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.

Privacy protection of communication networks using fully homomorphic encryption based on network slicing and attributes.

At present, social networks have become an indispensable medium in people's daily life and work. However, concerns about personal privacy leakage and identity information theft have also emerged. Therefore, a communication network system based on network slicing is constructed to strengthen the protection of communication network privacy. The chameleon hash algorithm is used to optimize attribute-based encryption and enhance the privacy protection of communication networks. On the basis of optimizing the combination of attribute encryption and homomorphic encryption,, a communication network privacy protection method using homomorphic encryption for network slicing and attribute is designed. The results show that the designed network energy consumption is low, the average energy consumption calculation is reduced by 8.69%, and the average energy consumption calculation is reduced by 14.3%. During data transmission, the throughput of the designed network can reach about 700 Mbps at each stage, which has a high efficiency.. The above results demonstrate that the designed communication network provides effective privacy protection. Encrypted data can be decrypted and tracked in the event of any security incident. This is to protect user privacy and provide strong technical support for communication network security.

Phosphorus prediction in the middle reaches of the Yangtze river based on GRA-CEEMDAN-CNLSTM-DBO.

Accurate and rapid prediction of water quality is crucial for the protection of aquatic ecosystems. This study aims to enhance the prediction of total phosphorus (TP) concentrations in the middle reaches of the Yangtze River by integrating advanced modeling techniques. Using operational and discharge data from the Three Gorges Reservoir (TGR), along with water quality parameters from downstream sections, we used Grey Relational Analysis (GRA) to rank the factors contributing to TP concentrations. The analysis identified turbidity, permanganate index (CODMn), total nitrogen (TN), water temperature, chlorophyll a, upstream water level variation, and discharge from the Three Gorges Dam (TGD) as the top contributors. Subsequently, a coupled neural network model was established, incorporating these key contributors, to predict TP concentrations under the dynamic water level control during flood periods in the TGR. The proposed GRA-CEEMDAN-CN1D-LSTM-DBO model was compared with conventional models, including BP, LSTM, and GRU. The results indicated that the GRA-CEEMDAN-CN1D-LSTM-DBO model significantly outperformed the others, achieving a correlation coefficient (R) of 0.784 and a root mean square error (RMSE) of 0.004, compared to 0.58 (R) and 0.007 (RMSE) for the LSTM model, 0.576 (R) and 0.007 (RMSE) for the BP model, and 0.623 (R) and 0.006 (RMSE) for the GRU model. The model's accuracy and applicability further validated in two sections: YC (Yunchi) in Yichang City and LK (Liukou) in Jingzhou City, where it performed satisfactorily in predicting TP in YC (R = 0.776, RMSE = 0.007) and LK (R = 0.718, RMSE = 0.007). Additionally, deep learning analysis revealed that as the distance away from dam increased, prediction accuracy gradually decreased, indicating a reduced impact of TGR operations on downstream TP concentrations. In conclusion, the GRA-CEEMDAN-CN1D-LSTM-DBO model demonstrates superior performance in predicting TP concentration in the middle reaches of the Yangtze River, offering valuable insights for dynamic water level control during flood seasons and contributing of smart to the advancement of water management in the Yangtze River.

Green transformation and performance synergy efficiency of china's thermal power enterprises on the basis of the environmental tax burden.

Global climate change and the collection of environmental protection taxes are accelerating the green transformation of thermal power enterprises. This study selected Chinese thermal power listed companies as samples and used a dynamic three-stage (operational, green transformation, and market performance) network DEA model to evaluate their transformation efficiency and corporate performance. This paper incorporates targeted indicators such as ESG (environment, society, governance) and stock prices into the model and conducts a comparative study on the basis of macro policies and the geographical location of the enterprise. A comparative analysis was conducted on the efficiency of enterprises before and after the adjustment of the environmental tax burden, using the environmental tax burden as an exogenous variable. Thus, the following conclusions can be drawn: there is a certain positive correlation between the collaborative efficiency of the two links of thermal power enterprises and the economic development of their respective regions. Moreover, the green transformation efficiency of most thermal power enterprises is superior to the market performance efficiency. The environmental tax burden mainly improves the overall efficiency of thermal power enterprises by improving their operational efficiency and efficiency in the green transformation stage without affecting market performance. To further improve efficiency, thermal power enterprises should actively communicate with stakeholders to strive for more financial relief.

