Extraction Approach Research Articles

Innovative Biophysical Approaches for Quercetin Extraction from Plant Cells

Innovative Biophysical Approaches for Quercetin Extraction from Plant Cells

Exploring the Use of Water-Extracted Flaxseed Hydrocolloids in Three-Dimensional Cell Culture.

Plant-derived hydrocolloids offer promising prospects in biomedical applications. Among these, Flaxseed hydrocolloid (FSH) can form a soft, elastic, and biocompatible hydrocolloid with tunable viscosity and superior swelling capacity, making it an attractive scaffold. This study introduces a green extraction method for FSH, employing a single-step aqueous extraction process and fabrication of FSH scaffold. Despite growing interest, the pristine form of FSH has not been investigated for sustainable long-term three-dimensional (3D) cell culture. Here, FSH scaffolds were thoroughly characterized for their morphological, chemical, mechanical, and biological properties. 3D cell culture experiments were conducted using NIH-3T3 mouse fibroblast cells, and cell viability was assessed using live/dead and Alamar Blue assays. High cell viability was sustained for long term compared with 2D cell culture. Cell adhesion and 3D cellular morphology on FSH scaffold for 30 days were monitored by scanning electron microscopy analysis. Also, collagen type-I and F-actin expressions were analyzed by immunostaining after 30 days of culture, resulting in 5- and 4-fold increments of fluorescence intensity, respectively. Results indicate sustained cell viability in the long term and favorable cell-material interaction, demonstrating the potential of FSH as a scaffold. This study emphasizes the importance of the green extraction approach, improving the biocompatibility and functionality of FSH tissue engineering applications.

Recognition of Conus species using a combined approach of supervised learning and deep learning-based feature extraction.

Cone snails are venomous marine gastropods comprising more than 950 species widely distributed across different habitats. Their conical shells are remarkably similar to those of other invertebrates in terms of color, pattern, and size. For these reasons, assigning taxonomic signatures to cone snail shells is a challenging task. In this report, we propose an ensemble learning strategy based on the combination of Random Forest (RF) and XGBoost (XGB) methods. We used 47,600 cone shell images of uniform size (224 x 224 pixels), which were split into an 80:20 train-test ratio. Prior to performing subsequent operations, these images were subjected to pre-processing and transformation. After applying a deep learning approach (Visual Geometry Group with a 16-layer deep model architecture) for feature extraction, model specificity was further assessed by including multiple related and unrelated seashell images. Both classifiers demonstrated comparable recognition ability on random test samples. The evaluation results suggested that RF outperformed XGB due to its high accuracy in recognizing Conus species, with an average precision of 95.78%. The area under the receiver operating characteristic curve was 0.99, indicating the model's optimal performance. The learning and validation curves also demonstrated a robust fit, with the training score reaching 1 and the validation score gradually increasing to 95 as more data was provided. These values indicate a well-trained model that generalizes effectively to validation data without significant overfitting. The gradual improvement in the validation score curve is crucial for ensuring model reliability and minimizing the risk of overfitting. Our findings revealed an interactive visualization. The performance of our proposed model suggests its potential for use with datasets of other mollusks, and optimal results may be achieved for their categorization and taxonomical characterization.

Advances in polysaccharide-based materials for biomedical and pharmaceutical applications: A comprehensive review.

Polysaccharides, the most abundant biopolymers in nature, have attracted the attention of researchers and clinicians due to its practicality in biomedical and pharmaceutical sciences. These biomaterials have high bioavailability and play structural and functional roles in living organisms. Polysaccharides are classified into several groups based on their origin, including plant polysaccharides and marine polysaccharides (like chitosan, hyaluronic acid, dextran, alginates, etc.) with specific applications. These biopolymers possess unique physicochemical (such as surface functional groups, solubility, and stability), mechanical (like mechanical strength and tensile), and biomedical (such as antioxidant activity, biocompatibility, biodegradability, renewability, and non-immunogenicity) characteristics which have made them excellent platforms for a wide variety of biomedical and pharmaceutical applications. Ease of extraction and different preparation approaches are mentioned as other potential properties of polysaccharides that further improved their practicality in biomedical sciences. They have high drug/bioactive encapsulation capacity and sustained/controlled release manner in in vivo microenvironments. The anti-inflammatory and immunomodulation, stimuli-responsive drug/bioactive release, and passive and active drug/bioactive delivery are considered the potential features of these biopolymers in pharmaceutical sciences. Polysaccharides have indicated practical applications in biomedical sciences, including biosensors, tissue engineering, implantation, wound healing, vascular grafting, and vaccines. This review highlights the advances of polysaccharide-based materials in biomedical and pharmaceutical sciences.

