Facile fabrication of a thermal/pH responsive IPN hydrogel drug carrier based on cellulose and chitosan through simultaneous dual-click strategy.

Herein, an interpenetrating network hydrogel (IPN-Gel) based on cellulose and chitosan was synthesized via simultaneous amino-anhydride and azide-alkyne click reaction in water in one pot. The samples were characterized by various analytical methods including FTIR, SEM, XRD, XPS, 1H NMR and so forth. The fabrication conditions were optimized by single factor experiments with water uptake (WU) and gel mass fraction (GMF) as two indexes. The WU and GMF of the IPN-Gel prepared under optimized conditions were 1192.37 % and 74.00 %, respectively. Its WU descended with the ascension in temperature, and first descended and then gradually ascended with the ascension in pH, confirming that the IPN-Gel had thermal/pH dual responsiveness. Using 5-Fu as a model drug, the release behavior of 5-Fu in IPN-Gel was explored. Its release behavior could be regulated by changing temperature and pH values, and it followed the Korsmeyer Peppas model. The viability of 4 T1 cells and HUVEC cells exceeded 80 % after 48 h of incubation at a high concentration of 200 µg/mL IPN-Gel, and hemolytic percentage was below the allowed limit of 5 %. The study provides a new strategy for the preparation of the IPN-Gel with biocompatibility, swelling reversibility and controllable drug release.

Optoelectronic neuron based on transistor combined with volatile threshold switching memristors for neuromorphic computing.

The human perception and learning heavily rely on the visual system, where the retina plays a vital role in preprocessing visual information. Developing neuromorphic vision hardware is based on imitating the neurobiological functions of the retina. In this work, an optoelectronic neuron is developed by combining a gate-modulated PDVT-10 channel with a volatile threshold switching memristor, enabling the achievement of optoelectronic performance through a resistance-matching mechanism. The optoelectronic spiking neuron exhibits the ability to alter its spiking behavior in a manner resembling that of a retina. Incorporating electrical and optical modulation, the artificial neuron accurately replicates neuronal signal transmission in a biologically manner. Moreover, it demonstrates inhibition of neuronal firing during darkness and activation upon exposure to light. Finally, the evaluation of a perceptron spiking neural network utilizing these leaky integrate-and-fire neurons is conducted through simulation to assess its capability in classifying image recognition algorithms. This research offers a hopeful direction for the development of easily expandable and hierarchically structured spiking electronics, broadening the range of potential applications in biomimetic vision within the emerging field of neuromorphic hardware.

Injectable double-crosslinked bone cement with enhanced bone adhesion and improved osteoporotic pathophysiological microenvironment for osteoregeneration in osteoporosis.

The osteoporotic bone defect caused by excessive activity of osteoclasts has posed a challenge for public healthcare. However, most existing bioinert bone cement fails to effectively regulate the pathological bone microenvironment and reconstruct bone homeostasis in the presence of osteoclast overactivity and osteoblast suppression. Herein, inspired by natural bone tissue, an in-situ modulation system for osteoporotic bone regeneration is developed by fabricating an injectable double-crosslinked PEGylated poly(glycerol sebacate) (PEGS)/calcium phosphate cement (CPC) loaded with sodium alendronate (ALN) (PEGS/CPC@ALN) adhesive bone cement. By incorporating ALN, the organic-inorganic interconnection within PEGS/CPC@ALN results in a 100 % increase in compression modulus and energy dissipation efficiency. Additionally, PEGS/CPC@ALN effectively adheres to the bone by bonding with amine and calcium ions present on the bone surface. Moreover, this in-situ regulation system comprehensively mitigates excessive bone resorption through the buffering effect of CPC to improve the acidic microenvironment of osteoporotic bone and the release of ALN to inhibit hyperactive osteoclasts, and facilitates stem cell proliferation and differentiation into osteoblasts through calcium ion release. Overall, the PEGS/CPC@ALN effectively regulates the pathological microenvironment of osteoporosis while promoting bone regeneration through synergistic effects of drugs and materials, thereby improving bone homeostasis and enabling minimally invasive treatment for osteoporotic defects.

