Extraction Mechanism Research Articles

Organization of malonamides from the interface to the organic bulk phase

Supramolecular organization of amphiphilic extractant molecules is involved in metal cation selectivity and separation kinetics during solvent extraction. The relationship between extractant associates/aggregates formed in the organic bulk phase and at the liquid–liquid interface is poorly understood even though it affects the extraction mechanism. The nanoscopic structures of the extraction systems N,N,N′,N′-tetrahexylmalonamide (THMA) in toluene and N,N′-dibutyl-N,N′-dimethyl-2-tetradecylmalonamide (DBMA) in n-heptane, used for either Pd(II) or Nd(III) selective extraction from an acidic aqueous phase, were examined. These systems present markedly different affinity for Pd(II) and Nd(III), and extraction kinetics. Extractant organization in the organic bulk phase and at the interface were compared by small-angle X-ray scattering, interfacial tension, and neutron reflectivity. THMA in toluene forms small associates in the organic bulk phase and accumulates in a diffuse layer at the interface, decreasing Pd(II) coordination probability and resulting in slow extraction. DBMA in n-heptane forms large aggregates and a compact, dense interfacial layer, resulting in rapid Pd(II) and Nd(III) extraction. Thus, Pd(II) extraction is driven by interfacial coordination alone, whereas the incorporation of Nd(III) into the core of large aggregates governs Nd(III) extraction in the interfacial layer. These results suggest that the interface should be described as a nanoscale interphase containing a high extractant concentration compared with the organic bulk phase.

Functionality and Health-Promoting Properties of Polysaccharide and Plant-Derived Substances from Mesona chinensis.

Mesona chinensis, in Thai called Chao Kuay and in Chinese Hsian-tsao, belongs to the Lamiaceae family. This herbal plant grows widely in Southern China, Taiwan (China), Malaysia, the Philippines, Indonesia, Vietnam, and Thailand. The Mesona plant is used to make functional products such as drinks and soft textured sweet treats, and also traditional medicine, to treat heat stroke, high blood pressure, heart attack, high blood sugar, hepatic diseases, colon diseases, inflammatory conditions, and to alleviate myalgia. The proximate composition of M. chinensis is a mixture of protein, fat, fiber, ash, and minerals. The main biological compounds in M. chinensis extracts are polysaccharides, terpenoids, flavonoids, and polyphenols, with wide-ranging pharmacological properties including antioxidant, antidiabetic, antilipidemic, carcinoma-inhibitory, renal-protective, antihypertensive, DNA damage-protective, and anti-inflammatory effects. This review investigated the proximate composition, polysaccharide type, and pharmacological properties of M. chinensis extracts. Phytochemical properties enhance the actions of the gut microbiota and improve health benefits. This review assessed the functional and medicinal activities of M. chinensis extracts. Future studies should further elucidate the in vitro/in vivo mechanisms of this plant extract and its impact on gut health.

Hypoglycemic effects of mountain caviar extract and inhibitory mechanism of saponins, including momordin Ic, on glucose absorption.

Mountain caviar is a fruit of Kochia scoparia that contains momordin Ic as a major saponin constituent. Its extract (MCE) has been shown to suppress blood glucose elevations in the human oral glucose tolerance test (OGTT) as well as increases in blood glucose in OGTT, gastric emptying (GE), and glucose incorporation in the small intestine in rats. However, the effects of MCE and momordin Ic on glucose absorption in mice and these action mechanisms have not been examined for more than 2 decades. Therefore, we herein investigated the effects of MCE, its saponin fraction, and momordin Ic on blood glucose elevations in mice. Mouse blood glucose elevation tests were performed on carbohydrate-loaded mice. The mountain caviar saponin fraction significantly delayed blood glucose elevations in glucose-, sucrose-, and soluble starch-loaded mice. In glucose-loaded mice, the saponin fraction, MCE, and momordin Ic significantly suppressed rapid glucose elevations after glucose loading, but not sucrose loading. A mouse GE study was performed by loading with glucose and phenolphthalein solution. Momordin Ic and MCE strongly suppressed mouse GE. Intestinal glucose absorption was evaluated by the incorporation of 2-deoxyglucose (2-DG) into Caco-2 cell layers and mouse duodenum wall vesicles. The results obtained showed that momordin Ic inhibited the incorporation of 2-DG into Caco-2 cells and mouse duodenum vesicles. Collectively, these results suggest that MCE, particularly the principal saponin, momordin Ic, preferably suppressed glucose-induced blood glucose elevations and delayed carbohydrate-induced glucose elevations in mice. The underlying mechanism was found to involve the suppression of GE and intestinal glucose absorption.

