, frequency grid stability. Gaining insight into the characteristics of frequency variations and defining precise evaluation metrics for these variations enable better assessment of the performance of forecasts and synthetic models of the power-grid frequency. Previous work on the power grid frequency analysis was limited to a few geographical regions and did not quantify the observed effects. In the present contribution, we analyze and quantify the statistical and stochastic properties of self-recorded power-grid frequency data from various synchronous areas in Asia, Australia, and Europe at a sampling resolution of one second. Revealing non-standard statistics of both empirical and synthetic frequency data, we effectively constrain the space of possible (stochastic) power-grid frequency models and share a range of analysis tools to benchmark any model or characterize empirical data. Furthermore, we emphasize the need to analyze data from a large range of synchronous areas to obtain generally applicable models.
Altering the Thermodynamics of Stimuli-Responsive Derivatives through Layered Hybrid Material Design
Global perspectives on infectious diseases at risk of escalation and their drivers
Abstract Infectious disease burden is dynamic and devastating across the globe. We need to better understand and predict these threats to mitigate harm from new, re-emergent and endemic pathogens. 3,752 globally diverse participants took part in this two-step, mixed-methods adapted Delphi study. Firstly, an online survey asked health workers and researchers to identify the infectious diseases they considered to be at greatest risk of escalation in their setting, along with the factors driving this. Secondly, structured thematic workshops were hosted in Africa, Asia and Latin America, to allow in-depth exploration of the factors driving the prioritisation of these diseases. Participants considered the primary threat to be the escalation of high burden, endemic diseases, rather than emerging or re-emerging pathogen outbreaks. This was driven by the high prioritisation of vector-borne diseases (primarily malaria and dengue), tuberculosis, and HIV/AIDS. Whilst the main finding from survey responses (n = 3,700) identified growing concern over tuberculosis, participants in the subsequent workshops (n = 169) emphasised the increasing threat of vector-borne diseases. Participants considered the impact of climate change, socioeconomic factors and increasing drug resistance patterns to be driving the escalation of these diseases. This study provides striking new insight into priority infection threats due to the large scale of participation, breadth of stakeholder experience, and wide global representation. These factors allowed us to accurately determine the consensus of a substantial component of the global infectious disease research community and share unprecedented insights from the lived experience of researchers and health workers in low resource settings. We consider these perspectives particularly valuable given the absence of biological data that concurrently assesses all infectious diseases across global regions at a single point in time. Our findings represent an important new evidence-based alarm call; the next pandemic may not be a sudden event, but a slow, ‘creeping catastrophe’, impacting the most impoverished regions and communities. We need to respond now, having heard these important, consistent opinions from these previously unheard and collective voices.
Biocatalytic Activation of Sulfur Heteroaromatics Facilitates Dearomatizing Cross-Couplings to Set Stereogenic Centers or Axes
Blood transfusion and mortality after spine osteotomy: a nationwide population-based retrospective cohort study
Conditional UNet emulation of CMAQ simulations for fine particulate matter concentration prediction
Identified endoplasmic reticulum stress-related molecular cluster and immune characterization in endometriosis
Abstract Endometriosis is a common disease among women of childbearing age, and endoplasmic reticulum stress (ERS), a response involved in regulating protein homeostasis, has been linked to its pathogenesis. To identify ERS-related hub genes, this study sequentially employed differential expression analysis, weighted gene co-expression network analysis (WGCNA), protein–protein interaction (PPI) network construction, and three machine learning algorithms. These methods led to the identification of four hub genes: Von Willebrand factor (VWF), vascular cell adhesion molecule 1 (VCAM1), endothelial PAS domain protein 1 (EPAS1), and coagulation factor VIII (F8). Unsupervised cluster analysis was conducted to categorize samples into ERS clusters, and the CIBERSORT algorithm was used to calculate immune infiltration scores, revealing two stable clusters. Cluster B was defined as “immune-enriched” with significantly higher immune scores, while Cluster A was “less immune-enriched”. Functional enrichment analysis of differentially expressed genes (DEGs) between the clusters highlighted cell adhesion and regulation of immune cell activation as key to cluster-specific phenotypes. A diagnostic model built with the four hub genes showed robust utility via validation curves, confirming their clinical relevance. DEGs from each cluster were screened in the Connectivity Map database to identify cluster-specific therapeutic agents. RT-qPCR and immunohistochemistry (IHC) validated that both mRNA and protein levels of the four hub genes were elevated in endometriosis tissues, supporting the bioinformatics findings. Overall, this study links ERS-related hub genes to endometriosis subtyping, immune infiltration, and diagnostics, providing a basis for personalized treatments and a potential clinical tool.
Integrating mass spectrometry and hyperspectral imaging for protoporphyrin IX detection in malignant glioma tissue
Abstract Maximal safe tumor resection is crucial for the treatment of high-grade gliomas (HGG). 5-aminolevulinic acid (5-ALA)-mediated fluorescence-guided surgery enhances tumor visualization by inducing protoporphyrin IX (PpIX) accumulation. However, current fluorescence-based observation devices lack the sensitivity for detecting tumor cells in low-density infiltrative zones. Hyperspectral imaging (HI) offers a potential solution. In this study, HI-derived PpIX measurements were compared to those obtained from reversed-phase liquid chromatography coupled to mass spectrometry (LC–MS), a method that delivers accurate concentrations. Additionally, we investigated coproporphyrins (Cp) I and III, since they potentially interfere with PpIX determination. Pig brain was used as a surrogate for protocol development and acquisition of comparative HI and LC–MS reference data, which were subsequently used to evaluate the results obtained from 27 biopsies from nine patients undergoing 5-ALA-mediated tumor resection. During sample preparation for LC–MS, 80% PpIX and 45% combined Cp I & III were recovered from brain tissue. For LC–MS quantification of PpIX, accuracy ranged from 98 to 137%, and coefficient of variation was 5–14%, indicating sufficient precision. For HI, the values were 77–121% and 11–31%, respectively. Notably, HI significantly overestimated PpIX concentrations compared to those determined by LC–MS. This study highlights LC–MS as a reliable method for porphyrin quantification and suggests that HI workflows need further optimization for accurate tumor delineation in HGG.