The Functional Comparison of Eukaryotic Proteomes: Implications for Choosing an Appropriate Model Organism to Probe Human Biology.

Phenotypic differences between species are, in significant part, determined by their proteomic diversity. The link between proteomic and phenotypic diversity can be best understood in the context of the various pathways and biological processes in which proteins participate. While the conservation pattern for individual proteins across species is expected to follow the phylogenetic relationships among the species, the diversity patterns of individual pathways may not: certain pathways may be much more conserved among distantly related species than two closely related species, owing to the ecological histories of the species. Thus, a pathway-centric analysis of proteome conservation and diversity has important implications for the appropriate choice of a model organism when investigating specific aspects of human biology. Exploiting the complete genome sequences and protein-coding gene annotations, here we perform a comprehensive gene-set-centric analysis of proteomic diversity between humans and 54 eukaryotic organisms, resulting in a catalog of organisms that are most similar to humans in terms of specific pathways, processes, expression patterns, and diseases. We corroborate our findings using species-specific mass spectrometry data.Our analysis provides a general framework to identify conserved and unique pathways in a group of organisms and a resource to prioritize appropriate model systems to study a specific biological system in a reference organism such as humans.

A tunable LDI-MS platform assisted by metal-phenolic network-coated AuNPs for sensitive and customized detection of amino acids.

This study introduces a novel approach for the sensitive and accurate detection of small molecule metabolites, employing metal-phenolic network (MPN) functionalized AuNPs as both adsorbent and matrix to enhance laser desorption/ionization mass spectrometry (LDI-MS) performance. The MPN comprising tannic acid (TA) and transition metal ions (Fe3+, Co2+, Ni2+, Cu2+, or Zn2+) was coated on the surface of AuNPs, forming metal-TA network-coated AuNPs (M-TA@AuNPs). The M-TA@AuNPs provided a tunable surface for specific interactions with analytes, displaying distinct enrichment efficacies for different amino acids, especially for Cu-TA@AuNPs exhibiting the highest affinity for histidine (His). Under the optimized condition, the proposed method enabled ultrasensitive detection of His, with good linearity (R2 = 0.9627) in the low-concentration range (50 nM-1 µM) and a limit of detection (LOD) as low as 0.9 nM. Furthermore, the method was successfully applied to detect His from human urine samples, showcasing its practical applications in clinical diagnostics, particularly in the realm of amino acid-based targeted metabolomics.

Calcium Imaging and Analysis in Beta Cells in Acute Mouse Pancreas Tissue Slices.

Ca2+ ions play a central role in the stimulus-secretion coupling cascade of pancreatic beta cells. The use of confocal microscopy in conjunction with the acute pancreas tissue slice technique offers valuable insights into changes in the intracellular calcium concentration following stimulation by secretagogues. This allows the study of beta cells on a single cell level, as well as their behavior on a multicellular scale within an intact environment. With the use of advanced analytical tools, this approach offers insight into how single cells contribute to the functional unit of islets of Langerhans and processes underlying insulin secretion. Here we describe a comprehensive protocol for the preparation and utilization of acute pancreas tissue slices in mice, the use of high-resolution confocal microscopy for observation of glucose-stimulated calcium dynamics in beta cells, and the computational analysis for objective evaluation of calcium signals.

A self-healing composite solid electrolyte with dynamic three-dimensional inorganic/organic hybrid network for flexible all-solid-state lithium metal batteries.