Ethanolic extract of Arctium lappa leaves alleviates cerebral ischemia reperfusion-induced inflammatory injury via HDAC9-mediated NF-κB pathway

BackgroundIschemic stroke (IS) is a major cause of mortality and disability worldwide. Inflammatory response is crucial in the pathogenesis of tissue injury in cerebral infarction. Arctium lappa leaves are traditionally used to treat IS. PurposesTo investigate the neuroprotective effects and molecular mechanisms of the ethanolic extract of A. lappa leaves (ALLEE) on cerebral ischemia-reperfusion (CIR). MethodsMiddle cerebral artery obstruction reperfusion (MCAO/R) rats and an oxygen-glucose deprivation/reoxygenation (OGD/R) cell model were used to evaluate ALLEE pharmacodynamics. Various methods, including neurological function, 2,3,5-triphenyltetrazolium chloride, hematoxylin and eosin, and Nissl, enzyme-linked immunosorbent, and TdT-mediated dUTP nick-end labeling assays, were used to analyze the neuroprotective effects of ALLEE in vitro and in vivo. The major chemical components and potential target genes of ALLEE were screened using network pharmacology. Molecular docking, western blotting, and immunofluorescence analyses were performed to confirm the effectiveness of the targets in related pathways. ResultsALLEE exerted potent effects on the MCAO/R model by decreasing the neurological scores, infarct volumes, and pathological features (p < 0.01). Furthermore, network pharmacology results revealed that the treatment of IS with ALLEE involved the regulation of various inflammatory pathways, such as the tumor necrosis factor (TNF) and chemokine signaling pathways. ALLEE also played key roles in targeting key molecules, including nuclear factor (NF)-κBIA, NF-κB1, interleukin (IL)-6, TNF-α and IL1β, and regulating the histone deacetylase (HDAC)-9-mediated signaling pathway. In vivo and in vitro analyses revealed that ALLEE significantly regulated the NF-κB pathway, promoted the phosphorylation activation of NF-κB P65, IκB and IKK (p < 0.01 or p < 0.05), and decreased the expression levels of the inflammatory factors, IL-1β, IL-6 and TNF-α (p < 0.01). Moreover, ALLEE significantly decreased the expression of HDAC9 (p < 0.01) that is associated with inflammatory responses. However, HDAC9 overexpression partially reversed the neuroprotective effects of ALLEE and its suppressive effects on inflammation and phosphorylation of NF-κB (p < 0.01). ConclusionsIn conclusion, our results revealed that ALLEE ameliorates MCAO/R-induced experimental CIR by modulating inflammatory responses via the inhibition of HDAC9-mediated NF-κB pathway.

Physicochemical properties of ionic liquids with indole removal function

Indole exists in coal tar, gasoline, diesel oil and other oil products, in the process of using oil products, will cause serious pollution to the environment, so it is necessary to remove indole from oil products. In this paper, 1-hydroxy-3-methylimidazolserine was synthesized and characterized. Its density, surface tension, and refractive index were measured every 5 K in the range of 293.15–333.15 K using standard addition method. The relationships between temperature and water content were discussed. The mass change with time was measured by thermogravimetric analyzer, and its evaporation enthalpy was discussed. Furthermore, the polarity was predicted using these data. Finally, it was used to separate indole from simulated oil. A simulated oil solution containing 0.2 g/L indole was extracted under conditions of a mass ratio of 1:5 between ionic liquid and simulated oil, temperature of 30 °C, and time of 60 min. The extraction rate reached 88.9 %, indicating that the prepared ionic liquid can be used as an extractant for removing indole from simulated oil. In addition, the extraction mechanism was also investigated, and it was found that the indole was successfully extracted from the simulated oil because of the hydrogen bond between the indole and the ionic liquid.