Acupuncture modulates emotional network resting-state functional connectivity in patients with insomnia disorder: a randomized controlled trial and fMRI study.

BACKGROUND: Insomnia disorder (ID) is one of the most common sleep problems, usually accompanied by anxiety and depression symptoms. Functional magnetic resonance imaging (fMRI) study suggests that both poor sleep quality and negative emotion are linked to the dysregulation of brain network related to emotion processing in ID patients. Acupuncture therapy has been proven effective in improving sleep quality and mood of ID patients, but the involved neurobiological mechanism remains unclear. We aimed to investigate the modulation effect of acupuncture on resting-state functional connectivity (rsFC) of the emotional network (EN) in patients experiencing insomnia. METHODS: A total of 30 healthy controls (HCs) and 60 ID patients were enrolled in this study. Sixty ID patients were randomly assigned to real and sham acupuncture groups and attended resting-state fMRI scans before and after 4 weeks of acupuncture treatment. HCs completed an MRI/fMRI scan at baseline. The rsFC values within EN were calculated, and Hamilton Anxiety Scale (HAMA), Hamilton Depression Scale (HAMD), Pittsburgh Sleep Quality Index (PSQI), Hyperarousal Scale (HAS), and actigraphy data were collected for clinical efficacy evaluation. RESULTS: Resting-state FC analysis showed abnormalities in rsFC centered on the thalamus and dorsolateral prefrontal cortex within EN of ID patients compared to HCs. After real acupuncture treatment, rsFC of the anterior cingulate cortex, hippocampus, and amygdala were increased compared with the sham acupuncture group (p < 0.05, FDR corrected). In real acupuncture group, the rsFC value was decreased between left amygdala and left thalamus after 4 weeks of treatment compared with baseline. A trend of correlation was found that the increased rsFC value between the right amygdala and left hippocampus was positively correlated with the decreased HAMA scores across all ID patients, and the decreased left amygdala rsFC value with the left thalamus was negatively correlated with the increased sleep efficiency in the real acupuncture group. CONCLUSION: Our findings showed that real acupuncture could produce a positive effect on modulating rsFC within network related to emotion processing in ID patients, which may illustrate the central mechanism underlying acupuncture for insomnia in improving sleep quality and emotion regulation. TRIAL REGISTRATION: http://www.chictr.org.cn ., ChiCTR1800015282, 20/03/2018.

Effectiveness of seven oral traditional Chinese medicines against mild or moderate COVID-19: An updated systematic review and network meta-analysis.

Background: Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is responsible for the outbreak of COVID-19 in Wuhan, China. As a highly infectious epidemic, SARS-CoV-2 rapidly evolves. Presently, COVID-19 coexists with humans, mainly with mild or moderate disease. The latest Guidelines for the Diagnosis and Treatment of COVID-19 (trial version of the 10th Edition) recommend several oral traditional Chinese medicines (TCMs) for treatment. This study aims to evaluate the evidence-based benefits of these TCMs as adjunctive therapies to conventional western medicine (CWM) for patients with mild or moderate COVID-19. Methods: We conducted a systematic review and meta-analysis adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, utilizing the PRISMA checklist. We searched PubMed, Cochrane Library, Embase, CNKI, and Wan-Fang databases to retrieve randomized controlled trials and retrospective cohort studies of TCM in combination with CWM on the treatment of mild or moderate COVID-19 that were published as of December 25, 2023. A network meta-analysis using the frequency model was employed to evaluate the benefits of different interventions. Results: A total of 30 eligible studies, enrolling 4144 participants, utilized 7 marketed oral TCMs in China. Compared with CWM alone, the integration of TCMs with CWM can significantly reduce severe conversion rate. This combined approach also enhances the clinical effective rate, shortens the negative conversion time of nucleic acid, and improves both symptoms and blood biochemical markers in patients. The network meta-analysis provided preliminary evidence of the superiority of specific TCMs for various outcomes: Qingfei Paidu for raising the CT improvement rate and clinical effective rate, and shortening the negative conversion time of nucleic acid; Huashi Baidu for reducing severe conversion and improving cough; Xuanfei Baidu for improving fatigue; Jinhua Qinggan for improving fever; Lianhua Qingwen for shortening the recovery time of fatigue and cough; and Shufeng Jiedu for shortening the recovery time of fever. Conclusions: TCM in combination with CWM may be beneficial for patients with mild or moderate COVID-19. Each TCM may have distinct benefits in COVID-19.