Composite solid electrolytes (CSEs), which combine the advantages of solid polymer electrolytes and inorganic solid electrolytes, are considered to be promising electrolytes for all-solid-state lithium metal batteries. However, the current CSEs suffer from defects such as poor inorganic/organic interface compatibility, lithium dendrite growth, and easy damage of electrolyte membrane, which hinder the practical application of CSEs. Herein, a CSE (PBHL@LLZTO@DDB) with polyurethane (PBHL) as the polymer matrix and Li6.4La3Zr1.4Ta0.6O12 (LLZTO) modified by silane coupling agent (DDB) as inorganic fillers (LLZTO@DDB) has been prepared. Disulfide bond exchange reactions between PBHL and LLZTO@DDB enable PBHL@LLZTO@DDB to form a dynamic three-dimensional (3D) inorganic/organic hybrid network, which promotes the uniform dispersion of LLZTO in PBHL@LLZTO@DDB, improves the Li+ conductivity (1.24 ± 0.08 × 10-4 S cm-1 at 30 â„ƒ), and broadens the electrochemical stability window (5.16 V vs. Li+/Li). Moreover, a combination of hydrogen bonds and disulfide bonds endows PBHL@LLZTO@DDB with excellent self-healing properties. As such, both all-solid-state symmetric and full cells exhibit excellent cycle performance at ambient temperature. More importantly, the healed PBHL@LLZTO@DDB can almost completely restore its original electrochemical properties, indicating its application potential in flexible electronic products.

Maternal ECG-Guided Neural Network for Improved Fetal Electrocardiogram Extraction.

Historically, acquiring a reliable and accurate non-invasive fetal electrocardiogram has several significant challenges in both data acquisition and attenuation of maternal signals. These barriers include maternal physical/physiological parameters, hardware sensitivity, and the effectiveness of signal processing algorithms in separating maternal and fetal electrocardiograms. In this paper, we focus on the evaluation of signal-processing algorithms. Here, we propose a learning-based method based on the integration of maternal electrocardiogram acquired as guidance for transabdominal fetal electrocardiogram signal extraction. The results demonstrate that incorporating the maternal electrocardiogram signal as input for training the neural network outperforms the network solely trained using information from the abdominal electrocardiogram. This indicates that leveraging the maternal electrocardiogram serves as a suitable prior for effectively attenuating maternal electrocardiogram from the abdominal electrocardiogram.

Influence of different exercise types on vascular endothelial function in middle-aged and older adults - A systematic review and network meta-analysis.

STUDY OBJECTIVES: Against the current backdrop of population ageing, the correlation between cardiovascular diseases and endothelial dysfunction is increasingly important. Exercise, a simple and accessible method of preventing and ameliorating numerous diseases, has been demonstrated to significantly enhance endothelial function. This study aimed to assess the effects of aerobic exercise (AE), resistance exercise (RE), combined exercise (CE) and high-intensity interval training (HIIT) on vascular endothelial function in middle-aged and older adults. Flow-mediated dilation (FMD) is a non-invasive ultrasound technique used to measure endothelial function. Direct and indirect comparisons were used to determine which exercise modality most effectively improved vascular endothelial function in this demographic. METHODS: This comprehensive systematic review and network meta-analysis examined randomised controlled trials (RCTs) comparing the effects of four different exercise interventions (AE, RE, CE and HIIT) to a control intervention on FMD in middle-aged and older adults. RESULTS: The analysis included 20 RCTs involving 1,123 participants. The surface under the cumulative ranking curve (SUCRA) analysis indicated that AE was the most effective in improving FMD (SUCRA = 68.9 %), followed by HIIT (SUCRA = 62.5 %), RE (SUCRA = 58.8 %), CE (SUCRA = 54.9 %) and CON (SUCRA = 4.9 %). CONCLUSIONS: This network meta-analysis of various interventions for FMD in middle-aged and older adults found that AE was the most effective in improving FMD (SUCRA = 68.9 %). These findings suggest that AE could be a valuable intervention in clinical practice for enhancing vascular health in this population.

Genome­wide identification of circular RNAs and MAPKs reveals the regulatory networks in response to green peach aphid infestation in peach (Prunus persica).

The green peach aphid (GPA), Myzus persicae (Sulzer), is a serious agricultural pest with a worldwide distribution and a vector of over 100 plant viruses. Various pathways, such as the mitogen-activated protein kinase (MAPK) cascades, play pivotal roles in signaling plant defense against pest attack, and circular RNAs (circRNAs) regulate the expression of mRNAs in response to pest attack. However, the mechanism underlying peach (Prunus persica) response to GPA attack remains unclear. The present study initially identified and characterized 316 circRNAs and 18 PpMAPKs from healthy and GPA-infested peach leaves by whole-transcriptome sequencing and predicted the differentially expressed circRNAs (DECs) after GPA infestation. PCR and Sanger sequencing confirmed the presence of six DECs in peach samples. Besides, RNA sequencing analysis detected 13 DECs, including 5 upregulated and 8 downregulated ones, in peach in response to the GPA attack. Gene ontology (GO) enrichment analysis indicated that specific DECs play crucial roles in the MAPK signaling pathway, and qRT-PCR revealed that GPA infestation altered the expression patterns of PpMAPKs. Finally, five circRNAs, three microRNA (miRNAs), and two MAPK target genes were identified to interact as a network and perform critical roles in modulating the MAPK pathway in the peach during GPA infestation.