Efficient extraction of Au(Ⅲ) by a piperidine-based ionic liquid and exploration of the mechanism driving force

Efficient gold extraction is of paramount importance, but how to improve the extraction efficiency and enhance the driving force of the extraction process is still to be explored. This study introduces an innovative approach using versatility ionic liquids (ILs) to overcome existing challenges. A tailored IL was synthesized and employed computational tools, including the Interaction Region Indicator, to predict its effectiveness in gold extraction. Quantum chemical calculations elucidated the interplay between hydrogen bonding capability and ion exchange properties of the ILs. Subsequent optimization of experimental conditions (0.1 mol/L HCl, 10.6 mg IL) yielded a remarkable 98.4 % extraction efficiency at 45 °C. Molecular dynamics simulations affirmed the IL's exceptional selectivity for Au(III) extraction, with analysis revealing that the extraction mechanism primarily hinged on electrostatic attraction and van der Waals interactions between ionic liquid cations and [AuCl4]−. This mechanism aligns with the established anion exchange mechanism, validated through Ultraviolet–visible spectrometer (UV–Vis), Nuclear Magnetic Resonance spectrometer (NMR), and Liquid Chromatograph Mass Spectrometer (LC-MS) analyzes. To complete the extraction cycle, 1.0 mol/L K2C2O4 was employed as a stripping agent for efficient Au(III) recovery from the loaded IL phase, facilitating repeated extraction cycles. This study underscores the sustainability of the IL-based Au(III) extraction process, maintaining consistently high extraction rates even after five cycles. In summary, this method does not use solvent, enabling environmentally friendly Au(III) extraction.

Drug-drug interactions with oral anticoagulants: information consistency assessment of three commonly used online drug interactions databases in Switzerland.

Background: Toxicity or treatment failure related to drug-drug interactions (DDIs) are known to significantly affect morbidity and hospitalization rates. Despite the availability of numerous databases for DDIs identification and management, their information often differs. Oral anticoagulants are deemed at risk of DDIs and a leading cause of adverse drug events, most of which being preventable. Although many databases include DDIs involving anticoagulants, none are specialized in them. Aim and method: This study aims to compare the DDIs information content of four direct oral anticoagulants and two vitamin K antagonists in three major DDI databases used in Switzerland: Lexi-Interact, Pharmavista, and MediQ. It evaluates the consistency of DDIs information in terms of differences in severity rating systems, mechanism of interaction, extraction and documentation processes and transparency. Results: This study revealed 2'496 DDIs for the six anticoagulants, with discrepant risk classifications. Only 13.2% of DDIs were common to all three databases. Overall concordance in risk classification (high, moderate, and low risk) was slight (Fleiss' kappa = 0.131), while high-risk DDIs demonstrated a fair agreement (Fleiss' kappa = 0.398). The nature and the mechanism of the DDIs were more consistent across databases. Qualitative assessments highlighted differences in the documentation process and transparency, and similarities for availability of risk classification and references. Discussion: This study highlights the discrepancies between three commonly used DDI databases and the inconsistency in how terminology is standardised and incorporated when classifying these DDIs. It also highlights the need for the creation of specialised tools for anticoagulant-related interactions.

Network Pharmacology and Component Analysis Integrated Study to Uncovers the Molecular Mechanisms of Lansium parasiticum Bark Extract in Colon Cancer Treatment

Side effects and risk of resistance are common consequences of colon cancer treatment based on chemotherapy. The medicinal plant originating in Indonesia, Lansium parasiticum bark extract (LPBE), has not been studied much. The purpose of this study is to identify the compounds present in LPBE and explain how the molecular mechanisms of the composite inhibit colon cancer cells. LC-MS/MS Liquid Chromatography Tandem Mass Spectrophotometry has been used to identify compounds in LPBE. The ADMET program is used to determine absorption profiles and bioavailability per oral. The tissue pharmacology approach uses Cytoscape 3.9.1, GeneCards, Disgenet, STRING 2.0.0, SRplot, and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway to predict the molecular anti-cancer mechanisms of these compounds. In vitro validation using PyRx Autodock Vina 9.0 and Biovia Discovery Studio with BAX (PDB ID:2YXJ), BCL2 (P DB ID:2W3L) and STAT3 receptors (PDB ID:6NJS). A total of 17 active compounds were identified through LC-MS/MS. The moronic acid compound showed the highest levels of 14.29% followed by 4-Morpholineacetic Acid 12.2% and ursolic aldehyde 8.37%. Pharmacological network analysis showed that the compounder works on the EGFR tyrosine kinase resistance path by targeting the BCL2, BAX, STAT3 genes. The results of the in silico validation support the results of tissue pharmacology findings. Ursolic aldehyde, and Moronic acid showed a higher affinity to the three receptors. Therefore, Lansium parasiticum bark extract (LPBE) is recommended for further study as a candidate anti-cancer drug both in vitro and in vivo.