Charting the Growth of Text Summarisation: A Data-Driven Exploration of Research Trends and Technological Advancements

Text summarisation plays a pivotal role in efficiently processing large volumes of textual data, making it an indispensable tool across diverse domains such as healthcare, legal, education, and journalism. It addresses the challenge of information overload by condensing or generating concise, meaningful summaries that improve decision-making, enhance accessibility, and save valuable time. Advances in artificial intelligence continue to propel the growth of text summarisation research, particularly with the evolution from traditional extractive approaches to cutting-edge abstractive models like BERT and GPT, as well as emerging innovations in multimodal and multilingual summarisation. To trace the development of this field, this study integrates bibliometric analysis and an in-depth survey, leveraging data from the Web of Science database to explore citation trends, uncover influential contributors, and highlight emerging research areas. Furthermore, bibliometric and critical evaluations are employed to outline strategic pathways and propose future directions for the continued advancement of the field. By incorporating sophisticated visualisation tools such as VOSviewer and RawGraphs, the analysis provides an enriched understanding of the field’s trajectory, identifying significant methodologies, landmark contributions, and existing gaps. This comprehensive exploration not only underscores the progress achieved in text summarisation but also serves as an invaluable resource for shaping forthcoming research endeavours and inspiring innovation in this dynamic area of study.

Obtaining Cellulose Nanocrystals in a Medium of Primary Monohydric Alcohols

The lack of a universal method for isolating cellulose nanocrystals (CNCs) has encouraged researchers to look for new methods and approaches as alternatives to traditional sulfuric acid hydrolysis. Acid alcoholysis has long been actively used in cellulose depolymerization processes to obtain a variety of alkyl glycosides and further alcoholysis products. In the present article, the authors continue their earlier research on the synthesis of CNCs in the presence of a sulfuric acid catalyst in an alcoholic environment. In this work, CNCs were obtained from sulfate-bleached pulp in a medium of primary monohydric alcohols (СnH2n+1OH, n = 5–8). A maximum CNC yield of 60 % was achieved with pentanol-1 at a sulfuric acid concentration of 50 %. The work revealed that the alcohols studied can be ranked in descending order based on both the acid concentration corresponding to the maximum CNC yield and the yield itself, as follows: pentanol-1, hexanol‑1, heptanol-1, and octanol-1. For octanol-1 the maximum CNC yield was 20 % at an acid concentration of 40 %. The physicochemical properties of the isolated CNCs were studied. No surface alkylation of the synthesized CNCs was found to occur during cellulose treatment in the media of the alcohols studied, as the properties of the CNCs, in general, were similar to those of CNCs obtained by standard sulfuric acid hydrolysis. This study broadens the scope of alternative methods to traditional sulfuric acid hydrolysis, and is likely to appeal to researchers engaged in developing novel approaches for CNC extraction.

DIAGNOSIS OF PROSTATE CANCER WITH ENHANCED EFFICIENCY USING FINE-TUNED CNN AND TRANSFER LEARNING

Cancer is one of the high-risk diseases for humans. Prostate cases are the second most common disease in men after lung cancer, and early diagnosis is vital. Artificial intelligence technologies have begun to be used in the diagnosis of prostate cancer, and more effective and sensitive results have been obtained, preventing potential errors in human-centered methods. In this study, in order to increase the classification performance in the diagnosis of prostate cancer, transfer learning methods and fine-tuning processes, which have higher success and learning ability with less training data, unlike machine learning methods, were applied. The two-class data set consisting of prostate cancer MR images, ‘significant’ and ‘not-significant’, was classified with Alexnet, Densenet201, Googlenet, and Vgg16 models with the feature extraction approach, and 71.40%, 72.05%, 65%, and 80.13% accuracy results were obtained respectively. To increase these rates, pre-trained transfer learning models were used and accuracy results of 89.74%, 94.32%, 85.59%, and 91.05% were achieved, respectively. A 98.10% validation result was obtained using the cross-validation method in the Densenet201 model. DenseNet201 model achieved the highest accuracy result of 98.63% in transfer learning with the combination of the RMSProp optimization method. The proposed transfer learning model provided an improvement of approximately 26% compared to the feature extraction method.