Kinematic network of joint motion provides insight on gait coordination: An observational study on Parkinson's disease.

The analysis of gait kinematics requires to encode and collapse multidimensional information from multiple anatomical elements. In this study, we address this issue by analyzing the joints' coordination during gait, borrowing from the framework of network theory. We recruited twenty-three patients with Parkinson's disease and twenty-three matched controls that were recorded during linear gait using a stereophotogrammetric motion analysis system. The three-dimensional angular velocity of the joints was used to build a kinematic network for each participant, and both global (average whole-body synchronization) and nodal (individual joint synchronization, i.e., nodal strength) were extracted. By comparing the two groups, the results showed lower coordination in patients, both at global and nodal levels (neck, shoulders, elbows, and hips). Furthermore, the nodal strength of the left elbow and right hip in the patients, as well as the average joints' nodal strength were significantly correlated with the clinical motor condition and were predictive of it. Our study highlights the importance of integrating whole-body information in kinematic analyses and the advantages of using network theory. Finally, the identification of altered network properties of specific joints, and their relationship with the motor impairment in the patients, suggests a potential clinical relevance for our approach.

Optimized neural network-based model to predict the shear strength of trapezoidal-corrugated steel webs.

Beam-like members use corrugated webs to increase their shear strength, stability, and efficiency. The corrugation positively affects the members' structural characteristics, especially those governed by the web parameters, such as the shear strength, while reducing the total weight. Existing code and analytical models for predicting the shear strength of trapezoidal corrugated steel webs (TCSWs) are summarized. This paper presents an optimized Artificial Neural Network (ANN)-based model to estimate the shear strength of steel girders with a TCSW subjected to a concentrated force. A database of 206 experimental results from the literature is used to feed the ANNs. Six geometrical and material parameters were identified as input variables, and the experimental shear strength at failure was considered the output variable. Four hyperparameter optimization techniques are applied to refine the ANN models: Bayesian Optimization (BO), Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS), Firefly Algorithm (FA), and African Buffalo Optimization (ABO). The performance metrics indicate that the ABO-ANN model is the most effective among these. The predictions of the developed ML model were also compared with those of existing code and analytical models. The comparisons illustrated that the ANN-based model outperforms the other existing models. The sensitivity analysis using the proposed ANN-based model captured the relationships and interactions among the geometric and material parameters and their impact on shear strength. One main finding is that the corrugation angle in the 35-45° range maximized the TCSW shear strength.

Automatic detection of pleural line and lung sliding in lung ultrasonography using convolutional neural networks.

Background: Lung ultrasonography (LUS) is a valuable diagnostic tool, but there is a shortage of LUS experts with extensive knowledge and significant experience in the field. Convolutional neural networks (CNNs) have the potential to mitigate this issue by facilitating computer-aided diagnosis. Methods: We propose computer-aided system by a CNN-based method for LUS diagnosis. As the first consideration, we investigated pleural line and lung sliding. The pleural line indicates the position of pleura in an ultrasound image, and LUS is performed after first confirming the position of pleural line. Lung sliding defined as the movement of the pleural line, and the absence of this feature is associated with pneumothorax. Results: Our proposed method accurately detected pleural line and lung sliding, demonstrating its potential to provide valuable diagnostic information on lung lesions.

Exploration of the pharmacological components and therapeutic mechanisms in treatment of Alzheimer's disease with Polygonati Rhizoma and its processed product using combined analysis of metabolomics, network pharmacology, and gut microbiota.