Multi-target mechanism of polyherbal extract to treat diabetic foot ulcer based on network pharmacology and molecular docking

Introduction: Diabetic foot ulcer (DFU) potentially leads to loss of function, infections, hospitalization, lower-extremity amputation, and even death. The potential therapeutic efficacy of a polyherbal candidate named TIP-Heal was identified for treating DFU. TIP-Heal, which stands for Tinospora crispa, Isotoma longiflora, and Piper betle L var nigra, consists of extracts from these three herbs in a ratio of 2:1:1. The Indonesian population commonly uses these herbs due to their wound-healing properties. It is our interest to analyse the mechanism of the polyherbal extract using network pharmacology and molecular docking. Methods: This study uses network pharmacology and molecular docking methods to analyze the multi-target mechanism of active compounds in TIP-Heal extract for DFU treatment. The proteins targeted by the bioactive chemical present in TIP-Heal and DFU were identified within a particular dataset with the keyword "homo sapiens." The identified target proteins were assessed using gene ontology (GO) analysis, the Kyoto Encyclopaedia of Gene and Genomes (KEGG) pathways, protein-protein interactions (PPIs), and molecular docking. Results: The critical proteins obtained were AKT serine/threonine kinase 1 (AKT1), caspase-3 (CASP3), epidermal growth factor receptor (EGFR), proto-oncogene tyrosine-protein kinase Src (SRC) and matrix metalloproteinase-9 (MMP-9). Several compounds, namely PubChem (Compound Identifier=CID: 5319898), 3-epiursolic acid, palmitic acid, and alpha-linolenic acid showed great potential as viable candidates to facilitate the healing process of DFU. Conclusion: The findings of this study indicate that the TIP-Heal extract has the potential to be used as a natural herbal treatment for DFUs with the involvement of AKT1, CASP3, EFGR, and SRC proteins.

Mechanism of n-butanol extract of Pulsatilla Decoction in treating ulcerative colitis by activating BMP signaling pathway

The study aimed to investigate the therapeutic effects of the n-butanol extract of Pulsatilla Decoction(BEPD) on ulcerative colitis(UC) via the bone morphogenetic protein(BMP) signaling pathway. C57BL/6 mice were divided into six groups: control, model, mesalazine, and BEPD low-, medium-, and high-dose groups. Except for the control group, the rest groups were treated with 3% dextran sulfate sodium(DSS) freely for seven consecutive days to establish the UC mouse model, followed by treatment with different concentrations of BEPD and mesalazine by gavage. The murine body weight and disease activity index(DAI) were recorded. After the mice were sacrificed, their colon tissues were collected for histological analysis. Alcian blue/periodic acid-Schiff(AB/PAS) staining was used to detect the number and mucus secretion status of goblet cells; immunohistochemistry was performed to measure the expression of ki67, cleaved caspase-3, mucin 2(Muc2), and matrix metalloproteinase-9(MMP9) in colon tissues; and immunofluorescence was used to analyze the expression of tight junction proteins in colon tissues, and enzyme linked immunosorbent assay(ELISA) was employed to quantify the levels of tumor necrosis factor-α(TNF-α), interleukin(IL)-1β, and IL-6. Western blot was conducted to evaluate the expression of BMP pathway-related proteins in mouse colon tissues. Quantitative real-time PCR(qRT-PCR) was performed to measure the expression of genes related to goblet cell differentiation in mouse colon tissues. In addition, this study also examined the protective effect and underlying mechanism of BEPD-containing serum on lipopolysaccharide(LPS)-induced barrier damages in LS174T goblet cells in vitro. The results showed that BEPD significantly alleviated UC symptoms in mice, restored goblet cell diffe-rentiation function, promoted Muc2 secretion and tight junction protein expression, and suppressed inflammatory factor secretion while activating the BMP signaling pathway. Therefore, BEPD may exert its therapeutic effects on UC by activating the BMP signaling pathway, providing a new strategy for drug intervention in UC.