How to identify patient perception of AI voice robots in the follow-up scenario? A multimodal identity perception method based on deep learning

ObjectivesPost-discharge follow-up stands as a critical component of post-diagnosis management, and the constraints of healthcare resources impede comprehensive manual follow-up. However, patients are less cooperative with AI follow-up calls or may even hang up once AI voice robots are perceived. To improve the effectiveness of follow-up, alternative measures should be taken when patients perceive AI voice robots. Therefore, identifying how patients perceive AI voice robots is crucial. This study aims to construct a multimodal identity perception model based on deep learning to identify how patients perceive AI voice robots. MethodsOur dataset includes 2030 response audio recordings and corresponding texts from patients. We conduct comparative experiments and perform an ablation study. The proposed model employs a transfer learning approach, utilizing BERT and TextCNN for text feature extraction, AST and LSTM for audio feature extraction, and self-attention for feature fusion. ResultsOur model demonstrates superior performance against existing baselines, with a precision of 86.67%, an AUC of 84%, and an accuracy of 94.38%. Additionally, a generalization experimentwas conducted using 144 patients’ response audio recordings and corresponding text data from other departments in the hospital, confirming the model’s robustness and effectiveness. ConclusionOur multimodal identity perception model can identify how patients perceive AI voice robots effectively. Identifying how patients perceive AI not only helps to optimize the follow-up process and improve patient cooperation, but also provides support for the evaluation and optimization of AI voice robots.

Surface feature extraction method for cloud data of aircraft wall panel measurement points

In the cloud, users need to connect to the data server to perform the file transmission via the Internet, and the Server transmits data to many servers. A machine or vehicle that can fly with the assistance of the air is known as an Aircraft. As an alternative to the downward thrust of jet engines, it uses either static lift or an airfoil's dynamic lift to combat gravity's pull. Drawing wall panel measurement points in the model is easy using the Aircraft Wall Panels (AWP) button. Draw wall panels between existing nodes or on the drawing grid using the relevant wall panel specifications. The technique intends to discover and extract information about undesirable defects such as dents, protrusions, or scratches based on local surface attributes gathered from a 3D scanner. Defects from a perfectly smooth surface include indentations and bumps on the surface. An image's features may be extracted by reducing the number of pixels in the picture to a manageable size so that the most exciting sections of the image can be recorded with Surface Feature Extraction (SFE). Some of the problems are the threat of drones and composite materials that do not break easily in oxymoronic. The aircraft's inner structure may have been damaged, although this is impossible to determine. A runway incursion severely threatens aviation safety because of the rise in aircraft movement on the airport surface and other human factors. An electronic moving map of airport runways and taxiways is shown to the pilot through a head-up display in the cockpit's head-down position. A practical feature extraction approach is required to ensure the safety of the airport scene in runway incursion prevention systems. All the drawbacks are rectified by AWP-SFE sensors installed along the runway centerline to detect magnetic signals generated by surface-moving targets, and this information is utilized to compute the target's length. The target length may extract peak features after regularizing the time domain data. Differentiation of target characteristics is used to determine the similarities between distinct targets. The suggested method's signal characteristics are more easily recognized than time domain or frequency domain feature methods. The experimental results show the proposed method AWP-SE to achieve a high-efficiency ratio of 88.2%, activity ratio of 73.3%, Analysis of aircraft in wall plane measurement point of 87.8% and an error rate of 32.3% compared to other methods.

