Extraction Method Research Articles

Oral Administration Properties Evaluation of Three Milk-derived Extracellular Vesicles Based on Ultracentrifugation Extraction Methods.

Milk-derived extracellular vesicles (M-EVs) are low-cost, can be prepared in large quantities, and can cross the gastrointestinal barrier for oral administration. However, the composition of milk is complex, and M-EVs obtained by different extraction methods may affect their oral delivery. Based on this, we propose a new method for extracting M-EVs based on cryogenic freezing treatment (Cryo-M-EVs) and compare this method with the previously reported acetic acid treatment (Acid-M-EVs) method and the conventional ultracentrifugation method (Ulltr-M-EVs). The new method simplifies the pretreatment step and achieves 25-fold and 2-fold higher yields than Acid-M-EVs and Ulltr-M-EVs. And it was interesting to note that Cryo-M-EVs and Acid-M-EVs had higher cellular uptake efficiency, and Cryo-M-EVs presented the best transepithelial transport effect. After oral administration of the three M-EVs extracted by three methods in mice, Cryo-M-EVs effectively successfully crossed the gastrointestinal barrier and achieved hepatic accumulation, whereas Acid-M-EVs and Ultr-M-EVs mostly resided in the intestine. The M-EVs obtained by the three extraction methods showed a favorable safety profile at the cellular as well as animal level. Therefore, when M-EVs obtained by different extraction methods are used for oral drug delivery, we can utilize their accumulation properties at different sites to better deal with different diseases. This article is protected by copyright. All rights reserved.

Predictive value of radiomic features extracted from primary lung adenocarcinoma in forecasting thoracic lymph node metastasis: a systematic review and meta-analysis.

The application of radiomics in thoracic lymph node metastasis (LNM) of lung adenocarcinoma is increasing, but diagnostic performance of radiomics from primary tumor to predict LNM has not been systematically reviewed. Therefore, this study sought to provide a general overview regarding the methodological quality and diagnostic performance of using radiomic approaches to predict the likelihood of LNM in lung adenocarcinoma. Studies were gathered from literature databases such as PubMed, Embase, the Web of Science Core Collection, and the Cochrane library. The Radiomic Quality Score (RQS) and the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) were both used to assess the quality of each study. The pooled sensitivity, specificity, and area under the curve (AUC) of the best radiomics models in the training and validation cohorts were calculated. Subgroup and meta-regression analyses were also conducted. Seventeen studies with 159 to 1202 patients each were enrolled between the years of 2018 to 2022, of which ten studies had sufficient data for the quantitative evaluation. The percentage of RQS was between 11.1% and 44.4% and most of the studies were considered to have a low risk of bias and few applicability concerns in QUADAS-2. Pyradiomics and logistic regression analysis were the most commonly used software and methods for radiomics feature extraction and selection, respectively. In addition, the best prediction models in seventeen studies were mainly based on radiomics features combined with non-radiomics features (semantic features and/or clinical features). The pooled sensitivity, specificity, and AUC of the training cohorts were 0.84 (95% confidence interval (CI) [0.73-0.91]), 0.88 (95% CI [0.81-0.93]), and 0.93(95% CI [0.90-0.95]), respectively. For the validation cohorts, the pooled sensitivity, specificity, and AUC were 0.89 (95% CI [0.82-0.94]), 0.86 (95% CI [0.74-0.93]) and 0.94 (95% CI [0.91-0.96]), respectively. Radiomic features based on the primary tumor have the potential to predict preoperative LNM of lung adenocarcinoma. However, radiomics workflow needs to be standardized to better promote the applicability of radiomics. CRD42022375712.

Effects of different extraction methods on the structure and function of blackberry seed polysaccharides

Effects of different extraction methods on the structure and function of blackberry seed polysaccharides

Quantitative determination of residue amounts of pesticide active ingredients used in grapes by LC-MS/MS and GC-MS/MS devices and evaluation of these pesticides in terms of public health.