Polygonati Rhizoma (PR, Huangjing in Chinese) and its processed product (PRP), which are used in Traditional Chinese medicine (TCM) for cognitive enhancement and treatment of Alzheimer's disease (AD), have not been fully explored in terms of the different mechanisms underlying their anti-AD effects. Therefore, we used APP/PS1 mice as an AD model to assess the effects of PR and PRP on anxiety-like behaviors, cognitive function, memory performance, and pathological changes in the murine brain. UPLC-HRMS was applied to identify the components of PR and PRP that entered into the blood and brain. Network pharmacology was used to elucidate potential mechanisms underlying the improvement of AD. Differences in the intestinal flora composition between mice treated with PR and PRP were investigated using 16S rRNA sequencing, establishing a correlation between pharmacological components and distinct flora profiles. The results revealed that both PR and PRP interventions ameliorated cognitive deficits and attenuated Amyloid ß (Aß) plaque deposition in the brains of AD mice. Seven specific blood-entering components, namely glutamic acid, Phe-Phe, and uridine, etc., were associated with PR intervention, whereas ten specific blood-entering components including (2R,3S)-3-isopropylmalate, 3-methylhexahydropyrrolo[1,2-a]pyrazine-1,4-dione, and 3-methoxytyrosine were related to PRP intervention. Uridine was identified as a common brain-penetrating component in both PR and PRP interventions. Network pharmacology analysis suggested that the NOD-like receptor signaling pathway, Calcium signaling pathway and Alzheimer's disease were specific pathways targeted in AD treatment using PR intervention. Moreover, the apoptosis pathway was specifically linked to AD treatment during PRP intervention. Furthermore, the administration of both PR and PRP enhanced the abundance and diversity of the intestinal flora in APP/PS1 mice. Western blotting confirmed that PR excels in regulates inflammation, whereas PRP balances autophagy and apoptosis to alleviate the progression of AD. This study offers valuable insights and establishes a robust foundation for further comprehensive exploration of the intrinsic correlation between TCM and AD.

Exploring the possible therapeutic mechanism of Danzhixiaoyao pills in depression and MAFLD based on "Homotherapy for heteropathy": A network pharmacology and molecular docking.

Objective: Danzhixiaoyao pills (DXP) is a traditional Chinese medicine formula that has been effectively used in clinical practice to treat depression and metabolic associated fatty liver disease (MAFLD), but its therapeutic mechanism is not yet clear. The purpose of this study is to explore the possible mechanisms of DXP in treating depression and MAFLD using network pharmacology and molecular docking techniques based on existing literature reports. Methods: By combining TCMSP, Swiss ADME, Swiss TargetPrediction, and UniProt databases, the active ingredients and potential targets of DXP were screened and obtained. By searching for relevant disease targets through Gene Cards, OMIM, and TTD databases, intersection targets between drugs and diseases were obtained. The network of "Disease - Potential targets - Active ingredients - Traditional Chinese medicine - Prescriptions" was constructed using Cytoscape 3.9.1 software, and the PPI network was constructed using STRING 12.0 database. The core targets were obtained through topology analysis. GO function enrichment and KEGG pathway enrichment analysis were conducted based on DAVID. The above results were validated by molecular docking using PyMol 2.5 and AutoDock Tool 1.5.7 software, and their possible therapeutic mechanisms were discussed. Results: Network pharmacology analysis obtained 130 main active ingredients of drugs, 173 intersection targets between drugs and diseases, and 37 core targets. Enrichment analysis obtained 1390 GO functional enrichment results, of which 922 were related to biological process, 107 were related to cellular component, 174 were related to molecular function, and obtained 180 KEGG pathways. Molecular docking has confirmed the good binding ability between relevant components and targets, and the literature discussion has preliminarily verified the above results. Conclusion: DXP can act on targets such as TNF, AKT1, ALB, IL1B, TP53 through active ingredients such as kaempferol, quercetin, naringenin, isorhamnetin, glyuranolide, etc, and by regulating signaling pathways such as pathways in cancer, MAPK signaling pathway, lipid and atherosclerosis, to exert its effect of "homotherapy for heteropathy" on depression and MAFLD. In addition, glyuranolide showed the strongest affinity with TNF (-7.88 kcal/mol), suggesting that it may play a key role in the treatment process. The research results provide a theoretical basis for elucidating the scientific connotation and mechanism of action of traditional Chinese medicine compound DXP, and provide new directions for its clinical application.

Efficient state of charge estimation of lithium-ion batteries in electric vehicles using evolutionary intelligence-assisted GLA-CNN-Bi-LSTM deep learning model.