Machine learning vs. regression models to predict the risk of Legionella contamination in a hospital water network.

Introduction: The periodic monitoring of Legionella in hospital water networks allows preventive measures to be taken to avoid the risk of legionellosis to patients and healthcare workers. Study design: The aim of the study is to standardize a method for predicting the risk of Legionella contamination in the water supply of a hospital facility, by comparing Machine Learning, conventional and combined models. Methods: During the period July 2021- October 2022, water sampling for Legionella detection was performed in the rooms of an Italian hospital pavilion (89.9% of the total number of rooms). Fifty-eight parameters regarding the structural and environmental characteristics of the water network were collected. Models were built on 70% of the dataset and tested on the remaining 30% to evaluate accuracy, sensitivity, and specificity. Results: A total of 1,053 water samples were analyzed and 57 (5.4%) were positive for Legionella. Of the Machine Learning models tested, the most efficient had an input layer (56 neurons), hidden layer (30 neurons), and output layer (two neurons). Accuracy was 93.4%, sensitivity was 43.8%, and specificity was 96%. The regression model had an accuracy of 82.9%, sensitivity of 20.3%, and specificity of 97.3%. The combination of the models achieved an accuracy of 82.3%, sensitivity of 22.4%, and specificity of 98.4%. The most important parameters that influenced the model results were the type of water network (hot/cold), the replacement of filter valves, and atmospheric temperature. Among the models tested, Machine Learning obtained the best results in terms of accuracy and sensitivity. Conclusions: Future studies are required to improve these predictive models by expanding the dataset using other parameters and other pavilions of the same hospital.

CineVN: Variational network reconstruction for rapid functional cardiac cine MRI.

PURPOSE: To develop a reconstruction method for highly accelerated cardiac cine MRI with high spatiotemporal resolution and low temporal blurring, and to demonstrate accurate estimation of ventricular volumes and myocardial strain in healthy subjects and in patients. METHODS: The proposed method, called CineVN, employs a spatiotemporal Variational Network combined with conjugate gradient descent for optimized data consistency and improved image quality. The method is first evaluated on retrospectively undersampled cine MRI data in terms of image quality. Then, prospectively accelerated data are acquired in 18 healthy subjects both segmented over two heartbeats per slice as well as in real time with 1.6 mm isotropic resolution. Ventricular volumes and strain parameters are computed and compared to a compressed sensing reconstruction and to a conventional reference cine MRI acquisition. Lastly, the method is demonstrated in 46 patients and ventricular volumes and strain parameters are evaluated. RESULTS: CineVN outperformed compressed sensing in image quality metrics on retrospectively undersampled data. Functional parameters and myocardial strain were the most accurate for CineVN compared to two state-of-the-art compressed sensing methods. CONCLUSION: Deep learning-based reconstruction using our proposed method enables accurate evaluation of cardiac function in real-time cine MRI with high spatiotemporal resolution. This has the potential to improve cardiac imaging particularly for patients with arrhythmia or impaired breath-hold capability.

Fusion of convolutional neural network with XGBoost feature extraction for predicting multi-constituents in corn using near infrared spectroscopy.