Real-time identification of multi-component periodic signals using Online Harmonics Extraction Approach

Real-time identification of multi-component periodic signals using Online Harmonics Extraction Approach

Soft interface instability and gas flow channeling in low-permeability deformable media

Understanding gas percolation through a clay layer or a shale formation is of great importance for the development of a geologic repository for nuclear waste disposal, a subsurface system for gas storage, and an engineering approach for hydrocarbon extraction from unconventional reservoirs. Gas injection experiments have revealed complex dynamic behaviours of gas percolation through water saturated compacted bentonite, characterized by a high breakthrough pressure, rapid breakthrough, a pressure/stress decay after the breakthrough, a relatively high migration rate, high-frequency periodic/nonperiodic variations in flow rate, stepwise rate reductions during relaxation, and low gas saturation over the whole process, all indicating channelling nature of the processes. Using linear stability analyses, we show that this channelling can autonomously emerge from the instability of the deformable interface between the injected gas and the compacted bentonite matrix driven by local stress concentration, pore dilation, and hydrologic gradient. Channel patterns formed would possess a fractal geometry. We further show that, once a percolating channel is established, the gas injected would percolate through the channel in a chain of gas bubbles, also due to the interface instability, resulting in periodic/chaotic variations in gas flow rate. Our work provides a unified explanation for key features observed for gas percolation in low-permeability deformable media. The work also suggests a possibility of designing an engineered barrier system for a nuclear waste repository that can have controllable gas release while limit water transport.

Optimization and greenness assessments of the supramolecular solvent-assisted cloud point extraction method for copper spectrophotometric determination in water and food samples

For the first time, the green supramolecular solvents (SUPRASs)-based cloud point extraction (CPE) approach was created for copper spectrophotometric determination in food and water samples. SUPRASs were synthesized in six different forms and used as synergistic reagents. SUPRAS (water, salicylic acid, and THF) mixed with TritonX-114 (TX-114) to be the most efficient extraction solvent. The validation investigation found that the linearity range, limit of detection (LOD), limit of quantification (LOQ), enhancement factor (EF), and preconcentration factor (PF) are 0.2-700.0 μg L-1, 0.1 μg L-1, 0.33 μg L-1, 85.0, and 50.0, respectively. The technique's accuracy was calculated using standard reference materials (SRMs) (GBW07605 Tea Leaves and GBW10019 Apple) and ranged between 96.0 and 98.9%. Lastly, a novel SUPRAS-CPE approach was utilized to determine copper concentrations in real water, food tests, and SRMs. Furthermore, the SUPRAS-CPE conforms to green chemistry principles by using less both time and energy than previous studies. Also, the greenness values were determined by three recent tools. The Analytical Greenness Metric for Sample Preparation (AGREEprep), Complex Modified GAPI (ComplexMoGAPI), and Sample Preparation Metric of Sustainability (SPMS) programs produced results of 0.79, 86.0, and 8.42, respectively. These results are strongly aligned with both green ideals and environmentally friendly practices. The finding increases the possibilities for using SUPRAS-CPE to extract additional mineral ions.

MGSAN: multimodal graph self-attention network for skeleton-based action recognition

Due to the emergence of graph convolutional networks (GCNs), the skeleton-based action recognition has achieved remarkable results. However, the current models for skeleton-based action analysis treat skeleton sequences as a series of graphs, aggregating features of the entire sequence by alternately extracting spatial and temporal features, i.e., using a 2D (spatial features) plus 1D (temporal features) approach for feature extraction. This undoubtedly overlooks the complex spatiotemporal fusion relationships between joints during motion, making it challenging for models to capture the connections between different temporal frames and joints. In this paper, we propose a Multimodal Graph Self-Attention Network (MGSAN), which combines GCNs with self-attention to model the spatiotemporal relationships between skeleton sequences. Firstly, we design graph self-attention (GSA) blocks to capture the intrinsic topology and long-term temporal dependencies between joints. Secondly, we propose a multi-scale spatio-temporal convolutional network for channel-wise topology modeling (CW-TCN) to model short-term smooth temporal information of joint movements. Finally, we propose a multimodal fusion strategy to fuse joint, joint movement, and bone flow, providing the model with a richer set of multimodal features to make better predictions. The proposed MGSAN achieves state-of-the-art performance on three large-scale skeleton-based action recognition datasets, with accuracy of 93.1% on NTU RGB+D 60 cross-subject benchmark, 90.3% on NTU RGB+D 120 cross-subject benchmark, and 97.0% on the NW-UCLA dataset. Code is available at https://github.com/lizaowo/MGSAN.