The battery's performance heavily influences the safety, dependability, and operational efficiency of electric vehicles (EVs). This paper introduces an innovative hybrid deep learning architecture that dramatically enhances the estimation of the state of charge (SoC) of lithium-ion (Li-ion) batteries, crucial for efficient EV operation. Our model uniquely integrates a convolutional neural network (CNN) with bidirectional long short-term memory (Bi-LSTM), optimized through evolutionary intelligence, enabling an advanced level of precision in SoC estimation. A novel aspect of this work is the application of the Group Learning Algorithm (GLA) to tune the hyperparameters of the CNN-Bi-LSTM network meticulously. This approach not only refines the model's accuracy but also significantly enhances its efficiency by optimizing each parameter to best capture and integrate both spatial and temporal information from the battery data. This is in stark contrast to conventional models that typically focus on either spatial or temporal data, but not both effectively. The model's robustness is further demonstrated through its training across six diverse datasets that represent a range of EV discharge profiles, including the Highway Fuel Economy Test (HWFET), the US06 test, the Beijing Dynamic Stress Test (BJDST), the dynamic stress test (DST), the federal urban driving schedule (FUDS), and the urban development driving schedule (UDDS). These tests are crucial for ensuring that the model can perform under various real-world conditions. Experimentally, our hybrid model not only surpasses the performance of existing LSTM and CNN frameworks in tracking SoC estimation but also achieves an impressively quick convergence to true SoC values, maintaining an average root mean square error (RMSE) of less than 1 %. Furthermore, the experimental outcomes suggest that this new deep learning methodology outstrips conventional approaches in both convergence speed and estimation accuracy, thus promising to significantly enhance battery life and overall EV efficiency.

Enhanced deep learning models for automatic fish species identification in underwater imagery.

Underwater cameras are crucial in marine ecology, but their data management needs automatic species identification. This study proposes a two-stage deep learning approach. First, the Unsharp Mask Filter (UMF) preprocesses images. Then, an enhanced region-based fully convolutional network (R-FCN) detects fish using two-order integrals for position-sensitive score maps and precise region of interest (PS-Pr-RoI) pooling for accuracy. The second stage integrates ShuffleNetV2 with the Squeeze and Excitation (SE) module, forming the Improved ShuffleNetV2 model, enhancing classification focus. Hyperparameters are optimized with the Enhanced Northern Goshawk Optimization Algorithm (ENGO). The improved R-FCN model achieves 99.94 % accuracy, 99.58 % precision and recall, and a 99.27 % F-measure on the Fish4knowledge dataset. Similarly, the ENGO-based ShuffleNetV2 model, evaluated on the same dataset, shows 99.93 % accuracy, 99.19 % precision, 98.29 % recall, and a 98.71 % F-measure, highlighting its superior classification accuracy.

Identifying factors related to mortality of hospitalized COVID-19 patients using machine learning methods.

Background: The COVID-19 pandemic has had a profound impact globally, presenting significant social and economic challenges. This study aims to explore the factors affecting mortality among hospitalized COVID-19 patients and construct a machine learning-based model to predict the risk of mortality. Methods: The study examined COVID-19 patients admitted to Imam Reza Hospital in Tabriz, Iran, between March 2020 and November 2021. The Elastic Net method was employed to identify and rank features associated with mortality risk. Subsequently, an artificial neural network (ANN) model was developed based on these features to predict mortality risk. The performance of the model was evaluated by receiver operating characteristic (ROC) curve analysis. Results: The study included 706 patients with 96 features, out of them 26 features were identified as crucial predictors of mortality. The ANN model, utilizing 20 of these features, achieved an area under the ROC curve (AUC) of 98.8 %, effectively stratifying patients by mortality risk. Conclusion: The developed model offers accurate and precipitous mortality risk predictions for COVID-19 patients, enhancing the responsiveness of healthcare systems to high-risk individuals.

Geometric feature extraction in nanofiber membrane image based on convolution neural network for surface roughness prediction.