The aim of this study was to quantitatively determine pesticide residues in grapes, one of the most produced and consumed fruits in Turkey and in the world. A total of 226 active ingredients were analyzed in 21 samples collected from Southeastern and Eastern Anatolia regions using QuEChERS (quick, easy, cheap, effective, rugged, and safe) extraction method and multiple residue analysis technique and LC-MS/MS and GC-MS/MS devices. In 11 out of 21 samples (52.4%), no active ingredient was detected, while at least one active ingredient was detected in 10 samples (47.6%). Thirteen different active substances (Ametoctradin, Azoxystrobin, Boscalid, Diphenoconazole, Dimethomorph, Fenhexamid, Fluopyram, Flutriafol, Metalaxyl- Metalaxyl-M, Metrafenone, Tebuconazole, Trifloxystrobin) were detected in the samples. The top 3 most detected active substances were Boscalid-Azoxystrobin and Fluopyram, respectively. The active ingredients were found between 0.015 and 0.499 mg kğ-1 values.

Locality Cross-domain Discriminant Analysis for Membranous Nephropathy Recognition using Microscopic Hyperspectral Imaging.

Cross-domain methods have been proposed to learn the domain invariant knowledge that can be transferred from the source domain to the target domain. Existing cross-domain methods attempt to minimize the distribution discrepancy of the domains. However, these methods fail to explore the domain invariant subspace due to the samples of different classes between two domains may overlap in the new subspace. They consider the features in the original space data that may be unnecessary or irrelevant to the final classification, and neglect to preserve the local manifold structure between two domains. To solve these problems, a novel feature extraction method called Locality Cross-domain Discriminant Analysis (LCDA) is proposed. LCDA first aligns the distributions and avoids overlap between two domains. Then, LCDA exploits the local manifold structure to maintain the discriminative capability of the low-dimensional projection matrices. Finally, a robust constraint is utilized to preserve the robustness of the projection matrices. The proposed LCDA not only avoids overlap between different classes but also explores the local manifold information. Experiment results on the medical membranous nephropathy hyperspectral dataset demonstrate that the proposed LCDA has better performance than other relevant feature extraction methods.

Reduction of precious metal ions in aqueous solutions by contact-electro-catalysis.

Precious metals are core assets for the development of modern technologies in various fields. Their scarcity poses the question of their cost, life cycle and reuse. Recently, an emerging catalysis employing contact-electrification (CE) at water-solid interfaces to drive redox reaction, called contact-electro-catalysis (CEC), has been used to develop metal free mechano-catalytic methods to efficiently degrade refractory organic compounds, produce hydrogen peroxide, or leach metals from spent Li-Ion batteries. Here, we show ultrasonic CEC can successfully drive the reduction of Ag(ac), Rh3+, [PtCl4]2-, Ag+, Hg2+, Pd2+, [AuCl4]-, and Ir3+, in both anaerobic and aerobic conditions. The effect of oxygen on the reaction is studied by electron paramagnetic resonance (EPR) spectroscopy and ab-initio simulation. Combining measurements of charge transfers during water-solid CE, EPR spectroscopy and gold extraction experiments help show the link between CE and CEC. What's more, this method based on water-solid CE is capable of extracting gold from synthetic solutions with concentrations ranging from as low as 0.196 ppm up to 196 ppm, reaching in 3 h extraction capacities ranging from 0.756 to 722.5 mg g-1 in 3 h. Finally, we showed CEC is employed to design a metal-free, selective, and recyclable catalytic gold extraction methods from e-waste aqueous leachates.

A novel method for extraction of high purity and high production Phytophthora sojae oospores.