Near-infrared (NIR) spectroscopy has been widely utilized to predict multi-constituents of corn in agriculture. However, directly extracting constituent information from the NIR spectra is challenging due to many issues such as broad absorption band, overlapping and non-specific nature. To solve these problems and extract implicit features from the raw data of NIR spectra to improve performance of quantitative models, a one-dimensional shallow convolutional neural network (CNN) model based on an eXtreme Gradient Boosting (XGBoost) feature extraction method was proposed in this paper. The leaf node feature information in the XGBoost was encoded and reconstructed to obtain the implicit features of raw data in the NIR spectra. A two-parametric Swish (TSwish or TS) activation function was proposed to improve the performance of CNN, and the elastic net (EN) was also applied to avoid the overfitting problem of the CNN model. Performance of the developed XGBoost-CNN-TS-EN model was evaluated using two public NIR spectroscopy datasets of corn and soil, and the obtained determination coefficients (R2) for moisture, oil, protein, and starch of the corn on test set were 0.993, 0.991, 0.998, and 0.992, respectively, with that of the soil organic matter being 0.992. The XGBoost-CNN-TS-EN model exhibits superior stability, good prediction accuracy, and generalization ability, demonstrating its great potentials for quantitative analysis of multi-constituents in spectroscopic applications.

Impact of residual antibiotics on microbial decomposition of livestock manures in Eutric Regosol: Implications for sustainable nutrient recycling and soil carbon sequestration.

The land application of livestock manure has been widely acknowledged as a beneficial approach for nutrient recycling and environmental protection. However, the impact of residual antibiotics, a common contaminant of manure, on the degradation of organic compounds and nutrient release in Eutric Regosol is not well understood. Here, we studied, how oxytetracycline (OTC) and ciprofloxacin (CIP) affect the decomposition, microbial community structure, extracellular enzyme activities and nutrient release from cattle and pig manure using litterbag incubation experiments. Results showed that OTC and CIP greatly inhibited livestock manure decomposition, causing a decreased rate of carbon (28%-87%), nitrogen (15%-44%) and phosphorus (26%-43%) release. The relative abundance of gram-negative (G-) bacteria was reduced by 4.0%-13% while fungi increased by 7.0%-71% during a 28-day incubation period. Co-occurrence network analysis showed that antibiotic exposure disrupted microbial interactions, particularly among G- bacteria, G+ bacteria, and actinomycetes. These changes in microbial community structure and function resulted in decreased activity of urease, ß-1,4-N-acetyl-glucosaminidase, alkaline protease, chitinase, and catalase, causing reduced decomposition and nutrient release in cattle and pig manures. These findings advance our understanding of decomposition and nutrient recycling from manure-contaminated antibiotics, which will help facilitate sustainable agricultural production and soil carbon sequestration.

Investigation of spatiotemporal distribution and formation mechanisms of ozone pollution in eastern Chinese cities applying convolutional neural network.

Severe ground-level ozone (O3) pollution over major Chinese cities has become one of the most challenging problems, which have deleterious effects on human health and the sustainability of society. This study explored the spatiotemporal distribution characteristics of ground-level O3 and its precursors based on conventional pollutant and meteorological monitoring data in Zhejiang Province from 2016 to 2021. Then, a high-performance convolutional neural network (CNN) model was established by expanding the moment and the concentration variations to general factors. Finally, the response mechanism of O3 to the variation with crucial influencing factors is explored by controlling variables and interpolating target variables. The results indicated that the annual average MDA8-90th concentrations in Zhejiang Province are higher in the northern and lower in the southern. When the wind direction (WD) ranges from east to southwest and the wind speed (WS) ranges between 2 and 3 m/sec, higher O3 concentration prone to occur. At different temperatures (T), the O3 concentration showed a trend of first increasing and subsequently decreasing with increasing NO2 concentration, peaks at the NO2 concentration around 0.02 mg/m3. The sensitivity of NO2 to O3 formation is not easily affected by temperature, barometric pressure and dew point temperature. Additionally, there is a minimum [Formula: see text] at each temperature when the NO2 concentration is 0.03 mg/m3, and this minimum [Formula: see text] decreases with increasing temperature. The study explores the response mechanism of O3 with the change of driving variables, which can provide a scientific foundation and methodological support for the targeted management of O3 pollution.

Quantitative predictions of protein and total flavonoids content in Tartary and common buckwheat using near-infrared spectroscopy and chemometrics.