As a technique in artificial intelligence, a convolution neural network model has been utilized to extract average surface roughness from the geometric characteristics of a membrane image featuring micro- and nanostructures. For surface roughness measurement, e.g. atomic force microscopy and optical profiler, the previous methods have been performed to analyze a porous membrane surface on an interest of region with a few micrometers of the restricted area according to the depth resolution. However, an image from the scanning electron microscope, combined with the feature extraction process, provides clarity on surface roughness for multiple areas with various depth resolutions. Through image preprocessing, the geometric pattern is elucidated by amplifying the disparity in pixel intensity values between the bright and dark regions of the image. The geometric pattern of the binary image and magnitude spectrum confirmed the classification of the surface roughness of images in a categorical scatter plot. A group of cropped images from an original image is used to predict the logarithmic average surface roughness values. The model predicted 4.80 % MAPE for the test dataset. The method of extracting geometric patterns through a feature map-based CNN, combined with a statistical approach, suggests an indirect surface measurement. The process is achieved through a bundle of predicted output data, which helps reduce the randomness error of the structural characteristics. A novel feature extraction approach of CNN with statistical analysis is a valuable method for revealing hidden physical characteristics in surface geometries from irregular pixel patterns in an array of images.

Mechanism investigation of anti-NAFLD of Shugan Yipi Granule based on network pharmacology analysis and experimental verification.

As a classical traditional Chinese patent medicine, Shugan Yipi Granule is widely used in China to treat non-alcoholic fatty liver disease (NAFLD) recently. Our previous study confirmed that Shugan Yipi Granule are effective in NAFLD. However, its underlying mechanism is still unknown. This study aims to investigate the mechanism of Shugan Yipi Granule on NAFLD based on network pharmacology prediction, liquid chromatography-mass spectrometry (LC-MS) analysis and in vitro verification. We obtained the active ingredients and targets of Shugan Yipi Granule and NAFLD from 6 traditional Chinese medicine databases, and the crucial components and targets screened by protein-protein interaction (PPI) network were used for molecular docking. Plasma metabolomics of NAFLD patients treated with Shugan Yipi Granule for one month was analyzed using LC-MS methods and MetaboAnalyst 4.0 to obtain significant differential metabolites and pathways. Finally, free fatty acid (FFA) induced HepG2 cells were treated with different concentrations of quercetin and kaempferol, then oil red o (ORO) and triglyceride (TG) level were tested to verify the lipid deposition of the cell. Network pharmacology analysis showed that the main active ingredients of Shugan Yipi Granule include quercetin, kaempferol and other 58 ones, as well as 188 potential targets. PI3K/Akt signaling pathway was found to be the most relevant pathway for the treatment of NAFLD. Non-targeted metabolomics showed that quercetin and kaempferol were significantly up-regulated differential metabolites and were involved in metabolic pathways such as thyroid hormone signaling. In vitro results showed that quercetin, kaempferol were effective in reducing lipid deposition and TG content by inhibiting cellular fatty acid uptake. Ultimately, with the network pharmacology and serum metabolomics analysis, quercetin and kaempferol were found to be the important active ingredients and significantly up-regulated differential metabolites of Shugan Yipi Granule against NAFLD, which we inferred that they may regulate NAFLD through PI3K/Akt signaling pathway and thyroid hormone metabolism pathway. The in vitro experiment verification results showed that quercetin and kaempferol attenuated the lipid accumulation and TG content by inhibiting the fatty acid uptake in the FFA-induced HepG2 cell. Current study provides the necessary experimental basis for subsequent in-depth mechanism research.

Integrating network toxicology and molecular docking to explore the toxicity of the environmental pollutant butyl hydroxyanisole: An example of induction of chronic urticaria.

The study aimed to comprehensively investigate environmental pollutants' potential toxicity and underlying molecular mechanisms, focusing on chronic urticaria (CU) induced by butylated hydroxyanisole (BHA) exposure, further drawing public awareness regarding the potential risks of environmental pollutants, applying ChEMBL, STITCH, and SwissTargetPrediction databases to predict the targets of BHA, CTD, GeneCards, and OMIM databases to collect the relevant targets of CU. Ultimately, we identified 81 potential targets of BHA-induced CU and extracted 31 core targets, including TNF, SRC, CASP3, BCL2, IL2, and MMP9. GO and KEGG enrichment analyses revealed that these core targets were predominantly involved in cancer signaling, estrogen and endocrine resistance pathways. Furthermore, molecular docking confirmed the ability of BHA to bind with core targets. The onset and development of CU may result from BHA by affecting multiple immune signaling pathways. Our study elucidated the molecular mechanisms of BHA toxicity and its role in CU induction, providing the basis for preventing and treating chronic urticaria associated with environmental BHA exposure.