Inula britannica ameliorates alcohol-induced liver injury by modulating SIRT1-AMPK/Nrf2/NF-κB signaling pathway

ObjectiveInula britannica is a traditional Chinese medicinal and functional food with various effects such as anti-liver injury, hypoglycemia, antioxidants, and anti-tumor. This study aimed to investigate the protective effects and mechanisms of the ethanolic extract of I. britannica (EEIB) on alcohol-induced liver injury in mice. MethodsFifty-six female C57BL/6 mice were randomly divided into seven groups: control group (Con), ethanol feeding model group (EtOH), Silibinin positive treatment group (EtOH + Silibinin 100 mg/kg), EEIB treatment group (EtOH + EEIB 100, 200, and 400 mg/kg), and EEIB control group (EEIB 400 mg/kg). The National Institute on Alcohol Abuse and Alcoholism (NIAAA) ethanol-feeding model was used to study the effects of EEIB on alcohol-induced lipid metabolism, inflammation, oxidative stress, and fibril formation in mice by histopathological evaluation, immunofluorescence staining, Western blotting analysis and molecular docking. ResultsEEIB reduced liver indices to different degrees to normal levels and improved liver morphology in mice. EEIB inhibited alcohol-induced liver injury by activating the sirtuin 1 (SIRT1)-adenosine monophosphate-activated protein kinase (AMPK) signaling pathway in the liver of alcohol-fed mice, in which sesquiterpenes may be the potential active ingredients, and also down-regulated the expression of alpha-smooth muscle actin (α-SMA), collagen alpha (Collagen I), tumor necrosis factor-alpha (TNF-α) and attenuated alcohol-induced liver injury. In addition, EEIB also activated the nuclear factor erythroid 2-related factor 2 (Nrf2) signaling pathway, which alleviated alcohol-induced liver injury at the level of oxidative stress. Notably, the EEIB control group demonstrated that EEIB had no toxic effects in mice. EEIB reduced alcoholic liver injury in a dose-dependent manner. Its therapeutic efficacy was comparable to, if not better than, that of Silibinin when administered at a dose of 400 mg/kg. ConclusionEEIB showed significant therapeutic effects on alcohol-induced liver injury in mice, and its mechanism of action was related to the SIRT1-AMPK, nuclear factor-kappa B (NF-κB), and Nrf2 signaling pathways, in which sesquiterpenes may be the potential active ingredients.

Revealing the active ingredients and mechanism of P. sibiricumm in non-small-cell lung cancer based on UPLC-Q-TOF-MS/MS, network pharmacology, and molecular docking