Phytophthora sojae, a soil-borne oomycete pathogen, has been a yield limiting factor for more than 60 years on soybean. The resurgence of P. sojae(Phytophthora sojae)is primarily ascribed to the durable oospores found in soil and remnants of the disease. P. sojae is capable of infesting at any growth periods of the soybean, and the succeed infestation of P. sojae is predominantly attributed to long-lived oospores present in soil. Comprehending the molecular mechanisms that drive oospores formation and their significance in infestation is the key for effective management of the disease. However, the existing challenges in isolating and extracting significant quantities of oospores pose limitations in investigating the sexual reproductive stages of P. sojae. The study focused on optimizing and refining the culture conditions and extraction process of P. sojae, resulting in establishment of an efficient and the dependable method for extraction. Novel optimized approach was yielded greater quantities of high-purity P. sojae oospores than traditional methods. The novel approach exceeds the traditional approaches with respect to viability, survival ability, germination rates of new oospores and the pathogenicity of oospores in potting experiments. The proposed method for extracting P. sojae oospores efficiently yielded a substantial quantity of highly pure, viable, and pathogenic oospores. The enhancements in oospores extraction techniques will promote the research on the sexual reproductive mechanisms of P. sojae and lead to the creation of innovative and effective approaches for managing oomycete diseases.

Combined multi-level context aggregation and attention mechanism method for photovoltaic panel extraction from high resolution remote sensing images

Combined multi-level context aggregation and attention mechanism method for photovoltaic panel extraction from high resolution remote sensing images

Enhancing multi-class lung disease classification in chest x-ray images: A hybrid manta-ray foraging volcano eruption algorithm boosted multilayer perceptron neural network approach.

One of the most used diagnostic imaging techniques for identifying a variety of lung and bone-related conditions is the chest X-ray. Recent developments in deep learning have demonstrated several successful cases of illness diagnosis from chest X-rays. However, issues of stability and class imbalance still need to be resolved. Hence in this manuscript, multi-class lung disease classification in chest x-ray images using a hybrid manta-ray foraging volcano eruption algorithm boosted multilayer perceptron neural network approach is proposed (MPNN-Hyb-MRF-VEA). Initially, the input chest X-ray images are taken from the Covid-Chest X-ray dataset. Anisotropic diffusion Kuwahara filtering (ADKF) is used to enhance the quality of these images and lower noise. To capture significant discriminative features, the Term frequency-inverse document frequency (TF-IDF) based feature extraction method is utilized in this case. The Multilayer Perceptron Neural Network (MPNN) serves as the classification model for multi-class lung disorders classification as COVID-19, pneumonia, tuberculosis (TB), and normal. A Hybrid Manta-Ray Foraging and Volcano Eruption Algorithm (Hyb-MRF-VEA) is introduced to further optimize and fine-tune the MPNN's parameters. The Python platform is used to accurately evaluate the proposed methodology. The performance of the proposed method provides 23.21%, 12.09%, and 5.66% higher accuracy compared with existing methods like NFM, SVM, and CNN respectively.

Evaluating the OpenAI's GPT-3.5 Turbo's performance in extracting information from scientific articles on diabetic retinopathy.

We aimed to compare the concordance of information extracted and the time taken between a large language model (OpenAI's GPT-3.5 Turbo via API) against conventional human extraction methods in retrieving information from scientific articles on diabetic retinopathy (DR). The extraction was done using GPT3.5 Turbo as of October 2023. OpenAI's GPT-3.5 Turbo significantly reduced the time taken for extraction. Concordance was highest at 100% for the extraction of the country of study, 64.7% for significant risk factors of DR, 47.1% for exclusion and inclusion criteria, and lastly 41.2% for odds ratio (OR) and 95% confidence interval (CI). The concordance levels seemed to indicate the complexity associated with each prompt. This suggests that OpenAI's GPT-3.5 Turbo may be adopted to extract simple information that is easily located in the text, leaving more complex information to be extracted by the researcher. It is crucial to note that the foundation model is constantly improving significantly with new versions being released quickly. Subsequent work can focus on retrieval-augmented generation (RAG), embedding, chunking PDF into useful sections, and prompting to improve the accuracy of extraction.