JianPiTongLuo (JPTL) Recipe regulates anti-apoptosis and cell proliferation in colorectal cancer through the PI3K/AKT signaling pathway.

Background: JianPiTongLuo Recipe (JPTL Recipe) is a traditional Chinese medicine formula commonly used in the clinical treatment of colorectal cancer. Clinical studies have found that it can significantly improve the prognosis of patients with colorectal cancer. However, its mechanisms of action are not well understood, which has limited its further clinical application. Methods: We investigated the potential mechanisms of action of the JianPiTongLuo (JPTL) Recipe on colorectal cancer (CRC) using a multi-step approach. Initially, network pharmacology and bioinformatics analyses were conducted using databases such as TCMSP, HERB, BATMAN-TCM, and STRING to identify active components of JPTL Recipe and predict their therapeutic targets. Interaction networks and functional enrichment analyses were constructed to hypothesize relevant biological processes and pathways. In vitro studies involved treating human CRC cell lines HCT116, LoVo and SW480 with varying concentrations of JPTL Recipe extract, measuring cell viability with the CCK-8 assay, assessing apoptosis via flow cytometry, and analyzing signaling pathways through Western blotting. To corroborate these findings, in vivo experiments were performed on BALB/c nude mice implanted with HCT116 cells, divided into control, JPTL Recipe-treated, 5-fluorouracil (5-FU)-treated, and JPTL Recipe combined with 5-FU groups, with tumor growth and histological changes monitored. Mechanistic studies focused on the PI3K/AKT signaling pathway, examining the phosphorylation status of key pathway proteins using immunofluorescence and Western blot analyses to elucidate JPTL Recipe 's interaction with pathway activity. Results: We demonstrated that JPTL Recipe effectively inhibits colorectal cancer cell proliferation, anti-apoptotic ability, and exerts synergistic therapeutic effects with fluorouracil. Further analysis revealed that JPTL Recipe affects the activity of colorectal cancer cells by inhibiting the phosphorylation of the PI3K/AKT signaling pathway. Conclusion: In summary, we have discovered and confirmed that the traditional Chinese medicine compound JPTL Recipe can serve as a novel adjuvant therapy for colorectal cancer, offering a new treatment approach for the integration of traditional Chinese and Western medicine in the treatment of colorectal cancer.

Study of factors affecting the magnetic sensing capability of shape memory alloys for non-destructive evaluation of cracks in concrete: Using response surface methodology (RSM) and artificial neural network (ANN) approaches.

Currently, the field of structural health monitoring (SHM) is focused on investigating non-destructive evaluation techniques for the identification of damages in concrete structures. Magnetic sensing has particularly gained attention among the innovative non-destructive evaluation techniques. Recently, the embedded magnetic shape memory alloy (MSMA) wire has been introduced for the evaluation of cracks in concrete components through magnetic sensing techniques while providing reinforcement as well. However, the available research in this regard is very scarce. This study has focused on the analyses of parameters affecting the magnetic sensing capability of embedded MSMA wire for crack detection in concrete beams. The response surface methodology (RSM) and artificial neural network (ANN) models have been used to analyse the magnetic sensing parameters for the first time. The models were trained using the experimental data obtained through literature. The models aimed to predict the alteration in magnetic flux created by a concrete beam that has a 1 mm wide embedded MSMA wire after experiencing a fracture or crack. The results showed that the change in magnetic flux was affected by the position of the wire and the position of the crack with respect to the position of the magnet in the concrete beam. RSM optimisation results showed that maximum change in magnetic flux was obtained when the wire was placed at a depth of 17.5 mm from the top surface of the concrete beam, and a crack was present at an axial distance of 8.50 mm from the permanent magnet. The change in magnetic flux was 9.50 % considering the aforementioned parameters. However, the ANN prediction results showed that the optimal wire and crack position were 10 mm and 1.1 mm, respectively. The results suggested that a larger beam requires a larger diameter of MSMA wire or multiple sensors and magnets for crack detection in concrete beams.