The alcohol extraction of P. sibiricum has exhibited significant inhibitory effects on the production of free radicals and the proliferation of non-small-cell lung carcinoma (NSCLC) A549 cells. Despite the diverse components found in alcohol extraction of P. sibiricum and its multiple targets, the active components and associated targets remain largely unidentified. Hence, there is a need for additional investigation into the pharmacodynamic elements and mechanisms of action. This study aimed to analyze and identify the components responsible for the anti-tumor activity of alcohol extraction from P. sibiricum using UPLC-Q-TOF-MS/MS for the first time. Subsequently, the targets of the active components were predicted using the SwissTargetPrediction database, whereas the targets for NSCLC were sourced from the Online Mendelian Inheritance in Man database (OMIM) and the GeneCards database. Next, the targets of chemical composition were integrated with disease targets via Venny online. GO and KEGG pathway enrichment analyses were performed utilizing DAVID. Subsequently, a network analysis of “components-targets-pathways” was established using Cytoscape 3.8.2 and assessed with the “network analyzer” plug-in. Molecular docking was conducted utilizing Autodock 1.5.6. The study aimed to examine the anti-proliferative impacts and underlying mechanisms of alcohol extraction from P. sibiricum on NSCLC through in vivo and in vitro investigations utilizing an animal model of transplanted tumor, CCK8 assay, cell scratch test, RT-qPCR, and western blotting. The study unveiled that 17 active components extracted from P. sibiricum alcohol demonstrated anti-non-small cell lung cancer (NSCLC) effects through the modulation of 191 targets and various significant signaling pathways. These pathways include Endocrine resistance, PI3K/AKT, Chemical carcinogenesis-receptor activation, Proteoglycans in cancer, EGFR tyrosine kinase inhibitor resistance, AMPK signaling pathway, and other related signaling pathways. Network analysis and molecular docking results indicated that specific compounds such as (25S)-26-O-(β-d-glucopyranosyl)-furost-5-en3β,22α,26-triol3-O-β-d-glucopyranosyl-(1→2)-β-d-glucopyranosyl-(1→4)-β-d-glucopyranoside, Timosaponin H1, Deapi-platycodin D3, (3R)-5,7-dihydroxy-6,8-dimethyl-3-(4′-hydroxybenzyl)-chroman-4-one, Disporopsin, Funkioside F, Kingianoside E, Parisyunnanoside H, and Sibiricoside B primarily targeted 17 key proteins (BCL2, EGFR, ESR1, ESR2, GRB2, IGF1R, JUN, MAP2K1, MAPK14, MAPK8, MDM2, MMP9, mTOR, PIK3CA, RAF1, RPS6KB1, and SRC) collectively. In conclusion, the alcohol extraction of P. sibiricum demonstrated inhibitory effects on cell proliferation, induction of apoptosis, and inhibition of metastasis through various pathways.

Short-term photovoltaic power forecasting with feature extraction and attention mechanisms

The uncertainty of weather conditions has always been a major challenge limiting the performance of photovoltaic (PV) power prediction. Enhancing the accuracy and stability of PV power prediction is crucial for optimizing grid operation. In response to this challenge, this study introduces a hybrid model that integrates an attention mechanism with the Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory Network (BiLSTM). This model aims to mitigate the adverse impact of weather variability on the accuracy of PV power prediction by effectively extracting key features from multidimensional time series data. Additionally, through ablation experiments, this research further assesses the contribution of each model component to performance. Validated with actual data collected from a 1 MW PV power station in China, our proposed model demonstrates significant performance advantages compared to eight advanced prediction models. Ablation study results reveal that removing the CNN component led to a 58.2% increase in MAE and a 53.9% increase in RMSE, while the removal of the attention mechanism resulted in an 83.8% increase in maximum error. These findings underscore the substantial enhancement in prediction accuracy achieved through the integration of CNN and BiLSTM, and the introduction of the attention mechanism significantly boosts the model's prediction stability.

Stochastic Economic Lot Scheduling via Self-Attention Based Deep Reinforcement Learning

The Stochastic Economic Lot Scheduling Problem (SELSP) is a difficult dynamic optimization problem with wide industrial applications. Traditional methods such as hyper-heuristics are manually designed based on substantial expert knowledge, which may limit their optimization performance. Recently, Deep Reinforcement Learning (DRL) is shown to be promising in automatically learning scheduling policies for SELSP. However, its performance is still quite far from that of hyper-heuristics, due to the lack of suitable deep models. In this paper, we propose a novel DRL method to learn dynamic scheduling policies for SELSP in an end-to-end fashion. Based on self-attention, our method can effectively extract useful features from raw state information, and is flexible in handling different numbers of products, which is not viable for previous methods. Experiments on a complex biopharmaceutical manufacturing process show that our method outperforms a recent DRL method and state-of-the-art hyper-heuristics. Moreover, the trained policy performs better in environments different from training with demand forecast errors and varying number of products, showing its strong robustness and generalization ability. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The Stochastic Economic Lot Scheduling Problem (SELSP) is an important problem for manufacturing enterprises, which is to optimally balance the production and inventory so as to minimize the total cost. However, SELSP is very challenging to solve due to the involvement of uncertain factors such as customer demands and machine failures. Traditional methods for solving SELSP, such as heuristic policies and hyper-heuristics, heavily rely on human experiences to design and hence the performance could be limited. This paper proposes a Deep Reinforcement Learning (DRL) based method to automatically learn scheduling policy for solving SELSP, which could alleviate the above limitation through a self-attention based feature extraction mechanism and reward based training. Experimental results on a realistic manufacturing process show that our method can deliver higher revenue than conventional manual policy and an existing DRL based method.