Hierarchical Indexing for Retrieval-Augmented Opinion Summarization

Abstract We propose a method for unsupervised abstractive opinion summarization, that combines the attributability and scalability of extractive approaches with the coherence and fluency of Large Language Models (LLMs). Our method, HIRO, learns an index structure that maps sentences to a path through a semantically organized discrete hierarchy. At inference time, we populate the index and use it to identify and retrieve clusters of sentences containing popular opinions from input reviews. Then, we use a pretrained LLM to generate a readable summary that is grounded in these extracted evidential clusters. The modularity of our approach allows us to evaluate its efficacy at each stage. We show that HIRO learns an encoding space that is more semantically structured than prior work, and generates summaries that are more representative of the opinions in the input reviews. Human evaluation confirms that HIRO generates significantly more coherent, detailed, and accurate summaries.

Intensification of extraction of bioactive compounds from pomegranate peel using an ultrasound-microwave assisted extraction approach: Parametric optimization, kinetics and thermodynamics

The expansion of fruit processing industries results in a significant amount of by-products, including peels, seeds, skins, pomace, and rinds, which are rich in bioactive compounds like polyphenols, flavonoids, carotenoids, and dietary fiber. These processing by-products are often deemed of minimal value relative to the processed fruit, largely due to the absence of efficient extraction methods. In this study, ultrasound-microwave assisted extraction (UMAE) technology was used to extract bioactive components from pomegranate (Punica granatum L.) peel using ethanol as a solvent. The influence of the combined power (ultrasonic 200 W + microwave 100–300 W), extraction time (10–30 min.), liquid to solid ratio (LSR) (20:1–60:1 mL/g) and solvent concentration (30–70 vol%) on extraction yield, total phenolic content (TPC), total flavonoids content (TFC) and 1,1-diphenyl-2-picrylhydrazyl- radical scavenging activity (DPPH-RSA) was investigated. The optimal conditions using response surface methodology (RSM) for UMAE were found to be an ultrasonic power of 150 W, LSR of 50:1 mL/g, a solvent concentration of 60 %, and an extraction time of 15 min. Under the optimized conditions, 64.964 % of extraction yield, 152.089 mg GAE/g DW of TPC, 38.566 mg QE/g DW and 88.892 % of DPPH-RSA were obtained. The concentration–time data were examined using a second-order rate kinetic model to calculate the extraction constant and activation energy. The thermodynamic analysis indicated that the UMAE process is both spontaneous and endothermic. Scanning electron microscopy (SEM) images, X-ray diffraction (XRD) and Fourier transform infrared (FTIR) spectra of resulted extract under optimal conditions revealed a disrupted structural network, analyzed the nature and identified the primary functional groups, respectively. This research showed that using UMAE with ethanol is a quick, effective, and eco-friendly method for extracting pomegranate peel.

An Intelligent Approach for Retinal Vessels Extraction Based on Transfer Learning

An Intelligent Approach for Retinal Vessels Extraction Based on Transfer Learning

Layered Bio-Inorganic MXene Membranes: A Green Approach for Uranium Extraction from Seawater Using Genetically Modified E. coli.

With the growing demand for clean energy, efficient uranium extraction technologies are needed, especially from seawater, where uranium reserves are huge. Here, we developed a composite membrane by inserting Escherichia coli engineered with super uranyl-binding protein (SUP) within a two-dimensional (2D) MXene (Ti3C2Tx) layer. SUP endowed the bioinorganic hybrid membrane with ultrahigh selectivity for uranyl ions, while the engineered E. coli improved the mechanical strength and economy of the membranes. Experimental results showed that the membranes achieved precise recognition of uranyl ions and excellent ion screening performance (SFU/V ≈ 43, SFNa/U ≈ 158). Excellent separation performance and cyclic stability tests demonstrated the industrial application potential of the membrane. This method offers a green and sustainable solution, combining biological engineering and nanomaterial innovation, providing an environmentally friendly and efficient approach for uranium extraction from seawater, marking a significant advancement in the field of clean energy resource development.