Overcoming Dimensionality Constraints: A Gershgorin Circle Theorem-Based Feature Extraction for Weighted Laplacian Matrices in Computer Vision Applications

(1) Problem Statement: In graph theory, the weighted Laplacian matrix is the most utilized technique to interpret the local and global properties of a complex graph structure within computer vision applications. However, with increasing graph nodes, the Laplacian matrix’s dimensionality also increases accordingly. Therefore, there is always the “curse of dimensionality”; (2) Methodology: In response to this challenge, this paper introduces a new approach to reducing the dimensionality of the weighted Laplacian matrix by utilizing the Gershgorin circle theorem by transforming the weighted Laplacian matrix into a strictly diagonal domain and then estimating rough eigenvalue inclusion of a matrix. The estimated inclusions are represented as reduced features, termed GC features; (3) Results: The proposed Gershgorin circle feature extraction (GCFE) method was evaluated using three publicly accessible computer vision datasets, varying image patch sizes, and three different graph types. The GCFE method was compared with eight distinct studies. The GCFE demonstrated a notable positive Z-score compared to other feature extraction methods such as I-PCA, kernel PCA, and spectral embedding. Specifically, it achieved an average Z-score of 6.953 with the 2D grid graph type and 4.473 with the pairwise graph type, particularly on the E_Balanced dataset. Furthermore, it was observed that while the accuracy of most major feature extraction methods declined with smaller image patch sizes, the GCFE maintained consistent accuracy across all tested image patch sizes. When the GCFE method was applied to the E_MNSIT dataset using the K-NN graph type, the GCFE method confirmed its consistent accuracy performance, evidenced by a low standard deviation (SD) of 0.305. This performance was notably lower compared to other methods like Isomap, which had an SD of 1.665, and LLE, which had an SD of 1.325; (4) Conclusions: The GCFE outperformed most feature extraction methods in terms of classification accuracy and computational efficiency. The GCFE method also requires fewer training parameters for deep-learning models than the traditional weighted Laplacian method, establishing its potential for more effective and efficient feature extraction in computer vision tasks.

Silicon extraction from x-ray amorphous soil constituents: a method comparison of alkaline extracting agents

The growing interest in amorphous silica (ASi) within the fields of soil science and ecology underscores the necessity for a reliable protocol to estimate ASi contents in soil. Alkaline wet chemical extraction methods are commonly employed for silicon (Si) extraction from operationally defined (x-ray) amorphous Si phases or short-range ordered mineral phases in soils and marine sediments. In our study we conducted a comparative analysis of four alkaline extraction methods (1% sodium carbonate, 0.5 M sodium carbonate, 0.2 M sodium hydroxide, and 0.1 M Tiron), assessing their extraction selectivity as well as effectiveness using soils artificially enriched with varying, defined amounts of ASi. While extraction effectiveness was evaluated by determining the recovery rate of initially added ASi, extraction selectivity was determined by measuring aluminum (Al) and iron (Fe) concentrations as indicators of the dissolution of non-target mineral phases. Microwave plasma atom emission spectrometry was used to analyze Al, Fe, and Si concentrations in the extracts. Our results indicate that extraction with 0.2 M sodium hydroxide yields the best outcomes in terms of both extraction effectiveness and selectivity. This more recent extraction technique is conducted at the most alkaline pH (13.3) of all four methods tested, but at ambient temperature (21°C) decreasing the dissolution of non-target mineral phases. Though, no wet-chemical extraction used on heterogeneous samples like soil is precisely selective, and thus able to quantify the target analyte only. Hence, data obtained by such procedures still need to be interpreted with caution considering all their limitations.