Gabor-net with multi-scale hierarchical fusion of features for fundus retinal blood vessel segmentation

This paper proposes a fundus retinal blood vessel segmentation model based on a deep convolutional network structure and biological visual feature extraction mechanism. It aims to solve the multi-scale problem of blood vessels in the fundus retinal blood vessel segmentation task in the field of medical image processing on the basis of increasing the biological interpretability of the model. First, the subject feature information of the retinal blood vessel image is obtained by using the non-subsampled Residual Bolck convolution main channel. Secondly, combined with the study of biological vision mechanisms, an information processing model of the Retina-Exogenius-Primary visual cortex (V1) ventral visual pathway was established. Gabor functions of different scales are used to simulate the structure of different levels of the visual pathway, and the scale information at different levels is integrated into the corresponding hierarchical stages of the convolutional main pathway network to enrich the information of small blood vessels and enhance the semantic information of the overall blood vessels. Finally, considering the imbalance of the ratio of vessel and nonvessel pixels, an adaptive optimization scheme using hybrid loss function weights is proposed to enhance the priority of blood vessel pixels in the calculation of the loss function. According to the experimental results on the STARE, DRIVE and CHASE_DB1 data sets, the model still achieves superior performance evaluation indicators overall compared with the existing optimal methods in the fundus retinal blood vessel segmentation task. This research is of great significance to the field of medical image processing and can provide more accurate auxiliary diagnostic information for clinical diagnosis and treatment.

Investigation of Thuja orientalis Leaves Extract's Anti-Arthritic Activity in Freund's Complete Adjuvant-Induced Arthritis in Wistar Rats

A severe auto-immune disease that destroys the body's joints and may render a person physically incapable is rheumatoid arthritis. In Ayurveda, herbs have been used to cure a wide range of illnesses. Herbal medicines are widely available and have fewer side effects. As a result, many people find herbal treatments to be intriguing. The Thuja orientalis is a valuable plant for Chinese and Indian medicine. The leaves of Thuja orientalis have long been used in traditional medicine to treat rheumatoid arthritis (RA). However, The current study investigates the anti-arthritic mechanism and phytochemical evaluations of methanolic extract of Thuja orientalis leaves. Researchers employed rats that had Freund's Complete Adjuvant (FCA)-induced arthritis to test the anti-arthritic potential of methanol extracts from Thuja orientalis. Among other things, we have examined paw edoema, body mass index, locomotor activity, haematological observations, biochemical assessment, arthritic score, radiography, etc. Rats were given 0.05ml of FCA subcutaneously to cause arthritis. Four groups of arthritic rats and one group of normal rats, each with six animals, were created. The arthritic rats in Groups IV and V got 200mg per kg and 400mg per kg of thuja orientalis extract, respectively. Diclofenac sodium (10mg per kg) was given as a routine medication to Groups III of the arthritic rats, respectively, and CMC (1ml/kg) was given to Group I of the normal animals. The disease control groups were Group II respectively. Paw volumes were measured using a plethysmometer at 1, 7, 14, 21 days. When compared to arthritis control rats, the Thuja orientalis extract (200 and 400mg/kg orally) significantly (p&lt;0.05, p&lt;0.01) decreased the size of the paws and changed the body weight in CFA rats at day 21. In statistical analysis, all data were reported as mean SEM. The mean values of the test groups and the control group were compared using one-way ANOVA and Dunnett's test. The results of the current investigation showed that a methanol extract from Thuja orientalis leaves may have anti-arthritic properties.