A rapid method was developed for determining the total flavonoid and protein content in Tartary buckwheat by employing near-infrared spectroscopy (NIRS) and various machine learning algorithms, including partial least squares regression (PLSR), support vector regression (SVR), and backpropagation neural network (BPNN). The RAW-SPA-CV-SVR model exhibited superior predictive accuracy for both Tartary and common buckwheat, with a high coefficient of determination (R2p = 0.9811) and a root mean squared error of prediction (RMSEP = 0.1071) for flavonoids, outperforming both PLSR and BPNN models. Additionally, the MMN-SPA-PSO-SVR model demonstrated exceptional performance in predicting protein content (R2p = 0.9247, RMSEP = 0.3906), enhancing the effectiveness of the MMN preprocessing technique for preserving the original data distribution. These findings indicate that the proposed methodology could efficiently assess buckwheat adulteration analysis. It can also provide new insights for the development of a promising method for quantifying food adulteration and controlling food quality.

A new method for the rapid identification of external water types in rainwater pipeline networks using UV-Vis absorption spectroscopy.

Ultraviolet-visible (UV-Vis) absorption spectroscopy, due to its high sensitivity and capability for real-time online monitoring, is one of the most promising tools for the rapid identification of external water in rainwater pipe networks. However, difficulties in obtaining actual samples lead to insufficient real samples, and the complex composition of wastewater can affect the accurate traceability analysis of external water in rainwater pipe networks. In this study, a new method for identifying external water in rainwater pipe networks with a small number of samples is proposed. In this method, the Generative Adversarial Network (GAN) algorithm was initially used to generate spectral data from the absorption spectra of water samples; subsequently, the multiplicative scatter correction (MSC) algorithm was applied to process the UV-Vis absorption spectra of different types of water samples; following this, the Variational Mode Decomposition (VMD) algorithm was employed to decompose and recombine the spectra after MSC; and finally, the long short-term memory (LSTM) algorithm was used to establish the identification model between the recombined spectra and the water source types, and to determine the optimal number of decomposed spectra K. The research results show that when the number of decomposed spectra K is 5, the identification accuracy for different sources of domestic sewage, surface water, and industrial wastewater is the highest, with an overall accuracy of 98.81%. Additionally, the performance of this method was validated by mixed water samples (combinations of rainwater and domestic sewage, rainwater and surface water, and rainwater and industrial wastewater). The results indicate that the accuracy of the proposed method in identifying the source of external water in rainwater reaches 98.99%, with detection time within 10 s. Therefore, the proposed method can become a potential approach for rapid identification and traceability analysis of external water in rainwater pipe networks.

Fast dentification of overlapping fluorescence spectra of oil species based on LDA and two-dimensional convolutional neural network.

Although most petroleum oil species can be identified by their fluorescence spectra, overlapping fluorescence spectra make identification difficult. This study aims to address the issue that fluorescence spectroscopy is ineffective in identifying overlapping oil species. In this study, an equivalent model of overlapping oil species with fluorescence spectra was established. The linear discriminant analysis (LDA)-assisted machine learning (ML) algorithms K nearest neighbor (KNN), decision tree (DT), and random forest (RF) improved the identification of fluorescent spectrally overlapping oil species for diesel-lubricant oils. The identification accuracies of two-dimensional convolutional neural network (2DCNN), LDA combined with the ML algorithms effectively all 100 %. Furthermore, Partial Least Squares Regression (PLSR) algorithm, Support Vector Regression (SVR) algorithm, DT regression algorithm, and RF regression algorithm were also used to identify the lubricant concentration in diesel-lubricant oils. The coefficient of determination of the DT was 1, and the root-mean-square error was 0, which identified the concentration of lubricant oils in them accurately and without error.

Exploring antithrombotic mechanisms and effective constituents of Lagopsis supina using an integrated strategy based on network pharmacology, molecular docking, metabolomics, and experimental verification in rats.