Evaluation of antioxidant and anti-inflammatory activity and identification of bioactive compound from the marine diatom, Odontella aurita extract

Increased production of reactive oxygen species (ROS) leads to oxidative stress, with its damaging effect extending to the mitochondria and plasma membrane. Further, prolonged inflammation can result in chronic disease development. The marine microdiatom Odontella aurita is recognized for its potential in food and pharmaceutical development. Moreover, it contains antioxidant and anti-inflammatory properties. However, studies regarding the efficacy of their varying extract forms and their underlying mechanisms remain scarce. Therefore, this study aims to explore the antioxidant and anti-inflammatory effects of Odontella aurita extracts obtained using various extraction methods (hot water, 70% ethanol, and chloroform:methanol (CM)). Among the three Odontella aurita extracts, the CM extract demonstrated superior efficacy in protecting RAW 264.7 cells from H2O2-induced cytotoxicity. It significantly lowered the levels of ROS and enhanced the expression of superoxide dismutase and glutathione peroxidase. Furthermore, the CM extract outperformed other extracts in inhibiting LPS-induced nitric oxide production, reducing mRNA levels in nitric oxide synthase, cyclooxygenase, and the proinflammatory cytokines interleukin IL-1β, IL-6, TNFα. Additionally, CM extract effectively suppressed the activation of NF-κB/IκBα and JAK2-STAT3 in LPS-induced RAW 264.7 cells. HPLC–UV analysis revealed a remarkable 33-fold higher fucoxanthin content in CM compared to the ethanol extract. GC–MS analysis identified elevated levels of cholest-5-en-3-ol, phytol, eicosapentaenoic acid methyl ester, methyl palmitate, palmitoleic acid methyl ester, and neophytadiene in the CM extract. These findings suggest that Odontella aurita CM extract is a promising antioxidant candidate for preventing or treating inflammatory diseases, consequently emphasizing its potential for further development.Graphical

Safe and genotyped. A non-invasive method for extraction of amphibian DNA from water baths and its application on Northern spectacled salamanders, Salamandrina perspicillata (Savi 1821)

AbstractThe global decline of amphibian populations, with 40.7% of species classified as threatened, calls for innovative and ethical approaches in conservation genetics. Molecular biology advancements have introduced environmental DNA (eDNA) analysis, primarily focusing on aquatic environments. However, the present study explores a novel non-invasive protocol using water samples to extract DNA from terrestrial and semi-terrestrial amphibians, specifically the endangered Italian endemic salamander, Salamandrina perspicillata (Savi, 1821). Unlike traditional invasive methods involving tissue sampling, this protocol immerses animals briefly, eliminating the need for digit or tail amputations or manipulation for buccal swabs. The study validated the protocol through DNA extraction, amplification, and sequencing, yielding results comparable to traditional methods. The non-invasive nature of the protocol aligns with the 3Rs principles (Replace, Reduce, Refine) and offers a streamlined, stress-minimizing alternative for studying protected and endangered species. Future experiments should also explore further refinements, including reduced soaking times and additional applications, such as skin microbiota analysis. This protocol represents a significant step towards ethical and effective research practices in amphibian conservation genetics, encouraging a paradigm shift in wildlife research ethics. Continued innovation in non-invasive methodologies is essential for comprehensive understanding and robust conservation strategies amid the ongoing biodiversity crisis.

Graph neural networks for automatic extraction and labeling of the coronary artery tree in CT angiography.

Automatic comprehensive reporting of coronary artery disease (CAD) requires anatomical localization of the coronary artery pathologies. To address this, we propose a fully automatic method for extraction and anatomical labeling of the coronary artery tree using deep learning. We include coronary CT angiography (CCTA) scans of 104 patients from two hospitals. Reference annotations of coronary artery tree centerlines and labels of coronary artery segments were assigned to 10 segment classes following the American Heart Association guidelines. Our automatic method first extracts the coronary artery tree from CCTA, automatically placing a large number of seed points and simultaneous tracking of vessel-like structures from these points. Thereafter, the extracted tree is refined to retain coronary arteries only, which are subsequently labeled with a multi-resolution ensemble of graph convolutional neural networks that combine geometrical and image intensity information from adjacent segments. The method is evaluated on its ability to extract the coronary tree and to label its segments, by comparing the automatically derived and the reference labels. A separate assessment of tree extraction yielded an score of 0.85. Evaluation of our combined method leads to an average score of 0.74. The results demonstrate that our method enables fully automatic extraction and anatomical labeling of coronary artery trees from CCTA scans. Therefore, it has the potential to facilitate detailed automatic reporting of CAD.