An Extraction Mechanism of Perception connected Semantic/Episodic Memory

An Extraction Mechanism of Perception connected Semantic/Episodic Memory

Collusion-proof mechanisms for full surplus extraction

The paper examines information structures that can guarantee full surplus extraction via collusion-proof mechanisms. Our collusion-proofness notion requires that there does not exist any coalition whose manipulation can affect the mechanism designer's payoff. When the mechanism designer is restricted to using standard Bayesian mechanisms, we show that under almost every prior distribution of agents' types, there exist payoff structures under which there is no collusion-proof full surplus extracting mechanism. However, when ambiguous mechanisms are allowed, we provide a weak necessary and sufficient condition on the prior such that collusion-proof full surplus extraction can be guaranteed. Thus, the paper sheds light on how the collusion-proofness requirement resolves the full surplus extraction paradox of Crémer and McLean (1985, 1988) and how engineering ambiguity in mechanism rules restores the paradox.

Isolation, characterization and pharmacological potentials of methanol extract of Cassia fistula leaves: Evidenced from mice model along with molecular docking analysis

The purpose of the current investigation was to conduct a detailed analysis of the chemical components and medicinal properties of the methanolic crude extract derived from the leaves of Cassia fistula. This analysis was carried out using both experimental (in vivo) and computational (in silico) methods. Eleven chemicals were chromatographically isolated using GC-MS/MS, which utilizes a library of NIST and Wiley 2020 versions. FTIR analysis of the extract was performed to identify the functional group of the compounds. The glucose-lowering capacity, analgesic, and anti-diarrheal activities of methanolic crude extract were analyzed utilizing a well-known oral glucose tolerance test, tail immersion method, writhing assay, and castor oil-induced diarrheal mice methods, respectively. After 60 min, 120 min, and 180 min of loading the drugs, a significant reduction of blood glucose levels was examined (p < 0.05) in all the extracts of this plant (200 mg/kg, 400 mg/kg and 600 mg/kg) utilized in this research at a time-dependent manner. Similarly, all the crude extracts showed significant (p < 0.05) effects against pain centrally and peripherally compared to the standard drug morphine (2 mg/kg bw) and diclofenac sodium (50 mg/kg bw). Moreover, the methanol extract (400 mg/kg bw) manifested anti-diarrheal efficacy by inhibiting 72.0 % of the diarrheal episode in mice compared to the standard drug loperamide (inhibition = 80.0%). The results of the computational investigations corroborated existing in-vivo findings. Greater or close to equivalent binding affinity to the active binding sites of kappa opioid receptor, glucose transporter 3 (GLUT 3), and cyclooxygenase 2 was indicative of the potential anti-diarrheal, hypoglycemic, and analgesic characteristics of the isolated compounds (COX-2). Moreover, anticancer and antimicrobial potentiality was also found impressive through evaluation of binding affinity with epidermal growth factor receptor (EGFR) and dihydrofolate reductase (DHFR) receptors. Results from this study indicated that C. fistula might be a beneficial natural resource for treating diarrhea, hyperglycemia, and pain. However, additional research is required to conduct a comprehensive phytochemical screening and establish precise action mechanisms of the crude extract or the plant-derived compounds.

Utilizing adaptive deformable convolution and position embedding for colon polyp segmentation with a visual transformer

Polyp detection is a challenging task in the diagnosis of Colorectal Cancer (CRC), and it demands clinical expertise due to the diverse nature of polyps. The recent years have witnessed the development of automated polyp detection systems to assist the experts in early diagnosis, considerably reducing the time consumption and diagnostic errors. In automated CRC diagnosis, polyp segmentation is an important step which is carried out with deep learning segmentation models. Recently, Vision Transformers (ViT) are slowly replacing these models due to their ability to capture long range dependencies among image patches. However, the existing ViTs for polyp do not harness the inherent self-attention abilities and incorporate complex attention mechanisms. This paper presents Polyp-Vision Transformer (Polyp-ViT), a novel Transformer model based on the conventional Transformer architecture, which is enhanced with adaptive mechanisms for feature extraction and positional embedding. Polyp-ViT is tested on the Kvasir-seg and CVC-Clinic DB Datasets achieving segmentation accuracies of 0.9891 ± 0.01 and 0.9875 ± 0.71 respectively, outperforming state-of-the-art models. Polyp-ViT is a prospective tool for polyp segmentation which can be adapted to other medical image segmentation tasks as well due to its ability to generalize well.