A Systemic Review on Phytochemicals and Novel Approaches for the Management of Hepatic Cancer

Introduction: Hepatic cancer, an aggressive tumour that often develops along with cirrhosis and chronic liver disease like infections with the hepatitis B and hepatitis C viruses, while nonalcoholic steatohepatitis, is linked to metabolic syndrome or diabetes mellitus. It is the third most prevalent cause of cancer-related mortality globally and ranks fifth in cancer incidence. According to GLOBOCON, the prevalence is expected to rise by 55.0% and the fatalities by 56.4% in the near future. Objective: The present review offered natural plant-based substances and compounds having curative effects on liver cancer, along with novel drug delivery systems and nanocarrier-based therapies. Methods: The literature has been taken from PubMed, Google Scholar, SciFinder, Springer Nature, Bentham Science, PLOS one, or other internet sites. Result: Treatment for heterogeneous malignancy is multidimensional, and care guidelines differ depending on the specialty and location. Several nutritional herbal remedies and their active phytoconstituents may have an abundance of impacts on the management of liver cancer, including preventing the growth and spread of tumor cells, shielding the body from liver carcinogens, boosting the effects of chemotherapy and immunomodulating the body. Conclusion: The treatment of liver cancer involves multidisciplinary and multimodel therapy. The literature is a compilation of extract, compounds, and novel approaches like nanoparticles, microsphere, liposomes, niosomes, phytosomes and microparticles. These approaches not only manage cancer but also boost the immunity of the individuals.

Understanding the Experience of Workplace Violence in Hospitals as Documented by Nursing Staff: Using the READ Approach.

Background: Workplace violence has a significant impact on patients, families, and staff safety. Workplace violence can produce traumatic results for those involved; the importance of preventive measures needs to be paramount in health service policy and process. Health care staff are required to document their experiences of violent incidents after every occasion, usually via an incident reporting system, which allows for a free text description of the event. There is a lack of understanding of how health care staff document reports of violence and how they explain the events. Methods: This study aims to determine the circumstances surrounding workplace violent events as documented by health care staff. The four-step Review, Extract, Analyze, and Document (READ) approach to document analysis was used to examine workplace violence incident reports over 12 months (September 2021-September 2022) in a tertiary referral hospital. Findings: Six categories of workplace violence were found: (a) "Escalation Dynamics"-patterns and progression of how violence incidents intensify; (b) "Warning Behaviors"-verbal or non-verbal signals that may foreshadow physical violence if not addressed; (c) "Authoritative Institutional Interventions"-how aggression correlates to protocols involving security personnel or law enforcement; (d) "Care Delivery Methods"-how certain treatment procedures and approaches might inadvertently elicit violence; (e) "Situational Stressors"-external circumstances or changes that act as triggers for violence; and (f) "Unprovoked triggers"-sudden and seemingly unprovoked violent outbursts. Conclusions and application to practice: Workplace violence in health care is a multifaceted interplay of events with the nurse involved in all aspects of the process. These findings can be used by occupational health nurses in education and policy development. The findings can be used to focus education on how violent incidents may escalate and provide more opportunities for de-escalation by health care staff.

Optimizing Genomic DNA Extraction from Avian Feathers: A Modified Phenol-Chloroform Approach for Enhanced Efficiency and Cost-Effectiveness.

The procurement of blood and tissue samples for DNA extraction in avian species intended for molecular studies is associated with the induction of discomfort and pain in the subjects, compounded by practical challenges in application and ethical considerations. Consequently, feathers have emerged as a more prevalent source for molecular investigations, particularly in the fields of poultry and ornithology. However, the effective extraction of DNA from feathers necessitates the breakdown of the hard keratinized tissue within the feather structure. This study aimed to devise a highly efficient, cost-effective, and easily adaptable Modified Phenol-Chloroform (MPC) approach for genomic DNA extraction from feathers, addressing shortcomings identified in previous studies on feather-based DNA isolation. The MPC method was employed to extract genomic DNA from feather samples obtained from six distinct avian species (chicken, guinea fowl, canary, pigeon, emu, and goose). Comparative evaluation of DNA isolation efficiency was conducted by employing two different commercial DNA kits alongside the MPC method. The results showed significantly higher DNA concentrations (ng/ml) from chicken feathers using the MPC method compared to those obtained with commercial kits (p < 0.05), along with high DNA purity (1.83 ± 0.11). Subsequent PCR experiments, employing nuclear and mitochondrial DNA-specific primers, illustrated the effective amplification of short and long fragments from MPC-isolated DNA samples. In contrast to commercial kits, the findings underscore the successful application of the MPC method in isolating high-quality genomic DNA from feathers characterized by elevated keratin content.