Evaluation of the time, labor, and money required for manual and automated nucleic acid (RNA) isolation for the detection of SARS-COV-2 by QRT-PCR using the qiamp viral RNA mini kit and kingfisher flex

We undertook this cross-sectional investigation to assess the time, manpower, and average run cost per sample using manual Qiamp Viral RNA micro kit (Qiagen) and automated kingfisher flex instrument extraction methods for SARS-Cov-2 identification.The study used 120 Viral Transport Media-collected nasopharyngeal/ oropharyngeal swabs.Magnetic bead-based RNA extraction was performed using the Thermo Fisher Scientific kingfisher flex instrument and manual Extraction was Silica membrane-based Qiagen spin column kits. The TaqPath™ COVID-19 Combo Kit from Thermo Fisher Scientific was used for detecting SARS-CoV-2 target genes.Human technique took 40 minutes longer than automation. It cost more to automate than to manually labor. These disparities in time, effort, and cost affect laboratory operations, offering pros and cons for each method. This suggests that positive or negative was consistent regardless of viral load or RNA concentration.The study found that automated RNA extraction yielded better results compared to manual extraction. The automated sample processing system saved time, people, and money. In resource-limited or low-throughput labs, manual extraction may be preferable. Manual methods are laborious, require more hands-on time, and risk cross-contamination and technical blunders.

FetchEEG: a hybrid approach combining feature extraction and temporal-channel joint attention for EEG-based emotion classification

Objective. Electroencephalogram (EEG) analysis has always been an important tool in neural engineering, and the recognition and classification of human emotions are one of the important tasks in neural engineering. EEG data, obtained from electrodes placed on the scalp, represent a valuable resource of information for brain activity analysis and emotion recognition. Feature extraction methods have shown promising results, but recent trends have shifted toward end-to-end methods based on deep learning. However, these approaches often overlook channel representations, and their complex structures pose certain challenges to model fitting. Approach. To address these challenges, this paper proposes a hybrid approach named FetchEEG that combines feature extraction and temporal-channel joint attention. Leveraging the advantages of both traditional feature extraction and deep learning, the FetchEEG adopts a multi-head self-attention mechanism to extract representations between different time moments and channels simultaneously. The joint representations are then concatenated and classified using fully-connected layers for emotion recognition. The performance of the FetchEEG is verified by comparison experiments on a self-developed dataset and two public datasets. Main results. In both subject-dependent and subject-independent experiments, the FetchEEG demonstrates better performance and stronger generalization ability than the state-of-the-art methods on all datasets. Moreover, the performance of the FetchEEG is analyzed for different sliding window sizes and overlap rates in the feature extraction module. The sensitivity of emotion recognition is investigated for three- and five-frequency-band scenarios. Significance. FetchEEG is a novel hybrid method based on EEG for emotion classification, which combines EEG feature extraction with Transformer neural networks. It has achieved state-of-the-art performance on both self-developed datasets and multiple public datasets, with significantly higher training efficiency compared to end-to-end methods, demonstrating its effectiveness and feasibility.

Quality of cellulose and biostimulant extracts from Oedogonium calcareum cultivated during primary wastewater treatment.