ETHNOPHARMACOLOGICAL RELEVANCE: Thrombosis is a common cause of morbidity and mortality worldwide. Lagopsis supina (Stephan ex Willd.) Ikonn.-Gal. ex Knorring is an ancient Chinese herbal medicine used for treating thrombotic diseases. Nevertheless, the antithrombotic mechanisms and effective constituents of this plant have not been clarified. AIM OF THE STUDY: This work aimed to elucidate the pharmacodynamics and mechanism of L. supina against thrombosis. MATERIALS AND METHODS: Systematic network pharmacology was used to explore candidate effective constituents and hub targets of L. supina against thrombosis. Subsequently, the binding affinities of major constituents with core targets were verified by molecular docking analysis. Afterward, the therapeutic effect and mechanism were evaluated in an arteriovenous bypass thrombosis rat model. In addition, the serum metabolomics analysis was conducted using ultra-high performance liquid chromatography coupled with Q-Exactive mass spectrometry. RESULTS: A total of 124 intersected targets of L. supina against thrombosis were predicted. Among them, 24 hub targets were obtained and their mainly associated with inflammation, angiogenesis, and thrombosis approaches. Furthermore, 9 candidate effective constituents, including (22E,24R)-5α,8α-epidioxyergosta-6,22-dien-3ß-ol, aurantiamide, (22E,24R)-5α,8α-epidioxyergosta-6,9 (11),22-trien-3ß-ol, lagopsinA, lagopsin C, 15-epi-lagopsin C, lagopsin D, 15-epi-lagopsin D, and lagopsin G in L. supina and 6 potential core targets (TLR-4, TNF-α, HIF-1α, VEGF-A, VEGFR-2, and CLEC1B) were acquired. Then, these 9 constituents demonstrated strong binding affinities with the 6 targets, with their lowest binding energies were all less than -5.0 kcal/mol. The antithrombotic effect and potential mechanisms of L. supina were verified, showing a positively associated with the inhibition of inflammation (TNF-α, IL-1ß, IL-6, IL-8, and IL-10) and coagulation cascade (TT, APTT, PT, FIB, AT-III), promotion of angiogenesis (VEGF), suppression of platelet activation (TXB2, 6-keto-PGF1α, and TXB2/6-keto-PGF1α), and prevention of fibrinolysis (t-PA, u-PA, PAI-1, PAI-1/t-PA, PAI-1/u-PA, and PLG). Finally, 14 endogenous differential metabolites from serum samples of rats were intervened by L. supina based on untargeted metabolomics analysis, which were closely related to amino acid metabolism, inflammatory and angiogenic pathways. CONCLUSION: Our integrated strategy based on network pharmacology, molecular docking, metabolomics, and in vivo experiments revealed for the first time that L. supina exerts a significant antithrombotic effect through the inhibition of inflammation and coagulation cascade, promotion of angiogenesis, and suppression of platelet activation. This paper provides novel insight into the potential of L. supina as a candidate agent to treat thrombosis.

Uncovering the pharmacological mechanisms of Patchouli essential oil for treating ulcerative colitis.

ETHNOPHARMACOLOGICAL RELEVANCE: Pogostemonis Herba has long been used in traditional Chinese medicine to treat inflammatory disorders. Patchouli essential oil (PEO) is the primary component of Pogostemonis Herba, and it has been suggested to offer curative potential when applied to treat ulcerative colitis (UC). However, the pharmacological mechanisms of PEO for treating UC remain to be clarified. AIM OF THE STUDY: To elucidate the pharmacological mechanisms of PEO for treating UC. METHODS AND RESULTS: In the present study, transcriptomic and network pharmacology approaches were combined to clarify the mechanisms of PEO for treating UC. Our results reveal that rectal PEO administration in UC model mice significantly alleviated symptoms of UC. In addition, PEO effectively suppressed colonic inflammation and oxidative stress. Mechanistically, PEO can ameliorate UC mice by modulating gut microbiota, inhibiting inflammatory targets (OPTC, PTN, IFIT3, EGFR, and TLR4), and inhibiting the PI3K-AKT pathway. Next, the 11 potential bioactive components that play a role in PEO's anti-UC mechanism were identified, and the therapeutic efficacy of the pogostone (a bioactive component) in UC mice was partially validated. CONCLUSION: This study highlights the mechanisms through which PEO can treat UC, providing a rigorous scientific foundation for future efforts to develop and apply PEO for treating UC.