A practical two-product cascading biorefinery was developed to extract a biostimulant and cellulose from the freshwater filamentous macroalga Oedogonium calcareum grown while treating primary wastewater. Biostimulant production provides a valuable extract with production of disinfected residual biomass for further product development. Both Escherichia coli and F-specific RNA bacteriophage, indicators of human pathogens contamination, were absent from the residual biomass. The chemical composition of the biostimulant was complex, consisting of growth-promoting substances, free amino acids, and minerals. The O. calcareum cellulose fractions yielded between 9.5% and 10.1% (w/w) with purities from 84% to 90% and closely resembled microcrystalline cellulose. Biostimulant extraction improved cellulose quality by increasing crystallinity from 59% to 62%. Biomass condition, drying process, and biostimulant production influenced the crystallinity index. This study demonstrates a two-step process of biostimulant and cellulose extraction from wastewater-grown Oedogonium, simultaneously disinfecting biomass and isolating high-quality cellulose as a sustainable alternative to conventional extraction methods.

Research on infrared spectroscopy detection of furfural content in transformer oil based on acetonitrile extraction

Abstract The stable operation of power transformers depends on the oil-paper insulation system, in which the aging degree of insulating paper is the key to evaluate the remaining life of the transformer. Currently, methanol extraction is widely used to detect the furfural content in oil to evaluate the aging state of insulating paper, but it ignores the influence of methanol produced during the operation of the transformer on the extraction results. To address this issue, this paper proposes a new method that can replace methanol extraction for detecting furfural in oil through simulation and experiment. Firstly, through simulation and experiment, it is proved that the strong vibration absorption peaks at 1677 cm-1 for furfural carbonyl and 2240 cm-1 for acetonitrile cyano can be used to establish a new method for extracting furfural content in oil using acetonitrile. A quantitative model between the concentration x of furfural in the furfural-acetonitrile mixed solution and the area y of the infrared absorption peak at 1677 cm-1 is established, with a goodness of fit of 0.9974. Secondly, a comparison between direct detection and acetonitrile extraction methods is conducted. The results show that direct detection is simple to operate, but the minimum detection concentration is 40mg/L, which is difficult to meet practical requirements. Acetonitrile extraction can reduce the minimum detection concentration to 0.1mg/L. At the same time, the extraction conditions are analyzed to determine the extraction ratio of 30, extraction times of 5, and extraction rate of 60%. Finally, the proposed detection method is applied to thermal aging tests on insulating paper of different types. The experimental results show that the proposed method has good repeatability and improves the detection resolution of furfural content. This paper combines infrared spectroscopy with acetonitrile extraction technology to open up a more efficient and practical new method for detecting furfural content in transformer oil.

Assessment of ethanol leaf extract from urtica dioica for in vitro Anti-Helminthic properties

Helminthic infections continue to be a significant public health concern worldwide, particularly in regions with poor sanitation and limited access to healthcare. With the increasing prevalence of drug resistance among helminths, there is a pressing need for alternative treatments derived from natural sources. Urtica dioica, commonly known as stinging nettle, has been traditionally used for various medicinal purposes, including its purported anthelmintic properties. This study aimed to evaluate the in vitro anti-helminthic activity of ethanol leaf extract of Urtica dioica against helminth parasites. Fresh leaves of Urtica dioica were collected, dried, and powdered. The ethanol leaf extract was prepared using standard extraction methods. Various concentrations of the extract were tested against helminth parasites using established in vitro assays. The efficacy of the extract was determined by assessing parameters such as paralysis and mortality of the parasites. Preliminary findings indicate that the ethanol leaf extract of urtica dioica possesses significant anti-helminthic activity against the tested parasites. The extract exhibited dose-dependent paralysis and mortality of the helminths, with higher concentrations demonstrating greater efficacy. These results suggest the potential of Urtica dioica as a natural source of anthelmintic agents. Further studies are warranted to elucidate the specific bioactive compounds responsible for the observed anti-helminthic activity and to evaluate the safety and efficacy of Urtica dioica extract in vivo. Additionally, investigations into the underlying mechanisms of action and potential synergistic effects with conventional anthelmintic drugs may provide valuable insights for the development of novel therapeutic interventions against helminthic infections. The study results when compared to standard drug of fenbendazole, ethanolic extract of Urtica dioica possess a good significant anti-helmentic activity compared to standard drug of fenbendazole.